PROGRAMME and ABSTRACTSInternational Symposium

Global Collaboration
on Data beyond
Disciplines

23

25 September 2020

Online ConferenceInternational Symposium- DSWS-2020 “Global Collaboration on Data beyond Disciplines”

PROGRAMME and ABSTRACTS

Online Conference
23–25 September 2020

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CircularInternational Symposium“Global Collaboration on Data beyond Disciplines”
23 – 25 September 2020
Online Conference

Background and Scope:
One of the important objectives of the current Open Science movement is the creation of new research fields
and technologies based on the vast quantities of data now being produced from almost all scientific domains.
Adherence to the FAIR (Findable, Accessible, Interoperable, and Reusable) Principles for data have beenrecognized as a norm for data-oriented activities in this Open Science era, and is leading to efforts to develop
Open Data infrastructure such as enhanced and integrated metadata catalogues, persistent identifiers (PIDs; e.g.,
Digital Object Identifiers) and metadata standards for research data management, certification of datarepositories addressing long-term management and stewardship of quality-assessed data, and so on. Moreover, inmany academic facilities (e.g., universities), the use of persistent identifiers for people, places, and other entities is
becoming best practice in preservation and provision of data produced by their research activities.
Further efforts are still needed to resolve the various challenges that currently exist for scientific research data;in particular, the sharing and reuse of such data. Although the importance of multidisciplinary data integration has
been widely advocated, data reuse by scientists either within or across disciplines is still not easy from the point ofview of the FAIR Principles: for example, there may be difficulties in discovering and accessing the data or
insufficient information on the data to enable easy analysis. Often additional information is also needed to assist
understanding when sharing research data with the general public, including policymakers. To improve thesituation, it is important to stimulate collaborations among scientists from various disciplines and to establish
systems that facilitate the interactions between the users and providers of research data.
On the basis of the above, the Joint Support-Center for Data Science Research (DS) of the Research
Organization of Information and Systems (ROIS) and the Committee of International Collaborations on Data
Science of the Science Council of Japan (SCJ) are organizing a symposium to share information on current
international research data activities addressing Open Science, data-centric science, and interdisciplinary datadriven science. A key step in making science open lies in improving data quality and transparency so that
researchers can easily share their work, and this necessitates a robust and global-scale infrastructure. The
symposium will be a remarkable opportunity to discuss the future development of such data-oriented
infrastructure in the Open Science era, and thus ensure the long-term preservation and equitable provision ofquality-assessed research data. The World Data System (WDS) of the International Science Council (ISC) and its
partner organization Open Researcher and Contributor ID (ORCID) are leading global initiatives that can galvanize
the community to find solutions to these data-related issues, and at the same time work with the community to
improve data discoverability and interoperability for effective data reuse.
2The goal of this symposium is to build consensus on various aspects of research data management by all
stakeholders in alignment with Open Research policies and initiatives. It will explore new paths for activities
significant in promoting interdisciplinary and collaborative research and data reuse under different scientificdisciplines based on evidence and feedback from data communities. Such global data activities are expected to bestrengthened by the endeavours and facilities provided by DS, ROIS.

Session Themes:
Potential session topics proposed for the symposium are:⮚Opening Session: Opening Addresses, Keynote Talks, 10 Years Event of the International Programme
Office of the World Data System (WDS-IPO).
Conveners: *Masaki Kanao, Yasuhiro Murayama, Takashi Watanabe, Rorie Edmunds⮚

WDS Members’ Forum 2020: This WDS-led session is an interim, online version of the biennial WDS
Members’ Forum (also known as ‘Data Repositories Day’), which has been postponed until 2021 such that italigns with International Data Week. The session will be split into two parts:• The first part will be a Scientific Session consisting of report backs from the WDS

membership on topics of interest and importance to the data repository community. All
WDS Members will have the opportunity to showcase the visions and foci of theirorganizations for the coming five years though a series of lightning talks—either in prerecorded videos or live—to facilitate discussions on opportunities created, challenges
faced, and areas in which WDS can assist.
• The second part will be a Plenary Session that provides a formal mechanism for the WDS
Scientific Committee to consult with the community on issues of relevance to datarepositories. It will share information on the current and prospective WDS activities, andask for feedback and buy-in from the WDS membership and beyond.
This virtual event is open to both WDS Members and general participants having an interest in theendeavours of WDS—those from outside of the WDS family will be very much welcomed. Since this meeting
incorporates the biennial WDS business meeting, Representatives of WDS Member Organizations—inparticular, those of WDS Regular and Network Members—are expected to participate.

Conveners: *Rorie Edmunds, WDS-SC, WDS-IPO, WDS-ITO⮚

Regional Activities on Data in the Asia & Oceania Area: Sharing information on data-oriented
collaborations on data in the Asia-Oceania area, e.g. plans of collaborating platforms have been going on inAustralia and Malaysia and discussions in the series of WDS Asia and Oceania Conferences, etc. Principal
Organizers are WDS and Regional Office of Asia and Pacific (ROAP).

Conveners: *Toshihiko Iyemori, Mazlan Othman, Juanle Wang, Takashi Watanabe⮚

WDS-ORCID Strategic Workshop: Adoption of PIDs in Asia–Oceania: This jointly hosted session by ORCID(Open Researcher and Contributor ID) and the World Data System of the International Science Council(WDS) will introduce to the Asia–Oceania community Persistent Identifiers (PIDs) from both global andnational perspectives, and their importance as a part of global open research infrastructure. It will also (1)3raise awareness about PIDs and their use across different scientific disciplines; (2) explore practical use
cases of PIDs by national data systems and data repositories, especially within the Asia–Oceania context;
and (3) showcase PID standards/best practices, as well as identify the resulting opportunities and benefits.
Convener: *Chieh-Chih Estelle Cheng, David Castle, Rorie Edmunds, Aminata Garba, Masao Mori,
Karen Payne, Hideaki Takeda⮚

Sharing of the COVID-19 Data: Sharing information on data involving COVID-19 pandemics for all relatedbranch of disciplines, including social, genome and biological aspects. As this session focus on international
interest involving the pandemics, all related topics on COVID-19 data issues are welcome.
Conveners: *Elaine Faustman, Marc Nyssen, Masaki Kanao, Tadahiko Maeda, Tomoya Baba,
Mari Minowa⮚

Forum of Early Career Data Scientists in the Asia & Oceania Area: A forum of young-generation data
scientists in the Asia-Oceania area to identify current problems and future plans. This part will be led by the
WDS ECR Network.
Conveners: *Akira Kadokura, WDS ECR Network members, Juanle Wang, Yoshimasa Tanaka,
Takashi Watanabe⮚

Promotion of Multi-Disciplinary Data Analysis: Presentations on research projects based on multi-,
and/or inter- disciplinary data use and data-led innovation of technology. Themes of space-weather data
analyses are also included.
Conveners: *Takashi Watanabe, Asanobu Kitamoto, Masahito Nose, Ryuho Kataoka

Keynote Speakers:
Mark A. Parsons (Editor-in-Chief, Data Science Journal)
Mazlan Othman (Regional Office of Asia and Pacific, International Science Council)
Jens Klump (Mineral Resources, CSIRO in Australia)
Lesley Wyborn (Australian National University)
Yusuke Komiyama (National Institute of Informatics)
Devika Madalli (Indian Statistical Institute)
Masanori Arita (National Institute of Genetics)
Priyanka Pillai (Research Data Stewardship and Health Informatics)
Kassim S. Mwitondi (Sheffield Hallam University)4Conference Programme:(JST=UTC+9h)

Dates

Time-slot 1

23 SEP(WED)

24 SEP(THU)

Regional Activities on Datain the Asia-Oceania Area

(01:00-03:30 UTC)
(10:00-12:30 JST)
25 SEP(FRI)

Time-slot 2

Time-slot 3

Opening Addresses, Key-note Talks,
10 Years Event of WDS-IPO

WDS Members’ Forum 2020

(05:00-08:00 UTC)
(14:00-17:00 JST)

(12:00-15:00 UTC)
(21:00-24:00 JST)

WDS-ORCID StrategicWorkshop(05:00-08:00 UTC)
(14:00-17:00 JST)

Sharing of the
COVID-19 Data
(10:00-16:00 UTC)
(19:00-25:00 JST)

Forum of Early Career Data Promotion of Multi-Disciplinary
Scientists in the Asia-Oceania Data Analysis
Area
Conclusive Addresses(01:00-03:30 UTC)
(10:00-12:30 JST)

(05:00-08:30 UTC)
(14:00-17:30 JST)

Detail Programme for all seven sessions is available from here;
Programme & Session detail: DSWS-2020_Programme & Session detail.pdf

Presentations:
Online Presentations:
General presenters are principally allocated 20 minutes including questions and discussion time.
Keynote speakers are principally allocated 30 minutes including questions and discussion time.
Several sessions prepare “lightning talk” presentations for few minutes, depending on the session content.

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There is no poster presentation in this conference, therefore, those who wants to make poster presentation could
submit the poster content into the Extended Abstract.
All presentations will be made via the online Meeting Application (Zoom).
Detail of the link information on the Zoom meeting room for all sessions is available by reply mail when you
registered from the Pre-Registration Form
"Pre-conference registration form".

Zoom meeting room:
Audio and Video control:
On entering the Zoom meeting room for each session, all delegates will be muted, and they should turn their
video mode to "OFF". While, delegates can change these settings, it is advised that they stay muted and keep their
video in the OFF mode throughout the time they are listening to the presentations to avoid causing disturbanceand save bandwidth. Presenters should turn on the audio and video modes only when it is their turn to present.
The buttons to turn on the audio and video mode will typically be at the bottom left corner of your screen; butmuting and unmuting can also be done by clicking on the dots at the top right corner of your image. Muting andunmuting can also be done via your audio headset if you are using one.
Asking questions and discussions:
During question time and discussions, delegates should only turn "ON" the "microphone" (unmute themselves),
when it is clear they have been given an opportunity to talk by the session chairs. This can happen in different
ways – for example, it could be a quick question to be raised in the middle of the presentation or it may follow thedirections of the session chairs. Delegates can raise their hand electronically. You can raise your hand by clicking
on the icon labelled "Participants" at the bottom of your screen and then click the button "Raise Hand." You canalso use the “Chat Room”, at the bottom of your screen, to send text messages to everyone or to an individual
participant. The chatroom will be monitored by the conveners of the session.
Sharing Screen:
Presenters will be expected to share their screens with the rest of the audience. Sharing your screen is astraightforward process. Simply select "Share Screen" at the bottom of your screen, which will display various
options for you to share. You will typically want to share your Desktop, as that is what you will be looking at. Onceyou have sone so, select your Power Point slides or any other application you may wish to share and click "Share"at the bottom right corner. You can end screen sharing by selecting the relevant button at the top of your screen.
If sharing external content with audio or video, such as YouTube clips, make sure you check the box "Share
Computer Sound" at the bottom left corner of your screen.
Full Screen Mode:
Please note that you might not be able to see some of the options if you are in Full Screen mode. You can use theEscape key to leave the mode and use the double arrow sign, at the top right corner of your screen to enter fullscreen mode.
For those who are not familiar with screen sharing, we advise that they send their presentation files to the session
conveners prior to the conference. Otherwise, you may send your presentation movie file to the conveners
instead of an actual oral presentation.

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Video recording:
All the sessions are planned to be recorded into the Zoom cloud. After the symposium has been completed,delegates can watch the session videos using the link information that will be provided by the Local Organizing
Committee (LOC). The video watching period is set to be terminated by the end of October 2020.
Please feel free to contact the LOC and/or the session conveners, if you have any questions or issues you would
like to raise.

Abstract submission:
All presenters are required to prepare an Extended Abstract to be included in the conference "Programme and
Abstracts" booklet. The booklet will be published on the conference website.
The following doc.file contains a set of guidelines for preparing an Extended Abstract submission. Guidance isgiven on layout, text formatting, figures/tables, and references to ensure that all submissions are clear and consistent.
The template is presented exactly as your extended abstract should appear, and it is highly advised to use it toprepare your submission in Word format. It is noticed that the manuscript should not exceed two pages in length, andshould fit within the margins given in this template.
Abstract Format: Guidelines for Extended Abstracts - dsws.2020.doc (please download from conference website)
All the manuscript file(s) for Extended Abstracts should be emailed to [dsws2020.admn (at) gmail.com] by 31
August 2020.
Each submission will be given a light review by a member of the LOC or AC (see below) to confirm the typesetting
and formatting adhere to the template, as well as the suitability of the presentation for inclusion in the programme.

Registration:
Pre-conference registration and presentation submission can be made from here. No registration fee is required
to attend the conference.
"Pre-conference registration form"
Detail Zoom meeting information for all sessions will be sent by reply mail when you registered.
For the participants who are staying in places/countries where the above Google Form cannot be accessed for any
reasons, please use the "Offline pre-registration form (xls.file) ". The "Offline pre-registration form” should be
emailed to [dsws2020.admn (at) gmail.com] prior to the conference.

Social Events (provisional):
The organizers are planning the following social events in conjunction with the symposium.
⮚

10-Year Anniversary Event for the International Programme Office of the World Data System (23September, 0500-0800 UTC). This will take place by online virtual meeting by Zoom.
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Local Organizing Committee (LOC): (* Chair)
Tomoya Baba (Data Science Promotion Section, DS, ROIS)
Akira Kadokura (Polar Environment Data Science Center, DS, ROIS)*Masaki Kanao (Polar Environment Data Science Center, DS, ROIS)
Mari Minowa (Data Science Promotion Section, DS, ROIS)
Yasuhiro Murayama (National Institute of Information and Communications Technology)
Koji Nishimura (Polar Environment Data Science Center, DS, ROIS)
Akihiko Nomizu (Data Science Promotion Section, DS, ROIS)
Masahito Nose (Nagoya University)
Hideaki Takeda (National Institute of Informatics, ROIS)
Yoshimasa Tanaka (Polar Environment Data Science Center, DS, ROIS)
Hironori Yabuki (Polar Environment Data Science Center, DS, ROIS)
Takashi Watanabe (WDS-IPO)

International Advisory Committee (AC): (* Chair)Phillippa Bricher (Australian Antarctic Division)
Taco De Bruin (Royal Netherlands Institute for Sea Research, Netherland)
Chieh-Chih Estelle Cheng (ORCID)
Shannon Christoffersen (University of Calgary, Canada)
Alexander de Sherbinin (NASA SEDAC; WDS-SC)
Rorie Edmunds (WDS-IPO)
Hiroyuki Enomoto (National Institute of Polar Research, ROIS)
Elaine Faustman (University of Washington; WDS-SC)*Asao Fujiyama (Joint Support-Center for Data Science Research, ROIS)
Susumu Goto (Database Center for Life Science, DS, ROIS)
Yuko Harayama (ORCID Board Researcher Member)
Kazuhiro Hayashi (National Institute of Science and Technology Policy)
Heidi J. Imker (Illinois University, USA)
Toshihiko Iyemori (Kyoto University; International Union of Geodesy and Geophysics; WDS-SC)
Ryuho Kataoka (National Institute of Polar Research, ROIS)
Asanobu Kitamoto (Center for Open Data in the Humanities, DS, ROIS)
Jens Klump (Mineral Resources, CSIRO in Australia)
Yuji Kohara (Database Center for Life Science, DS, ROIS)
Tadahiko Maeda (Center for Social Data Structuring, DS, ROIS)
Masao Mori (Tokyo Institute of Technology)
Hideki Noguchi (Center for Genome Informatics, DS, ROIS)
Tsuneo Odate (National Institute of Polar Research, ROIS)
Mazlan Othman (Regional Office of Asia and Pacific, International Science Council)
Mark Parsons (Rensselaer Polytechnic Institute, USA)Peter Pulsifer (University of Colorado, USA)
Seiji Tsuboi (Japan Agency for Marine-Earth Science and Technology)
Genta Ueno (Center for Data Assimilation Research and Applications, DS, ROIS)
Juanle Wang (China Academy of Science; WDS-SC)
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Organized by:
Joint Support-Center for Data Science Research (DS), Research Organization of Information and Systems (ROIS)
Committee of International Collaborations on Data Science, Science Council of Japan (SCJ)

Supported by:
National Institute of Information and Communications Technology (NICT)
Research Organization of Information and Systems (ROIS)World Data System (WDS), International Science Council (ISC)
Open Researcher and Contributor ID (ORCID)
Science Council of Japan (SCJ)

Workshop Website:
https://ds.rois.ac.jp/article/dsws_2020/

Important Dates:
Registration & Abstract submission open: 01 April 2020
Presentation submission deadline: 15 August 2020
Extended Abstract submission deadline: 31 August 2020
Programme booklet online: 15 September 2020
Registration deadline: 23 September 2020
Symposium: 23–25 September 2020

Contact Address:
dsws.loc-2020 (at) nipr.ac.jp

9International Symposium- DSWS-2020 “Global Collaboration on Data beyond Disciplines”

PROGRAMME

Online Conference
23–25 September 2020

10

PROGRAMMEInternational Symposium“Global Collaboration on Data beyond Disciplines”
23 – 25 September 2020
Online Conference

Wednesday 23 September 2020

Opening Session (05:00-08:00 UTC)
Conveners: *Masaki Kanao, Yasuhiro Murayama, Takashi Watanabe, Rorie Edmunds
05:00–05:20 Opening Addresses (Chair: Masaki Kanao)••

Asao Fujiyama (Director-General, Joint Support-Center for Data Science Research (DS), ROIS) (10’)
Masaki Kanao (LOC Chair), outline of symposium & practical information (10’)

05:20–06:35 Keynote Talks (Chair: Takashi Watanabe)
Keynote 1: Past, present and future of global data collaboration
Mark A. Parsons (Editor-in-Chief, Data Science Journal) (25’)
Keynote 2: Open Science for Asia and the Pacific
Mazlan Othman (Regional Office of Asia and Pacific, International Science Council) (25’)
Keynote 3: Do we all need to become data scientists?
Jens Klump (Mineral Resources, CSIRO in Australia) (25’)
06:35–06:45 Group Photo @ Zoom Screen & Coffee Break
06:45–08:00 10 Years Event of the International Programme Office of the World Data System (WDS-IPO)(Chair: Yasuhiro Murayama; each talk is allocated for 5’-10’)•••

Hideyuki Tokuda (President, National Institute of Information and Communications Technology (NICT))
Ryoichi Fujii (President, Research Organization of Information and Systems (ROIS))
Kazuhiko Takeuchi (Vice President, Science Council of Japan)•••

Daya Reddy (President, International Science Council)
Alexander de Sherbinin (Chair, Scientific Committee of World Data System)
Elaine Faustman (Vice Chair, Scientific Committee of World Data System)••

Chieh-Chih Estelle Cheng (Manager APAC Engagement, ORCID)
Rorie Edmunds (International Program Office, World Data System)•

Discussion
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Wednesday 23 September 2020

WDS Members’ Forum 2020 (12:00-15:00 UTC)
Conveners: * Rorie Edmunds, WDS-SC, WDS-IPO, WDS-ITO
12:00–13:30 Scientific Session – Member Lightning Talks(Chairs: Alex de Sherbinin, Rorie Edmunds)
●
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●

●

●
●

Welcome & Introduction (5’)
Alex de Sherbinin (Chair, WDS-SC), Rorie Edmunds (Executive Director, WDS)
Panel Discussion 1: FAIR Data and Interoperability (15’)
Marc Nyssen (Moderator)
Nick Thieberger (PARADISEC)
Wendy Gross (World Data Service for Paleoclimatology)
Michael Diepenbroek (PANGEA – Data Publisher for Earth & Environmental Science)
Panel Discussion 2: Automation, Scalability, and Cloud Use (20’)
Aminata Garba (Moderator)
Jeanne Behnke(NASA-ESDIS Project)
Martina Stockause (DKRZ – WDC Climate)
Jerry Carter (Incorporated Research Institutions for Seismology)
Andrii Shelestov (Ukrainian Geospatial Data Center)
Panel Discussion 3: Sustainability: Making the case for domain repositories through value of
information, operational use, & web services (20’)
Robert Chen (Moderator)
Mamoru Ishii (WDC – Ionosphere and Space Weather)
Adam Shepherd (Biological and Chemical Oceanography Data Management Office)Doug Schuster (National Center for Atmospheric Research)
Frederic Clette (WDC – Sunspot Index and Long-term Solar Observations)
Breakout Discussions (20’)
Karen Payne, David Castle, Loana Popescu (Moderators)
Wrap-up of Scientific Session (5’)
Alex de Sherbinin (Chair, WDS-SC)

13:30–13:50 Coffee Break
13:50–15:00 Plenary Session – Community Consultation(Chairs: Alex de Sherbinin, Rorie Edmunds)●●●●

Welcome & Introduction (5’)
Alex de Sherbinin (Chair, WDS-SC), Rorie Edmunds (Executive Director, WDS)
WDS-ITO Activities (+ Discussion)
Karen Payne (Associate Director, WDS-ITO) (15’)
Data Together: WDS & Member Involvement (+ Discussion)
Ingrid Dillo (Vice Chair, WDS-SC) (15’)
Candidate Membership & Mentoring (+ Discussion)
Wim Hugo (Director of Strategy, WDS) (15’)
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●

Domain vs Generalist Repositories (+ Discussion)
Alex de Sherbinin (Chair, WDS-SC) (15’)
Wrap-up of Plenary Session
Alex de Sherbinin (Chair, WDS-SC) (5’)

Thursday 24 September 2020

Regional Activities on Data in the Asia & Oceania Area (01:00-03:30 UTC)
Conveners: *Toshihiko Iyemori, Mazlan Othman, Juanle Wang, Takashi Watanabe
01:00–03:30 Regional Activities on Data in the A & O Area
(Chairs: Mazlan Othman, Toshihiko Iyemori)• National Cross-Domain Activities on Research Data in Australia and Connections with International
Activities (Invited)
Lesley Wyborn (Australian National University) (20’)• WDS-led Activities on Data in the Asia-Oceania Area
Takashi Watanabe (International Program Office, World Data System) (15’)• Open Science and its Policy towards Open Science Paradigm
Kazuhiro Hayashi (National Institute of Science and Technology Policy) (15’)•

General-purpose research-data management service for international research collaboration (Invited)
Yusuke Komiyama (National Institute of Informatics) (20’)
Coffee break (10')•••

Scientific Data Standard System Construction and its Application for Data Archiving in China
Juanle Wang (Chinese Academy of Sciences) (15’)
Collection, Archive and Sharing Space Weather information in NICT
Mamoru Ishii (National Institute of Information and Communications Technology) (15’)
Making historical data Re-useable: a case study of the challenges and success of Bangladesh Bureau of
Statistic’s historical data conversion project
Chandra Shekhar Roy (Bangladesh Bureau of Statistics) (15’)•

Data Stewardship: Indian Perspective (Invited)
Devika Madalli (Indian Statistical Institute) (20’)•

Discussion / Summary

Thursday 24 September 2020

WDS – ORCID Strategic Workshop: Adoption of PIDs in Asia-Oceania(05:00-08:00 UTC)
Conveners: *Chieh-Chih Estelle Cheng, David Castle, Rorie Edmunds, Aminata Garba,
Masao Mori, Karen Payne, Hideaki Takeda
1305:00–06:20 Part 1 – PIDs: A Vital Component of Global Research Data Infrastructure(Chairs: Rorie Edmunds, Estelle Cheng)•

Introduction by Chairs (5’)•

Adopting Persistent Identifiers in Open Research Infrastructure: ORCID and beyond
Chieh-Chih Estelle Cheng (ORCID, APAC Engagement Manager) (20’)
Japanese DOI RA, Japan Link Center (JaLC) and its collaboration with ORCID
Masashi Hara (Japan Science and Technology Agency) (20’)
The IGSN Global Sample Number - A PID for Physical Objects
Jens Klump (Mineral Resources, CSIRO in Australia) (20’)•••

Discussion & Wrap Up (15’)

06:20–06:40 Coffee Break
06:40–08:00 Part 2 – Practical Use Cases: From National PID Systems to Sector Applications(Chairs: Rorie Edmunds, Estelle Cheng)•

Introduction by Chairs (5’)●

The roles of PIDs in Scientific Data Management in China
Xiaoyan Hu (National Space Science Center, CAS, Space Science Big Data Technology
Laboratory) (15’)●

Korea Research Data Management Platform and PID
Sa-kwang Song (Korea Institute of Science and Technology Information) (15’)
DataCite Commons – A new service to explore the PID Graph
Martin Fenner (DataCite, Germany) (15’)●●

The Power of PIDs at the Cambridge Crystallographic Data Centre
Ian Bruno (Cambridge Crystallographic Data Centre) (15’)•

Discussion & Wrap Up (15’)

Thursday 24 September 202

Sharing of the COVID-19 Data (10:00-16:00 UTC)
Conveners: *Elaine Faustman, Marc Nyssen, Masaki Kanao,
Tadahiko Maeda, Tomoya Baba, Mari Minowa
10:00–12:00 Sharing of the COVID-19 Data - 1(Chair: Tomoya Baba, Tadahiko Maeda)
Keynote 1: Open Access and Data Sharing of Nucleotide Sequence Data
Masanori Arita (National Institute of Genetics) (25’)• Time-series analysis of directional sequence changes in SARS-CoV-2 genomes and an unsupervised
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•
•
•

explainable AI for studying corona virus genomes
Toshimichi Ikemura (Nagahama Institute of Bio-Science and technology) (20’)
Susceptibility to COVID-19 infection: Insights from mathematical modelling
Ryosuke Omori (Research Center for Zoonosis Control, Hokkaido University) (20’)
Wastewater-based epidemiology for COVID-19: Perspectives for environmental surveillance of SARSCoV-2 in wastewater
Ryo Honda (Faculty of Geosciences and Civil Engineering, Kanazawa University) (20’)
Spatial analysis of COVID-19 spread using compositionally-warped Gaussian process
Daisuke Murakami (Center for Data Assimilation Research and Applications, DS, ROIS) (20’)•(Discussion)•

Coffee Break

12:00–14:00 Sharing of the COVID-19 Data - 2(Chair: Marc Nyssen, Mari Minowa)
Keynote 2: Sharing data for a coordinated response during COVID-19 pandemic
Priyanka Pillai (Research Data Stewardship and Health Informatics) (25’)• Sharing literature annotation regarding COVID-19 through PubAnnotation
Jin-Dong Kim (Database Center for Life Science, DS, ROIS) (20’)• COVID-19 Data and Knowledge Hub
Liu Chuang (Global Change Research Data Publishing & Repository and GCdataPR -WDS) (20’)• COVID-19 Data Sharing Initiative by WDCM and NMDC
Linhuan Wu (Institute of Microbiology, Chinese Academy of Sciences) (20’)• Machine Learning based Peptide Therapeutics for Covid-19
Shailza Singh (National Center for Cell Science, India) (20’)•(Discussion)•

Coffee Break

14:00–16:00 Sharing of the COVID-19 Data - 3(Chair: Elaine Faustman, Masaki Kanao)
Keynote 3: Monitoring Sustainable Development Goals Amidst COVID-19
Through Big Data, Deep Learning and Interdisciplinarity
Kassim S. Mwitondi (Sheffield Hallam University) (25’)• The RDA Community Response to COVID-19
Mark Leggott (Research Data Canada) (20’)• VODAN-in-a-box: a FAIR toolkit for improving reusability of COVID-related data
Luiz Olavo Bonino da Silva Santos (GO FAIR International Supporting
and Coordination Office) (20’)• Detailed Methodology and Application Guidelines for “The Global Covid-19 Index (GCI)”
Woody Ang Woo Teck (PEMANDU Associates, Malaysia) (20’)• Leveraging open data towards containing COVID-19
Obwaya Mogire (South Eastern Kenya University) (20’)•

Discussion
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Friday 25 September 2020

Forum of Early Career Data Scientists in the Asia & Oceania Area (01:00-03:30 UTC)
Conveners: *Akira Kadokura, WDS ECR Network members,
Juanle Wang, Yoshimasa Tanaka, Takashi Watanabe
01:00–03:30 Forum on Early Career Data Scientists in the A & O Area
(Chairs: Jesse Xiao, Akira Kadokura)
•
•
•
•

•
•
•
•
•
•
•

Introduction for WDS ECR Network (Invited)
Lianchong Zhang, Jesse Xiao (The University of Hong Kong, WDS ECR Network) (15’)
Information platform promoting global collaboration in microbial community (Invited)
Linhuan Wu (Institute of Microbiology, Chinese Academy of Sciences) (15’)
Disaster Rapid Damage Mapping with Remote Sensing: benchmarking datasets and methods (Invited)
Junshi Xia (Institute of Physical and Chemical Research (RIKEN)) (15’)
Introduction of FUXI Platform -Global Integrated Observation Data Management and Service System(Invited)
Yuanyuan Wang (Zhejiang University) (15’)
Activities toward Open Data and Research Data Management at NIES/CGER (Invited)
Yoko Fukuda (National Institute for Environmental Studies) (15’)
Standardization of biological sample information database (Invited)
Tazro Ohta (Database Center for Life Science, DS, ROIS) (15’)
Official Micro Data, Causal Inference and Evidence-Based Policy Making (Invited)
Junchao Zhang (Center for Social Data Structuring, DS, ROIS) (15’)
Recent Achievements of Deep Learning on Recognition of Modern Japanese Magazines (Invited)
Anh Duc Le (Center for Open Data in the Humanities, DS, ROIS) (15’)
A data-scientific approach toward understanding of the goldfish genome and morphological diversity(Invited)
Tetsuo Kon (Nagahama Institute of Bioscience and Technology) (15’)
Modelling the Suitability of Parcel Pick-up Lockers Using the Multi-Source Open Data (Invited)
Zilai Zheng (Graduate School of Life and Environmental Sciences, University of Tsukuba) (15’)
Discussion

Friday 25 September 2020

Promotion of Multi-Disciplinary Data Analysis,
Conclusive Adresses (05:00-08:30 UTC)
Conveners: *Takashi Watanabe, Asanobu Kitamoto, Masahito Nose, Ryuho Kataoka
05:00–06:30 Promotion of Multi-Disciplinary Data Analysis - 1(Chair: Takashi Watanabe, Asanobu Kitamoto)••

Introduction of the Session
Takashi Watanabe (International Program Office, World Data System) (5’)
Developing Open-Source Data Analytics and Tools to Address Interdisciplinary Issues in Political Science,
Human Geography, and Environmental Science
Joshua Brinks (iSciences, Limited Liability Company, Burlington) (20’)
16

•
•
•
•

Implementation of OA/FAIR Principles in Six Years - A Case Study of Global Change Research Data
Publishing & Repository
Liu Chuang (Chinese Academy of Sciences) (20’)
Data analysis and intelligent application in modern agricultural technology extension
Ying Deng (National Engineering Research Center for Information Technology in Agriculture,
China) (20’)
A Comparison between Logistic Regression and Decision Tree Methods for Predicting the Pre-term BirthRakesh Kumar Saroj (SRM University Sikkim-Gangtok, India) (20’)
Coffee Break

06:30–08:00 Promotion of Multi-Disciplinary Data Analysis – 2(Chair: Takashi Watanabe, Asanobu Kitamoto)
•
•
•
•

•

Importance of a Multi-disciplinary Study of Peculiar Environmental and Socio-economic Movements in
18/19 Century
Takashi Watanabe (International Program Office, World Data System) (20’)
A unique website JCDP that aims to disseminate scientific information on historical climate data
Takehiko Mikami (Tokyo Metropolitan University) (20’)
Historical records of red auroras in Japan: Collaboration between literature and science
Ryuho Kataoka (National Institute of Polar Research) (20’)
Research Across Disciplinary Boundaries: Data Challenges and Solutions in the Environmental and Ecosocial Sciences
Alison Specht (the University of Queensland, Australia) (20’)
Discussion

08:00–08:30 Conclusive Addresses (Chair: Masaki Kanao; each talk is allocated for 5’-10’)•••

Yasuhiro Murayama (National Institute of Information and Communications Technology (NICT))
Myung-seok Choi (Korea Institute of Science and Technology Information (KISTI))
Masaki Kanao (LOC Chair), summary of symposium & conference proceeding

Extended Abstract only @ conference web-site
Extended Abstracts will be included in the conference “Programme and Abstracts” online booklet.
•••

EA-1: Application of machine learning techniques to weather forecasting
V. Sakthivel Samy (National Centre for Polar and Ocean Research, India)
EA-2: Analyzing the early 19th century's geomagnetic declination in Japanese archipelago from
Tadataka Inoh's 67 volumes magnetic survey azimuth ledger Santou-Houi-Ki by Interdisciplinary study
Motohiro Tsujimoto (no affiliation)
EA-3: Data and metadata sharing among Asian countries
Masaki Kanao (Joint Support-Center for Data Science Research, ROIS)

17

International Symposium- DSWS-2020 “Global Collaboration on Data beyond Disciplines”

ABSTRACTS

Online Conference
23–25 September 202018

Open Access and Data Sharing of Nucleotide Sequence Data
Masanori Arita1*,2
Bioinformation & DDBJ Center, National Institute of Genetics, Yata 1111, Mishima, Shizuoka 4118540, Japan
2
RIKEN Center for Sustainable Resource Science, 1-7-22 Tsurumi, Yokohama, Kanagawa 230-0045,
Japan
Email: arita@nig.ac.jp

1*

Summary. Open access, free access, and public domain are different notions. For nucleotide sequence

data (NSD), the best practice is the policy of INSDC, or the International Nucleotide Sequence

Database Collaboration. INSDC partners, consisting of DDBJ in Japan, EMBL-EBI in Europe, and NCBI in
the US, have been the core framework of sharing NSD for over 30 years. Recent virus information,
however, are mainly distributed through the GISAID repository supported by the World HealthOrganization. The historical background of NSD databases can explain the outcome and future
prospect of the different publication policies.

Keywords. nucleotide sequence data (NSD), database, repository, INSDC, data policy

1. Open access, free access, andfundamental data-sharing policy of INSDC was

published in 2002 by its advisory board: freepublic domain

and unrestricted access to all of the data

Open access, free access, and public domain

records, without use restrictions, licensing

are all different. Although all of them seem torequirements, or fees on the distribution orprovide access without cost, licensing terms

use by any party including commercial sectors

may differ. Open access implies the clear[1]. INSDC is the core framework for sharing

statement of user rights in licensing terms.

NSD for over 30 years and consists of three

Free access, often associated with the sentence

partner nodes: the DNA Data Bank of Japan“all rights reserved”, indicates the requirement(DDBJ) at the National Institute of Genetics inof an official inquiry when the contents are

Mishima, Japan [2]; the European Nucleotidecommercially used or re-distributed. This isArchive (ENA) at the European Molecular

why commercial companies do not use ‘free’

Biology Laboratory's European Bioinformatics

software programs. Lastly, public domain

Institute (EMBL-EBI) in Hinxton, UK [3]; andindicates no copy- or other rights; any type of

GenBank at National Center for Biotechnology

usage is allowed even without credit. This

Informationdifference is evident for artificial products such

LibraryofBethesda, Maryland, USA [4]. GenBank andsequence data (NSD) from nature, however,Embl-Bank were established in 1982 andthe best licensing is not always evident.
INSDC

National

Medicine, National Institutes of Health inas artworks or literature. For nucleotide

Empirically,(NCBI),

followed(International

by

DDBJ

in

1987.

The

three

identifiers

calledAccession

institutions keep collecting and providing NSD

Nucleotide Sequence Database Collaboration)

withprovides a key to viable solutions. The

19

uniqueNumbers(ANs).

ANsaremandatory

deposited data in connection with any othermajority of life science and medical journals,

availability of data from each nation. The

information for sequence studies in the vast

database.

and the relationship with editorial offices wasThestrictconditionaffects

United States and European countries registerestablished to guarantee data accessibility andcovid-19 sequences to both INSDC and GISAID.

to assist reproducibility of published results.

China does to both its national Center(https://bigd.big.ac.cn/) and GISAID. Japan,

2. Traceability of NSD

however, deposit sequences only to GISAID.

This difference is affecting research

NSD are often the crucial part of life science
theiractivities in each country. The number ofimportant for the bioresource access andexceeds 50,000, and China is the most prolific

metadata information to describe the originof articles from Japan is relatively small,and the /COUNTRY qualifier in the NSD

Japanese data. Although the number ofthe sequenced organism indicated in terms ofscientific quality, the size of research bodystudies.

At

the

same

time,

becomecovid-19 related articles in PubMed database

benefit sharing (ABS). The INSDC system offers

country. Among major countries, the number

information using the BioSample repository

probably due to the restricted accessibility forannotation, defined as “locality of isolation ofresearch paper is not an ideal measure ofpolitical names for nations, oceans or seas,scales well with the article output.

metadata with ANs, scientific origin and

4. Conclusions

The same is true for patented sequences. Inof research body and therefore outputs. Since

sequences are tagged with the ANs andwith unique identifiers even for open data,

country/regional

originmay

followed by regions and localities”. With these

Data access and sharing policy affects the size

responsible authors become traceable for NSD.

data traceability is achieved through metadata

the US, Europe, Japan and Korea, patented
publicizedwithinthe

INSDC

coronavirus sequences are better publicizedframework.

without access restriction.

Although tracking every usage is difficult, thecurrent system offers realistic and sustainable

Acknowledgments. The author is grateful tosolution for data traceability.

the INSDC members. DDBJ is supported by

3. GISAID and virus sequences

Ministry of Education, Culture, Sports, Science

The GISAID repository (https://gisaid.org) by

and Technology (MEXT) of Japan, Japan

2008 on request from virus-affected countries

Development (AMED), and National Bioscience

World Health Organization was launched in

Agencyhesitant to publish sequences from the INSDC.

Database Center (NBDC).

influenza and coronavirus with support from

References

GISAID is the default repository of avian

1.

World Health Organization. The fundamental

difference from the INSDC is its accessagreement: GISAID users are forbidden toaccess, modify, distribute, or even display

20

for

MedicalResearchandBrunak, S., Danchin, A., Hattori, M.,
Nakamura, H., Shinozaki, K., et al.
Nucleotide sequence database policies.
Science,298(5597),
1333,
DOI:
10.1126/science.298.5597.1333b, 2002

2.

3.

Ogasawara O, Kodama Y, Mashima J,
Kosuge T, Fujisawa T. DDBJ Database
updates and computational infrastructureenhancement. Nucleic Acids Res, 48(D1),
D45-D50, DOI: 10.1093/nar/gkz982, 2020
Amid C, Alako BTF, Balavenkataraman
Kadhirvelu V, Burdett T, Burgin J, et al. The

4.

21

European Nucleotide Archive in 2019.
Nucleic Acids Res, 48(D1), D70-D76, DOI:
10.1093/nar/gkz1063, 2020
Sayers EW, Cavanaugh M, Clark K, Ostell J,
Pruitt KD, et al. GenBank. Nucleic Acids Res,48(D1), D84-D86, DOI: 10.1093/nar/gkz956,
2020

Developing Open-Source Data Analytics and Tools to Address
Interdisciplinary Issues in Political Science, Human Geography, andEnvironmental Science
Joshua Brinks1*, Thomas Parris1, Robert Chen2, Eileen Shea3, Cynthia Crowley1,
Dan Baston1* ISciences, LLC, Burlington, VT 05401, United States
CIESIN/Columbia University; Manager, NASA Socioeconomic Data and Applications Center (SEDAC),
United States
3
CASE Consulting International, North Carolina, United States
1

2

Email: jbrinks@isciences.com

Summary. Environment-security researchers and practitioners must overcome several technicalchallenges in the course of their efforts. In the absence of an open-community platform, eachresearcher or institution compiles and curates their own data repositories, analytical tools, andcomputational environments. This results in significant duplication of effort, limits the replication andvalidation of research, and diminishes the interoperability of analytic tools. The Data ANalytics andTools for Ecosecurity (DANTE) Project is a cooperative partnership between the United States Army
Corps of Engineers, CIESIN, ISciences, L.L.C., and CASE International that aims to develop a community

platform with a suite of open-source tools and content that reduce common barriers to entry forinterdisciplinary research in human geography, social and political science, and global environmental
change. In addition to providing a curated catalogue of data sets and custom R programming tools

tailored to interdisciplinary research, one of the primary goals of the DANTE Project is to demonstrate
reproducible research through replication of recent high impact peer reviewed studies.
Keywords.

Open-source

tools,

R,

environment-security,

1. Introduction

emerging

griddeddata

challenges such as pandemics,

Social and political scientists, analysts, andextreme climate events, resource conflict, mass

environment and security must work closely

argue that the development of sustained andtargeted data products describing our rapidly

from

the

predictions

theenvironment-securityof

migration, and supply chain disruption. We

with Earth scientists to access reliable andtrusted partnerships between stakeholders

changing planet. These include scenarios andhumanitarian, and private sectors is needed inaccelerating

human-

Establishing such partnerships also requires

information is a tremendous opportunity fordisciplines--including the natural, social, health,society respond effectively to a range of

new tools to facilitate the integration ofpolicymakers

in

of

the

futureevolving

conditions

changes incoupled

area

due

to

academic,

national

security,

community.

environment systems. Providing this data andcontributions from a wide variety of scientificbothengineering, and data sciences--along with

scientific advancement andhelping22

science into operational products and services

global climate, migration, political conflict, andthat are reliable and replicable.

agriculture can more easily adhere to open

data and open-access science principles toCentral to this premise, and ultimately successreproduce their research.

in the environment-security arena, is therecognition of recent trends in scientific

2. Conclusions

inquiry and theory that have witnessed aThis presentation will highlight current issues

movement towards replicable science andinopen data practices. These include open-

multidiscipinaryintersection

source code and proper attribution of data

of

the

partnerships
human

at

the

geography,

environmental stressors, and political scienceand conflict research sectors. Additionally, wesets, software, and packages used for analyses.

will showcase recent open-science efforts from

Although replication is a core component of

the DANTE Project. This includes, but is not

the scientific method, it is often the mostlimited to, spatial analysis tools, open-access R

overlooked. In the absence of replication,

packages, gridded datasets, and materials

scientific endeavours may be rewarded bypromoting open-science and replication in thepoor methods that result in negative policy

environmental,

implementations and erroneous theoreticalsciences.

assumptions. While close replication presents asignificant challenge in psychological research,

political,

andgeographicalAcknowledgments. This presentation, and theclinical trials, and field ecology, research efforts

DANTE Project, are funded by United States

focused on standardized global datasets ofArmy Corp of Engineers STTR program.

23

The Power of PIDs at the Cambridge Crystallographic Data
CentreIan Bruno1*, Eric Rogers1, Suzanna Ward1* Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge, CB2 1EZ, United Kingdom
Email: bruno@ccdc.cam.ac.uk

1

Summary. The Cambridge Crystallographic Data Centre (CCDC) is responsible for the management andcuration of the Cambridge Structural Database (CSD) which contains the experimentally-determined 3D

crystal structures of over 1 million organic and metal-organic chemical compounds. This workshop

presentation outlines the use of persistent identifiers (PIDs) at CCDC. Particular attention is paid to theintegration of PIDs into CCDC’s data management, preservation and publication workflows and theutility of PIDs for aligning the repository with FAIR data initiatives. It will also outline how PIDs can beused to enable linking between related research objects and data repositories and highlight
opportunities for further channelling the power of PIDs.

Keywords. Persistent identifiers, data repository, Cambridge Structural Database (CSD), chemistry,
crystallography

1. Introduction

2.1 Identifiers assigned from deposition to

Structural Database (CSD) [1] over 50 years ago,

Each structure receives a unique accession ID or

mechanisms for improving the efficiency of how

back to the depositor to be cited in theiruser communities. Throughout this, persistentidentifiers are added to datasets, including atools for enabling the reliable management and

DataCite, the metadata for the structure is

Since the establishment of the Cambridge

publication

the CCDC has sought to develop and adopt

Deposition Number which is communicateddata is stored, processed and shared with its

manuscript.

identifiers (PIDs) have proved to be important

CCDC Data DOI. By registering the DOI throughpreservation of data by the CCDC.

made openly accessible and searchable via

2. Using PIDs to make data FAIR

Since 2016, users can provide an ORCID ID

Uponpublication,

additional

DataCite.

The value which PIDs can add to research datawhen depositing data which will be shown

Principles for scientific data management andUsing the CCDC online data submission form,

alongside the data record once published.

has been highlighted through the FAIR Guiding

depositors can also provide a DOI link to thestewardship [2]. As a proponent of FAIR data,

raw data of their structures if this is archived in

the CCDC has adopted PIDs where possible

another repository.

with the aim of rendering the data it holds more
findable, accessible, interoperable and reusable.

2.2 PIDs for CSD Communications

CSD Communications is a platform thatprovides researchers with the opportunity to24

publish datasets independently of a journal

article [3]. All CSD Communications receive a

4.1 Adoption of InChI

CCDC DOI which is communicated directly to

The International Chemical Identifier (InChI) [6]

publicly available, allowing for immediate

identify a particular chemical compound. The

the depositor as soon as the data is madeprovides a way to uniquely and unambiguously

access and citation. In 2019, an ISSN was also

CCDC has used InChIs internally to help

acquired [4] to help publishers, institutions andestablish links between the CSD and otherCommunications.

to enable linking of relevant datasets acrossresearchers track and record citations to CSD

chemistry resources and we aim to build on thischemical and biological domains [7].

3. Using PIDs to link data to articles

4.2 Capturing and linking funding data

The CCDC has agreements in place with journal
publishers

who

communicate

Building on the Open and FAIR data movement,

publicationinternational policy makers have put forward

information for articles associated with datasets

proposals for making research more open anddirectly to us in a machine-readable format.

transparent. To help those who must monitor

Using PIDs as a central component forcompliance with such policies, it is our intention

matching records, metadata is automaticallyto explore the use of organisational and otherupdated in the CCDC system so data can be

identifiers

unembargoed and made publicly available

organisations

alongside an article. Given the amount of X-ray
crystaldata

published

each

year,

these

datasets,

research

was established through global collaboration

andlifecycle

andacrosspublication

are discoverable, citable and reusable long after

Scholix-based

they were first generated.

mechanisms are underpinned by the use oftheorganisations,workflows. PIDs help ensure research datasets

between journal publishers, data centres andenableresearchers,

between them to be tracked throughout theuniversal framework for data-article linking thatfurtherwithof PIDs enables research objects and the links[5] since its conception. Scholix defines aandassociatedandentities such as chemical structures. Adoption

The CCDC has been part of the Scholix initiative

PIDs

are

funding

PIDs enable the reliable identification oftimely publication.

providers.

that

the

5. Conclusions

addition of metadata to datasets and theirservicetrack

published data sets.

automated systems are vital for accurate

other

to

automatic

References

identification and publication of links between

1. Groom, C. R., Bruno, I. J., Lightfoot, M. P. &Ward, S. C., The Cambridge Structural
Database, Acta Crystallogr. Sect. B Struct. Sci.
Cryst. Eng. Mater., 72, 171–179, 2016
https://doi.org/10.1107/S2052520616003954
2. Wilkinson M., Dumontier M., Aalbersberg I.
et al., The FAIR Guiding Principles forscientific data management and stewardship.

resources.

4. Future directions for PIDs at the
CCDCWork is currently underway at the CCDC tofurther harness the opportunities afforded by
PIDs.

25

6. Heller, S., McNaught, A., Stein, S.,
Tchekhovskoi, D. & Pletnev, I., InChI - theworldwide chemical structure identifier
standard. J. Cheminform. 5, 7, 2013.
https://doi.org/10.1186/1758-2946-5
7. CCDC, BioChemGRAPH project will improvedata synergy to facilitate drug development,
The Cambridge Crystallographic Data Centre(CCDC),2020
https://www.ccdc.cam.ac.uk/News/List/bioch
emgraph-projectSci Data, 3, 160018, 2016
https://doi.org/10.1038/sdata.2016.18
3. CCDC, CSD Communications, The Cambridge
Crystallographic
Data
Centre(CCDC).
https://www.ccdc.cam.ac.uk/Community/csdcommunications/
4. ISSN, CSD Communications, International
Standard Serial Number International Centre.
https://portal.issn.org/resource/ISSN/26319888
5. Burton A. et al., The Scholix framework forinteroperability in data-literature informationexchange.
D-Lib
Mag.
23,
2017.
https://doi.org/10.1045/january2017-burton

26Adopting Persistent Identifiers in Open Research Infrastructure:
ORCID and beyond
Chieh-Chih Estelle Cheng1** 10411 Motor City Drive, Suite 750, Bethesda, MD 20817, United States

1

Email: e.cheng@orcid.org

Summary. One key factor in making science truly open lies in improving data quality and transparency,

so that researchers can easily share their work, and organizations can easily acquire the most accurate
and up-to-date research information in an open and interoperable way. Such interoperability requires
a robust and global-scale infrastructure that enables the open creation of linkages between researchers,

research institutions, publishers, and funding organizations. The use of persistent identifiers (PIDs) like

ORCID supports the research community in collecting, managing, analyzing and showcasing datatransparently and reliably. For Open Science to become a reality, communities and organizations needto collaborate to establish a shared vision and implement policies, technologies, and services.
Keywords. Persistent identifiers, ORCID, Interoperability, Trusted Connections, Transparency

1. Introduction

the use of Digital Object Identifiers (DOIs) inorganization supported by a global community

use of arXiv identifiers, PubMed and PubMed

institutions, publishers, funders, professionaland other organization identifier (OID) likestakeholders in the research ecosystem. ORCID

Organization Registry(ROR). A prime example

Interface) that allows systems and applications

connect

collecting data from and connecting data to theillustrated as follows.

ORCID is a non-profit community driven

publishing and data repositories, and with theof member organizations, including researchCentral identifiers and most ISBN identifiers,

associations, service providers, and other

Ringgold ID, GRID ID, LEIs, and Researchoffersfor how ORCID in alignment with other PIDs to

an

API

(Application

Programming

researchers,

contributions,

to connect to the ORCID registry, including

and

their

the

grants,theirorganizations

is

ORCID Registry.

Openness is one of the key ORCID values, and

ORCID works with the global community andpartners to build and sustain a technology
infrastructure that supports sharing of research
information between systems.

The ORCID Registry supports a wide variety ofexisting identifiers, which manifests a wide
range of connections between ORCID iDs andother PIDs. For instance, ORCID connects with

Figure 1. The virtuous circle of interoperability27

2. Interoperability

andConnections

Trustednational perspective based on regions orcountries, where ORCID services and resources

can be applied in regional and national contexts.

Such interoperability and connections amongpeople (researchers and(researchorganizations,

In the Asia-Pacific Region, there are 4 ORCID

curators), places

facilities,

Consortia: Australia, Japan, New Zealand, andfunders,

Taiwan [2]. They have joined ORCID’s consortia

repositories, and publishers) and things (such as

program to take a coordinated national

equipment, facilities, publications, and datasets)are realized

through

researchers

withapproach to building ORCID into their national

ORCID integrations.

research information services or platforms.

Organizations use the ORCID API to link
theiraffiliationsor

3.1 National Apporaches to ORCID in thecontributions. All this begins from researchers

Asia-Pacific (APAC) Region

sharing their ORCID iDs with their affiliated

Recognizing the importance, many countries

institutes as their personnel record is created,have statements on adopting ORCID at aand in turn the institute shares back thenational level, including Australia and New

researcher’s affiliation or education information

Zealand. The former issued the Joint Statement

into the researcher’s ORCID record, using theorganization

ID

for

the

of Principle: ORCID - Connecting Researchersorganization

and Research, with the funders releasing a joint(person/ORCID + affiliation/OID). When astatement on ORCID in which they encouraged

researcher goes to a publisher website to

all researchers applying for funding to have an

submit a manuscript, they can use their ORCID

ORCID iD. The latter developed the New

record to share their iD with publishers as well

Zealand ORCID Hub, which allows all New

as their institutional affiliation information. And

Zealand Consortium members to productively

by the time the paper is published, PIDs

engage with ORCID regardless of technical(person/ORCID + paper/DOI + publisher andresources.

research institute/OID) are all interconnected,

which can be easily shared with the researcher’s

4. Conclusions

home institution or funder [1].

Each sector in the research community has thepower to improve the value and openness of its

Because the connections are made as a

research information. This will require them toresearcher interacts with trusted researchfulfil their roles and responsibilities by adopting

information systems, it both imbues verification

PIDs to foster an open research infrastructure.

into the connection and reduces the workneeded for researchers and institutes toReferences

manage research information and research

1. Haak Laurel L., Meadows Alice, Brown Josh,
Using ORCID, DOI, and Other Open
Identifiers in Research Evaluation, Frontiers in
Research Metrics and Analytics., 3, 28, 2018https://doi.org/10.3389/frma.2018.00028
2. ORCID Consortia,
https://orcid.org/content/orcid-consortia[accessed on: August 2020]

outputs.

3. ORCID Consortia

As a community-driven organization, ORCID
has been in partnership with the international

research community, from both a sector

perspective collaborating with organizations

such as World Data System and DataCite to a28

COVID-19 Data and Knowledge Hub
Liu Chuang1** Global Change Research Data Publishing & Repository and GCdataPR -WDS,
No.11A, Datun Road, Chaoyang District, Beijing , 100101, China
Email: lchuang@igsnrr.ac.cn

1

Summary. On January 31, 2020, the World Health Organization (WHO) announced the
COVID-19 as a Public Health Emergency of International Concern (PHEIC). The
Consultant Committees on Communication and Information Technology (CCIT) and Life
Sciences and Human Health (CCLH) of China Association for Sciences and Technology(CAST) for the United Nations jointly initiated COVID-19 Knowledge & Data Hub
immediately. Professor GONG Ke, President of World Federation of Engineering
Organizations (WFEO) and Chairman of CCIT leads the initiative.Member of the leadinggroup includes Prof. LIANG Xiaofeng, Secretary-general of the CCLH, Mr. QIN Jiuyi,
Deputy Director of Consultant Committee Office of CAST and Prof. LIU Chuang, memberof CCIT. Prof. LIU Chuang was appointed to be the leader of the technical group andProfessors. LIANG Xiaofeng and YI Heya, Secretary of the CCLH, as the co-leaders of theinformation resources group of the COVID-19 KD Hub. The Global Change Science
Research Data Publishing & Repository (GCdataPR) (http://www.geodoi.ac.cn) hosts theonline system of the COVID-19 Knowledge & Data Hub. The GCdataPR is jointly
established in 2014 by the Institute of Geographic Sciences and Natural Resources
Research of Chinese Academy of Sciences (IGSNRR/CAS) and the Geographical Societyof China (GSC).The GCdataPR is one of Regular Members of the World Data System of
the International Science Council (WDS/ISC) and China Data Publishing Center of
Global Earth Observation Systems. The China Association for Science and Technology(CAST) is the largest non-governmental organization of scientific and technologicalprofessionals in China, serves as a bridge that links the Communist Party of China andthe Chinese government to the country's science and technology community. Throughits 210 national member societies and local branches all over the country, CAST
maintains close ties with millions of Chinese scientists, engineers and otherprofessionals working in the fields of science and technology. CAST attained UN
Economic and Social Council (ECOSOC) consultative status in 2004 and took active partin UN activities since then.(http://english.cast.org.cn/).
Keywords.

29

Data analysis and intelligent application in modern agricultural
technology extension
Chunjiang Zhao1, Huarui Wu 1, Ying Deng 1** National Engineering Research Center for Information Technology in Agriculture, Beijing 100097,
China
Email: yingdeng87@163.com

1

Summary. It is difficult to popularize agricultural technology by offline manpower because of thelarge population and wide distribution of farmers in China. (Chinese) National Engineering ResearchCenter for Information Technology in Agriculture (NERCITA) has assisted the Ministry of Agriculture

and Rural Affairs of the People's Republic of China (MoA) in developing the China Agro-Tech
Extension Platform, The CATE, which is dedicated to assisting the implementation of agricultural

technology promotion with information technology as the technical support. Through statisticalanalysis of the data such as agricultural conditions, user logs, Q&A, GPS tracking, Etc., CATE provides

the basis for overall decision making for the management department. Throughout monitoring theonsite IoT data, CATE is able find out abnormalities and early warn the farm owners. Through natural
language processing, the intelligent question and answer system of agricultural technology is trainedby the users’ Q&A data, so as to assist experts and agricultural technology promoters to solve farmers'
production technology problems more quickly and effectively. According to the user's historical accesspath, analyse the user's personal interests and areas of expertise in agriculture to carry out
personalized recommendation for both agricultural technology personnel and farmers.

Keywords. Agro-tech extension, agricultural condition analysis, NLP, Intelligent Q&A, personalizedrecommendation

1. Introduction

Agri-Tech Extension Platform, The CATE, which

In order to provide Chinese farmers withis dedicated to assisting the agricultural

agricultural technical guidance services, theof information technology.

extension stations throughout the country to

2. Core resources

However, due to the large number and thetechnicians, 2,690 experts and more than 6higherqualityandmoretechnology promotion work with the support

immediate

MoA has set up agricultural technology

vigorously promote agricultural technology[1].

Nearly all the 440,000 front-line agricultural

distribution of a wide range of Chinese farmers,
theofflineextensionisartificial
work

way

of

million farmers have registered.

agricultural

time-consuming

CATE has collected a total of 10.35 million

andagricultural technical questions online so far,popularization of the Internet, NERCITA has

more than 47 million answers, of which 7.05

laborious. With the rapid development andof which nearly 90% have been answered, with

been assisting the MoA to develop the China

million have been answered by other users

30

4. Make It Smarter

thumb up, 1.75 million have been adopted by
the questioner, and 83.8% of the questions

Previously, when a farmer came up with ahave been approved or responded by thequestioner after being answered.
Totally

more

than

3.9

millionquestion he may submit a new post rather

than check up from the existing posts due tovalid

the low accuracy of the key words search

agricultural condition messages and 14 million

algorithm. It imposed a great burden on thevalid daily logs were uploaded. And more thanwork of agricultural technician and also

30,000 agricultural condition messages weregenerated a lot of redundant data. In order toposted, and 3.5 million kilometres service

resolve the problem, CATE finds out threeroute GPS data was received from the agro-

ways:

technicians each day.

• A

agricultural

agricultural technology. Then the selected

technicians'

valid Q&A text was iteratively input into theagricultural condition reporting and posted
logs,

CATE

agricultural

constructedsituationareal-time

agricultural technology knowledge base.

nationwide• An intelligent question answering system ofmonitoring

agricultural

those queries with incomplete structure. And

the hot spots of agricultural condition.

then

The platform analyses the service of agro-

knowledge

fromsematic searching

reordered by their relevance using deep

technology extension can be aware of the dailylearning model. Finally the optimized answer

service content and service coverage of eachwill be generated according to fixed thresholdagricultural technician, so as to provide

parameters. The intelligent Q&A system replies

decision basis for assessing the performance

users’ questions immediately and accurately,of each agricultural technician and planningtechnology extension system.

relatedRecalled knowledge text list will then be

the administrative department of agriculturalofrecallmodel according to the query words list.

historical interviews, GPS tracking, etc. So that

content

theknowledge base using

technicians based on data such as online Q&A,

work

constructed.

completes and does anaphora resolution to

detection, rapid early warning and processing

futurewas

technology[2]According to history dialogue, the model

network, providing data support for the

the

text

and quickly remove data content irrelevant toThrough the analysis of farmers, agricultural
andgarbageclassification NLP model was built to efficiently

3. Data Analysis
expertssemi-supervisedso that repeated questions and the work of

agricultural

agricultural technicians are reduced.

• The Personalized Recommendation system

The platform is connected to onsite IoT

analyses users’ recent browse history, abstracts

devices of thousands of farms receiving theenvironmental data and real-time images so

related keywords and computes the sematicthe meteorology, moisture content, diseases

users’

representation. It computes interestingness bythat CATE is able to monitor and early warn for

visit

frequency

andkeep-in-page-correction.

Integrated

with

text

seconds along with the forgetting curveand insect pests, and provides remote control
interface of Internet of Things equipment tosimilarity

weights, the model outputs the ordered

realize integrated remote management.

recommendation result list to users, and it

31

auto tunes itself refer to users’ satisfaction
feedback.
contents

Therefore,areadvancedrecommended

experience is improved.

technical

and

user

5. Conclusions

Through the analysis of agricultural situation

logs and other data, a nationwide agricultural
condition monitoring network has been built,

and the analysis of agricultural technicians’
working data assists administrators making

management

decisions.

Through

artificial

intelligence methods, the junk filter, intelligent

agro-technical Q&A system and personalizedrecommendation system are constructed, andmakes up the real-time, accuracy and advanceof the artificial service of CATE platform, so as

to further improve the efficiency of agrotechnical promotion service.

References
1.

2.

Research Group on Reform of Agricultural
Technology Extension System in China.
Current
Situation,
Problems
andCountermeasuresof
Agricultural
Technology
Extension
inChina[J].Management World, 000(005):50-57,75,
2004, (In Chinese)
ZHANG Mingyue,WU Huarui,ZHU Huaji.
Analysis of Extraction of Semantic Feature
in Agricultural Question and Answer Basedon Convolutional Model[J].Transactions of
the Chinese Society for Agricultural
Machinery, 49(12):203-210, 2018, (In
Chinese)32

DataCite Commons –A new service to explore the PID Graph
Martin Fenner1** DataCite, Am Welfengarten 1B, Hannover, 30167, Germany
Email: martin.fenner@datacite.org

1

Summary. In August 2020, DataCite launched DataCite Commons, an easy to use new discovery service

for scholarly works, people and organizations, and their connections. This abstract provides backgroundinformation about the new service.

Keywords. Persistent identifier, PID Graph, Connections, FREYA

1. Introduction

Research Data Alliance (RDA) Scholarly Link

metadata describing the content identified by

the Make Data Count initiative (Fenner et al.,discovery, reuse and understanding of scholarly(Fenner et al., 2016), and more.

research data has been the focus of the non-

2. User Stories

Registration Agency DataCite since its founding

Graph are user stories, important problems in

Persistent identifiers (PIDs) and standardized

Exchange (Scholix) initiative (Aryani et al., 2017),

these PIDs are centrally important for the2018), work by the EC-funded THOR project

research. The identification and description of
profit membership organisation and

The driver for all technical work on the PID

DOI

in 2009.

scholarly

themselves, but they are also essential for

infrastructure. One of the first activities of theadequately

Metadata not only describe scholarly resources
unequi-vocally describing

betweenresources.

forexisting

August 2018. A total of 45 user stories havebeen initially documented in GitHub issues, andtheir connections (edges) form a graph. As PIDs

then made available in the PID Forum forare essential for building and exploring thiscommunity feedback (The PID Forum, n.d.). An

graph, we call it the PID Graph(Fenner & Aryani,

example user story that has been documented2019) . One focus of the European Commission

in detail elsewhere is(EC)-funded FREYA project (FREYA, n.d.) thatstarted in December 2017 and wraps up in

As a researcher, I want to get a list of all my

November 2020 was to identify the mostresearch outputs (publications, datasets, software, etc.)

important use cases, agree on the technical
build

be

stories, cumulating in an in-person workshop in

funding. Together these resources (nodes) and

and

with

can’t

discussion and prioritization of these user

theseconnections include citations, authorship andarchitecture,addressed

that

FREYA project was therefore the collection,

the connections

Examplescommunication

supported by grant funding, and how often they are

production

cited, to demonstrate the impact of my work.

infrastructure to explore this PID Graph. This
work builds on earlier work that includes the33

3. GraphQL API

resources described by PIDs and associatedmetadata (Tab. 1).

The next step in PID Graph development was
the exploration of existing PID infrastructure,

Table 1. Number of PIDs in DataCite Commons as

and how this infrastructure needs to beof

adopted to address PID Graph user stories. By

6 Sep 2020.

February 2019 it became clear that extending

Identifierand connecting the existing PID provider REST

Works

APIs was not enough to address the PID Graph

Peopleuser stories in a scalable way. We therefore

Organizations

looked at other technology options, including

Total

SPARQL, and eventually picked GraphQL, aquery language that is a good fit for the existingbackendservicessuch

19,798,202

ORCID

9,622,911

CrossrefROR

8,771,341

98,332
38,290,786

DataCite, ORCID and ROR. For Crossref it is asource technology, and can work with existing
provider

DataCite

DataCite Commons includes all PIDs fromuser stories, has become a widely adopted open
PID

Count

subset (7.51%) of the total number of DOIs

as(116,810,696 as of 6 Sep 2020). Many more

relational databases and Lucene-based search

Crossref DOIs will be imported into the service

indexes (Solr, Elasticsearch). We launched a pre-in the coming months, with the number of

release GraphQL API in May 2019, and a pro-

Crossref DOIs expected to exceed the number

duction version in May 2020 (Fenner, 2020a).

of DataCite DOIs before the end of 2020.

4. DataCite Commons

GraphQL greatly simplifies the backend

services architecture powering the PID Graphbut is an API that few users will directlyinteract with. We started writing Jupyter

notebooks to interact with the GraphQL API,

including 10 notebooks that directly address
10 PID Graph user stories, for example(Petryszak et al., 2020).

The next step was then to build a web

service powered by the GraphQL API, and on
August 27, 2020 the DataCite Commons

service was launched for initial public feedback(Fenner, 2020b). The official launch will be in

October 2020, as the EC-funded FREYA projectis wrapping up.

DataCite Commons was built with React,the currently most popular Javascript

framework that integrates seemlessly with

GraphQL backend services. The new service

provides access to 28 million scholarly

34

3. Fenner, M. (2020b). DataCite Commons—
Exploiting the Power of PIDs and the PID
Graph. https://doi.org/10.5438/F4DF-4817
4. Fenner, M., & Aryani, A. (2019). Introducing
the
PID
Graph.
https://doi.org/10.5438/JWVF-8A66
5. Fenner, M., Demeranville, T., Kotarski, R.,
Dasler, R., M0cEntyre, J., de Mello, G., Vision,T., Dappert, A., & Farquhar, A. (2016). Thor:
Conceptual Model Of Persistent Identifier
Linking.
Zenodo.
https://doi.org/10.5281/ZENODO.48705
6. Fenner, M., Lowenberg, D., Jones, M.,
Needham, P., Vieglais, D., Abrams, S., Cruse,
P., & Chodacki, J. (2018). Code of practice forresearch data usage metrics release 1(e26505v1).
PeerJ
Inc.
https://doi.org/10.7287/peerj.preprints.265
05v1
7. FREYA. (n.d.). Retrieved 6 September 2020,
from https://www.project-freya.eu/en
8. Petryszak, R., Fenner, M., Lambert, S., Llinares,
M. B., & Madden, F. (2020). FREYA PID
Graph—Grant Outputs (1.1.1) [Computer
software].
DataCite.
https://doi.org/10.14454/QAYM-KT26
9. The PID Forum. (n.d.). The PID Forum.
Retrieved 6 September 2020, fromhttps://www.pidforum.org/

5. Conclusions

DataCite Commons wraps up the FREYA workon launching a production service to explore

the PID Graph, but it also is the starting point

for a totally new discovery platform for open
science.

Acknowledgments. The work on DataCite

Commons and the DataCite GraphQL API is
supported by funding from the European

Union’s Horizon 2020 research and innovation
program-me

777523.

under

grant

agreement

No

References

1. Aryani, A., Burton, A., Manghi, P., Bruzzo, S.
L., Stocker, M., Schindler, U., Diepenbroek,
M., Fenner, M., & Koers, H. (2017). Scholix
Framework: Building a Bridge Between
Research Data and Publications. 304151
Bytes.
https://doi.org/10.4225/03/58CA546A18C5
0
2. Fenner, M. (2020a). Powering the PID Graph:
Announcing the DataCite GraphQL API.
https://doi.org/10.5438/YFCK-MV39

35

Activities towards Open Data and Research Data Management at
NIES/CGER
Yoko Fukuda1*, Tomoko Shirai1* National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki, 305-8506, Japan

1

Email: fukuda.yoko@nies.go.jp

Summary. The Center for Global Environmental Research (CGER), National Institute for Environmental

Studies (NIES), has been managing and operating the Global Environmental Database (GED) since2014 as a platform for publication and search of data, focusing on global environmental research. As adata management platform, the NIES/CGER started designing and developing the research data
management system (RDMS) in 2018. In this presentation, we introduce activities toward open data

and the RDMS at NIES/CGER.

Keywords. Open data, Open science, Research data management

1. Introduction

The Center for Global Environmental Research

3. ResearchStudies (NIES) conducts research on global

The CGER started designing of the research

System for CGER

(CGER), National Institute for Environmental
environmental issues with particular emphasis

April 2018. The CGER RDMS mainly supports

the NIES. The CGER keeps intensive monitoringthe

networks of the atmosphere, ocean, forest

metadata,

management with a team regardless of thewide range of fields such as water environment

open/close status of data. Research data andand biosphere in addition to the above fields.

metadata registered in the CGER RDMS can bepromptly released from the GED. Research

2. Global Environmental Databasethecreating

RMDS also enables collaboration of data

CGER also supports to develop databases for adata,

works:

data and supplementary material. The CGER

regions with international cooperation. The

thesefollowing

Licensing, DOI minting, and management of

ecosystem in the Asia, Oceania, and Pacificdisseminate

Management

data management system (CGER RDMS) inon climate change as the core organization of

To

Data

data also can be visualized in Quick Plot whichCGER

has been developed for both the RDMS andconstructed Global Environmental Database

the GED.

(GED) in May 2014 as a data publication

In order to collect opinions about whatplatform of the CGER. The GED mainly

NIES staffs want for the RDMS, test trials of theanalysis support tools. The CGER has been

During the last half of FY 2020, the RDMS will

response to the request from researchers sinceand the operation of the CGER RDMS will be

provides research data, real-time data, andCGER RDMS were conducted in August 2020.

supporting DOI minting to research data in

be improved based on a user’s perspective,

2016.

partially started in or after FY 2021.

36

4. Information

serviceToeffective

Pacific Monitoring sites
promotetheof

5. Challenges

Asia-

utilization

The author joined NIES after getting a phD in

the field of earth science. The experience inobservations

of

and

data

analysisfacilitatesobservational data on greenhouse gases andsupporting data activities, especially metadata

website "Asia-Pacific Monitoring Sites" wasperspectives of a data user and a researcher.

provides basic information on the observation

the expert knowledge and IT skills are lacking.

such as the location of the observation site

rewarding since many NIES researchers show amoment, there is no system to download data

Remaining concerns are lack of clear career

website so that it is easier to use data, further

persons who are working on the open sciencecreation and the RDMS designing from both

air pollutants in the Asia-Pacific region, aThis website

Meanwhile, there are many situations where

which has been conducted mainly by NIES

Nevertheless, my works are definitely very

and the observation parameters. At thestrong interest in research data management.

files directory from this site. To improve thepaths and role models. The network ofconsideration will be needed.

is important.

released in February 2018.

And

37

Japanese DOI RA, Japan Link Center (JaLC) and its collaboration
with ORCID
Masashi Hara1** Japan Science and Technology Agency, Tokyo, 102-8666, Japan
Email: mhara@jst.go.jp

1

Summary. Digital Object Identifier (DOI) is becoming more common recently and is used for not only

journal articles but also for research data, research report, e-learning and so on. Japan Link Center (JaLC)
started in 2012 with the authorization by the DOI foundation and is a Registration Agency in Japan. JaLC
takes a role to register DOIs to academic contents which are produced in Japan and already hasregistered more than 6 million DOIs. In 2020 JaLC has provided a function in which the authors could

link their research outputs to ORCID my-page. JaLC will continue to offer new functions to provide amore efficient academic ecosystem in Japan.

Keywords. DOI, ORCID, Japan Link Center (JaLC), Registration Agency (RA), Persistent Identifier (PID)

1. Introductionof Japan Link Center (JaLC) and its newly

A researcher does research, submits new papers

function which is linked to ORCID.

new positions, and reports his/her outputs to

2. DOI and Japan Link Center

do so, it is important for stakeholders like

Persistent Identifiers (PIDs) and registered toto publishers, applies new research grants andDOI (Digital Object Identifier) is one of the

FA and/or belonging organizations. In order toapprox. 220 million contents as of March 2020.

evaluators, reviewers, policy makers to access

The number of DOIs exceeded 50 million insame unique information. So, the concept of

2011, 100 million in 2015, and 200 million in

Persistent Identifier (PID) is developed and for

2019, doubled in about 4 years. The number ofexample, DOI for articles and ORCID for

DOI resolution is 500 million to 600 million

researchers become common nowadays.

every month. These figures show that DOI

system is a -fundamental infrastructure.

The International DOI foundation (DOI

foundation) was founded in 1997 to develop

and manage DOI systems. The DOI foundation
andCorporationfor

National

Research

Initiatives (CNRI) established DOI system, using
the handle system CNRI had developed. After
that, DOI became an international standard
ISO2632:2012 in 2012.

This presentation will include what DOI is,

The DOI operation is governed by the DOI

what a registration agency is in Japan, the rolefoundation and the DOI Registration Agencies

38

(RAs). The DOI foundation makes policies andresearcher’s ORCID record automatically. To doregister DOIs, assign Prefix and keep the DOI

metadata.

manages RAs. At present, there are 11 RAs, they,so, the ORCID ID should be included in the JaLC

registration. Statistics show that110 million

In the case of Search & Link function, a

DOIs out of the total 220 million are from theresearcher searches her/his research outputs in

RA Crossref (52%), with 33 million from ISTIC

the “JaLC contents search page” and then these(15%), 21 million from Datacite (10%) andJaLC

outputs can be connected to the researcher’s

The JaLC was authorized as a RA in March

not need the ORCID ID to be included in the

covers 6 million DOIs (3%).

ORCID record one by one. This function does2012 by the DOI foundation. The JaLC isJaLC metadata.

governed by the Japan Science and Technology
Agency(JST),the

National

InstituteJaLC released these functions this spring andforwith a few months passing by,the login fromof Informatics (NII) and the National Diet Library

the Search & Link function is used about 2,000

is to collect the academic contents created infunction usage is unfortunately still low.

Materials Science (NIMS), the National Institute

ORCID web site has counted 10,000 times and(NDL) and operated by JST. The purpose of JaLC

times per month. However, the Auto-Update

Japan, to promote the usage of DOI in Japanand to bringbetter

4. Conclusions

access to the Japanese

research outputs for oversea users JaLC covers

JaLC is willing to bring more services utilizing

DOI registration for the journal articles, books,

DOIs to reduce researcher's burden and toresearch reports, research data, e-learning andbring a better research environment. Next year

so on.

we are planning to start to provide abstracts

which are in the metadata. Furthermore, JaLC is

3. New Function with ORCID

trying to collaborate with more international

JaLC released Auto-Update and Search & Link

partners.

function integrated with ORCID. Nowadays it isReferences

becoming more common that researchers

manage their research outputs in ORCID and1.

using ORCID in publishing workflow during or
during grant proposal submission. These new2.

functions make it possible for researchers tohttps://doi.org/10.11502/JaLC_policy

connect their contents which are in JaLC to[accessed on: August 2020]

researchers’ ORCID my-page. It enables correct

information with ease so we expect this to

reduceresearchers’

management.

burdenon

Hara, M., Sato, R., Mimura, N., DOI and JaLC
activities. Journal of Information Science andTechnology Association, vol. 70, 432, 2020
Japan
Link
Center,data

If a researcher sets the Auto-Update function

as “on” in advance, as long asthe researcher’sauthenticated ORCID iD is a part of DOI

registration metadata of his/her research

outputs, these outputs will be added to the39Open Science and its Policy towards Open Science Paradigm
Kazuhiro Hayashi1** National Institute of Science and Technology Policy, 3-2-2 Kasumigaseki, Chiyoda, Tokyo, 100-

10013, Japan
Email: khayashi@nistep.go.jp

Summary. Open Science is a movement to transform Science, Society, and “Science and Society” with

opening up knowledge drastically by the advancement of ICT.

Starting from e-journals and Open

Access, scholarly communication has been transformed and currently preprint and preprint server isbeing focused on the COVID-19.

Research Data has also been a potential media for scholarly

communication, still it needs incentives for researchers. Especially, it needs some arrangement ofregulation which allows data providers and users to handle data with confidence. With the acceleration

by COVID-19, we are recognizing more and more of the gap between current system and what we areaiming, which is a signal to leap towards Open Science paradigm. From policy perspectives, the essenceof Open Science is re-organization of the system and regulations for Science and Society, which isfundamentally for fostering a new culture but with more concrete strategy to mitigate the gap.
Keywords. Open Science, Research Data, regulation, social change, re-organizing

1. Introduction

Nevertheless, it is still based on the system of

Science and Technology Policy. In addition topreprint server is being focused on the COVID-

for a recommendation. Open Science does not

information, but it casts the basic problem of

Science is a movement to transform Science,

sceptical to peer-review system itself with someup knowledge drastically by the advancement

retraction. On the other hand, we found that

include most of initiatives related to Openprovides a new and rapid landscape of research

Open Science has been a hot topic among

peer-review journals.

In 2020, preprint andOECD, and G7, UNESCO[1] is now working on

19.

have a sole definition but, most generally, Openquality control, and ironically, it also make us

Society, and “Science and Society” with opening

incidents

Preprint provides immediate scientificrepresented

by

some

papers’of ICT. [2] Under this explanation, we canaccumulating preprints regarding COVID-19

Science.

without “papers and citations.”[3] COVID-19 isrevealing something that has been existed but

2. Current

evolutionandacceleration by COVID-19

had not been seen.

its

Starting from e-journals and Open Access,scholarly communication has been transformed
towards digital native paradigm. E-journals with

subscription model and OA model consists aninevitableinfrastructureforresearchers.

40

Figure 1. Preprint landscape of COVID-19 with
its natural language processing

Research Data has also been a potential

media for scholarly communication, which is acentral pillar of Open Science Policy of the mostcountries that are challenging.

3. What policy has done and what it
reveals

Figure 2. Open Science Policy in Japan 2020

As of 2020, many countries have Open AccessPolicy with publicly-funded researches. Funding
agencypromotes/requires/mandates

Despite of Research Data’s potential and its

Openproactive policy development, Open Science

Access to their research articles. However,Policy and its implementation found that itprogress of Open Access provides other issues

needed much incentives for researchers andrepresented by APC (Article Processing Charge)

otherwith embracement of OA by commercial

Open

ResearchData,

first pillar of recommendation is “The need forrulemaking in an era where data plays a central

research data with FAIR Data Principles or therole.”[5]philosophy of “as Open as possible, as Close as

With our experiences towards research datathsharing

Science and Technology Basic Plan (2016-2020)

utilization.[4]theresearch

with

its

unexpected

system and what we are aiming. We’ve knew it

Strategy which has specific measures and clear
on

also

more and more of the gap between our current

manner followed by the Integrated Innovation
focusing

andacceleration by COVID-19, we are recognizing

has promoted Open Science in a general

goals

some

a recommendation for Open Science and its

some

For example, in Japan, the 5needsconfidence. Science Council of Japan released

countries are challenging to share theirnecessary.”itproviders and users to handle data with

culture based on peer-review journals have not
Regarding

Especially,

arrangement of regulation which allows data

publisher as a business. It seems that wholechanged so much.

actors.

from outset, but in the end, incremental reformdata

is not going to work, which is a signal to leap

It supports Data Policy, DMP,

towards Open Science paradigm.

Data Infrastructure, Data Repository and Datapublication together with pilot programs and4. Conclusions

monitoring.(Figure 2)

From policy perspectives, the essence of OpenScience is re-organization of the system andregulations for Science and Society, which isfundamentally for fostering a new culture butwith more concrete strategy with practices tomitigate the gap we have observed or to solve
problems for the leap.

41

References
1.

2.

UNESCOOpen
Sciencehttps://en.unesco.org/science-sustainablefuture/open-science [accessed on: Sep
2020]
Kazuhiro Hayashi. Progress of Open

4.

Science and Transforming Citizen Scienceto Co-creative Research. Trends in Science(Gakujutu no Doko), Vol. 23(11), PP. 11_12-

11_29:

5.

https://doi.org/10.5363/tits.23.11_12
3.

(Japanese), 2018

Hitoshi Koshiba, Kazuhiro Hayashi, Yuko Ito.
A Trial of early detection system forresearch trends through the preprints data42

─ Research status around COVID-19 /SARS-CoV-2. NISTEP Discussion Paper. No
186: http://doi.org/10.15108/dp186
Kazuhiro Hayashi. Perspective of OpenScience in Japan driven by Integrated
Innovation
Strategy.
International
Workshop on Data Science-Present &Future
of
Open
Data
&
Open
Science–:https://ds.rois.ac.jp/wpcontent/uploads/2018/10/Abstract_bookle
t_dsws_2018.pdf [accessed on: Sep 2020]
Toward deepening and promoting OpenScience:
http://www.scj.go.jp/ja/info/kohyo/pdf/ko
hyo-24-t291-1.pdf (Japanese) [accessed on:
Sep 2020]

Wastewater-based epidemiology for COVID-19: Perspectives forenvironmental surveillance of SARS-CoV-2 in wastewater
Ryo Honda1*, Akihiko Hata2* Faculty of Geosciences and Civil Engineering, Kanzawa University,
Kakuma-machi, Kanazawa 920-1192, Japan2
Department of Environmental and Civil Engineering, Toyama Prefectural University,
5180 Kurokawa, Imizu, Toyama 939-0398, Japan
Email: rhonda@se.kanazawa-u.ac.jp
1

Summary. Wastewater-based epidemiology (WBE) by monitoring of SARS-CoV-2 RNA in wastewater is
expected to enable early warning and prediction of overall status of COVID-19 outbreaks. Detection of

SARS-CoV-2 RNA in wastewater and its correlation with the number of clinically reported COVID-19

cases has been reported in several countries. However, the number of clinically reported cases are notstatic but affected by situations of clinical surveillance because, for example, it does not include patients

who do not visit clinics due to asymptomatic and mildly symptomatic infections. Therefore, theadvantage of WBE for COVID-19 could be capability of tracking the trend of outbreaks andconvergences independent of such clinical situations.

Keywords. SARS-CoV-2 RNA; fecal shedding; wastewater monitoring

1. Introduction
Wastewater-based

research progress on WBE for COVID-19 andepidemiology(WBE)

discuss its future perspectives.

issurveillance of epidemiological information ofpeople in a sewer catchment by monitoring of

2. SARS-CoV-2 RNA in wastewatertool for early warning and prediction of overall

were reported in Australia, India, Italy, Japan,

Detection of SARS-CoV-2 RNA in wastewater

wastewater. WBE is expected to be an effective

Spain, Netherlands, USA, etc [1,2]. Typically, 10-

status of COVID-19 outbreaks. The potential

advantage of WBE is capability of surveying the

100 mL of untreated wastewater sample issample, while clinical surveillance needs a large

wastewater sample was first concentrated andrequired for detection. The viruses in aentire catchment by testing a single wastewater

its RNA is extracted. The viral RNA is usuallynumber of samples collected from individuals.

concentrated up to approximately 100 times

SARS-CoV-2 RNA is shed in feces of COVID-19

during this process. The extracted RNA ispatients and therefore can be present in

detected by RT-qPCR assay, which mostly same

wastewater. Detection of SARS-CoV-2 RNA in

wastewater

has

been

reportedinas clinical tests. There are several options in

manyconcentration methods and primer-probe setscountries [1]. However, there are still needs offor PCR assay. Although currently selection ofdata accumulation and development of aprediction

model

to

interpret

the

these

RNA

methods

islikelytodependonpreferences of each research group, theconcentration into epidemic situation. This talkefficiency and sensitivity of these methods areaims to provide the up-to-date situation of43

being evaluated. The detected concentrations

be able to track the trend of outbreaks andwith the number of COVID-19 cases reported insituations. Therefore, it could be useful toof viral RNA in wastewater reportedly correlates

convergences independent of such clinicalclinical surveillance [3]. Some preprint studies
also reported the viral RNA in wastewater was

predict

theoutbreak/convergenceeffectof

lockdown

frompeople’s mobility data, and to evaluate thedetected 1-week earlier than increase of clinicalreported cases [4]. Therefore, it is expected tocountermeasures.

be applicable as an early warning tool orandvarioussurveillance of catchment-scale outbreaks.

3. Principle of WBE and uncertainty

Since viral RNA shedding in feces ranged 104-106 copies/g, viral RNA could be detected inwastewater when no. of COVID-19 infections ina sewer catchment reaches 5-10 cases per
100,000[5].

Meanwhile,

viral

RNA

Figure 1 A part of true number of infections

concentrations detected in wastewater ranged

are apparent as clinically reported cases.

104-106 copies/L. It is often detected at a very

low concentration of 104 copies/L level, which isslightly above the typical detection limit. In such

4. Conclusions

CoV-2 RNA depends not only on the lower limit

WBE for COVID-19 by environmental

perspectives

a low concentration, the detection of SARSof detectable concentration but also probability

could be effective tools for early warning andassay preparation. The number of the clinically

prediction of COVID-19 outbreaks andreported COVID-19 cases is the most applicableconvergence. However, its limitations andindicator to verify the prediction of COVID-19

uncertainty are still under investigation. Dataoutbreak situation by WBE. However, theaccumulation and statistical analysis of WBE

number of clinically reported cases are notdata with clinical epidemiological data,

static but affected by situations of clinical(Figure1).

Forpeople’s mobility data, and possible factors

example,which affect spread and convergence ofasymptomatic and mildly symptomatic patients

COVID-19 outbreaks are expected as further

may not visit clinics and not be identified byclinicalreportedsurveillance.
cases

The

numbermonotonicallyoffuturesurveillance of SARS-CoV-2 in wastewaterof successful transfer of viral RNA into PCR

surveillance

andstudies.

theincrease

References

independent of the epidemic situation when1.

more people are tested by clinical PCR assays.

Therefore, WBE dataset obtained as RNA

concentration in wastewater is unlikely tosimply correlate with the clinically reported

epidemic situations. Instead, its advantage is to44

Michael-Kordatou, I., Karaolia, P. &Fatta-Kassinos, D. Sewage analysis as atool for the COVID-19 pandemic
response and management: the urgent
need for optimised protocols for SARSCoV-2 detection and quantification. J.
Environ. Chem. Eng. 8, 104306, 2020

2.

3.

4.

5.

Hata, A., Honda, R., Hara-Yamamura &Meuchi, Y. Detection of SARS-CoV-2 inwastewater in Japan by multiple
molecular
assays-implicationforwastewater-based epidemiology (WBE).
medRxiv 2020.06.09.20126417, 2020,
doi:10.1101/2020.06.09.20126417
Medema, G., Heijnen, L., Elsinga, G.,
Italiaander, R. & Brouwer, A. Presence of
SARS-Coronavirus-2 RNA in Sewage
and Correlation with Reported COVID19 Prevalence in the Early Stage of the
Epidemic in The Netherlands. Environ.
Sci. Technol. Lett. 7, 511–516, 2020
Peccia, J. et al. SARS-CoV-2 RNA
concentrations in primary municipal
sewage sludge as a leading indicator of
COVID-19 outbreak dynamics. medRxiv,
2020, doi:10.1101/2020.05.19.20105999
Hata, A. & Honda, R. Potential Sensitivityof Wastewater Monitoring for SARSCoV-2: Comparison with Norovirus
Cases. Environ. Sci. Technol. 54, 6451–
6452, 2020

45

The Roles of PIDs in Scientific Data Management in China
Xiaoyan Hu1*, Ziming Zou1, Lianglin Hu2, Jia Liu2, Xin Chen2, Qi Xu1* National Space Science Center, Chinese Academy of Sciences, NO.1 Nanertiao, Zhongguancun,
Haidian district, Beijing, 100190, China
2
Computer Network Information Center, Chinese Academy of Sciences, NO.4 Nansijie,
Zhongguancun, Haidian District, Beijing, 100190, China
Email: huxiaoyan@nssc.ac.cn

1

Summary. Persistent identifiers (PIDs) play an important role in scientific data management, especiallyin data publication and citation to support the findability and accessibility of scientific data. Dataorganizations in China are increasingly aware of PIDs as required for implementation of the FAIR
principles, and several good practices have been developed. It is worth noting that China has alsoestablished a persistent identifier, namely the China Science & Technology Resource identifier (CSTR).
This paper presents a typical case of National Space Science Data Center (NSSDC) embedding fourtypes of PIDs into its workflows, exemplifying the application of PIDs in scientific data management.
Keywords. FAIR data, data ecosystem, data citation, metadata, persistent identifiers

1. Introduction

DOI is a well-known digital identifier for

Persistent identifiers could benefit research inobjects of any type [3]. More familiar toauthors and published results [1], but also for

publication and data publication. Some global

management. The FAIR principles are the mostfor dataset registration and discovery.

curation and management. Properly managed

ORCID is a persistent digital identifier thatprimary access point to enable Findability inresearchersbasis for all the other FAIR attributes [2].

activities [4]. ORCIDs are currently used to

2. Global Persistent Identifiers

depositors and data users, enabling data

researchers, DOIs are widely used in academic

many ways, not just for clear tracking of

more automated, higher quality scientific data

platforms such as DataCite are based on DOI

widely accepted theoretical model for data

2.2 ORCID

PIDs with their associated metadata are thedistinguishes

that

supports

2.3 Domain-oriented identifiers

There are also some domain-oriented PIDs,

some general identifiers, e.g. Digital Object
Researcher

recordother

provenance and tracking of data usage.

repositories and research communities are
Open

a

from

identify specific individuals of data producers,

The most commonly used PIDs in Chinese data

(DOI),

andresearcher

automatic links among all his professional

the FAIR data ecosystem, and to provide a

Identifier

onesuch as IVOID, SPASE ResourceID, ATCC, etc.

andMetadata of domain-oriented identifiers prefer

Contributor Identifier (ORCID), and somegreaterdomain-oriented identifiers.

granularityorrichness.

These

identifiers and associated metadata not only

2.1 DOI

support data findability and accessibility, but

46

also address domain-specific demands fordata management and sharing. When thedata understanding and data interoperability.

dataset is released, this internal identifier will

be used as the suffix to generate the full PID

3. CSTR

name, which will be registered via the APIinterface provided by the service agencies, e.g.

CSTR is proposed by the Ministry of Scienceand Technology (MOST) of China for the

CNKI DOI and DataPid. Based on these PIDs

publication, maintenance and management ofsome integrated data platform. Meanwhile,

resources in China [5].

citation of NSSDC dataset.

identification,
all

kinds

registration,NSSDC have published data catalogues ontechnologicalthese widely accepted PIDs support normative

cataloguing,ofscientificandA CSTR name in turn consist of a uniform

prefix, a registration authority code, a resource

5. Conclusions

authority code indicates the registrant who

importance of PIDs in achieving data FAIR.

Data practitioners in China have realized thetype code and a suffix. The registration
issues the CSTR. The resource type code

Some best practices have yielded good results,scientific equipment, major S&T infrastructure,research ecosystem.

providing a basis for building a healthy

identifies 23 types of S&T resources, including

specimen, scientific data, paper, etc. The suffix

is assigned by the registration authority, using

Acknowledgments. This work was supporteddelimiters of indeterminate length.

Plan of Chinese Academy of Sciences，Grantacombinationofletters,

numbers

in part by the 13th Five-year InformatizationandAn example of a CSTR would be:

No. XXH13505 and No. XXH13514.

CSTR:14800.11.2012-100101-13215-00001-V1

References

4. NSSDC Practice1.

Four types of PIDs are embedded in theworkflows of NSSDC, which are DOI, CSTR,

Handle and ORCID. To support these PIDs,

2.

NSSDC self-formulated dataset core metadata

covers all elements of DOI, CSTR and Handle

metadata, thus all the information required forregistration of these PIDs can be derived from3.

this core metadata.

In the phase of deposition, submitting

adequate

standardized

metadata

information

introduction

document

4.

and

is

necessary, and filling in the ORCIDs of data
authorsisstrongly

recommended.

5.

Then

NSSDC will assign a unique internal identifier

for the qualified dataset, which will be usedthroughout the full subsequent process of47

Meadows A., DOIs and other persistentidentifiers have much more to offer
science[J]. Nature, 558(7710), 372-372,
2018
Juty, N., Wimalaratne, S. M., Soiland-Reyes,
S., Kunze, J., Goble, C. A., & Clark, T,
Unique, Persistent, Resolvable: Identifiers
as the foundation of FAIR. DataIntelligence, 2:1-2, 30-39, 2020
International
DOI
Foundation,
https://www.doi.org/doi_handbook/1_Introduction.html [accessed on: October, 2015]
ORCID,
https://support.orcid.org/hc/enus/articles/360006973993-What-is-ORCID[accessed on: August, 2020]
China National Technical Committee ofStandardization for Science & Technology
Infrastructure, Science and technology
resource
identification:
GB/T32843—
2016[S]. China Standards Press, Beijing,
2016 (in Chinese)Time-series analysis of directional sequence changes in SARSCoV-2 genomes and an unsupervised
explainable AI for studying the corona virus genomes
Toshimichi Ikemura 1,2*, Kennosuke Wada1, Yoshiko Wada1, Yuki Iwasaki1,
Yasushi Hiromi2, Takashi Abe3
* Nagahama Institute of Bio-Science and Technology, Nagahama, Shiga-ken 526-0829, Japan
2
National Institute of Genetics, Mishima, Shizuoka-ken 411-8540, Japan
3
Faculty of Engineering, Niigata University, Niigata-ken 950-2181, Japan
Email: t_ikemura@nagahama-i-bio.ac.jp

1

Summary. This study conducted time-series analysis of mono- and dinucleotide compositions for
SARS-CoV-2 genomes and found clear time-series changes in these compositions on a monthly basis.

We then developed a sequence alignment-free method that extensively searches for advantageous

mutations and ranks them in an increase level of their population frequency. Unsupervised machinelearning is highly desirable for datamining of big data, and we conducted BLSOM (batch-learning SOM)
of the virus genomes, using the oligonucleotide composition in each genome and found the obtained

clustering (self-organization) of the genomes is primarily related to known clades. Since BLSOM is an

explainable AI, it can tell us what features of the oligonucleotide composition are responsible for theclade separation.

Keywords. COVID-19, big data, zoonotic virus, oligonucleotide, advantageous mutation

1. Introduction

previously established a batch-learning self-and human cells may not present an ideal

compositions, which can reveal various new

SARS-CoV-2)

present BLSOM study analysed over 40,000

Many host factors are involved in viral growth,organizing map (BLSOM) for oligonucleotide

growth condition for zoonotic viruses (e.g.,characteristics of genome sequences [6]. The

nonhuman hosts. Therefore, efficient growth

genomes of SARS-CoV-2, which were isolated

that

have

invadedfromafter the invasion will require changes in virus

from December 2019 to June 2020.

analysed time-series changes in mono- and2. Results

viruses, including SARS-CoV-2, and found time-

dinucleotide composition

genomes. To study the viral adaptation, we

2.1 Time-series changes in mono- andoligonucleotide compositions for zoonotic RNA

In the time-series analysis in Fig. 1a, weseries directional changes that are detectableeven on a monthly basis [1-5].

tabulated virus strains for each collection

new knowledge from big data without priordinucleotide composition (%). The monthly

month

Unsupervised machine learning can discoverandcalculated

the

mono-

and

results clearly show monotonic time-series

knowledge or particular models and is highly

desirable for datamining of big data. We

48

increase/decrease

in

a

part

of

these

containing genomes of a single clade are

2.2 Rapidly increasing 20-mers and searchcontaining genomes of multiple clades are

nucleotides; for details, see [5].

coloured to indicate each clade, and grid points

for advantageous mutations

displayed in black. BLSOMs for 4 ~ 7-mers and

For the highly mutable RNA viruses, the time-

15-mers

as 20-mers is important for designing PCR

BLSOM of 15-mers, which are rapidly increasing

series analysis of long oligonucleotides such

show

a

good

separation(self-

organisation) by clade. Fig. 2a shows theprimers and therapeutic oligonucleotides thator decreasingin their monthly population.

can be used for a sufficiently long period [4].

BLSOM

mers and almost all 15-mers are present only

organisation) according to the clade; theSince the polyA-tails were removed, all 20-

canprovideinformation

about

oligomers responsible for the clustering (self-

once on each genome, and thus theircontribution level of each 15-mer in each node

population corresponds to the occurrence(red), moderate (white) and low (blue). Thereoccurrence

level

(%)

in

each

month'scan be visualized by a heatmap diagram: highlevel (%) of the strains with the oligomer

were many cases in which occurrence levels aresequence in the viral population. When wereversed for a pair of 15-mers in which only one

happened to search for 15 and 20-mers thatbase has changed. Figure 2b shows the casewere absent in all strains isolated in December
2019,

then

emergedandincreased

where the respective mutation corresponds tointhe No.7 mutation in Table 1 and is responsible

occurrence, we unexpectedly found a group of

for clustering of GR genomes (bright green in

rapidly increasing 15 and 20-mers. When
analysingtheirtime-series

Fig. 2a). The increase/decrease pattern of theoccurrence

respective 15-mers is presented in Fig. 2c.

frequency, a very similar pattern of monotonic

increase was observed for sixty 15-mers (Fig.

1b), as well as eighty 20-mers. Figure 1cpresents mutations responsible for the rapid
increasing oligomers; for details, see [5].

3. Conclusions

The time-series analysis of oligonucleotide
composition and an unsupervised-type AI(BLSOM) 0f the composition, which are notdependent on sequence alignment, are shown
to be powerful methods for obtaining novel

knowledge of viral genome sequences and2.3 Oligonucleotides BLSOM

molecular evolutionary processes.

of short (2~7-mer) and long (e.g., 15-mer)

Acknowledgments. This work was supported

We next conducted BLSOMs with compositions

oligomers in the over forty thousands of SARS-

by a Grant-in-Aid for Scientific Researchcomposition is given in the learning process. To

Counterplan Research Project (supervised by(18K07151) from JSPS and by COVID-19

CoV-2 genomes; importantly, only the oligomer

Prof. Tatsumi Hirata, NIG) from the Researchsee if the clustering (self-organization) by the
BLSOM is related to known clads, grid points

49Organization of Information and Systems(ROIS).

References
1.

2.

3.

4.

5.

6.

Iwasaki Y., et all., Prediction of directionalchanges of influenza A virus genome
sequences with emphasis on pandemic
H1N1/09 as a model case. DNA Res. 18,
125-136, 2011
Iwasaki Y., et all., Novel bioinformatics
strategies for prediction of directionalsequence changes in influenza virus
genomes and for surveillance of potentiallyhazardous strains. BMC Infect. Dis. 13, 386,
2013
Wada Y., et all., Directional and reoccurring
sequence change in zoonotic RNA virus
genomes visualized by time-series word
count. Sci. Rep. 6, 36197, 2016
Wada K., et all., Time-series oligonucleotide
count to assign antiviral siRNAs with longutility fit in the big data era. Gene Ther. 24,
668–673, 2017
Wada K., et all., Time-series analyses of
directional sequence changes in SARSCoV-2 genomes and an efficient searchmethod for candidates for advantageous
mutations for growth in human cells.
Gene:X;
doi.org/10.1016/j.gene.2020.100038, 2020
Abe, T., et all., Informatics for unveilinghidden genome signatures. Genome Res.
13, 693-702, 2003

50

Collection, Archive and Sharing Space Weather
Information in NICT
M. Ishii1*, Y. Kubo1, S. Sakaguchi1, D. Shiota1, M. Den1, M. Nishioka1, H. Jin1,
H. Ishibashi1, K. Marubashi1, K. Fukunaga1* National Institute of Information and Communications Technology (NICT),
4-2-1 Nukui-kita, Koganei, Tokyo, 184-8795, Japan
Email: mishii@nict.go.jp

1

Summary. We, NICT, has a long history to observe the sun and ionosphere, and the amount ofobservational and analysis data has become huge. Some parts of the database are obtained during highsolar activity period

and include precious information for discussing social impact with extreme events, which is difficult to
get in low solar activity periods, such as cycle 23 and 24. Over the past decade, we have been digitizing
ionogram data recorded in microfilms which is easy to lose in the long days. Recently, we add the actionof data rescue for optical and radio solar activity data observed at Hiraiso observatory. In addition, we
began to digitize the data named “Solar Activity Chart" in which the real time data of solar disk,

geomagnetic field, radio black out and cosmic ray with every cycle of 27 days, and a part of data madepossible to show in the website freely. Another remarkable topic is to provide space weather information
to ICAO as a part of global centers sinceNov. 7, 2019. In the present status, three global centers use their own dataset independently, but now

we discuss to unify the dataset and evaluate prediction models for providing with the same quality. We
will present the detail of present status and issues to be solved.

Keywords. Space Weather, legend data retrieve, real time data sharing, ICAO

1. Introduction

Since the beginning of 21st century,

Space weather is electro-magnetic phenomenaInternational Civil Aviation Organization (ICAO)

affects on radio utility, Global network of

information in civil aviation. In addition to thenear the Earth occurred by solar activities which

has been discussing the use of space weather

satellite system, electric power grid, etc. NICT

preparation of regulations, we worked for theand forecasting [1]. We have been observing

weather information to ICAO. As a result ofregion since 1930s, publishing radio alert just

PECASUS (Consortium of Finland (leader), UK,started operational space weather forecast

Netherland and Poland) and ACFJ (Consortium

has a long history for space weather monitoring

selection of organization who provide space

ionosphere not only in Japan but also in Asian

multiple audits, three global canters, US

after the world war II for stable radio use andAustria,

service in 1988 as a member of Internationalof Australia, Canada, France and Japan) were

Belgium,

Cyprus,Germany,

Italy,

selected in 2018 and the operational service

Space Environment Services (ISES)[2].

started in 2019.

51

to the next generation. In the present status,

2. Digitization of Legend dataionosphere and solar data are already digitizedand open on our web. Now we try to digitize

We started ionospheric observation in 1924

temporality and 1934 operationally. Because

the Solar Activity Chart. [3-5]military

3. International

the ionospheric information was important inmission,many

ionosphericplan) were operated in Asian region during

Asia-Oceania Region

World War II. After the WWII, we started
ionosphericmeasurement

forSpace Weather monitoring inobservatories (14 sites in operational, 15 sites inoperational

Cooperation

We started an international project namedin

SEALION

Wakkanai, Fukaura, Shibata, Kokubunji and(SouthEast

Asia

Low-latitude

equatorial

ionospheric

Ionospheric Network) in 2003 for monitoring

Yamagawa since 1946-47. In present, we

andoperate four stations, Wakkanai/Sarobetsu,

forecasting

disturbances, especially plasma bubbles (EPB)

Kokubunji, Yamagawa and Ogimi.

which affect on the use of satellite positioning

We had operated solar radio and opticalseriously. In present, we have a cooperative

measurement for monitoring the solar activities

in Hiraiso observatory. The solar radio telescopeobservationinChiangMai,

KototabanginIndonesia,

Phuketand

Chumphon in Thailand, Bac Lieu in Vietnam,

has been launched in 1962 for measuring

200MHz solar radiation. We added antennas for

and

Cebu

in

Philippine. The observed data are sharing in real

expanding measuring bandwidth from 100MHz

time and shown in web site.[6] We built a new

to 9.5GHz. With closing Hiraiso Observatory in

VHF radar in Chumphon, Thailand for real time

2016, The function of solar radio measurementmonitoring EPB as a part of SEALION in Jan.

moved to Yamagawa observatory. The optical2020.

measurement of H alpha started in 1986 in
Hiraiso observatory. The system improved in

4. Data

1994 and continued the observation until

Sharing

in ICAO

Weather Centersclosing.

Space

There are various kinds of data for radio

As described in the introduction section, thegeomagnetic field, radio black out, cosmic ray,started in Nov. 2019 with three global centers.

ones. We have been preparing a diagram

database independently from others, and therelation among these parameters since 1951,

situation could be a cause of discrepancies ininclude important information as sun-solar

is quite necessary to harmonize the dataset andThese data have been recorded on micro-

NICT prepares the data server for gathering realdismissing in long years. We started to save

GPS and satellite occultation. This system will bealert/space weather forecast, e.g., solar surface,

space weather information service alreadyand upstream events effect on the downstream

However, each center has it’s own observation

named “Solar Activity Chart” for visualizing theprediction model is also same situation. This

which is a kind of relational database andthe space weather forecast information, and it

wind-magnetosphere-ionosphere interactions.

validate the models using same dataset. Now

films

exposedtime ionospheric data observed by ionosondes,

these legend data with digitizing for succeeding

available since the early 2021 and hope toandpapers

and

can

be

contribute to improve the precision of global

52space weather information for aviation before

weather monitoring sometimes under thethe next high solar activity period start.

difficult situations. A part of this work is

supported by the spectrum user fee, the5. Conclusions

Ministry of Internal affairs and Communications.

NICT has a huge amount of data since the early

References

20th century not only in domestic but also Asiaand already Opened them via websites. As a

1.
2.
3.

https://swc.nict.go.jp/en/
http://www.spaceweather.org/
http://wdc.nict.go.jp/IONO/wdc/index.html

ICAO global centers for improving space4.

http://solarobs.nict.go.jp/

5.

http://wdc.nict.go.jp/hsv/

6.

https://aer-nc-web.nict.go.jp/sealion/

Oceania region. We started to digitize the data
new attempt, we try to share real time data inweather forecast services for aviation.

Acknowledgments. We appreciate all legend
leaders who started the observation for space53

Data and metadata sharing among Asian countries
Masaki Kanao1** Joint Support-Center for Data Science Research, Research Organization of Information and
Systems, 10-3, Midori-cho, Tachikawa-shi, Tokyo 190-8518, Japan
Email: kanao@nipr.ac.jp

1

Summary. The Polar Environmental Data Science Center (PEDSC) of the Joint Support-Center for Data

Science Research (DS), the Research Organization of Information and Systems (ROIS) has a

responsibility to manage and publish the data involving Japanese research activities as one of a

National Antarctic Data Center (NADC). At the International Polar Year (IPY2007-2008), a significant
number of multi-disciplinary data have been compiled. These collected data/metadata have a tightcollaboration with the Global Change Master Directory (GCMD), the Polar Information Commons (PIC),

as well as several data centers belonging to the World Data System (WDS). In terms of data activities

in polar communities of the Scientific Committee on Antarctic Research (SCAR) and the International

Arctic Science Committee (IASC), tighter linkages of data/metadata sharing within the Asian Forum for
Polar Sciences (AFoPS) countries has been discussed and should be further promoted by the involved
Asian countries, in particular China, India, South Korea, Malaysia and Japan.

Keywords. Polar Environmental Data Science Center, data sharing, polar communities, AFoPS.

1. Introduction

established a data policy in February 2007,

disciplines make up the most significant legacy

Committee on Antarctic Data Management

The Polar Data Center (PDC) of the National

Antarctic Research (SCAR). This contributed to

the Polar Environment Data Science Center

Management

Science Research (DS) have responsibility toregion have great influence on the global

Antarctic

tight

an effective framework for long-term strategy

PDC/PEDSC and the Global Change Mastershould be made available promptly and newInformation

newly

network

service,

IPY. In this Abstract, a history of data

sciencecommunities

summarized, focusing on the last one decade.

tighter linkages have to be established withsignificant function of the NADC for Japan andunder ISC, such as CODATA, and WDS.

based on the requirements of the Standing

Diverse data accumulated by several scienceof the International Polar Year (IPY2007-2008).

(SCADM) of the Scientific Committee on

Institute of Polar Research (NIPR), followed by

the subsequent SCAR Data and Information(PEDSC) of the Joint Support-Center for Data

aspects of scientific data collected in the polar

manage these polar data one of the National

environmental research. In order to constructcollaboration has been established betweenof the polar data management, the data

Directory

Data

Center(GCMD)

ofCommons(NADC).

NASA,(PIC),

A

the

and

Internet

Polar

Strategy.

technologies

cloud

Severalsuchasystemdifferent

repository
must

be

and

the

established World Data System (WDS) after theemployed. In addition to the activities in polar

management in polar region by Japan isInternational Arctic Science Committee (IASC),The PDC/PEDSC has been performed aother cross-cutting data science initiatives

54

of

SCAR

scientists

to

AFoPS

meetings;

provide2. Asian collaboration perspective

personnel

involving polar sciences, the Asian Forum foron the polar sciences.

governmental organization established in 2004

communities of SCAR and the International

advance of polar sciences among countries inclose linkages in the data sharing among

members, i.e, the national polar research

involved members of the Asian countries.

training

andcooperationin

In terms of a collaboration in activities

outreach activities; produce joint publications

Polar Sciences (AFoPS) has been a non-Regarding the data related activities in polar

Arctic Science Committee (IASC), addition,

to encourage and facilitate cooperation for thethe Asian region. The Forum consists of its six

AFoPS countries should be promoted byinstitutions representing China, Japan, South

Establishing a new portal server inside the

Korea, India, Malaysia and Thailand. AFoPS

GCMD,

also has four observer countries: Indonesia,

step

for

Directly (AMD) within the GCMD, the sharing

bi-polar regions for the benefit of humanimportancefirst

have been compiled into the Antarctic Masterrecognized as the value of scientific research in
the

the

The metadata from involved Asian countries

The objectives of the AFoPS has beenrecognizing

example,improving data sharing and inter-operability.

Philippines, Sri Lanka and Vietnam, respectively.

activities,

forthe metadata from AFoPS nations could beofinternational cooperation in polar regions andrather smoothly conducted prior to the actual

operators, together with aiming to serve the

Detail discussion and consultation have been

the need to work closely with other national

data compilation.

common interests in both polar sciences andcarried out in last few years at the SCAR

logistics. Member countries will work together

general meetings and relating Polar Datafor the tasks as follows; provide a foundation

Forums in Canada (2015), Finland (2018),

for cooperative research activities; present

Norway (2021 as planned), as well as the WDS

Asian achievements toward international polar

initiative

communities; encourage more Asian countries’

conferences(Kyoto,2017).

Continuous discussion might be necessary

involvements in polar sciences.

among Asian communities so as to construct

Major Activities of the AFoPS include; provide

a forum to seek a common view on polar

the whole data and metadata sharing system.

supportone of the mile-stone to achieve the common

affairs among member countries; develop andThis online conference of DSWS-2020 could be

activities (i.e., joint science projects, logistic

goals.

between polar expeditions and institutes, etc.);

Acknowledgments. The authors would like tocooperative

programs

on

polar

cooperation and personnel exchange program

convene joint symposium and workshops forexpress their appreciation to collaborators

sharing scientific results, information andexperience

joint

symposium;

involving polar data communities, particularly

conference

the member

activities within AFoPS working groups (WGs);of

PEDSC,

data committee

members of the SCAR, IASC, WDS, CODATA

support non member countries to develop

and IPY.

their national polar programs; invite scientists

to field expeditions and institutes; invite

55

Historical records of red auroras in Japan: Collaboration between
literature and science
Ryuho Kataoka1** National Institute of Polar Research, 10-3 Midori-cho, Tachikawa, 190-8518 Tokyo, Japan
Email: kataoka.ryuho@nipr.ac.jp

1

Summary. During the largest magnetic storms, red auroras can be seen by naked eyes even in Japan

regardless of the low magnetic latitude. Such a large magnetic storms are rare (e.g., one in 100 years),and it is hard to investigate the basic properties in detail by modern observations alone. Collaboration
project between Japanese literature and polar science, called aurora4d project, was therefore conducted

to identify such historical records of red auroras. Several successful examples are briefly reviewed in thisarticle.

Keywords. space weather, magnetic storm, aurora, pre-modern Japanese text

1. Introduction

found in the 300-year history book of China,“red sign in the heaven” in AD 620, which canfound to be associated with the 11-year solar

magnetic storm [1]. Such a characters of red

Modern space observations revealed thatcalled Soushi, and the occurrence pattern is

The oldest astronomical record of Japan is thebe red aurora occurred during the greatest

cycles [2].

sign, or Sekki, can be found in many other old

the largest solar flares hardly stop by a single

always needed in terms of both historical

successive occurrence of largest magnetic

texts in Japan, although careful analyses are

eruption. The prolonged property of Sekki, or

aspects and scientific aspects to identify the real

storms, is therefore the natural consequence of

make the meaningful analysis to the historical

we can learn that such a nature of eruptive solar

auroral events. We report our latest efforts tothe largest eruptive solar flares. In other words,

accounts.

activity was hold even in 1000 year ago.

2. Meigetsuki event

3. Seikai eventthe event on 1204 Feb 19-21, recorded inchanges the game. A fan-shaped red sky was

and the magnetic latitude was 36 deg when the

September 17 in Kyoto. Although the magnetic

Japan.

most famous red auroral event in Japan [3].

auroral event is the prolonged activity, i.e.

geomagnetic field lines over Kyoto, it is found10 similar events, prolonged Sekki, can also be

aurorasOne of the most popular examples of Sekki isGraphical evidence in literature sometimes

Meigetsuki [2]. The observation site was Kyoto,

painted in the book called Seikai, on 1770

geomagnetic north pole largely declined tolatitude was only 24 deg, it was also one of the

The most notable characteristic of thisFrom the geometrical analysis of inclinedthat the field-aligned emission pattern ofsuccessive occurrence over 3 nights. More than

56

makes

the

shape

of

fan.

The

reconstructed geometry of the auroral oval

science. This may be an interesting example ofthen gives an estimate of the possible

citizen science.

amplitude of the magnetic storm.

5. Conclusions

The estimated amplitude is approximately

10 nT in the Dst index, which is comparable to3

A variety of examples of historical auroral study

or even larger than the Carrington event, theare reviewed, in which the close collaboration

historically largest magnetic storm occurred in

between literature and science was essentially

September 1859.

important and meaningful.

4. Taro-Jiro event

Acknowledgments. The author would like tois a challenging work. At the end of the

supporters, and Kazama-san.

Connection of the old data to the modern data

thank all the members of aurora4d project,aurora4d project, we noticed that in Japan, theReferences

first photograph of aurora was taken on 1958
February 11. From the modern analysis of the1.

successive photograph of 1 min cadence, we

revealed the westward drift of the fan-shaped
aurora [4].

Fortunately,

hand-written

diaries2.

andpaintings also existed in this event [5], and it

gives the firm connection among the words,

3.

paintings, and photographic modern data. The
most surprising present for the author was thatthe high-school student who wrote ad painted

the red sky 60 years ago, Kazama-san, recently

4.

painting. He recently read a newspaper article

5.

visited our institute, bringing another auroraof the press-released products of our aurora4d
project, and noticed that his 60-year-old

painting can contribute to the new field of

57

Kataoka, R., et al., Pheasant Tail:
Consideration of the shape of the red signin the Nihon-Shoki, SOKENDAI Review ofCultural and Social Studies, 16, 17-28, 2020
Kataoka, R., et al., Historical space weather
monitoring of prolonged aurora activities inJapan and in China, Space Weather, 15(2),2017
Kataoka, R., and K. Iwahashi, Inclined zenith
aurora over Kyoto on 17 September 1770:
Graphical evidence of extreme magnetic
storm, Space Weather, 15, 1314-1320, 2017
Kataoka, R., et al., Fan-shaped aurora as
seen from Japan during a great magnetic
storm on 11 February 1958, 9, A16, 2019
Kataoka, R., and S. Kazama, A watercolor
painting of northern lights seen above
Japan on 11 February 1958, J. Space
Weather Space Clim., 9, A28, 2019

Sharing literature annotation regarding COVID-19
through PubAnnotation
Jin-Dong Kim1** Database Center for Life Science,
Joint Support-Center for Data Science Research,
Research Organization of Information and Systems
178-4-4 Wakashiba, Kashiwa, Chiba 277-0871, JAPAN
Email: jdkim@dbcls.rois.ac.jp
1

Summary. To accelerate collaboration for fighting Covid-19, collections of scientific literature related to

Covid-19 have been made available, by publishers and several initiatives, for research purposes.

Consequently, many research groups which have expertise in natural language processing or text mining
have produced annotations to the literature collections, to enable computer-aided access to the content.

Toward integration of the datasets, the Covid19-PubAnnotation project was launched to provide an open
platform for voluntary contribution and integration, based on the PubAnnotation system. So far,contributions from 6 groups have been integrated, which are publicly available in various ways e.g.,
download, Web API, or SPARQL search.

Keywords. literature, annotation, textmining, search

1. Background

Since the release of the datasets, many

Covid-19 is giving unprecedented challenges togroups which had expertise in natural language

people are struggling to find ways to overcome

pre-processing the literature [3, 4], e.g., forextend knowledge about the virus, for thecomputer-aided access to the content of

on. Particularly, the community of science isannotation,

mankind on almost the whole world. While

processing (NLP) or text mining (TM) worked onit, many scientists are also taking their efforts tosemantic

development of treatments, medicines, and soliterature. Such pre-processing is often calledhighly motivated to share useful resources, e.g.,annotations datasets.

publishers are opening the content of scientifictimely access to specific pieces of information

are compiling and releasing collections oflarge amount of literature. However, when they

LitCovid dataset by NCBI [1], and the CORD-19

websites, separately from each other, theirrecognized as important resources for sharing

integrative use. It is expected that collecting

potential further discoveries.

integrated interface to them would make themdata, and tools.

indexing,

and

to

the

enableresults

ways

are

for

calledThose annotation datasets are useful for

In the same line, many

literature. There are also public initiatives, which

which otherwise might have been hidden in theCovid-19-related literature. To name a few, theare only accessible from their individual project

dataset by the Allen Institute of AI [2] are widely

utility is limited, missing the chance for an

uptodate scientific knowledge, and for mining

the annotation datasets and providing an

58

Figure 1. Example of integrated annotations contributed by PubTator, OGER-BB, and PubDictionaries.

even further useful, opening a chance for

It is an ongoing project. Based on the analysis

synergic use of them.

improved particularly for sustainability, and the

2. Covid19-PubAnnotation
The

Covid19-PubAnnotationon the 6 integrated datasets, the platform issecond call for contribution will be soon issued

project

to solicit more datasets.

waslaunched to address the need for integration ofannotation datasets made to the Covid-19-

Acknowledgments.

This

work

has

been

an

Database Center (NBDC) of Japan Science andrelated literature collections. PubAnnotation isfinancially supported by National Biosciencebiomedical literature whose goal is to collectTechnology Agency (JST).

global NLP community [5]. One of the core

References

open

repositoryofannotationsofand integrate annotations contributed by the

1.

functions which the system features is theautomatic alignment function, thanks to whichannotation datasets contributed by various

2.

groups can be aligned together.

The PubAnnotation system was set up

to collect annotation datasets to the two

3.

literature collections, and relevant researchgroups were invited to contribute. For the call,

6 groups responded and contributed theirannotation datasets. Figure 1 is an example of4.

integrated annotation datasets, which includes
annotations contributed by three initiatives:

PubTator [3], OGER-BB [4], and PubDictionaries
[5].

It

was

found

that

the

contributed

5.

annotation datasets are highly complementary
to each other, showing the usefulness of the

setup. The results of the collected andintegrated annotation data sets are publicly
available for search, visualization, and finegrained access.

59

Chen Q, Allot A, Lu Z. Keep up with thelatest coronavirus research, Nature,
2020;579(7798):193, 2020
Lu Wang L, Lo K, Chandrasekhar Y, et al.
CORD-19: The Covid-19 Open Research
Dataset, ArXiv, 2020;arXiv:2004.10706v2,
2020
Chih-Hsuan Wei, Hung-Yu Kao, andZhiyong Lu, PubTator: a web-based text
mining tool for assisting biocuration,
Nucleic Acids Res. 2013 Jul; 41(Web Serverissue): W518–W522. 2013
Marco Basaldella, Lenz Furrer, Carlo Tasso,
and Fabio Rinaldi, Entity recognition in thebiomedical domain using a hybrid
approach, Journal of Biomedical Semantics,
8 (51), 2017
Jin-Dong Kim, Yue Wang, Toyofumi
Fujiwara, Shujiro Okuda, Tiffany J Callahan,
and K Bretonnel Cohen, Open Agile text
miningforbioinformatics:
the
PubAnnotation ecosystem, Bioinformatics,
35 (21), 4372–4380, 2019

Do we all need to become data scientists?
Jens Klump1** CSIRO Mineral Resources, 26 Dick Perry Avenue, Kensington, WA, 6151, Australia
Email: jens.klump@csiro.au

1

Summary. The digital transformation changes the way we do research. A previously unimaginable increasein the availability of data and compute resources enables us to put the data first and search for emerging

patterns in highly complex datasets, thus accelerating research and innovation. Our response to thistransformation needs to balance domain knowledge with digital skills in order to leverage the power of the

new research tools. To benefit from this trend, we need to increase the digital literacy of our entire workforce
in research. In exchange, we gain new insights through data-driven research.

Keywords. A list of up to 5 words describing the main concepts in the abstract, separated by commas

1. Introduction

for research and engineering. The shift brought

research has changed. We have conducted

driven research is the ability to put the data first

theoryadded

complex datasets rather than stating the

research, and ten years ago, data-driven

this way, data-driven analysis supplements

paradigm” of research [1]. In recent years weData-driven research is now part of the way

Over the last decade, the way we conduct

forward by the “Fourth Paradigm” of data-

millennia of empirical research and centuries ofand search for emerging patterns in highly

simulation as a new way of conducting

hypothesis first and then prove or disprove it. In

research

hypothesis-driven analysis.

development.

wasComputingpostulated

as

the

“fourthhave seen the “fourth paradigm” evolving into

we do research, and it comes with its own set ofunimaginable increase in the availability of data

be expert users of their research tools, even if

make the best use of the new resources data

generation. This distinction between the expert

science change the way we do research?

useful in describing the relationship between

previously

tools. As in any discipline, researchers need toand compute resources. Are we prepared tothey are not the ones building the next

science adds to our toolbox? How will data

user and the developer of new tools might bedatascience,poweredbyathe disciplinary and digital domains.

2. From the “Fourth Paradigm” to

3. Digital Transformation

Data Science

The tools of data-driven research rely heavily on

While exploratory data analysis is not a newnew

concept, data-driven research goes a step

digital

tools

that

go

beyondthespreadsheet and sometimes beyond the power

further in its use of digital tools. Dramaticof the desktop computer. While this shift inincreases in computing power and data

scale

volumes have made machine learning andartificial intelligence available as common tools

60

might

sound

intimidating,manysophisticated data analysis tools are now

Artificial Intelligence Future Science Platform

The barrier to broader adoption of thesetwenty-five postdoctoral researchers matchedavailable as web-based services.

(MLAI FSP). The MLAI FSP consists of about

new tools is both technical and social. The paceby an equal number of researchers fromof technological change has become so fast

CSIRO’s business units, thus pairing digitalthat we can no longer wait for it to be absorbed
by generational change.
Researchorganisations

like

CSIRO

specialists with domain specialists.

5. Conclusions

inAustralia or the Helmholtz Association in

The digital transformation changes the way weGermany have realised that for solving researchdoquestions, disciplinary and digital expertise

research.

In

ourresponsetothistransformation, we need to balance domain

have to go hand in hand. At the same time theyknowledge with digital skills in order toalso recognised that it takes a concerted effort

leverage the power of the new research tools.

to upskill their entire workforce to increase their

In exchange, we gain new insights throughdigital literacy.

data-driven research.

4. Organisational Response

Acknowledgments. The author would like torecognised that the scale and velocity of theDigital Earth, and in the Research Data Alliance

In its Digital Megatrends report [2], CSIRO

thank his colleagues in CSIRO, ARDC, Helmholtz

digital transformation of industry, society andfor the fruitful discussions that informed thisscience will continue to accelerate over theabstract. Special thanks go to the Digitalcoming decade. This trend is likely to impact

Transformation Teams in CSIRO.

three areas: (1) Data-driven scientific discovery,

References(2) opportunity for industrial innovation, and (3)key enablers for innovation.

1.

To raise the level of digital literacy in theirorganisations,

CSIRO

Association

have

goes

further

and

both

the

initiated

Fourth Paradigm: Data-Intensive ScientificHelmholtz

Discovery (1.1). Redmond, WA: Microsoft

training

Research. 2009

programmes called “Digital Academies”. CSIRO
even

than

the

2.

Helmholtz

Association in aiming these digital training
programmes

not

only

at

early

Brisbane. 2019

managers.

The digital literacy programme in CSIRO ispart of a broader initiative, Digital+Domain,that includes expanding and innovating the
and

a

SA,

DawsonD.

Digitaldecade of digital disruption, CSIRO Data61,

training for senior researchers and executive

infrastructure,

Hajkowicz

Megatrends: A perspective on the coming

careerresearchers but also offering digital literacy

digitalHey, T., Tansley, S., & Tolle, K. (Eds.). The

research

programme. An important part of the research

programme is the Machine Learning and

61

The IGSN Global Sample Number - A PID for Physical Objects
Jens Klump1*, Kerstin Lehnert2, Sarah Ramdeen2, Lesley Wyborn3* CSIRO Mineral Resources, 26 Dick Perry Avenue, Kensington, WA, 6151, Australia
Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY, 10964, USA
3
Australian National University, 143 Ward Road, Acton, ACT, 2601, Australia
Email: jens.klump@csiro.au

1
2

Summary. Physical samples are at the heart of many research disciplines, yet it is challenging tounambiguously identify and trace samples outside of their immediate institutional context. The IGSN

Global Sample Number transferred the concept of persistent identifiers championed through the useof Digital Object Identifiers by DataCite and applied it to physical samples. The application of IGSN
allows persistent identification of physical samples across institutional boundaries. Through linking

IGSN with DOI of data and literature, physical samples are linked to scholarly communication andbecome part of the record of science.

Keywords. Persistent Identifier, Metadata Catalogues, Collection Management, Knowledge Graph

1. Introduction

research nor future science. In many cases,

Physicalsampleshavescientificinvestigations,

been

a

criticalsample sites can no longer be accessed (e.g.

in(e.g. lunar samples, remote locations, ocean

component of research since the beginning ofmine sites) or were too expensive to recollectnatural and environmental sciences, material

drilling). As time progressed, single institutions

archaeology, and biomedicine. Millions of

instruments: samples were shipped around theespeciallythesciences, agriculture, physical anthropology,nosamples have been acquired from all over the

globe for analysis and reuse by other research

globe and beyond.

longerhad

full

suitesofanalyticalcommunities. However, unless the samples

thesewere uniquely identified, confusion reigned

typically stored within individual institutions.

whom and when, and it was often not known

government agencies valued their collections

who had originally collected them.

recent decades, as costs rose, administrators

2. Sharing Information About Samples

become too expensive and that samples could

Number (IGSN) [1], which provides a globally

no longer valued and became buried in deskaremaineddata acquired on these samples, and any

Traditionally,

research

has

seen

samples described, locally processed and thenover exactly which sample was analysed, byOver the last centuries, most universities andwhich institution the samples came from nor

and locally curated and catalogued them. In

A solution lies in the IGSN Global Sample

argued that preservation of samples had

be recollected when required. Samples wereunique identifier for physical samples. It allows

drawers, sheds, or basements, where theysamples (or the digital representation of them),

supporting neither the transparency of current

publications that result from these data. It

unknownandinaccessible,

62

researcher

toestablish

links

betweenenables any researcher, institution or funderidentification of samples and actionable links

be acknowledged and credited and can(landing pages) and to other data generated

that supported the collection of the sample tofrom publications to online metadata profiles

demonstrate value for the contribution that aby other studies of the same sample, thus

particular sample has made to scientificlinking

research. More importantly, it can ensure thatinvested by institutions into sharing them andgeosciences confirms the power of its concept

Originally developed for the solid Earthand implementation but imposes substantial

Sciences with funding from the US Nationalpressures

Science Foundation, the IGSN has evolved into

on

the

other

make the transition from project to productand there are lessons to be learnt from otherphysical samples. More than seven million

PID services. To this end, the project invited

samples have been registered so far.

experts

in

the

field

and

Sciencetechnologicalproject

fundedbyelement of research data infrastructure andFoundation.

reference digital and5. Conclusions

scholarly communication. PIDs are essential tonon-digital objects,

theAlfredP.

Sloan

DOI. This opens new ways for embedding

communication between information systems.

physical samples in the record of science.

If broadly adopted, they lead to convergence
thisroadmap

technological base with other PID systems likemachine-to-machineinstrategy anddata and literature. It shares a commonand facilitating unambiguous identification ofsystems

data

Using IGSN as PID links physical samples tomaking these citable and more interoperable,data

researchtowards long-term sustainability of the IGSN, a

Persistent Identifiers (PID) are a foundational

diverseofinfrastructures to develop an organisational

3. Linking Samples to the Record of

of

and

IGSN is not the first PID service provider todisciplines that need to uniquely identify

incapacity

governing organization.

adopted by a growing number and range ofresources

existing

capabilities of the IGSN architecture and its

an international PID system that is now
byscholarly

The recent expansion of the IGSN beyond

preserving them for reuse in future science.

and

to

4. Outlook

samples but also for the efforts and expertiseworldwide,samples

communication and the record of science [2].

credit is given not only for the collection of the

stakeholders

physicalhighlyfragmented ecosystem. PIDs such as DOI and

Acknowledgments. Building the IGSN Global

globally and across disciplines to identify, track

years by the member organisations of the

Sample Number has been supported over the

ORCID have been successfully implemented
andrelaterespectively.
IGSN

digitalfollows

objects

theandexampleIGSN Implementation Organization (IGSN e.V.)

persons,ofReferences

these

successful PID systems. The use of IGSN as1.

globally unique, persistent, and resolvable2.

identifiers for samples ensures unambiguous

63

IGSN e.V. https://www.igsn.org
Lehnert, K. A., Vinayagamoorthy, S., Djapic,
B., & Klump, J. The Digital Sample:
Metadata, Unique Identification, and Links

to Data and Publications. EOS, Transactions,
AGU, 87(52, Fall Meet. Suppl.), Abstract
IN53C-07. 2006

64

General-purpose research-data management service
for international research collaboration
Yusuke Komiyama1*, Mao Tsunekawa1* National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-Ward, Tokyo 101-8430, Japan
Email: komiyama@nii.ac.jp

1

Summary. There is an information technology infrastructure system for a research-data management(RDM) service called the National Institute of Informatics’ Research Data Cloud (NII RDC) in Japan. TheNII RDC has three stages of research-data platforms—discovery, publication, and management—andthey work harmoniously together. The web service that manages the closed data under researchamong the NII RDCs is called GakuNin RDM (GRDM). The GRDM is based on the Open Science
Framework by the Center for Opens Science in the US; we modified to fit the GRDM to Japan’s current

open science policy. In particular, we added a function of research-data trail preservation from theperspective of research integrity and a storage customization function for academic institutions into

the GRDM. As of 2019, the GRDM is in the demonstration phase and is in the progress of
implementation in collaboration with domestic and international academic institutions. In this article,
we introduce the current development progress of the GRDM in Asia and Oceania.

Keywords. research-data management, data sharing, data platform, open science, researchcollaboration

1. Introduction

science network—SINET [3] —for all academic

To date, research-data management (RDM)

institutions across the country. There is anacademic

for an RDM service called NII Research Data

services have been required by Japanese

information technology infrastructure system

misconduct incorporate

Cloud (NII RDC) in Japan. The NII RDC has

research-data infrastructure is increasing todiscovery, publication, and management—andpolicymaking in Japan. For example, theservice that manages the closed data underdevelopment

RDM (GRDM) [4].

institutions

to

avoid

the context ofscientificgovernance [1]. Recently, the demand forthree stages of research-data platforms—

thesciencethey work harmoniously together. The web

guidelines for the large-scale research &research among the NII RDC is called GakuNin

researchpromotingproject

open“Moonshot”

have

described the need for advanced research

2. System Design

management and research-data infrastructure

2.1 Enhancing RDM service from Opensystems [2].

Science Framework

At present, the national research andeducation network of Japan—the National

Firstly, We developed a GRDM based on theestablish RDM services over the high-speed

and modified it to fit with Japan's current open

OSF [5] by Center for Opens Science in the US

Institute of Informatics (NII)—is trying to65science policy. In particular, we added a19

academic

institutions

from the perspective of research integrity andinterdisciplinary

projects

institutions into the GRDM. In addition, theexample, there is a case of the GRDM usage inmerely configuring the identity provider ofdoctors and mathematicians. There are alsofunction of research-data trail preservation

in

Japan

were

used

the

participating in the GRDM trial service. Several

a storage customization function for academic

demonstrations service of the GRDM. For

GRDM can build a data-sharing system byneurology by a collaboration of medical

overseas research institutions with eduGAIN

applications in the life sciences where the RDM[6] —a global authentication and authorization

services are combined with business process

because the GRDM has been registered to it as

paper before publication in an organization.

infrastructure(AAI)

federation

service—systems for reviewing invalid images in aa service provider.

3.2. International deployment

2.2. Linking between RDM service and data

Focusing on the Asian region, we are providing

Secondly, we designed features that would

softwaremanagement plan (DMP)

the source code of the GRDM as open-source

enable RDM service to be compliant with the

to

the

Malaysian

Research&

Education Network (MYREN). We held an

DMP. DMPs are an essential part of theinternational workshop in Tokyo in February

needs to have the functionality to prepare an

the GRDM source code for the installation toresearch-data life cycle. The RDM service

2020 with MYREN members. They modified

appropriate research environment compliant

enable the RDM service to run on an on-

with the data management plan. Therefore, we

premises virtual machine. They are currently

are customizing a data management planningrunning the same system as the GRDM for 12

tool that is called ReDBox 2.0 [7] and linking toinstitutions in Malaysia.

GRDM as a plugin. ReDBox 2.0 is an open-

4. Conclusion

source software provided by the Queensland

Cyber Infrastructure Foundation (QCIF) of

We are building the NII RDC. It is a nationwide

Australia. The software has functions that not
only support

tocreate

RDM service platform on a high-speed science

DMPs but also

network in Japan, which supports each phaseorchestrate repositories and RDM service to beof the research-data life cycle. In particular, thecompliant with the DMPs. In the initial stage ofdevelopment,

weachieved

to

GRDM is a platform that focuses on handlingprovidesensitive research data. We have adopted theresearchers functions for deploying research-

OSF as the basis for the GRDM and weredata projects compliant with DMPs into GRDM

enhancing it to fit with Japan’s open scienceand extracting research data from GRDM forpolicy. Besides, the GRDM is being used bydefining research data sets.

research institutions in Japan and Malaysia.
The NII RDC is a global standard RDM service

3. Development

for advanced research management that works

We are running a demonstration of the GRDM

supports AAI federation services. Therefore, we

3.1. Domestic deployment

servicefordomesticusetowith a DMP tool that called RedBox 2.0 andacademic

believe the NII RDC is useful for international

institutions, starting in April 2019. As of 2020,

research collaboration.

66

References
1.

2.

3.

4.

5.

6.

7.

Funamori, M., Hayashi, M., Komiyama, Y.,
Tsuchiya, M. & Yamaji, K. Requirements
Analysis of System for Research DataManagement
toPrevent
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Advanced
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CabinetOffice. Moonshot Reseach andDevelopment
Program
Science,Technology and Innovation- Cabinet
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https://www8.cao.go.jp/cstp/english/moonshot/top.html, 2020
Kurimoto, T. et al. SINET5: A low-latency
and high-bandwidth backbone network for
SDN/NFV Era. in 2017 IEEE International
Conference on Communications (ICC), 1–7,
2017
Komiyama, Y. & Yamaji, K. Nationwide
Research Data Management Service ofJapan in the Open Science Era. in 2017 6th
IIAI International Congress on Advanced
Applied Informatics (IIAI-AAI 2017) 129–
133, 2017
Sullivan, I., DeHaven, A. & Mellor, D. Openand Reproducible Research on OpenScience Framework. Curr. Protoc. Essent.
Lab. Tech. 18, 2019
Michael, S. & Ziegler Jule, A. An Identity
Provider as a Service platform for theeduGAIN
researchandeducationcommunity. in 2019 IFIP/IEEE Symposiumon Integrated Network and Service
Management (IM), 739–740, 2019
Nairnsey,J.
for
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DataManagement Plans (RDMPs), Connecting
to cloud services, and Archiving Using
ReDBox 2. in eResearch Australasia 2019,
1–19, 201967

A data-scientific approach toward understanding of the goldfish
genome and morphological diversity
Tetsuo Kon1*, Yoshihiro Omori 1* Laboratory of Functional Genomics, Graduate School of Bioscience, Nagahama Institute of
Bioscience and Technology, Nagahama, Shiga, 526-0829, Japan
Email: t_kon@nagahama-i-bio.ac.jp

1

Summary. Goldfish strains show highly diversified phenotypes in morphology and coloration.

However, the genetic basis underlying these phenotypes is still waiting for elucidation. We recently

reported a de novo assembly of the goldfish genome. The goldfish genome has 50 chromosomes,

which are further divided into two asymmetrically evolved subgenomes consisting of 25 chromosomes
each. Furthermore, we performed whole-genome sequencing of 27 goldfish strains and wild goldfish.

We found more than 60 million genetic variations and established a population structure of major

goldfish strains. Genome-wide association studies and analysis of strain-specific variants showed

genetic loci associated with several goldfish phenotypes, including the dorsal fin loss, long-tail,telescope-eye, albinism, and heart-shaped tail. Our results suggest that accumulated mutations in theasymmetrically evolved subgenomes led to the generation of diverse phenotypes in goldfish strains.

Taken together, we conclude that data-scientific approaches are effective for understanding thegenetic basis of the phenotypic diversity among goldfish strains.

Keywords. Goldfish strains, Diverse phenotypes, Genome assembly, Whole-genome duplication,
Genome-wide association study

1. IntroductionThe

goldfishto genetic and phenotypic diversity in goldfish(Carassius

auratus)isstrains have been largely unknown. To solveadomesticated cyprinid teleost, which is closely

this problem, we adopted a data-scientific

180 variants and 70 genetically establishedgenome assembly of the goldfish genome [3].

related to the crucian carp. There are at least

approach.

First,

wegoldfish strains [1]. They show highly diverse

On top of that, we performed whole-genome

fin,strains [4]. These studies provide us clues why

sequencing

phenotypes contain biologically interesting

goldfish strains show diversified phenotypes in

and

hood

morphology.

These

27

a wholephenotypes in body shape, colouration, scales,
eyeofestablisheddomesticatedgoldfishphenotypes that have not been observed inmorphology and coloration.

showing diverse phenotypes, goldfish are

2. De novo assembly of the goldfish

because their genome experienced a recent

Byzebrafish or medaka mutants. In addition tointerestingfromevolutionary

perspectives

whole genome duplication (WGD) event [2].

genome
using

long

read

DNA

sequencing

technology, we generated a high-quality draft

However, whether and how WGD contributes

sequence

68

and

gene

annotations

of

the

goldfish genome [3]. The total length of thisevolution

genome assembly was 1.82 giga base pairs. It
arefurther

dividedintotogenetic

phenotypic diversity in the goldfish strains.

contains the 50 chromosome-scale scaffolds
which

contributes

4. Conclusions

twosubgenomes, the L- and S- subgenome. We

By collecting and analysing the huge genomic

found that the L-subgenome is preserved to

data,

we

suggestthegenetic

basis

stay more similar to the ancestral state,phenotypic diversity in the goldfish strains.

gene losses, and changes in levels of gene

Referenceswhereas the S-subgenome experiences more
expression [4]. This phenomenon is called as1.

asymmetric subgenome evolution.

3. Whole-genome sequencing of 27

2.

goldfish strains

To identify genes associated with diverse

3.

phenotypes in goldfish strains, we performed
whole-genome sequencing of 48 individuals

from 27 strains bred in Japan and a wild

goldfish lineage. We compared the genome

sequencing data of each individual and ourgoldfish reference genome sequence, andidentified more than 60 million variant sites [4].
Genome-wide association studies and analysisof strain-specific variants showed genetic loci4.

associated with several goldfish phenotypes,
includingthedorsalfin

loss,

long-tail,telescope-eye, albinism, and heart-shaped tail.
These mutations were more related to the Ssubgenome

than

the

L-subgenome.

and

This

suggests that the asymmetric subgenome

69

ofOmori Y., Kon T. Goldfish: an old and newmodel system to study vertebrate
development, evolution and humandisease. J Biochem, 165, 209-218, 2019
BraaschI.
GenomeEvolution:
Domestication of the Allopolyploid
Goldfish. Curr Biol, 30, R812-R815, 2020
Chen Z.*, Omori Y.*, Koren S., Shirokiya T.,
Kuroda T., Miyamoto A., Wada H., FujiyamaA., Toyoda A., Zhang S., Wolfsberg TG.,
Kawakami K., Phillippy AM., NISC
Comparative
Sequencing
Program;Mullikin JC., Burgess SM. De novo
assembly of the goldfish (Carassius
auratus) genome and the evolution ofgenes after whole-genome duplication. Sci
Adv,
5,eaav0547,2019(*equally
contributed)
Kon T., Omori Y., Fukuta K., Wada H.,
Watanabe M., Chen Z., Iwasaki M., MishinaT., Matsuzaki SS., Yoshihara D., Arakawa J.,
Kawakami K., Toyoda A., Burgess SM.,
Noguchi H., Furukawa T. The genetic basisof morphological diversity in domesticatedgoldfish. Curr Biol, 30, 2260-2274, 2020

Recent Achievements of Deep Learning on Recognition ofModern Japanese Magazines
Anh Duc Le1*
* The Center for Open Data in the Humanities, DS, ROIS
Email: anh@ism.ac.jp

1

Summary. Inspired by the recent successes of deep learning on computer vision, natural language
processing, we present our recognition of modern Japanese Magazines by using deep learning. The

recognition system has two main stages: text line detection and text line recognition. We employ
Character Region Awareness Network for text line detection and Attention-based Encoder-Decoder
for text line recognition. The system can convert an image of modern Japanese to a text file. It can be

the main component for many applications such as text retrieval from image documents, document
storage. We also open the source code of the recognition system for research communities.

Keywords. Modern Japanese Magazines, text line detection, text line recognition, deep learning

1. Introduction

Character Region Awareness Network (CRAFT)

resource for historians in exploring social

Encoder-Decoder

Since historical documents are an invaluablefor text line detection and Attention-based

aspects, lifestyles, even weather in the previous

recognition.

historical documents. The popular method is

2. Overview of Recognition System

historical documents and made them available

extraction and text line recognition. Theera, many countries have been preserved their

(AED)

for

text

line

The system has two main modules: text line

to construct digital libraries to preserve

detailed architectures are shown in Figures 1

to the public. This helps researchers fromand 2.

domestic and aboard to access historical

2.1 Text line detection by CRAFT

documents. The traditional method is scanning

CRAFT [1] inherits the UNet model to detectbooks to images. Then, experts read them andtext characters and affinity between characters.

provide transcriptions. This approach is labor-

We combine the bounding boxes of charactersintensive, time-consuming, and requiring many

and affinity between them to create theexperts. So, it is not feasible to provide

bounding boxes of text lines.

transcription for a large number of historical

2.2 Text line recognition by AED

documents in libraries. Document analysis and

The AED model [2] is used for the sequence torecognition can speed up the digitalization

sequence. We employ the AED model toprocess.

convert an image of text line to text. It has two

The goal of this research is to develop amain parts: DenseNet for extracting features

recognition system for the full page of modern

from an image of a text line, and an LSTM

Japanese magazines. The project will inherit

the recent deep learning techniques such as70

integrated

with

an

attention

model

generating the output text.

forFigure 1. The architecture of text line detection.

Figure 2.The architecture of text line recognition.

Table 2 shows the CER of our AED modelfor text line recognition. We achieved 22.52%

3. Experimental resultsof CER on the testing sets. Since the training

We employ a dataset of modern magazines indataset is imbalanced

Japan from 1870 to 1945. It has 922 pages,
and we randomly select 80%

AED in the tfuture. Moreover, we have released

our source code for the research communities

rest for testing. The number of categories iswhich

contains

manycharacterclasses, we have to improve the accuracy ofof pages fortraining, 10% of pages for validation, and the5,398,

between[3].

character

categories that do not use in the current
Japanese character system.

4. Conclusions

for text line detection. Our retrain model

achievements in the recognition of modern

In this paper, we have presented the recent

Table 1 shows the Precision, Recall, and F1

achieves 90.8% of F1 score.

Japanese magazines. Our recognition system isTable1. The result of CRAFT for text line detection.

Japanese magazines. We hope our source

able to recognize a full page of modern

MethodPrecision

Recall

CRAFT

89.6

92.0

code is useful for the communities.

F1
90.8

References
1.

Table 1. The results of AED for text line recognition.

MethodAED

Character Error Rate (%)
22.52

2.

71

Baek Youngmin et al., Character Region
Awareness for Text Detection, CVPR, pp.
9365--9374, 2019
Anh Duc Le et al., Recognition of Japanese
historical text lines by an attention-based
encoder-decoder and text line generation.
In Proceedings of the 5th International

Workshopon
Historical
Document
Imaging and Processing, pp. 37–41, 2019

3.

72

Kindai
OCR:
https://github.com/ducanh841988/KindaiOCR

The RDA Community Response to COVID-19
Mark Leggott1** Research Data Canada, 45 O'Connor St., Suite 1150, Ottawa, ON K1P 1A4, Canada
Email: mark.leggott@rdc-drc.ca

1

Summary. In March 2020 The European Commission approached the Research Data Alliance (RDA) todevelop guidelines for data sharing for research into the COVID-19 pandemic. By the end of March

RDAlaunched a process using its open platform for community engagement, and within a few weeks
over 600 community members had volunteered their time to develop the guidelines. The final versionof the “COVID-19 Recommendations and Guidelines on Data Sharing” (the Guidelines) was released onJune 30, and provides advice in 4 thematic areas (Clinical, Omics, Epidemiology, Social Sciences) and 4

cross-cutting areas (Community, Indigenous Data Guidelines, Research Software, Legal and Ethical).

Since their release, the Guidelines have been endorsed by a wide range of stakeholder groups, and will

help inform the research into the COVID-19 pandemic. In addition to the Guidelines, a series of
supporting navigational aids (infographic, mindmap, decision wizard) and journal articles are being
developed.

Keywords. COVID-19, data sharing, research data management, best practices

73

Data Stewardship: Indian Perspective
Devika P. Madalli1** National Documentation Research and Training Center, Indian Statistical Institute,1

8th Mile Mysore Road, Bangalore 560 059, India
Email: devika@drtc.isibang.ac.in

Summary. The talk intends to give an overview of Data initiatives in India. India is diverse and host to a large
number of research institutes and universities. There are a number of national nodal centers to foster work in
particular domains such as Agriculture, Astrophysics, Medical sciences, cultural studies among many others. It
is evident that the data landscape is wide and varied.

Data sharing at national level is fostered by the National Data Sharing and Accessibility Policy (NDSAP) by theGovernment of India. The national level mandate is binding on all government departments, institutes andprojects funded by the central government. Basically the policy ensures that data generated out of publicfunds will be openly available. One of the significant implications of the policy and mandate is that it resulted

in the National Data sharing portal http://data.gov.in which is the national platform for sharing data invarious domains. The portal hosts over 400,000 resources in various domains such as agriculture, health,transport, education etc; all openly accessible.

Other than the national portal there are domain based communities that are data intensive. The centre
initiated task forces on data management in domains such as climate, health and biosciences. As a responseto the present pandemic situation data.gov.in has focused data on COVID19 and presents data (granular),infographics and visualizations.

The Government of India has initiated the Smart Cities Mission. The mission aims to aide urban planners toplan and implement all facilities and citizen services based on needs assessment of communities.

The

mission is data intensive. Smart cities planning is aided by arrays of datasets such as governance data,demographic data, transport data, employment data, water, sanitation among others.

There are however, issues in data management such as provision of metadata, ensuring quality of data, data
curation methods and multilingual issues that need to be addressed. The talk intends to discuss some theissues and possible solutions by adopting best practices for the same.
Keywords.

74

A unique website JCDP that aims to disseminate
scientific information on historical climate data
Takehiko Mikami1*, Masumi Zaiki2, Junpei Hirano3* Tokyo Metropolitan University, 1-1 Minami-Osawa, Hachioji City, Tokyo, 192-0397, Japan
2
Seikei University, 3-3-1 Kichijoji-Kitamachi, Musashino City, Tokyo, 180-8633, Japan
3
Teikyo University, 359 Otsuka, Hachioji City, Tokyo, 192-0395, Japan
Email: mikami@tmu.ac.jp

1

Summary. JCDP (Japan-Asia Climate Data Program) is a unique website that aims to disseminate

scientific information on historical climate data.

Historical climate data include various kinds ofmeteorological documentary records, such as old instrumental temperature and pressure observations,since the 19th century before the JMA official data were available. In Japan, daily weather records infeudal domains during the Edo period (the 1600s-1860s) provide detailed climate information from allover the country. Our website JCDP will contribute to the development of historical climatology not
only in Japan and Asia but also in Europe and America, where meteorological observations started
earlier than those in Asian countries.

Keywords. climate data, documentary record, JCDP

1. Introduction

around Japan. Historical documents, including

In Japan, official meteorological observations

diaries of individuals, logs of clan offices,

Observatory in 1872, founded by the present

temples and shrines, have been preserved inour research group discovered dozens ofcontain daily weather descriptions such as

meteorologicalpeculiar climate-related natural phenomena

started at Hakodate Local Meteorologicalgovernment documents, and reports fromJapan Meteorological Agency (JMA). However,

local libraries and museums. These often

documents

sub-daily“cold”, “fine”, “rainy” and “windy”, and mention

locations in Japan, such as in Nagasaki, Kobe,

such as “lake freezing” and “flower blooming”.

so on during the 19th century. Also, in East Asia

extreme events such as floods, droughts, andmeteorological records, which cover the 19th

documents.

been kept safe in libraries and museums, or in

information on historical climate data.

which

data

includeobservedthroughout

Osaka, Yokohama, Tokyo, Mito, Hakodate andInformation

andheavy snowfalls are also contained in historicalSoutheast

Asia,

a

vast

amountofJCDP

and 20th centuries up to World War II, havesome cases left to decay.

reconstructionsoftoclimate-related

disseminate

scientific

JCDP has six drop-down navigation menus;

several kinds of historical documentary sources
enableaims

severe2. Contents of JCDP

Apart from meteorological data, there are

which

about

Climate Information (Historical Documents, OldclimateInstrumental

variations before the 19th century in and

75

Data,

and

Lighthouse

Observations),

Data(Instrumental

which prevents foreign climate scientists from

Meteorological Data, Reconstructed Climate

understanding and examining climate changes

Indices, Historical Weather Database, and Daily

in Japan during the historical period.

Diary Weather Records), Publications, Column,

In contrast, collaborative research efforts in

Links, and People (Fig.1). You can download

Europe have made remarkable progress in(EXCEL) format.

language English (e.g., Brazdil et al.,2005).

various types of climate data in the text file

historical climatology with the aid of common

The JCDP site has been constantly accessed

Therefore, we will continue disseminating

since it was opened in February 2018, and 42%

scientific information on Japanese historicalof the access is from overseas. The number ofclimate data in English.

access from overseas is higher from the United

Notes

States (26.6%), France (4.3%), United Kingdom(2.4%), etc., and access has been made from 72

URL of JCDP website:countries in total so far.

https://jcdp.jp/

3. Conclusions

References

In Japan, we have a tremendous amount of1.

historical documents that include various kindsofclimate information, such as weather

conditions, natural disasters, and so on.

However, almost all such documents were
written in Japanese and Chinese characters,

76

Brazdil,R., Pfister,C., WannerH., Von
Storch,H. and Luterbacher,J., Historical
Climatology in Europe – The state of theArt. Climatic Change, 70, 363-430, 2005

Figure 1. A top page of the JCDP website

77

Spatial Analysis of COVID19 using Compositionally-warped
Gaussian Process
Daisuke Murakami1** Institute of Statistical Mathematics, 10-3 Midoricho, Tachikawa, Tokyo, 190-8562, Japan
Email: dmuraka@ism.ac.jp

1

Summary. This study first develops a regression approach to model number of COVID19 infectious byprefectures, which are noisy and non-Gaussian distribution. A compositionally-warping function isused therein. After that, factors determining ease of infectious are analysed using the developed
approach. The result demonstrates that commuting, concentration of young people in metropolitanarea, people concentration in commercial area, and other factors are likely to increase infectious.

Keywords. COVID19, Spatial analysis, Additive model, Compositionally-warped Gaussian process

1. Introduction

2. Model for number of infectious

available owing to the development of senor

per area 𝑦𝑦𝑖𝑖,𝑡𝑡 on t-th day in i-th prefecture using

More and more spatial data are becomingThis study analyses the number of infectious

and other technologies. Together with that,the following regression model:

statistical methods for spatial data have beendeveloped.

Yet,

these

methods

are𝐾𝐾

𝜑𝜑�𝑦𝑦𝑖𝑖,𝑡𝑡 � = � 𝑥𝑥𝑖𝑖,𝑡𝑡,𝑘𝑘 𝑏𝑏𝑖𝑖,𝑘𝑘 (𝑝𝑝𝑖𝑖 , 𝑥𝑥𝑖𝑖,𝑘𝑘 ) + 𝜀𝜀𝑖𝑖,𝑡𝑡

not

𝑘𝑘=1

necessarily adaptive to recent data that are

where 𝑥𝑥𝑖𝑖,𝑡𝑡,𝑘𝑘 is the k-th covariate. The coefficientnoisy and have non-normal distribution.

𝑏𝑏𝑖𝑖,𝑘𝑘 (𝑝𝑝𝑖𝑖 , 𝑥𝑥𝑖𝑖,𝑘𝑘 ) is assumed to vary depending on

Daily number of COVID19 infected people,

prefecture 𝑝𝑝𝑖𝑖 and covariate value 𝑥𝑥𝑖𝑖,𝑘𝑘 . Given

which we will analyse, is one of such data. As

the assumption, map pattern such as high risk

shown in Figure 1, the data have severely

nearbyskewed (i.e., non-normal) distribution because

we

first

develop

covariate-dependent

A difficulty is the non-normality of 𝑦𝑦𝑖𝑖,𝑡𝑡 .

appropriately analyse factors behind COVID19
that,

and

age is used as a covariate) are estimated.

method modelling such data is required toGivenTokyo,

pattern such as high risk of aging people (ifof the rareness of infected people. Statisticalspread.

𝜀𝜀𝑖𝑖,𝑡𝑡 ~𝑁𝑁(0, 𝜎𝜎 2 )While logarithmic has typically been used toatransform non-normal variable 𝑦𝑦𝑖𝑖,𝑡𝑡 to normal

regression model for such non-normal data.

one 𝜑𝜑�𝑦𝑦𝑖𝑖,𝑡𝑡 �, we use a compositionally-warping

Then, it is applied to an analysis of COVID19.

approach,concatenatingtransformation

functions to achieve better transformation andmodel accuracy.

3. Analysis

We analyse the daily number of infectious byprefecture between March 1 and June 3

Figure 1: Histogram of # of infectious per area

78

(source: https://gis.jag-japan.com/covid19jp/).

Regarding Pop and DayPop, both are

The covariates are week, age, age × prefecture,

estimated to increase number of infectious.

(Pop), and day-night ratio of population

infectious increases as population density

mean people density in residential area at 7PM

The result suggests that, while the number ofdensity at 7PM (DayPop).

increases,

Figure 1 summarizes estimated effects

thistendencygets

severeincommercial area with high daytime population.

from week, days of the week, and age. The

Figure 3 plots residual factors that could

major findings are as follows: (a) infectious

not be explained by the covariates. This figure

number of infectious on Monday and Sunday;

extraordinarilysmoothly varying over week; (b) smaller

demonstrates that number of infectious is(c) high risk of people aged between 20 and 59.
risk.

Figure2

plots

nearby

Tokyo

even

subtracting the effect of population density.

Based on (c), commuting might increase

infectionhigh

In summary, commuting, concentration of

estimatedyoung people in Tokyo and Osaka, people(A) prefectures nearby Tokyo and Osaka havenearby Tokyo, are factors increasing infectious.

influence from age by prefecture. Interestingly,
different

properties

from

(B)

the

concentration in commercial area, and locationother

Acknowledgement

prefectures. 20s and 30s tend to have higher

risk in (A) whereas 40s and 50s has the highest

People density data is provided by NTT

risk in (B). Concentration of young people

DoCoMo Inc.

might spread infection in (A). On the other
hand, high risk in (B) might mean commuting
is the major source of COVID19 spread.

Figure 3: Residual spatial process (ease of
infectious that are not explained by the covariates).

Figure 1: Effects from covariates

Figure 2: Effects from age by prefecture79

Monitoring Sustainable Development Goals Amidst COVID-19
Through Big Data, Deep Learning and Interdisciplinarity
Kassim Said Mwitondi1**Sheffield Hallam University, College of Business, Technology and Engineering; Sheffield S1 2NU.

1

United Kingdom
Email: k.mwitondi@shu.ac.uk

Summary. As the coronavirus disease 2019 (COVID–19) ravaged across the globe, in the first half of2020, the world was once again reminded of the huge gaps in our knowledge, despite our current
scientific and technological capacities. The pandemic has had a severe impact on our ways of life, anddespite its devastating impact, it has presented us with an opportunity for paying greater attention to

the challenges we face. It is in that context that we associate the fight against COVID-19 with monitoring

Sustainable Development Goals (SDG). Considering each SDG as a source of Big Data, we present a
generic framework for combining Big Data, machine learning and interdisciplinarity to address global

challenges. The work delivers descriptive and prescriptive findings, using data visualisation andanimation techniques, on the one hand, and predictive results, based on convolutional neural networks,

on the other. The former is based on structured data on cases and deaths from COVID–19 obtained

from the European Centre for Disease Prevention and Control (ECDC) and data on the impact of thepandemic on various aspects of life, obtained from the UK Office of National Statistics. Predictive

findings are based on unstructured data–a large COVID–19 X–Ray data, 3181 image files, obtained from

Github and Kaggle. The results from both sets are presented in the form that resonates with crossdisciplinary discussions, opening novel paths for interdisciplinary research in tackling global challenges.

Keywords. Big Data, Convolutional Neural Networks, Data Science, Data Visualisation, Interdisciplinarity,

Predictive Modelling, Sustainable Development Goals

The complex interactions of the SDGs, the1 Introduction

magnitude and dynamics of inherent data

The United Nations Sustainable Development

attributes and the deep and wide socio–

Goals (SDGs) SDG [2] were signed up by 193

economic and cultural variations across themember states in 2015, outlining measurable

globe provide both a challenge and antargets and indicators to be attained by the year

opportunity to the SDG project [8, 9]. In the2030, hence its commonly used pseudonym,

light of the impact of COVID–19 on SDGs andagenda 2030. They were drawn to address thethe computing power, each SDG is a source of

global challenges–poverty, inequality, climate

Big Data [8-12]. This work is motivated by thechange, environmental degradation, peace andforegoing aspects, and it seeks to answer thejustice, with a target set for the year 2030.

question: How can interdisciplinarity, Big Data
and

80

deep

learning

combinetodeliver

1sustainable solutions in the wake of the COVID–

COVID–19 in a descriptive and prescriptive way.

To provide a descriptive mapping of skills

both the battle against COVID–19 and in

addressing global challenges.

parameters, before and during the pandemic.

19 pandemic? We set the following objectives.
andinterdisciplinary

knowledge

Results provide clear insights to stakeholders infor2

To illustrate the impact of COVID–19 based3

To demonstrate the efficacy of combining

monitoring SDGs. We mainly focus on the key

Visualisation

on structured and unstructured data.

animation

adaptable to handle new cases.,

codes

are

2.3 Convolutional Neural Networks

data, modelling techniques and skills in an

We apply Convolutional Neural Networks

interdisciplinary context.

4and(CNN)-a machine learning

To present analytical results through datatechnique,classifying the X–Ray data, as in Figure 2.

visualisation, animation and modelling.

for

2 Methods

The general framework for this work is
illustrated in Figure 1, in which the intersections
highlights the setup for developing robust

solutions for global challenges like COVID–19.

Figure 2: A CNN model for classifying imagery data

The mechanics of CNN are well-understood [17,18, 19]. Its architecture is illustrated in Figure 3.

Figure 3: A typical CNN architecture
Figure 1: Iintersection of challenges, data & skills

2.1 Data Sources
The

work

is

based

on

structured

The CNN output, 𝑌𝑌𝑖𝑖,𝑗𝑗,𝑘𝑘 denotes the neuron
output in the 𝑖𝑖 𝑡𝑡ℎ row and the 𝑗𝑗𝑡𝑡ℎ column offeature map 𝑘𝑘 of the 𝑙𝑙 𝑡𝑡ℎ convolutional layer.
We highlight the technical challenges in
obtainingtheconvolutional
values,demonstrating them via sliding the kernel overthe input data, as shown in Figure 4.

andunstructured data. The former came from theEuropean Centre for Disease Prevention andControl (ECDC) [13] and the UK Office of

National Statistics [14]. Unstructured data is acombination of 1840 COVID–19 X–Ray image

files, downloaded from a Github[15] and 1341
normal data collected from Kaggle [16].

2.2 Data Visualisation

Data visualisation and animation techniques areused on structured data to reflect the impact of

Figure 4: Convolutional values obtained via kernel sliding

81

The vertical and horizontal strides in Figure 4 asconditions, doctors and radiologists are underthey impinge on the model’s capability ofpressure to distinguish such images.

feature capturing. The pooling layer in Figure 3

reduces the dimensionality of the rectified
feature map, using different filters to identify

different parts of the image. The Fully
Connected layer then receives this as input, forclassifying the image. The Rectified Linear Unit

(ReLU) applies the activation function such thatthe output is zero for all negative inputs (x < 0)and x otherwise.

ThesigmoidandFigure 6: Covid-19 positive (L) and normal (R)theWhile the application of CNN in modelling

hyperbolic tangent are also commonly used.

imagery data is not new, challenges haveremained

3 Analyses

generalisation.

For structured data, analyses are presented as

unstructured data, a CNN model in Python isadaptable

accuracy

demonstrate

and

these

modelling in an interdisciplinary context.

patterns using the Gapminder library in R. For
with

We

to

challenges and opportunities relating to CNN

graphical images captured from animatedimplemented,

relating

4 Concluding Remarks

parameters

This paper highlighted the potentials oftested via a Sample-Measure-Assess algorithm.

combining underlying domain knowledge, on

3.1 Data Visualisation

the one hand, and data science–technical skills

on COVID-19 and non-COVID-19 related

challenges, as described in the SDGs. While

Multiple illustrations are given, based on UK

and soft skills, on the other. It underlined thedata on production and employment, as well asrole of interdisciplinarity in addressing global

deaths. Figure 5 shows COVID-19 related cases

CNN models can detect patterns that might go

and deaths for May 2020, in Brazil, France, Italy,

unnoticed to the human eye, for all their power

Japan, South Africa, US and the UK.

and complexity, they do not provide thoroughinterpretations of the imagery data. Further,

they may perform poorly on new data.

The ECDC acknowledges that the data might

not be very accurate the calculations by the
ECDC Epidemic Intelligence are affected byvariations

laboratory

Figure 5: COVID-19 related cases and deaths in May

testing

strategies,

effectivenessofcare, possibly in

combination with other factors like "...testing

COVID–19 X–Ray data described in Section 2.1.

policies, number of tests performed, test

The image on the left-hand side is from thepositivity, excess mortality and rates of hospital

COVID–19 positive sample of 1840 cases, whileand Intensive Care Unit (ICU) admissions." In

the one to the right is from the 1341 non–Undercapacities

should be made with

The two images in Figure 6 represent two

samples.

national

surveillance systems. Thus, any comparisons

3.2 Convolutional Neural Networks

COVID–19

inparticular, such comparisons must be done bypandemic82

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June 2018, Pretoria, RSA, 2018
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of Urban Big-data for Sustainable
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S. Challenges and Opportunities of Big Data
in Health Care: A Systematic Review. JMIR
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Ranjan, R.; Zomaya, A.; Wei, J. Remotesensing big data computing: Challenges
and opportunities. Future Generation
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13. ECDC,COVID-19
Data.
2020;
https://www.ecdc.europa.eu/en/publicatio
ns-data
14. ONS, Office for National Statistics. 2020;
https://www.ons.gov.uk/
15. Cohen, J. P.; Morrison, P.; Dao, L. COVID-19
image data collection. arXiv 2003.11597
2020
16. Kaggle, Chest X-Ray Images (Pneumonia).
2020;
https://www.kaggle.com/paultimothymoo
ney/chest-xray-pneumonia
17. Fukushima, K. Neocognitron: A selforganizing neural network model for amechanismofpatternrecognition
unaffected by shift in position. Biological
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18. LeCun, Y.; Jackel, L. D.; Boser, B.; Denker, J.
S.; Graf, H. P.; Guyon, I.; Henderson, D.;
Howard, R. E.; Hubbard, W. Handwritten
Digit Recognition: Applications of Neural
Net Chips and Automatic Learning. IEEE
Communication, 41–46, invited paper, 1989
19. LeCun, Y.; Boser, B.; Denker, J. S.; Henderson,
D.; Howard, R. E.; Hubbard, W.; Jackel, L. D.
Backpropagation Applied to Handwritten
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20. Krizhevsky, A.; Sutskever, I.; Hinton, G. E. In
Advances in Neural Information Processing
Systems 25; Pereira, F., Burges, C. J. C.,

teams of data scientists, epidemiologists, andother medical and social experts.

Acknowledgments. The author would like tothank the Joint Support-Centre for Data Science

Research (ROIS-DS) for inviting him to all its
events in the last few years. The partnership hassignificantly enhanced his involvement ininterdisciplinary research.

References
1.

2.
3.

4.

5.

6.

7.
8.

Rothan, H. A.; Byrareddy, S. N. The
epidemiology andpathogenesis ofcoronavirus disease (COVID-19) outbreak.
Journal of Autoimmunity, 109, 102433,
2020
SDG, Sustainable Development Goals. 2015;
https://www.un.org/sustainabledevelopme
nt/sustainable-development-goals/
Zambrano-Monserrate, M. A.; Ruano, M. A.;
Sanchez-Alcalde, L. Indirect effects of
COVID-19 on the environment. Science of
The Total Environment, 728, 138813, 2020
Bartik, A. W.; Bertrand, M.; Cullen, Z.;
Glaeser, E. L.; Luca, M.; Stanton, C. The
impact of COVID-19 on small businessoutcomes and expectations, 117, 17656–
17666, 2020
Pan, S. L.; Zhang, S. From fighting COVID19 pandemic to tackling sustainable
development goals: An opportunity for
responsible information systems research.
International Journal of Information
Management,102196, 2020
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to COVID-19 in Taiwan: Big Data Analytics,
New Technology, and Proactive Testing,323, 1341–1342, 2020
IUCN, In the spirit of nature. 2018;https://www.iucn.org/news/europe/20181/
spirit-nature-everything-connected
Mwitondi, K.; Munyakazi, I.; Gatsheni, B.
Amenability of the United Nations
Sustainable Development Goals to Big Data
Modelling. International Workshop on Data
Science-Present and Future of Open Data
and Open Science, 12-15 Nov 2018, Joint
Support Centre for Data Science Research,

83Informatics Association, 27, 1147-1148,
2020

Bottou, L., Weinberger, K. Q., Eds.; Curran
Associates, Inc., 1097–1105, 2012
21. Impact of the digital divide in the age of
COVID-19. Journal of the American Medical

84

Standardization of biological sample information database
Tazro Ohta1*, Shuya Ikeda1, Takatomo Fujisawa2, Shuichi Kawashima1* Database Center for Life Science, Joint Support-Center for Data Science Research, Research

1

Organization of Information and Systems, 178-4-4 Wakashiba, Kashiwa, Chiba 277-0871, JAPAN2
DNA Data Bank of Japan, National Institute of Genetics, Research Organization of Informationand Systems, 1111 Yata, Mishima, Shizuoka 411-8540, JAPAN
Email: t.ohta@dbcls.rois.ac.jp
Summary. Genomic data sharing is an essential concept for biological and medical sciences.
Researchers submit the biological sample information as metadata of their genome sequence data to

the public repository called BioSample, where researchers can freely utilize the information. However,the sample information is often described without using controlled vocabulary, which causes
problems in searching public data. To provide a better form of sample information, we designed adata model in Resource Description Format (RDF) to improve interoperability and added ontology
terms to the existing entries to standardize the description.

Keywords. Data sharing, Genome sequencing, Biological sample information, Resource Description

Framework, Ontology

1. Introduction
SharingtheThis ecosystem of genomic data sharing has

genomic

data

among

been a key to the success of the field.

theHowever, still there is a big challenge in

researchers have advanced biological andmedical sciences [1]. Since the adoption of the

sharing genomic data. In the past 10 years, thepublication of genomic data as a condition formeasurement

require researchers to release data before thedata produced per project [3]. Genomic data,

the researchers all over the world have been

are now available at a finer granularity, such as

Bank

The

Bermuda principles, most journals require

rapid

paper publication. Even some research fundings

sequencer resulted in the enormous amount of

paper publication. The genomic data shared bywhich used to be collected for single species,

collected by three organizations, DNA Datacell line, cell culture, or numerous single cells.

Bioinformatics Institute (EBI) in Europe, andaccomplished(NCBI) in the United States. Anyone can use theof the genomic data per project. Researchers

charge. Thus, many secondary databases have

which occurs sample information management

secondary databases would be utilized to

The public DNA database accepts various

Japan(DDBJ)

inJapan,

Europeanadvancelow-costoftheequipment

DNA

by

the

DNA

molecule

so-calledsequencing
latestDNAmethods

technologies

National Center for Biotechnology Information

resulted in the massive growth of the amountcollected genomic data for any purpose free of

now get hundreds of samples to sequence,

been built based on shared data [2]. The

tasks, a new bottleneck for data sharing.

generate new data, which is then shared again.

kinds of data. The target species are fromhuman to microorganisms. The sequencing

85

methods

capture

DNA,

RNA,

or

those

facilitate the merging of public genome data

molecules enriched by a special experimental

and the secondary data generated by third-

protocol. Therefore it is not feasible to control

party database developers.

samples. Sample information is written in

existing BioSample entries based on thepairs. This lack of a fully standardized way toenables us to identify different words that

all the vocabularies used to describe the used

Second, we added ontology terms to thenatural language, often in the form of key-value

description by the original data submitters. This

describe the sample information causes a

indicate the same concept. We developed aproblem when a user searches public data

pipeline to process the entries by using a tool

based on sample information [4]. Many sample

used to develop MetaSRA, a database that

information descriptions include synonyms andorganizes the public information of RNA

typographical errors. For example, a protein

sequencing of human samples [6]. The current

name Oct4 has many different forms; forimplementation of the pipeline can process

instance, a hyphenated style "Oct-4", its

only human samples and process over 400

synonym "POU5F1", or a Microsoft Excel

million samples in nearly 10 hours.

induced error "October 4th". It is not likely that

3. Conclusion

all the researchers look for hundreds ofthousands of samples to find those differences

Providing sample information in RDF format

between sample information to obtain a set ofand assigning ontology terms can solve the two

samples by an attribute.

big challenges in utilizing public biological

To solve the problem of the sample

information. This allows users to get the sample

information description, we developed an

information without preprocessing data which

improved sample information database with astandardized

formatandcosts crucial. Our approach also helps use cases

controlledof modern machine learning applications thatvocabularies. In this research, our target is therequire input data with standardized metadata.

BioSample database, the largest biologicalsample information database that principallycollects

samplesexperiments.

ofgenomic

References

sequencing

1.

2. ResultFirst, we designed a data model based on the2.

Resource Description Framework (RDF), a W3C
standard

toenableinterchange

between3.

different resources available on the web [5]. We
employed

RDF

becausedatabases

andknowledge

the

BioSample

4.

database is a hub to connect various biologicalbases.

Many

biological databases publish their data in RDF

format for its interoperability (cite: KERO, ChIP-

5.

Atlas). Modeling BioSample data in RDF can

86

Lakiotaki K, Vorniotakis N, Tsagris M et al.
BioDataome: a collection of uniformly
preprocessed and automatically annotateddatasets for data-driven biology. Database
2018;2018
Rung J, Brazma A. Reuse of public genomewide gene expression data. Nat Rev Genet,
14, 89–99, 2012
Heather JM, Chain B. The sequence ofsequencers: The history of sequencing DNA.
Genomics, 107,1–8, 2016
Gonçalves RS, Musen MA. The variable
quality of metadata about biologicalsamples used in biomedical experiments.
Sci Data;6, 2019
Kawashima S, Katayama T, Hatanaka H et al.
NBDC RDF portal: a comprehensive

6.

repository for semantic data in life sciences.
Database 2018
Bernstein MN, Doan A, Dewey CN.
MetaSRA: normalized human sample-

specific metadata for the Sequence Read
Archive. Wren J (ed.). Bioinformatics, 33,
2914–23, 201787

Susceptibility to COVID-19 infection: Insights from mathematicalmodelling
Ryosuke Omori1*, Ryota Matsuyama2, Yukihiko Nakata3* Research Center for Zoonosis Control, Hokkaido University, Kita-20 Nishi-10 Kita-ku, Sapporo,
Hokkaido, 001-0020, Japan
2
Hiroshima University, 1-2-3 kasumi, Minami-Ku, Hiroshima, Hiroshima, 734-8553, Japan3
Aoyama Gakuin Univeristy, 5-10-1 Fuchinobe, Sagamihara, Kanagawa, 252-5258, Japan
Email: omori@czc.hokudai.ac.jp

1

Summary. Since the emergence of COVID-19, its pandemic has been observed all over the world. In

spite of the large heterogeneities in the number of confirmed cases and deaths by COVID-19 between

countries, the trend in the age distribution of mortality is quite similar between countries; most deaths

are elderly persons. To understand the mechanism of a common trend in age distribution of mortality,
the assumptions regarding the cause for age-dependency of mortality by COVID-19 were evaluatedby employing a mathematical model describing transmission process and natural history of COVID-19.

Our results suggest that the susceptibility to COVID-19 is not differed largely by age, but the agedistribution of mortality by COVID-19 can be explained simply by the age-dependent mortality or therate becoming severe cases among all cases.

Keywords. COVID-19, Mathematical modelling, Epidemiology

1. Introduction

deaths are elderly persons. Between Italy,(COVID-19), the pandemic of COVID-19 has

and confirmed cases are varied largely, theper million population in Italy, 507 cases inmortality by COVID-19 have been observed.

confirmed until 29th May 2020. Also, 29525

transmissibility of COVID-19 (measured by R0)

deaths in Japan have been confirmed until 12th

COVID-19.

are varied largely among countries. Estimates

mortalityfrom a single primary case, are also varied

heterogeneity in susceptibilities (the rate oftransmissibility of COVID-19 are varied largely

COVID-19) and another is the heterogeneity inOn the other hand, a common trend in theinfected individuals. We evaluated that whichamong countries has been observed; most

distribution of mortality by COVID-19 among

Since the emergence of novel coronavirus

Spain, and Japan, where the number of deaths

been observed around the world; 382 cases

quite similar pattern in the age distribution of

Spain, and 13 cases in Japan have beenThis suggests a weak association between thedeaths in Italy, 18818 deaths in Spain, and 400

and the age distribution of mortality by

May 2020. Both of confirmed cases and deaths

The causes for the age-dependency ofbyinfected cases among exposed cases to

that

one

is

be

among

suggests

groups,canclassified

it

two

diseasesof R0, the expected number of secondary cases
countries,

into

infectious

theamong countries.

the rates becoming severe cases amongage-distribution of mortality by COVID-19

factor shapes a common trend in the age-88

countries by fitting a mathematical model

mortality by COVID-19 comes from the age-describing a transmission process and naturaldependency of susceptibility or not.

history of COVID-19 with the observed data of

3. Results

the age-distribution of mortality by COVID-19.

First,

2. Modelweevaluated

whethertheage-

distribution of mortality by COVID-19 can be

Since the existence of latent period, recovery

explained by only the age-dependency ofor death after infection of COVID-19 aresusceptibility or not, by estimating φ withknown, a mathematical model, SEIRD model

assuming no age-dependency in mortality ( δnsemployed to model a transmission process

assumed that δns are constant between age(an extended model from SIR model) was
and natural history of COVID-19;

E’n= βσnSn(Σmkn,m Im) - εEn,I’n= εEn - (γ+δn)In,(2)

R’n= γIn,
D’n= δnIn,

(4)

En,

In

Rn

and

groups,(1)

S’n= -βσnSn(Σmkn,m Im),Sn,

are constant between age groups). If it isestimate, δns are also difficult to estimate. If
this assumption is suitable, the estimate of δn is

the

not

rate,

and

with φ. However, the age-distribution ofmortality

cause is the heterogeneity of contact rate

thisdependent

dependency

parameterized using the estimates per country

R0,mortality
in

the

δn

but

rate

no

age-

becoming

dependency of age-distribution of mortality toof heterogeneity of susceptibility by age, we

R0 is quite low. Here we estimated δn from themodelled the susceptibility among age groupσn = cn .

withsymptomatic cases among all cases, thein the previous study [1]. As for the modellingφdependent

On the other hand, if we assume the age-heterogeneity can affect to the result, kn,m was

n as

also

age-distribution of mortality to R0.

in the model. It can be considered that kn,m canandiscontradicting to the observed low sensitivity of

between different age groups described as kn,mcountries

association

the age-distribution of mortality is dependent

age distribution of mortality, a most important

among

the

analyses of equation (1)-(5), we observed thatcauses for the association between R0 and thevariedargue

mortality (Dn( ∞ )/ΣmDm( ∞ )). From numericalmortality rate among age group n. Among thebetobe measured by φ) and age-distribution ofγ and δn are transmission coefficient, the raterecoverynecessary

between age-dependency of susceptibility (canrecovered and death among age group n. β, ε,
infectious,

mortalityof asymptomatic and mild cases are difficult toproportions of susceptible, exposed, infected,

becomingofregardless of the value of δn. Since the number(3)

represent

age-distribution

Dn(∞)/ΣmDm(∞) is equal to Rn(∞)/ΣmRm(∞)

(5)

Dn

the

observed data of the mortality rate among
confirmed cases.

(6)

Also, estimated values φ to explain the age-If φ = 0, the susceptibility is independent with

distribution of mortality in Italy, Spain andage. From the estimated values of φ explaining

Japan with the each of two assumptions as

the age-distribution of mortality by COVID-19,

above are quite different between three

we evaluated whether the age-distribution ofcountries. Although the age-dependency insusceptibility can be differed by country, the89difference is unrealistically large. This suggeststhat these two assumptions i) no agedependency

insymptomatic

cases

dependency

inmortality
the

ii)

no

all

cases

rate

among

age-

becoming

isunrealistic to explain the age-distribution ofmortality.

4. Conclusions

The age dependency of susceptibility to
COVID-19 is difficult to explain the age-

distribution of mortality with low sensitivity to

R0 values. This suggests that the susceptibility

to COVID-19 is not differed largely by age.

Also, the age-distribution of mortality by

COVID-19 can be explained simply by the age-dependent mortality or the rate becoming

severe cases among all cases. Further detailcan be found in [2].

References1.

Prem K, Cook AR, Jit M. Projecting social
contact matrices in 152 countries using
contact surveys and demographic data.
PLoS Comput Biol. 2017;13(9):e1005697.

2.

doi:10.1371/journal.pcbi.1005697

Omori R., Matsuyama R., Nakata Y., Does
susceptibility to novel coronavirus (COVID19) infection differ by age? : Insights frommathematicalmodelling,
medRxiv,
doi:10.1101/2020.06.08.20126003

90

Open Science for Asia and the Pacific
Mazlan Othman1*, Sufyan Aslam2* Director, ISC ROAP, 902-4 Jalan Tun Ismail, 50480, Kuala Lumpur, Malaysia
Science Officer, ISC ROAP, 902-4 Jalan Tun Ismail, 50480, Kuala Lumpur, Malaysia
Email: mazlan.othman@council.science12

Summary. Open Science is a global movement for doing science in the future. This movement isbeing championed by UNESCO, which is undertaking a survey for the development of an international

standard-setting instrument on Open Science. The APEC platform provides the political foundation forcollaboration within Asia Pacific.

Keywords. Open Science, International Science Council, UNESCO, APECI.

Introduction

The International Science Council (ISC), in its

Open Science represents an approach to the

Action Plan 2019-2021 (1), highlighted theworkdisseminating

particularly in the Global South. Open Science

new collaborative tools. The idea captures ascience systems in the Global South toresearch have been carried out for the last fifty

cutting edge of data-intensive open science. In

publishingscientificbeen working with its Regional Offices to startknowledge at an earlier stage in the research

Platforms that will convene and create regional

The recent response of the scientificresources needed to advance data-intensive,

scientific process which is based on cooperative

importanceknowledge by using digital technologies andin the Global South aims to allow scientists andsystemic change to the way science andcollaborate and position themselves at theyears: complementing the standard practices ofcollaboration with CODATA, the Council has

publications by sharing and using all available

the discussions on regional Open Science

process.

interests,

community to the COVID-19 pandemic has

solutions-oriented research in the Global South

Science and how it can accelerate the creation

A pilot study for a Pan-African Open

and

new

ways

researchofresults

inofideas,

addressing

people,Open

Science,institutions

andvividly demonstrated the importance of Open(2).

of scientific solutions to a global challenge. The

Science Platform (AOSP) was launched inposted in an open access repository and made

African Department of Science and Innovationcompanies made the designs for protective face

Science of South Africa and the South African

be freely 3D printed in cities and societies where

the National Research Foundation agreed togenetic sequence of the SARS-CoV-2 virus was

December 2016 with the support of the South

freely available for all researchers. Several

and in collaboration with the Academy ofshields open source, allowing these shields to

National Research Foundation (3). In April 2020,

host the African Open Science Platform Project

they are needed the most.

II.

Office for the next 3 to 5 years.

International Science Council’s

III.

Vision for Open Science

91

UNESCO’s

Recommendation

on Open ScienceIn spite of the encouraging open science

the gap of knowledge divide between APEC

growing number of national and regional

The key objectives to the initiative include:

Economies.

actions in response to COVID-19, and theinitiatives, there is currently no international•

Building a community of purpose thatframework nor common policy guidance forworks together to identify platformof UNESCO’s General Conference, 193 Member

know-how;governance measures and technicalopen science globally. During the 40th session

States tasked the Organization with the•

Economies and international policies

development of an international standard-

for platform governance;

setting instrument on Open Science in the formof a UNESCO Recommendation on Open

•

among

Science and identify concrete measures ontoscienceandcommitments facilitating the production andV.

dissemination of scientific knowledge aroundto

promoteAPEC

Economies

through

Way Forward

Open Science is a necessary transformation to

the world.

IV.

Science

Open Science.

Open Access and Open Data, with proposals tocloserOpen

engagement and knowledge sharing

to define shared values and principles for Open

citizens

Developing an APEC PPSTI Statementon

Science (4). The Recommendation is expected

bring

Sharing of best practices in APEC

the scientific practice in adapting to thechanges, challenges and opportunities of theOpen Science for Asia and the

21st century digital era to advance knowledge

Pacific

and to improve our world.

With UNESCO currently developing its

In looking for a political platform to advocate
for Open Science, ISC Regional Office for Asia

Recommendation, it is imperative for all

Academy of Sciences Malaysia (ASM) and thetowards the consultations in defining shared(MOSTI) Malaysia, decided to work through thewill continue to champion collaboration and

Policy Partnership on Science, Technology andstakeholder members involved in the UNESCO

and the Pacific (ROAP), together with thestakeholders to engage with and contribute

Ministry of Science, Technology and Innovation

values and principles for Open Science. The ISC

Asia-Pacificstrengthen

Economic

Cooperation(APEC)

partnerships

with

the

multi-

Innovation (PPSTI) Working Group, which aims

Global Open Science Partnership.

connectivity network in an open, accessible, andScience Statement and ISC ROAP will continue

and technological collaboration (5).

PPSTI. This will include future capacity buildingtostrengthen

the

STI

ecosystem

APEC PPSTI has endorsed the Openandto push the Open Science agenda througheffective way to accelerate knowledge sharing
The

initiativewillstrengthen

initiatives

theamongstcapacity of practitioners in APEC Economies inbeyond.

andReferences

APEC

Economies

andapplying: (i) best practices in APEC Economies
international

policiesforplatformgovernance; and (ii) a joint statement topromote engagement and knowledge sharing

1. ISC Action Plan 2019 - 2021. International

This will enable Economies to participate morehttps://council.science/actionplan/

Open Science approach would assist in bridging

Scienceamong APEC Economies through Open Science.

Sciencefully in the global scientific enterprise since an

2. ISC Action Plan, Open Science. International

92

Council.

Council.

[Online][Online]https://council.science/actionplan/open-

5. Policy Partnership on Science, Technolpogy

3. African Open Science Platform: Strategy andCooperation

and Innovation (PPSTI). Asia-Pacific Economic

science/
Vision.

CODATA.

https://codata.org/initiatives/strategicprogramme/african-open-science/
4.

Open

Science.

UNESCO.

(APEC).

[Online]https://www.apec.org/Groups/SOM-Steering-[Online]Committee-on-Economic-and-TechnicalCooperation/Working-Groups/Policy-

Partnership-on-Science-Technology-and-[Online]Innovation

https://en.unesco.org/science-sustainablefuture/open-science/recommendation

93

Past, present and future of global data collaboration
Mark A. Parsons1** CODATA Data Science Journal

1

https://orcid.org/0000-0002-7723-0950
parsons.mark@gmail.com
Summary. International data collaboration has been going on for centuries, but it really exploded withthe dawn of the digital age and especially in the last decade. Although we continue to grapple withmany of the same issues, increased mediation has allowed us to deal with ever increasing complexity
through additional layers of abstraction. Global data collaboration is more critical than ever.

Keywords. Global data collaboration, standards, mediation, socio-technical relationships, FAIR DigitalObjects

With global data collaboration, you are never

System in 1957 and the Committee on Data

standardization exercise we have been pursuingwhat is now the International Science Council.

done. Consider biological classification, a data

(CODATA) in 1966. Both under the auspices offor centuries. Linnaeus published his Systema

Then, of course, with the birth of digital data

Naturae in 1735. Darwin published On theand especially the internet, things started

Origin of Species in 1859 changing conceptions

changing very rapidly, and global collaborationof what a species is. Today, as genomics again

became increasingly imperative. The late 1990stransforms our conception of species, we

saw the growth of the open data and open

continue to work on the Encyclopedia of Life,

access movements, and initial realization thatwhich was first released in 2007. At the samedata stewardship was essential to science andtime, we are increasingly recognizing how

society.

global standards can obscure valuable local

With the dawn of the new millennium, we

knowledge [1]. Biological classification, it turns

began to see changing expectations in howof the challenges of data collaboration.

International Council of Science conducted aout, is really difficult, and it is but one exampleThis

generaldesiretosciencestandardize

wasconducted.

In

2004,

the

comprehensive review of its efforts related tocontributed to the establishment of many

scientific data and information [2], and theinternational scientific organizations in the late

United Nations launched the World Summit onwere the precursors of many contemporary

Gray put forth his vision an entirely new “fourth

1800s and early 1900s. These organizations

the Information Society in 2003. In 2007, Jim

organizations that are grappling with data

paradigm” of scientific exploration [3]. Gray

issues today. It was not until the post-war era,called for new tools and new infrastructure to

specifically devoted to collaborative issues

community has responded.

however,thatorganizations

emergedenable this data-driven science, and the global

around data, notably the World Data Centers94

openness of the 20th century remains a

In the last decade, there has been an

explosion of collaborative data activity. In 2009,

challenge. Now as we focus on FAIRness towere reformed and reinvigorated to meet theand exploitation of massive data, we continue

the World Data System (WDS) and CODATA

enable machines to automate the exploration

new needs and expectations of data-driven

to struggle with many of the same challenges

science [4]. In 2011, the FORCE11 manifesto was
published

for“Improving

while some of the old challenges now seem

future fesearchtrivial as they have disappeared under layers ofcommunication and e-scholarship” [5]. The

abstraction.

Research Data Alliance (RDA) launched in 2013

We

are

dealing

with

much

greaterto enable the necessary data infrastructure andcomplexity of both data and the insights theycollaboration communities [6]. In 2016, a large

increasing layers of complex mediation [9]. The

did much to create and unite myriad data

reveal, but we have dealt with this through everinfluential community published the FAIR

nascent concept of precisely specified and(Findable, Accessible, Interoperable, Reusable)

identified “FAIR digital objects” is the latest

principles [7], which have created a common

example of a conceptualization that allows aframework for discussion on the detailed

new layer of cyber-mediation, and it shows

specifics of machine interoperability and a

great

focus on implementation through the GOFAIR

global data collaboration are greater than ever.

WDS) have outlined their “joint commitment toReferences

work together to optimize the global researchtoidentifythe1.

opportunities and needs that will trigger
federated infrastructures to service the new2.

Together” statement 1. This has resulted in the3.

reality of data-driven science” in a joint “DataVirus Outbreak Data Network (VODAN) and the
RDA

COVID-19

Recommendations

andGuidelines on Data Sharing [8]. These are4.

important and timely accomplishments, but if

the COVID-19 pandemic has shown us anything,
it is that we have a long way to go before we

5.

collaboration.

6.

have robust and open data sharing andJust like the 19thsocio-

remain central issues. The imperatives fororganizations (CODATA, GOFAIR, RDA, andandNonetheless,machines, and between people and machines

Today, the four major international data

ecosystem

10].

technical relationships among people, among

initiative.

data

promise[century promise of7.

standardization was never fully realized, thehttps://www.go-fair.org/wpcontent/uploads/2020/03/DataTogether_March-2020.pdf

1

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connection. Princeton University Press, 2005
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Panel on Scientific Data and Information.
ICSU, Paris, 2004
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Data-intensive
scientificdiscovery. Microsoft Research, Redmond,
2009
ICSU, Decisions, 29th ICSU general assembly,
Maputo, Mozambique, 21-24 October 2008.
ICSU, Paris 2009
Bourne, P. et al., Force11 white paper:
Improvingthefutureofresearch
communication and e-scholarship. 2011
Lannom, L., Special issue on the researchdata alliance. D-Lib 20, 1-2, 2014
Wilkinson, M. D. et al., The FAIR guiding
principles for scientific data management

8.
9.

NSF Task Force on Cyberlearning. NSF,
Washington, 2008
10. Wittenburg, P, Strawn, G, Mons, B., Boninho,
L., and Schultes, E., Digital objects as drivers
towards convergence in data infrastructures.
2019

and stewardship. Scientific Data, 3,160018–
160018, 2016
RDA COVID-19 WG, RDA COVID-19
recommendations and guidelines on data
sharing (version 1.0), 2020
Borgman, C. et al., Fostering learning in the
networked world: The cyberlearning
opportunity and challenge. Report of the96

Sharing data for a coordinated response during COVID-19
pandemic
Priyanka Pillai1*,* The University of Melbourne, Parkville, Melbourne, Australia, Victoria 3010 Australia
Email: priyanka.pillai@unimelb.edu.au

1

Summary. This keynote speech will describe the composition of the infectious disease data ecosystemand highlight some challenges from the past outbreaks associated with building the data ecosystem fora response. This speech will also describe how making data consistent and shareable has strengthened
preparedness and response activities in present-day scenario of the ongoing COVID-19 pandemic.
Keywords. COVID-19, Infectious Diseases, Data Management, Data Sharing, Health Informatics

Globally, the health and medical research

epidemiologists, public health researchers andstrategic plans emphasise enhanced data

policymakers.

collection, efficient reporting systems andbuilding advanced infrastructure to respond to

Pastmanner. How does data support preparedness

with rapid aggregation, integration and sharing

infectious diseases emergencies in a timely

infectious

diseaseoutbreaks

havedemonstrated several challenges associatedtowards infectious disease emergencies? Whatof data to inform a response during an outbreak.

information is needed to identify the start of an

It is essential to improve data collection,

outbreak? How does data inform the potential

facilitate data sharing and support data usage

role of a data specialist?

community.

severity and spread of an outbreak? What is thefor decision-making in the infectious diseases

The infectious diseases data ecosystem is

The conclusion is that the challenges in sharing

sources like general practices, jurisdictionalbuilding

comprised of information from a wide range ofsurveillancesystems,laboratories,

epidemiology

emergency

clinical

departments,

and aggregating data can be addressed byresearch,

among

data

custodians,

promoting collaboration and implementing

diagnostic

studiestrust

data stewardship practices. The infrastructure

andsolutions to leverage big data in infectious

genomics. The carefully distilled knowledge

diseases should be agile, comply with ethicalfrom this diverse data ecosystem enables better

requirements

preparedness for and response towards anandlegislation,

facilitate

equitable data access and expedite cross-outbreak. A data specialist is a bridge or aborder data sharing globally. There are many

central point of information for clinicians, publiclessons learnt from past pandemics that have

health practitioners, virologists, microbiologists,

strengthened the preparedness and response97capabilities in the present-day scenario of theongoing COVID-19 pandemic.

98

Making historical data Re-useable:a case study of the challenges and success of Bangladesh Bureau of
Statistic’s historical data conversion project
Chandra Shekhar Roy 1*
1* Senior Maintenance Engineer-IT, Bangladesh Bureau of Statistics (BBS),
Statistics & Informatics Division,
Ministry of Planning, E27/A, Agargaon, Dhaka-1207, Bangladesh
Email: csroy.sme@bbs.gov.bd

Summary. Bangladesh starts journey focusing on ICT with a view to realize the vision of ‘Digital
Bangladesh 2021’. Thus, at the long run census and survey data can be used exhaustively in the planning

process to transform into digital Bangladesh by 2021. In Bangladesh, several types of official data arereleased under the Statistics Act. BBS played a significant role in the field of historical Statistical data

preservation. After the independence of Bangladesh in 1971, there was a rich repository of statistical

microdata in IBM 360 to ES/9000 model mainframe tapes. Almost 8600 nine-track ½ inch spool tapes
were used to preserve those data. Recently BBS has converted all those data from EBCDIC format to ASCII

format. Near about 165 data set recovered which has been declared as digital asset. Since independence,

2,391 of BBS surveys and census publications have been digitized and converted to e-book system.
Alternative back-up for data reservation 200 kilometers away from NSO.

Keywords. Historical data, Data preservation, Statistical microdata, Statistics Act.

1. Introduction

The overarching objective is to strengthen theanalysisacademic and scholarly debates can take place

1. a. importance of historical data forprevailing national statistical archiving system,

Bangladesh Bureau of Statistics (BBS) has vowedtaking cognizance of historical data. By revisitingtime series data, it is hoped that well-informed

to convert Census/Surveys micro data for theand meticulous policies can be designed andnext generation technology format processed informulated in the future. Several types of legacy

IBM proprietary technology. Most fundamentally,

micro-data are released across a variety ofavailability and easy accessibility to such a large

formats and based on data confidentiality andvolume of Big Data will inspire reassessing
economictheories

andindicators

user needs.

ofdevelopment informing Bangladesh’s position in

2. The process of data conversion

global rankings like Sustainable Development
Goals (SDG).

1.b. specific data and their value (who will use& why they should use it)99

3. History of data publication of BBS

Since the post-independence period, BBS has
been compiling various census and survey based
statistics and conducting surveys and publishingvarious reports / publications. All thesepublished reports could not be saved properly
due to lack of proper technology. Reports
published by BBS are available only in official
Library. Due to insufficient copies of thesepublications, they cannot be made available tothe ordinary people and these valuablepublications cannot be provided as per thedemand of local and foreign students-teachers,
researchers, stakeholders and various institutions.
In the interest of the overall development of
the country, there is a need for historicalinformation on determining the course of socioeconomic conditions and policy making. There is
no alternative to digitization of all the reports
published by BBS to meet the demand for allthese valuable information needs. Moreover,digital version of information / publication is
now the time demand in building a digital
Bangladesh.
Since independence, 2,391 of BBS surveys andFigure 1: 9-track tape cleaner

2.a. Institutional challenges• Total 8600 of 9-track spool data tapes
are needed to be read and convert.
• Technology outdated• More than 20 years old backup
magnetic-tape• Historical data from 1972 to year 2019 isneeded for developing time series data
2.b. technical challenges• In-depth
understandingofthefunctionality of the COBOL code
structure.
• Source data quality, if poor, needs to becleansed in order to be successfully
converted.
• Metadata
preparationanddatacataloguing.
• Microdata documentation.
2.c. the success of the project
•
•
•
•

•

census publications have been digitized and a
web based software titled 'Digitization of BBS

Publications' has been developed and data

IBM mainframe backward compatible
technology exists;
EBCDIC to ASCII conversion software isavailable in the USA;All the backup data was stored instructured way (from year 1972 to 1999);
All the statistical published reports (2391
e-book & .pdf) have been digitized(from year 1972 to 2018);
A total of 165 Microdata sets including
50 censuses and 115 surveys have beencreated/transformed with the help ofexisting COBOL programming and BBS
publication.
159 set of legacy-data
documentation has been prepared forBBS data portal. It is fully humanintervention with tedious job. Some
Cobol programmers were associatedwith this activities.

searching optimization have been incorporated.

Consideration of launching the digitization of

BBS Publication started in April, 2015. all thosedigital version reports have been converted to ebook system and integrated into web based
software.

figure: 2 e-book website in BBS domain.

100

4. Statistical description/metadata of1)On-grid Solar power backup system; 2) Inhouse power generator; 3) Intelligent humanAccess Control System; 4) Fire suppression
system for DPC; 5) C³- S.P.E.A.R Data
preservation unit. (next generation data Centre
solutions); 6) Power and environment control
EMS unit.; 7) NSO data center to DPC (P2P) link.

data that were restored

Census & Survey Metabase= (Metadata +Database) for big data analysis Twointeroperable database and application havebeen developed by the country local vendor. A
web portal called “data.bbs.gov.bd” has beencreated and following developing tools has beenused.
Linux; PHP, Codigniter framework, Metabase;
Apache; Oracle, MySQL; HTML reporting.

Fig: 4

Figure 3 data & metadata portal (data.bbs.gov.bd)

5.

Digitalpreservation(DP)earthquake free area in the countryinJessore is one of the remote districts of
Bangladesh which is internationally declared as
earthquake free zone
Three tier data backup called• Online storage, both in NAS and adequate
storage server has been installed• Online digital archiving, 50GB capacity
optical blue ray disk has been used for the
purpose.
• Offline LTO-5 tape backup, 1.5 TB capacities
is being used for off-the- shelf data backup.
Low power consumption has been ensured
by calculating upcoming data volume forfive years, while the small footprint can help
keep capex low. Initially 80 terabyte storage
capacity has been installed with one tapebackup facility. The following initiatives havebeen taken for DPC.

6. Future work

Data community in Bangladesh is realizing it as‘Digital Assets’ of the country. This project canbe shared in the international community bothdata rescue, Big Data and dissemination groups.
•

DDI can be initiated in BBS where 47 yearsof data are available.

•

SDMX can be implemented easily by using
all those data along with current data.

•

Data preservation is a vital issue in adeveloping country. Special attention may
need to support in this data-security field.

101

7. Conclusions

The methods used for data conversion for

official statistics in Bangladesh are largely basedon those developed in USA. In order to promote

the secondary use of official statistics in
Bangladesh, further research into data sharing

methods for official statistical data should be
pursued.

References1.

Bangladesh

bureau3.

Digitizationof4.

Onlineofstatistics,

http://www.bbs.gov.bd
2. BBS Data portal, http://data.bbs.gov.bd
BBS

http://203.112.218.73:8082
data

Publication,

Dissemination

http://redatam.bbs.gov.bd

portal,102

Application of machine learning techniques to weather
forecasting
V. Sakthivel Samy1*, Veena T.2* National Centre for Polar and Ocean Research, Goa, 403804, INDIA
2
National Institute of Technology, Goa, 403401, INDIA
Email: vssamy@ncpor.res.in

1

Summary. The Polar climate system is paid more attention to atmospheric scientific community as it isregarded to be sensitive for anthropogenic induced climate changes, ozone depletion and melting of

ice shelf due to global warming. The temperature difference between equator-tropical region and polar

region is the major driver of the general circulation of whole atmosphere. The meteorological and polar

data sets collected from Polar Regions such as Antarctica, Arctic, Southern Ocean and Himalaya arebeing archived at National Polar Data Center (NPDC) for ease access and retrieval of the same.
Keywords. Machine Learning, Data Analytics, Antarctica, Long Short-Term Memory

1. Introduction

At present, weather forecasting relies on

2. Objective

and the following factors: (i) Past weather data

methodologies to solve problems and findThe proposed application of machine learning

numerical weather prediction (NWP) models

alternative solutions to weather forecasting(ii) Present atmospheric conditions (iii) Patterntasks. Weather forecasting is currently basedof climatic conditions based on history. But here

on a combination of explicitly-solved physicalatmospheric conditions are chaotic, which isnumerical and empirical models that represent

non-linear in nature.

the current state andIn nutshell chaotic nature of atmosphere,

evolutionoftheatmosphere. Using the data produced by thesecomplexity involved in numerical weathermodels as benchmark, we aim to identify

predictions, assumptions of initial conditions

machine learning methodologies that canwhich are prone to error, issues involved in

improve the accuracy of the forecasts andparameterizations make the forecast lessautomate activities in the forecasting process.

accurate. It has been also established by India

Meteorological Department (IMD) that nomodel except the Artificial Neural Network(ANN) predicts the long- range forecasting so

accurately districts-wise. ANN’s also work
effectively for a non-liner input condition [2, 3].

So, the objective of this work is to analyse thesignificance of machine learning approaches
over other traditional methods in weather

Fig.1: The traditional NWP and machine

forecasting.

learning approaches to weather forecasting103

Table 2: LSTM model details

ITEMS

DETAILS

Prediction

Temperature forecasting for 210 /Input

Temperature

TargetVariable

240 daysmeteorologicaldatastation.

of

Hourlydata is resampled to Daily Data

Training

Parameter

Learning rate = 0.001; Optimizer =adam; RMS prop

Number of Units: 180,200 ;Numberof Epochs: 700, 100

4. Proof of Concept using Long
Short-Term
Model

Memory(LSTM)

Carried out proof of concept using Long ShortTerm Memory model and the predicted resultsof the LSTM model is depending on the actual
collected

data

at

the Bharati Station, Antarctica. The LSTM

models use the input data to update numerous

3. Analyzing the time series data

values in the internal cell states. However, theObservations of air temperature, air pressure,

LSTM models learn these physical principles

and other several synoptic variables are carried

from the input data and observed data, and are

Antarctica, and at Bharati Station (69°24’41”S,accurately as possible. The setup details arewind speed, wind direction, relative humidity,

during the training and calibration processes

out at Maitri Station (70°45’52”S, 11°44’03”E),

optimized to forecast the discharges as

76°11’72”E), Antarctica. Using these data,summarized in Table 2 as given above:-different stations), analysis is done on the data

As per the model setup details are summarized

analysis with their Trends, Behavioural, Seasonal

options and parameters of the model have

we have used the observations carried out atparameters of the neural network were also

Meteorological Department (IMD), New Delhi.

the iteration step.

Data Center (http:/npdc.ncaor.gov.in ). The

three-dimensional (3D) vectors to match the2015 to Feb 2020.

vector (3D) comprises of (samples, time steps,

data: The statistical data of hourly TEMP

num_timesteps, num_features, respectively. The

Direction) and AP (Air Pressure) time series used

time steps collected. The num_timesteps arecollected over the years 1985-2018(varying forcount and visualization is performed throughin the above Table 2, the several training

and Wavelet Spectrum Analysis. For this studychanged to get best results. The adaptable

BharatiIndia

updated depending on a given loss function of

These records are available from National Polar

This input data layer must be reformatted into

records used in this study cover the period Feb

architecture of the LSTM model. The input

Identifying and eliminating anomalies in theand features) with the shape num_samples,(Temperature), WS (Wind Speed), WD (Windnum_samples are the data rows or the total ofin this study.

the past observation for the feature, i.e., a lag

station,collected

byvariable.

Table 1: Sample data format

The data / input vector is (1131, 240, 1) where
1131 is the total samples, 240 is the time steps

and 1 is the feature. Therefore, the sequence iscreated with 240 data elements in one batch.

Hence based on the past observation of 240

104

timestamps

thefuturetimestamps is predicted.

valuesof240

Training cases:
Case 1: Training Year: 2016 – 2018 Prediction Year:
2019

References
1.

Shrivastava, G., Karmakar, S., Kowar, M., &Guhatakurta, P., Application of artificial
neural networks in weather forecasting: A
comprehensive
International

Table 3: Summary of the results and parametersof the day model forecast
Forecast
for

Case

Batch
Size

No. ofUnits

No. ofEpochs

RMSE

32

200

700

-

240

Case 1

32

200

700

10.82

Case 232

ofComputer

200

500

2.

Somasundar, Ministry of earth sciences,
Data.gov.in, Jan 2017

Case 1

days

Journalreview.

Applications, 51(18), 17–29, 2012

210

days

literature

3.

12.72

Crosby, D. S., & Ferraro, R., Estimating theprobability of rain in an SSM/I FOV using
logistic regression. Journal of Applied
Meteorology, 34, 2476–2480, 1995

105

VODAN-in-a-box: a FAIR toolkit for improving reusability of
COVID-related data
Luiz Olavo Bonino da Silva Santos1*,2,3, Barend Mons1,2* GO FAIR International Support and Coordination Office, Leiden, the Netherlands

1

2

Leiden University Medical Center, Leiden, the Netherlands
3
University of Twente, Enschede, the Netherlands
Email: luiz.bonino@go-fair.org

Summary. The COVID-19 pandemic highlighted the need for improvements in data management andreuse to allow for quicker and better understanding of the situation, which leads to better informed

decision making and ultimately a better response to the challenges imposed by the outbreak. With theexperience of using the FAIR principles as guidelines to tackle these issues, the GO FAIR community

launched the Virus Outbreak Data Network initiative with the aim of applying the FAIR principles andrelated technologies to increase the availability of FAIR data and services focusing on the needs of virus

outbreak in general and the SARS CoV-2 pandemic in particular. The first result of VODAN is the
VODAN-in-a-box, a toolkit containing a data entry tool compliant with the World Health Organization’s
COVID-19 Rapid Case Report Form, a semantic data model for this CRF and a FAIR Data Point to expose
the metadata of the dataset containing the case reports.
Keywords. FAIR, data, interoperability

1. Introduction

GO FAIR community, with is inherent bottom-

Since the publication of the FAIR principles [1]up approach, launched the Virus Outbreak Data

guidelines for improving data stewardship.

existing

SARS-CoV 2, researchers, decision makers,of FAIR data and services relevant to thethe world population struggled with the

The first deliverable of VODAN is thein March 2016 they have been used as

Network (VODAN) [2,3] initiative to reuse theFrom the initial period of the 2020 pandemic oftechnologies to help improving the availability

health care providers and to a certain extend

reaction against virus outbreaks.

difficulties in finding, accessing, interoperating

VODAN-in-a-box, which is a toolbox consistingof:

and, ultimately, reuse data related to the•outbreak. These data were, and still are, needed

FAIR-related

methods

andA data-entry application named CRF

for many different purposes including early

Wizardtoallow

healthcarebiological implications of the infection andabout their COVID-19 cases. The

The aforementioned difficulties are, notform (CRF) template defined by thedetection of the outbreak, understanding of theproviders to enter the information

discovery of preventive and curative measures.

CRF Wizard uses the case reportcoincidently, the very reason for the emergence

World Health Organization (WHO)for the COVID-19 pandemic.

of the FAIR principles. With this realisation, the

106

•

A semantic data model for the WHO

the WHO COVID-19 CRF does not contain manymodel provides machine-actionable

can be deployed on-premise gives confidence

COVID-19 CRF. This semantic data

identifiable data, the fact that the whole toolkit

semantic information about theto the hosting entity that their data is kept

questions and answers contained inunder control, so that compliance with GDPR

the WHO CRF. The CRF Wizard uses
thissemantic

data

model

(or other privacy-related regulations) can betoguaranteed.

transform the data entered in its

3. Future work

web form into properly annotated

RDF, which is stored in a triple-store.
•TheA VODAN FAIR Data Point (FDP).
about

datasets.

The

toolkitthat

exposesthetypes of COVID-19-related datasets, richer

metadata schemas and improvements in thefindability of the indexed metadata.

19-related datasets. The CRF Wizard

The

is configured to interact with theof

new case is reported, the metadata

data

of

stewardshipmanagement,of the dataset is automaticallythetoolkitalreadysuchasmetadata(meta)data

findability

andinteroperability

andinteroperability, among others. However, toupdated.

further

The VODAN-in-a-box is available under the

improveonreusability we need to adapt and develop end-

MIT license and can be deployed using theimages.

adoption

demonstrated improvements in some aspects

related VODAN FDP so that, when aDockerwill

the package with semantic models of othermetadata of the available COVID-

available

VODAN-in-a-box

deploy and use the toolkit but also to expand

VODAN FDP is the component ofthearoundcontinue to not only support other entities to

The FDP is a tool to expose FAIR
metadata

work

user tools that can make use of this emerging

The

FAIR infrastructure.

documentation of the toolkit can be foundat

4. Conclusions

https://docs.vodan.fairdatapoint.org/en/last

The

est.

experience

with

the

development,

deployment and usage of the VODAN-in-a-box

demonstrated that the realisation of the FAIR

2. Adoption of the toolkit

principles in terms of a toolkit supporting the

As of September 11 2020, the VODAN-in-a-Box

improvements of data stewardship is feasible.

has been already deployed in 8 locations in 6

Work will continue on improvement of thedifferent countries. More locations are in theexisting tools as well as on expansion of theprocess of validating the solution for use.

toolkit, metadata schemas, semantic data

Health care personnel are being instructed tomodel and end-user applications.

enter the information of their case reports.

One characteristic of the VODAN-in-a-box

Acknowledgments.

that has been mentioned as decisive for its

We

would

like

to

acknowledge the contribution of ZonMW andadoption is the deployment freedom. Health-the Philips Foundation for funding the initial

related data are highly sensitive. Even though107development and deployment support of the2.

References

3.

VODAN-in-a-box.

1.

Wilkinson, M., et al, The FAIR Guiding
Principles for scientific data management
and stewardship. Scientific Data, 3(1), 1-9,
2016

108

The Virus Outbreak Data Network(VODAN):https://www.gofair.org/implementationnetworks/overview/vodanMons, B., The VODAN IN: support of a FAIRbased
infrastructurefor
COVID-19,
European Journal of Human Genetics, 28,
724-727, doi: 10.1038/s41431-020-0635-7,
2020

“A Comparison between Logistic Regression and Decision Tree
Methods for Predicting the Preterm Birth"
Rakesh Kumar Saroj1*, Madhu Anand2*Department of Mathematics and Statistics, SRM University Gangtok, Sikkim/Gangtok -737 102,
India
2
Department of Chemistry, Dr. B.R. Ambedkar University, Agra, India
Email: rakeshkumarsaroj.b@srmus.edu.in

1

Summary. Pre-term birth is an increasingly prevalent complex condition with multiple risk factors
including environmental pollutants. This is also leading cause of neonatal death of developing

countries. Machine learning techniques play a key role in predicting the factors in the pre-term birth.

The objective of this study is to compare the performance of logistic regression and decision treeclassification methods and to find the significant determinants that cause pre-term birth.

For this study we have used a case–control study of 50 cases of full-term births and 40 cases of preterm births. We have been tested the logistic regression and decision tree classifier methods in thisdataset and to evaluate the accuracy of the logistic regression and decision tree methods throughvarious indices.

The logistic regression is determined to be suitable classifier model for this dataset as compare todecision tree methods. The variables like alpha HCH, total HCH and MDA are the most influential

factors with respect to association with preterm birth. The final result revealed that logistic regression
classifier is accurate model to predict the pre-term birth with better accuracy.

Keywords. Machine learning, Pre-term birth, Classifier, Decision tree, Logistic regression

1. Introduction

to improve maternal and child health. The

Preterm birth is the birth of a baby at less thanmachine learning scientists have worked on

usual about 40 weeks. Preterm birth (PTB) has

places [4-6]. The previous research article

37 weeks' gestational age, as opposed to theinterpretable prediction techniques in several

found through various reason including low

recommended that if early risk factors are

consumption of alcohol [1]. Previous research

acute kidney injury then can beorganochorines and increased risk of abortion,

logistic and classification models to predict themalformations,andfactors and covariates in order to reduce the[2-3] There are various machine learning

performance of LR and DT classificationsocio-economic status, smoking, race andidentified through classification methods inhave suggested the associations between

Overall aim of this study to construct the

small

minor

high-risk groups for PTB based on several

hypospadias in the infants have been reported

risk of PTB and compare the predicting

predictionforregression,

component

gestational

and

,

age

babies,cryptochildismclassification

logistic

analysis,modelslikemethods.

tree

and

2. Material and Methods

regression,decisionprincipal

maximum likelihood method have been used

109

For this study we have used a case–control

We developed the logistic and decision treecases of pre-term births at Dr. Bhim Rao

help of 70% of training dataset including all

study of 50 cases of full-term births and 40

classification model for prediction with theAmbedkar University (located at Agra, Uttar

variables. The logistics and decision tree

Pradesh, India).

models are tested on 30% testing dataset and

The dependent variable in this study is pretermmodel evaluation results are given in Table

birth and it is classified between full term birth

1.The result shows that logistics classifiers withand pre-term birth. The risk factors considered

the better accuracy of predictions compared toin this study include variables age, BMI,

decision tree.

addiction,

exposure,significantly different from the decision tree

gender and organ-chlorine pesticides in theof the both classifier models include allnumberofchildren,residence,lactation

pesticide

duration,

The accuracy of logistic

regression for classifying preterm birth is 0.96,

drinking water sources; dietary habits baby

method. The comparison of the performance

placenta of the females.

variables

revealsthat

logistic

regression

performs the better in terms of metrics(precision = 0.92, F1-score = 0.96 and AUROC= 0.97), while decision tree performs the poor

3. Statistical Analysis(precision = 0.75, F1-score = 0.86 and AUROC

In this research paper, we used the decision= 0.87).The Figure 2 shows the receiverbecause decision tree creates a binary tree andvariables and it is found that logistic regression

next step is measure to rank of the variables

After that we have used the top six ranked

technique. Finally, we have used the decision

reason behind repeat the same experimentmodel in the training dataset (70% of cases)

how efficient is the use of information gain

(30% of cases).

using the top six variables are given in Table 2.

tree model in whole dataset and train datasets

operating characteristic (ROC) curve for all

it is very useful in classification problems. The

model is better than decision tree model.

from the data sets through information gain

variables and apply the both model. The

tree and logistic regression both classifier

with top six ranked variables is to emphasize

and evaluate the model using the test sample

measures in the data. The results obtained

The result found that top six variables do notaffect the accuracy performance of decision

4. Results

tree method and again the accuracy of logistic

In this study the variable rank shows in the

regression for classifying preterm birth is 0.78,

Figure 1 using information gain measure.

significantly different from the decision tree

Higher values of information gain indicate

method. The performance gain is shown by

those variables are important for preterm birth.

logistic regression (precision = 0.65, F1-score

The alpha HCH, total HCH, MDA, p_p_DDT,= 0.79 and AUROC = 0.81) and decision tree

beta HCH and p_p_DDE are the highly ranked

performs (precision = 0.56, F1-score = 0.69

variables. In other words, it declares that theseand AUROC = 0.71). In figure 3, we present

variables play important role in preterm birth.

ROC curves for both classifiers models with the

The higher rank variables like alpha HCH, total

help of top six ranked variables. It shows that

HCH and MDA are the most influential factorsthat logistic regression model is again betterwith respect to association with preterm birth.

than decision tree model.

110

5. Discussion
While

the

final

objectiveclassification/predictionofmachineReferences

specialist

1.

learning

technique development is to predict preterm

birth risk, the definition of preterm birth anddata needed to analyze preterm birth risk are2.

less amenable to study presently. Therefore,

the purpose of this study is to determine thefeasibility of using classification/prediction

machine learning to generate expert system(knowledge-base)

rules

for

predictionof

3.

preterm birth. In this study we use the logistic
regression and decision tree method on
preterm birth data and it is found that LR is themost accurate in terms of predicting preterm

birth. With this method, scientists, researchers4.

and practitioners are able to predict anddetect the preterm birth that is at a higher ofdata sets.

5.

6. Conclusion

This study is limited due to small amount ofdata because the machine learning techniques

6.

give better result in big datasets. The result

finds

that

pretermbirthissignificantly

associated with only γ-HCH and MDA. Finallythe results revealed that the logistic regression
is better accuracy in classifying preterm birthcompared to the decision tree method.

Metzger, M. J., Halperin, A. C., Manhart, L.
E., & Hawes, S. E., Association of maternalsmoking during pregnancy with infant
hospitalization and mortality due toinfectious diseases. The Pediatric infectious
disease journal, 32(1), e1,2013
Birnbaum, S. C., Kien, N., Martucci, R. W.,
Gelzleichter, T. R., Witschi, H., Hendrickx, A.
G., & Last, J. A. Nicotine-or epinephrineinduced uteroplacental vasoconstriction
and fetal growth in the rat,Toxicology,94(1-3), 69-80,1994
Hosie, S., Loff, S., Witt, K., Niessen, K., &Waag, K. L., Is there a correlation between
organochlorinecompounds
andundescended testes.European Journal of
Pediatric Surgery, 10(05), 304-309,2000
Jacob Bien, Robert Tibshirani, et al.
Prototype selection for interpretableclassification. The Annals of Applied
Statistics, 5(4):2403–2424, 2011
Emilio Carrizosa, Amaya Nogales-G´omez,
and Dolores Romero Morales. Strongly
agree orstrongly disagree?: Rating featuresin Support Vector Machines. Information
Sciences,329:256–273, 2016
Amin Emad, Kush R Varshney, and Dmitry
M Malioutov. A semiquantitative group
testing approach for learning interpretableclinical prediction rules, In Proc. Signal
Process. Adapt. Sparse Struct. Repr.
Workshop, Cambridge, UK, 2015.

Table 1. Evaluation of classification models using all factors.
Performance metrics

LR
0.96
1.00
0.92
0.96
0.97

Accuracy
Sensitivity (Recall)
Precision (Positive predictive value)F1-score
AUROC

Model

DT
0.85
1.00
0.75
0.86
0.87

Table 2. Evaluation of classification models using the important factors.
Performance metrics
Accuracy

LR
0.78

111

Model

DT
0.67

Sensitivity (Recall)
Precision (Positive predictive value)F1-score
AUROC

1.0
0.65
0.79
0.81

0.91
0.56
0.69
0.71

Figure 1. Variables ranking according to information gain

Figure 2 . Area under the curve analysis of the logistic regression and decision tree methods using all

factors112

Figure 3. Area under the curve analysis of the logistic regression and decision tree methods using
important factors

113

Machine Learning based Peptide Therapeutics for Covid-19
Shailza Singh1** National Centre for Cell Science, NCCS Complex, Ganeshkhind, SP Pune University Campus, Pune-

1411007, India

Email: singhs@nccs.res.in
Summary. The work aims to target the main protease enzyme (Mpro) of SARS-CoV-2, a variant ofcoronavirus causing disease Covid-19. As we all are aware that the current pandemic situation has
been caused by SAR-CoV-2 and due to the presence of large number of mutants, development of aneffective drug or vaccine is not yet possible. Thus, we studied these diverse mutants of SARS-CoV-2

virus to identify conserved sequential motifs in main protease enzyme (Mpro). Mpro is a key enzyme
responsible for maturation of many proteins or transcription factors from just two polypeptides, hence

acting as a very promising target. We applied a novel technique of combinatorial machine learningalgorithm with an optimization algorithm to design novel set of peptides (library of 97 peptides)

which on one hand are conserved in coronavirus species and on the other hand are non-toxic tohumans. The computational studies have confirmed the good binding affinity of two peptides with

Mpro protein. Thus, blocking the functionality of Mpro may result in regulating the replication of viralRNA. Also, by studying the evolvability of virus of all the mutants available till date, machine learning

strategy ensures the efficiency of the peptides against viral variants that might arise in future. In brief,
the machine learning and optimization approach used by us might provide an efficient way to combat
against SARS-CoV-2 virus that warrants further experimental validation.
Keywords..

114

Korea Research Data Management Platform and PID
Sa-kwang Song+1*
1+

Korea Institute of Science and Technology Information, Research Data Sharing Center, 245 Daehakro, Seogu, Daejeon, 34141, Republic of Korea
1*
University of Science and Technology, Department of Data and HPC Science, 217 Gajeong-ro,
Yuseong-gu, Daejeon, 34113, Republic of Korea
Email: esmallj@kisti.re.kr

Summary. Recent rapid increasing interest on research data in Korea makes the government to prepare
for constructing research data platform designated for Korean R&D environment as well as theregulation related to sharing and utilization of research data. Korea Institute of Science and Technology
Information(KISTI) has been developing Korea research data platform, DataON, since 2018 and opened

its first version to the researchers in Korea in Jan 2020. it employed three persistent identifiers fordatasets, researchers, and projects, which are based on DOI, Researcher Number and Project Numberof NTIS(National Institute of Science and Technology Information, Korea). For the global users, theDataON allows ORCID to be added in the metadata as well.

Keywords. Research Data Management Platform, PID, DOI, Data Management Plan, ORCID

1. Introduction

big data since 2018. In order to meet the these

Considering the recent research trends such as

kinds of demands of the times, KoreanIntelligence, various movements are underway

related to research data from 2018 andvarious countries, including EU's OpenAIRE,

platform, and officially opened the first version

Artificial

government has been promoting legalizationin the sharing and utilization of research data inestablishing DataON, a national research data

Australia's ARDC, Japan's NII, etc. In response toin January 2020.

research paradigm, the Korean government

2. DataON:

implemented

DataON has been designed to gather andOpen

Science,

Big

Data,

and

the change to the global fourth-generation

Management System

recognized the importance of data and hasvariouspolicies.

Research

Especially,

domestic interest in the open science paradigm

Datautilize research data managed by Institutional

for easy access and utilization of publiclyData

funded research results has been rapidly

Registries(IDRs)or

Domainspecific

Research Data Platforms(DRDPs) in various

increasing. As a result, led by the Ministry offields such as bio, materials, chemistry, geology,

Science and ICT in Korea, a strategy to promote

etc. since 2017. It is an aggregated researchsharing and utilization of national research data

data management environment to preserve,

has been promoted in 2017, and the National

share, and reuse research data that is especially

Science and Technology Research Association

essential for multidisciplinary research.

in Korea has also been preparing a plan toItsupports convenient search of research data

promote the conversion of research data into

115

which is distributed both domestically in IDRs &•OpenAIRE, Australia’s ARDC, Japan's NII, etc.

•

collaborative•

DRDPs and overseas

aggregators such EU's

etc.)

Moreover, it provides secure sharing andanalysisenvironment

duringresearch,including

Storing&

Preserving

PlanManagement, Backup, Method)for

supporting individual and But all information
created

Summary(Data Type, Producing Method,

•

raw,

(Storage,

Sharing Plan(Scope, Method, Time, DOI)Management & Sharing Director (Name,

Contact)

intermediate, and final data could be part of its

The ministry of Science and ICT has three

convergent research.

project management organizations which have

2.1 Government Strategy

more projects by the end of 2020.

planned to apply the DMP process to 300 or

DataON has started based on the government

3. PIDs on DataON

strategy entitled "Research data sharing andutilization strategy＂ by the ministry of Science

Actually, DataON is the first step of its roadmap.
However,and ICT in 2017. Its goal is to promote sharing

andutilization

through

identifiers

knowledge

itfor

employeddatasets,

threepersistentresearchers,

andprojects though it needs further elaborate

capitalization of national research data. The

strategy comprises four key parts including

design.

resource, constructing infrastructure, activating

identification because KISTI is the first DOI

changing

legal

system,fostering

As a PID for datasets, DataOn adopted DOI

human

RA(registration agency) in Korea. Whereas

research communities related to research data.
2.2 Research Data

Researcher Number of National Institute of
Science and Technology Information (NTIS) is

Koreanadopted as a PID for researcher identification indata for reproduction of research results,

number that is essentially given to participants

observations, surveys, and analyses of national

managed by the NTIS, commissioned by the

DefinitionofResearchDatainregulation is the essential and objective factual

Korea. The Researcher Number is a unique

obtained

experiments,

in the Korean national R&D project created andR&D projects (Regulations on management of

Korean government to KISTI. For the global

But all information created during research,the metadata as well. At last, a unique identifier

could be part of it.

been adopted for persistent identifier for athroughvariousnational R&D projects, Article 25 Section 28).

users, the DataON allows ORCID to be added inincluding raw, intermediate, and final data

for national R&D projects, Project Number, has

project.

2.3 Data Management Plan(DMP)

4. Conclusions

DMP is a formal document that outlines

how data are to be handled both during a

KISTI has been developed Korean research data

research project, and after the project is

platform, DataON, since 2018, in which threecompleted. The main elements of DMP in thepersistent identifiers for datasets, researchers,

Government Policy are

and projects has been applied. Each of them are116

based on DOI, Researcher Number and Project
Number imported from NTIS respectively.

117

Research Across Disciplinary Boundaries: Data Challenges andSolutions in the Environmental and Eco-social Sciences

Alison Specht1*, Jeaneth Machicao2, Shelley Stall3, Danton Vellenich2, PedroCorrêa2 and the PARSEC consortium4* School of Earth and Environmental Sciences and TERN, the University of Queensland, 4072, Australia;

12

Department of Computer and Digital Systems, Av. Prof. Luciano Gualberto, 158, University of Sao
Paulo, S-P, 05508-010, Brazil;

3

American Geophysical Union, 2000 Florida Ave., N.W., Washington DC, 20009, USA;4

www.parsecproject.org.

Email: a.specht@uq.edu.au
Summary. The synthesis of data across disciplinary domains is required to achieve desperately needed
understanding about modern, complex, global challenges. Complex questions in today’s world such as
the challenges of climate change, carry with them a complex range of data, all of which need to beharmonised to permit analysis. In the PARSEC project [1], a team of synthesis and data scientists has
been assembled from across the globe. We come from varying disciplines, including data science, themanagement of protected areas, ecology, remote sensing, AI, sociology, economics, and modelling. We

discuss some of our solutions to enable successful collaboration as we: (1) blend data of different scales,time-frames and types; (2) work together across large geographic boundaries; and (3) ensure the data
management is of international standard from the beginning of the project to publication.
Keywords. inter-disciplinary, PARSEC, DDOMP, data sharing, workflow
boundaries; and (3) ensuring data management

1 Introduction

is to international standards from the start to

The PARSEC project (Building New Tools for
Data

Sharing

Transnational

andRe-useInvestigation

throughofpublication.

athe

2 Challenges and solutions

Socioeconomic Impacts of Protected Areas [1])has a unique cross-cutting objective: to conduct

Our scientific objective is to detect changes ininterdisciplinary, transnational synthesis science

the socio-economic status of communities inwhile developing and testing novel approaches

the environs of Protected Areas (PA’s) into the management and preservation ofenvironmentalandsocioeconomic

response to the creation of the PA. We are using

data.

remotely sensed spatial data, trained using

PARSEC is funded through the Science-driven

Artificial Intelligence techniques to detect

e-Infrastructure Innovation (SEI) Collaborativechanges in detectable spatial characteristics

Research Action of the Belmont Forum [2] bythat have socio-economic meaning.

four countries, Japan, the USA, France and Brazil,
with associates from Australia, the UK and

2.1 Blending data of different scales, timeinsight into the challenges of: (1) blending data

To analyse the impact of PAs on nearby regions

frames and types

elsewhere. The project provides a uniqueof different scales, time frames and types; (2)we are using a data science framework (Figure

working together across large geographic

1) whose central machinery is a deep neural118

2.2 Working across geographic boundaries.

network model to map inputs to outputs. We
are accessing data from several sources such as

The project relies on co-ordinated practicegeostatistical national census data, which is at

across the project teams, who conduct different

different spatial and temporal scale andcomponents of the analysis. Development of

resolution from the remotely sensed data, e.g.

algorithms is primarily occurring in France, butfrom decades for the socio-economic data, tothe workflows and large data sets need to bedays for the remote-sensed data. The firstshared with the other teams safely, robustly,

challenge, therefore, is to disaggregate theseand in a timely manner. We intend to use

heterogeneous data to establish a common

Amazon Web Services (AWS) infrastructure, for

granularity which can be used across countries.

its

We need the most fine-grained data available,

substantial

working

space,AWS

Organization to create team space, and AWS

but there will be various cycles to refine theLake Formation for satellite image

main goal, and we may need to adjust the

granularity to reveal the relationships.

Figure 1: Proposed workflow to handle acquisition from different scales and types. A common data granularity,

as fine as possible, will be used to annotate the satellite image data, and then inputted into the deep learningmodel for training and testing.

storage. Amazon EC2 will be used for machine

The PARSEC project, as a grantee of thehas been created to handle versioning.

new Data and Digital Output Management Plan

Belmont Forum, is required to implement thelearning experiments, and a GitHub account [4](DDOMP) format [3, 5]. One of the objectives ofAn important aspect of co-working in atransnational

consortium

is

PARSEC is to create guidance for researchers

goodthat transforms the DDOMP into a living

communication. As well as email, we have a

document with guides and templates on how tosystem of regular Zoom meetings, multiple

makeSlack channels, a dedicated Google Drive and aasynchronous).

written,synchronous

creationandthe existing information provided by the

dictate the use of multiple communication(verbal,concerning

management of scientific data. We build upon

Zotero community. Time zone differences
modes

decisionsBelmont Forum and created checklists for PIs toandgive their researchers to follow during the
project, suggestions on how to communicate

2.3 Ensuring data management is of

and use resources for better tracking of datainternational standard.

and digital objects during the project. As a case119

(Japan), NSF (US), and the synthesis centre

study, the PARSEC project has implemented

CESAB of the FRB (France).

these tools for the use of our researchers. This
makes yearly reporting requirements much

Referenceseasier as well as communication across the

1. Building New Tools for Data Sharing and Re-

team. We look forward to sharing these tools
with

the

community.

GlobalCollaborationon

use through a Transnational Investigation of

Data

the Socioeconomic Impacts of Protected
Areas <www.parsecproject.org>

2. Science-driven e-Infrastructure Innovation

3 Conclusions(SEI) Collaborative Research Action of theBelmont Forum <www.belmontforum.org>

This project, which exclusively uses existing

data, has another two and a half years of life.

3. Bishop, B., Gundermann, H., Davis, R., Lee, T.,

along the way. By following open work practices

J. Data curation profiling to assess datasome of the socio-economic data), we intend toto inform a toolkit. Data Science Journal,

We expect we shall need to adjust many plans

Howard, R., Samors, R., Murphy, F., Ungvari,where we can (we anticipate limitations on

management training needs and practices

share what we learn along the way.

19(1), 4-11, 2020. doi: 10.5334/dsj-2020-002

4. PARSEC

Acknowledgements. This research is a producthttps://github.com/PARSECworldof the PARSEC group funded by the Belmont

5. Data And Digital objects Management Plan:

Forum as part of its Collaborative Research
Action(CRA)

on

Science-Driven

github:https://www.belmontforum.org/resources/de-ata-and-digital-outputs-management-plan-

Infrastructures Innovation (SEI), with partners,

ddomp/

the ANR (France), FAPESP (Brazil) and JST

120

Detailed Methodology and Application Guidelines for“The Global Covid-19 Index (GCI)"
Woody Ang Woo Teck1** PEMANDU Associates, Level 21, Sunway Putra Tower, Jalan Putra, 50350 Kuala Lumpur, Malaysia

1

Email: woody.ang@pemandu.org

Summary. The Global Covid-19 Index (GCI) is a fully data-driven and objective approach to assess thetrue severity and recovery progress of any given country. It is meant to be instructive to Governments

in answering the key questions of what measures have been applied globally, and from there, what

appears to be working and which ones do not. The GCI is derived from our very own proprietaryalgorithms that incorporate all the metrics that truly matter from well recognised and validated open

source data. Furthermore, unlike other indexes in the world that are published at a slow frequency, the
GCI can be updated LIVE on a daily basis.

Keywords. COVID-19; Index; Severity; Recovery; WHO; Pathfinder

1. Introduction•

The GCI Severity Index is designed towasexhibit a ‘scarring’ characteristics so thatpartnership with the Ministry of Science,from a health perspective by COVID-19 canof Malaysia. The intent of the GCI was to identify

similarly affected to compare their progress

their battle against COVID-19, and to be able tomore

implemented by these countries.

without having to recover from a significant

2. GCI – Severity and Recovery

directly comparable with those that wereThe

GlobalCOVID-19

Index

(GCI)

in

countries that have been affected badly

Technology and Innovation of the Government

be compared with countries that have beencountries that were making positive progress inin tackling the pandemic. This also enables

identify common best practices that werecountries may have been more successful

developedby

PEMANDU

Associatescomparisons

as

some

number of infections and may not belate to react and may be looking for more

Indices

relevant best practices.

The GCI is intentionally designed to be

The following key dynamic parameterscomprised of 70% dynamic components ofwhich data is updated

relevant

contribute 70% of the GCI Severity Index:

daily whilst theConfirmed Cases per Population: Thisremaining 30% is comprised of less dynamic

parameter is intended to ensure cases areparameters that may only be updated once orseen in relative to the size of each country.

twice yearly.

This data is updated daily currently basedon Johns Hopkins University’s Center for

2.1 GCI Severity Index

Systems Science and Engineering (JHU

CSSE) Open Data sources[1]. However, we121will be migrating to utilise the World Health

We will cover this in further detail in Section

now that the WHO COVID-19 dashboard isIn

Organisation’s (WHO) database instead

3.1.

also live. Population data is retrieved fromWorld Bank[2]

based on the 2019 revision.

the crude death rates of each country andfactors its population size, and thencompares this to the death rates due to
COVID-19 since the first case was reported

in the country. This gives a true reflection of
how death rates are being affected by

2.2 GCI Recovery Index

COVID-19 in these countries. On a daily•

basis, the number of deaths is cumulatively
theincrease by a single day.
•denominator

will

health

We apply an outlier checks and min-max

andperspective

and

takes

into

infectious) in the country.
•

The following key dynamic parameters
contribute 70% of the GCI Recovery Index:

which we flag any samples which fall

Activeoutside either limits. These outliers will be

Cases

parameter

given a score of 0 for those below the lower

is

per

Population):

calculated

by

This

removing

recoveries and deaths from the total

limit and 100 for those above the upper

confirmed cases. It also visually enables thelimit.

representation of the actual flattening and

All other non-outlier samples will then be

reduction of the COVID-19 epidemic curve.

analysed and normalised using the min-This data is updated daily from the JHU

max statistical method which will give each

CSSE Open Data repository.

country a relative score for each dynamic

Recoveries per Confirmed Case. This

component, and this enables us to identify

parameter factors a country’s success incountries performing better or worse thantreating patients that have been diagnosed

their peer.

COVID-19 positive. It is also updated daily

The remaining 30% of the Severity Index isbased on data from JHU CSSE.

comprised of semi-dynamic components

Tests Conducted per Confirmed Case: This

which are aimed at enabling a relative
other

containing

which represents the number of people still

component’s interquartile range (IQR), afteronisand remaining active cases (the latter of

To eliminate outliers, we determine eachcomparison

country

consideration the recoveries, testing efforts,

the above parameters.

•therecovering from the epidemic based on thescore to each country in the GCI sample for•

The Recovery Index is a measure of how
wellnormalisation methods to apply a relative•

Severity Index

𝐺𝐺𝐺𝐺𝐺𝐺 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 𝐼𝐼𝐼𝐼𝑑𝑑𝑑𝑑𝑑𝑑
𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶=
𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃+ 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷ℎ 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 𝑑𝑑𝑑𝑑𝑑𝑑 𝑡𝑡𝑡𝑡 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶19+ 𝑈𝑈𝑈𝑈𝑈𝑈 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 + 𝐻𝐻𝐻𝐻𝐻𝐻 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 + 𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵+ 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 (𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖+ 𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺 + 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶+ 𝑃𝑃ℎ𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶)

19: This parameter takes into considerationwhilstthe GCI

components are as follows:

Proportionate Death Rate due to COVID-

trackedsummary,

reflects how much effort a country has

comprehensive

invested in testing. This metric normalises

considerations that expose the risk andfor both mass testing and controlled

recovery ability of each individual country.

testing as the lesser that is found for the122number of tests conducted would usually•indicate that a better recovery to countries

member states score a maximum score forwhich are finding a high number ofconfirmed

positivecases.

Test

this component:

data

Data , an Open Data platform project of[3]

Measureson

Testingby the Blavatnik School

Saudi Arabia
Switzerland

•

to

•

In summary, the GCI Recovery Index

known case, returned from overseas)

components are as follows:

2: Testing of anyone showing Covid-19

symptoms

3: Open public testing (eg "drive

GCI Recovery Index
Active Cases
Recoveries=+
Population Confirmed Cases
Tests Conducted+
Confirmed Cases+ Country Measures on Testing & Contact Tracing+ UHC Billion + HEP Billion + HPOP Billion+ INFORM (Socioeconomic Vulnerability+ Governance + Communication+ Phyiscal Connectivity)

toGovernment policies on contract tracing
after a positive diagnosis:−−

0: No contact tracing

1: Limited contact tracing; not done forall cases
2:

Comprehensive

contact

done for all identified cases

The remaining 30% of the Recovery Index isdetail in Section 3.1.

hospital, came into contact with a−

For all the components above, the samethe pandemic. We will cover in further

criteria (eg key workers, admitted toasymptomatic people)

United Arab Emirates

country’s healthcare system to respond to

symptoms AND (b) meet specificavailable

South Korea

which reflect the inherent abilities of the

1: Only those who both (a) havetesting

South Africa

San Marino

comprised of semi-dynamic components

0: No testing policy

through"RwandaQatar

Index Parameters.

to PCR testing:−Mauritius

Luxembourg

the GCI sample for the above Recovery

Government policies on who has access

−

Kenya

Icelandobtain a relative score for each country infollows:−

GreeceGermany

described in Section 2.1 were applied totheinformation that the OxCGRT tracks as

−

Gabon

Cuba

methods for the GCI Severity Index as

Governments of each country. We havemethodChina

outlier checks and min-max statisticalContact Tracing Policies implemented byscoringDjibouti

RussiaOxCGRT tracks the ongoing Testing andaDenmark

Malaysiaof Government of Oxford University. The

appliedCyprusKazakhstan

andOxford COVID-19 Government Response

Tracker (OxCGRT)

Bahrain

Ghana

Contact Tracing: We utilise the data from
[6]

Austria

Dominica

the Global Change Data Lab based in theCountry

Australia
Canadacurrently is obtained from Our World In

United Kingdom.

As of 20th August 2020, at least 29 WHO

tracing;123

3. Upcoming Improvements in the
We

GCI Methodology
have

taken

on

board

its key components. This data will be
updated on an annual basis from all WHO
Member States.

many

•

recommendations based on the discussions

Billion: The HEP Billion aims for 1 billion

with the World Health Organisation team of

more people to be better protected fromdata experts. This section will elaborate all newhealth emergencies. Its key indicators rely

components to the GCI Methodology that haveon IHR State Party Self-assessment Annual

been built as part of the new version that will be

Reporting (SPAR), vaccine coverage of at-

made available soon.

risk groups for epidemic- or pandemic-

prone diseases; and timely detection and

3.1 Change in Semi-Dynamic Indicators•

response to potential health emergencies.

We recognise that the originally applied

This data will be updated on an annual

subindicators from the Global Healthbasis from all WHO Member States.

Secruity Index (GHSI) funded by the Bill and•Melinda Gates Foundation may have somepeople to enjoy better health and well-recommendation to explore other Indicesbeing. The HPOP Billion marks the first timethat would achieve the same objectives ofthat WHO has created a single measure ofassessing a country’s preparedness andchange in the domain of the behavioural,ability to respond to a pandemic. For theenvironmental and socially determined

new prototype, we have now applied thehealthiness of global populations. It utilises

following sub-indicators as part of the new

16 subindicators which are linked to

GCI methodology.

Sustainable Development Goals (SDGs) andWHO’s Triple Billions Concept: Based on

WHO’s resolution WHA66.10. This data will

the Thirteenth General Programme of Work
(GPW13)

Methodsforalso be updated on an annual basis from all

Impact

WHO Member States.

Measurement[4] published by the WHO, 3

newoutcomeindicators

have

•

been

(DRMKC) INFORM Risk Index[5]: The

Concept; the Universal Health Coverage
Billion,Protection

Populations(HEP)

HealthBillion(HPOP)

Billion.

INFORM model is the DRMKC’s efforts to

Emergencies

andestablish an index based on the risk

HealthyWeconcepts

have•

Universal

HealthCoverageof:hazards&

exposure,vulnerability and lack of coping capacity

applied individual weightages to the GCIfrom these individual Billion scores:

European Commission’s (EU) Disaster
Risk Management Knowledege Centre

proposed as part of the Triple Billions(UHC)

Healthier Populations (HPOP) Billion:
The HPOP Billion goal is for 1 billion moredisputed results. We have taken on WHO’s•

Health Emergencies Protection (HEP)

dimensions. Each country is scored basedon these 3 risk areas,(UHC)

of which acomprehensive list of components are

Billion: The UHC Billion aims to ensure thatutilised. For the purpose of the GCI, we havean additional 1 billion people receive thezoomed in specifically on the aspects ofquality health services they need without

Socio-Economic Vulnerability, Governance,

financial hardship. It uses both Average

Communication Infrastructure and Physical

Service Coverage and Financial Hardship as

Connectivity Infrastructure. These scores

124

are currently updated on a half-yearly basis

utilities and also for help to reach them. A

INFORM Socio-Economic Vulnerability

We apply this directly to the GCI Recovery

by the EU DRMKC.
•

Score:The

INFORMhigher score indicates better infrastructure.

Soci-Economic

Index but inverse it for the GCI Severity

Vulnerability Score ranks countries on their
level

of

development,

inequality

Index.

and•dependency on foreign aid. A higher score

Billions and INFORM risk has further

indicates a higher vulnerability that thestrengthened the semi-dynamic section ofmember state is exposed to, and will mean

the GCI and also leverages on an ongoing

they are at a higher risk should a pandemic

commitment by both the WHO and EU to

GCI Severity Index but inverse it for the GCIan annual basis.

affect them. We apply this directly to the
Recovery Index.
•

INFORM

Governance

Score:

keep these assessments updated at least on

The

3.2 Change in Monitored Duration of GCI

Governance score is derived from two

Index Dynamic Calculationlenses; the Government Effectiveness Index•

and Corruption Perception Index. This is a

waves

mechanisms to effectively respond to a

Currently,

epidemic. A higher score indicates that aandquickly

andwidespreadcountry’s

population.

effortsA

canhigher

start

of

the

commencing in countries that may havebeen successful before.

4. Finalascore

Observations

Conclusions

indicates better infrastructure. We apply

andWe believe the new version will further

this directly to the GCI Recovery Index but

strengthen the original objective of the Global

inverse it for the GCI Severity Index.
•

thesituation, especially if new waves are

public

reach

since

days, and better reflects the current

Literacy Rates. This is a measure of how
communication

dynamic

be tracked and updated for only the past 90

Infrastructure score considers Electricity,
Accessrecords

means that all the dynamic components will

INFORM Communication Infrastructure

CellularGCI

version of our GCI Methodology. This

Index.

MobileCOVID19

states, we have implemented a 90-day

Index but inverse it for the GCI Severity

Internet,

original

ever-changing circumstances in all member

apply this directly to the GCI Recovery

and

the

pandemic. In order to better reflect themore effective in handling COVID-19. We

Communication

thecomponents

country’s existing governance will likely beThebeyondoutbreak within their respective borders.

national-level hazard such as a health

Score:We recognise that since the initial outbreakin many countries, some are now seeing

measure of existing country’s Governance•We believe utilising a combination of WHO

COVID-19 Index; comprehensively combining

INFORM Physical Connectivity Score: Thedata from highly reputable data sources, which

Physical Connectivity score considers Road

now includes the WHO Billions Concept, andDensity, Water and Sanitation Access. This

the EU Inform Risk Ratings.

is a measure of how easy it will be for acountry’s population to connect to needed125

We remain committed together with theWorld Health Organisation team comprising Dr

updated for the remainder of 2020 and 2021

Dr Brett Archer for the multiple constructive

Malaysian Government to keep the GCI live andNirmal Kandel, Dr Bochen, Dr Amit Prasad, andand hope to be able to use it to enable theconsultations and inputs in our efforts to bringdevelopment of more insight in addition to theabout a new version of the GCI for in-principle

full Global Pathfinder Report which is now

endorsement. We would not have been able topublicly available fpr download on the GCIdevelop new version at this pace without theirwebsite and we hope more member states willvalued inputs.

be able to benefit from the learnings in thisReferences

report:(https://covid19.pemandu.org/download/GPF

1.

John Hopkins Center of Science and CenterWe are also committed to continue working

2.

World Bank Population Data

that the GCI continues to be a tool that can be

4.

World

%20Full%20Report.pdf)

3.

closely with the World Health Organisation so

helpful to member states and will be morefor Systems Science and Engineering

Our World in Data – COVID19 Testing Data
HealthOrganisation(WHO)

Thirteenth General Programme of Work
(GPW13) Methods for Impact Measurement

willing to extend our assistance in its ongoing
efforts to battle this global pandemic.

5.

Acknowledgments. On behalf of PEMANDU

Associates and the Government of Malaysia, we

6.

would like to accord our special thanks to the

GCI Advisory Council Members for the feedback(Billions Concept)
EuropeanCommission’s

DisasterManagement Knowledege Centre (DRMKC)
INFORM Risk Index

Oxford COVID-19 Government Response
Tracker (OxCGRT) by the Blavatnik Schoolof Government of Oxford University

on the original GCI model, and subsequently

126

Risk

Analyzing the early 19 th century’s geomagnetic declination in
Japanese archipelago from Tadataka Inoh’s 67 volumes
magnetic survey azimuth ledger Santou-Houi-Ki byinterdisciplinary study
Motohiro Tsujimoto1** Sakai Shi Osaka Japan 592-8347

1

Email: motori-7map@wine.ocn.ne.jp

Summary. The “Santou-Houi-Ki” is recorded by Japanese cartographic surveyor Tadataka Inoh in 1800

to 1816 consist of 67 volumes survey ledger to produce the first survey map of Japan, called“Coastal

map of Japan” or Inoh Map (1:3600.1:21600.1:432000). In the Santou-Houi-Ki estimate 200,000
magnetic compass survey azimuth data by accuracy of 0°05’unit were written ,with the name of survey
reference point and Target points. The surveyed region extends from North eastern Hokkaido toYakushima island in western Japan. We execute interdisciplinary and simultaneous analysis ofgeomagnetic declination, real azimuth, precise position of the survey execution point and target points(latitude and longitude less than 0.1 second). And add the historical importance of each precise positionof survey reference point. Calculate backward the precise position of survey reference point, where thevalue of magnetic declination, subtracting the magnetic survey azimuth from the true azimuth to any

target points is similar or approximate. When I compare the analysis of magnetic declination from
Santou-Houi-Ki to NOAA’s Historical declination viewer, NOAA’s progress of declination west in Kyushu

and in western Honshu is more than 3 to 10 years late than the analysis from Santou-Houi-Ki and 20

years late in Tsushima Island in particular. I must continue to analysis in Kyushu and Tsushima Islandand supply analysis data from Santou-Houi-Ki to NOAA’s historical declination Viewer.

By conducting verification by analysis that fuses the disciplines of humanities and sciences that havenever been learned with each other, we can obtain epoch-making highly accurate data that has neverexisted before, and create a chain that can be reused in various fields of humanities and sciences to beborn.

Keywords. Santou-Houi-Ki, Tadatakaka Inoh, magnetic compass survey, magnetic declination,

NOAA’s Historical Declination Viewer

127

Scientifc Data Standard System Construction and its Application
for Data Archiving in China
Juanle Wang1*, and Yujie Wang1* State Key Laboratory of Resources and Environmental Information System, Institute of Geographic
Sciences and Natural Resources Research, Chinese Academy of Sciences, A11, Datun Road, Chaoyang
District, Beijing, 100101, China
Email: wangjl@igsnrr.ac.cn
1

Summary. Scientific data standards are the basis for the long-term acquisition, processing, preservation,and sustainable access and utilization of scientific data. The degree of standardization of scientific datais also a significant index to measure the accumulation and effective utilization of scientific data
resources in the world. This paper analyzes the current status of the scientific data standards system

and its development, closely follows the standardization requirements of scientific data management,

analyzes the full life circle process of scientific data, and establishes a reference model of scientific data
standard system. This system is established based on the three levels of basic standard, general standard

and specialized standard. On this basis, some suggestions are put forward, such as perfecting thereference model of scientific data standard system, covering the whole life cycle chain, implementing
the system step by step according to the plan, coordinating the multilevel standards, and publicizing
and implementing the standard application.

Keywords. standards system, scientific data, scientific data centers, data management and sharing,reference model

1. Introduction

2. Method.

run through the whole life cycle of scientific

data management activities are analyzed from

Scientific data standard and its standardization

The standardization requirements of scientificdata. In order to strengthen and enhance thethe aspects of organization of content, life cycle

necessary to carry out top-level design andservice and assessment and evaluation ofstandardization level of scientific data, it ismanagement, security guarantee, application

system construction for the standard system ofscientific data. The reference model of scientificscientific data. The lack of overall coordination

data standard system is constructed under theof the standard system will make the standards

guidance of hierarchical structure. The standard

is divided into three categories: basic standard,

among different scientific data centers or

general standard and specialized standard.

repositories be incompatible, forming new data

Accordingbarriers. This study will explore a scientific data

to

the

methodsoffunction

centralization and life cycle, the specificstandard system that includes multidisciplinary

contents of the three kinds of standards arecontent, covers multi domain platforms, anddesigned. Based on the basic business process

runs through the life cycle of scientific data.

of scientific data management standardization,

128

4. Conclusions.

the concept of life cycle after appropriate

simplification is integrated into the analysis,This

and the content and function classification ofscientificdataestablished.

standard

specification

fully

considersthebusinessrequirements of scientific data management

arestandardization

and

the

commoncharacteristics of scientific data in different
disciplines and fields, and coordinates with the

3. Results.

whole

In Summary, the scientific data standard system

datacollection

systemofscienceandlevels of basic standard, general standard andguide standard, scientific data description
scientificstandard

technology platform. According to the threeconsists of eight subsystems: definition andstandard,

study

specialized standard, the reference model ofandscientific data standard is established. Eightprocessing standard, scientific data archiving

sub-systems including definition and guidestandard, scientific data preservation andstandard, scientific data description standard,

maintenance standard, scientific data sharing

scientificand service standard, scientific data assessment

data

collection

andprocessing

standard, scientific data archiving standard,and evaluation standard, and scientific data

scientific data preservation and maintenancesecurity standard. Based on this model, data

standard, scientific data sharing and service

archiving standards are drew up and practiced

standard,

for scientific research program data archiving inscientificdataassessment

andevaluation standard, and scientific data security

China.

standard are constructed. This standard systemisexpected

toprovidestandardizationreference for National Science Data Centers andSpace science

High-energy physics

7 Scientific data assessment andevaluation standard（Data evaluation,
service evaluation, institution
evaluation, destruction and reuse）

Genomics

6 Scientific data
3 Scientific data
5 Scientific data
2 Scientificsharing andcollection andpreservation anddata description
service standard
processing
maintenance4 Scientific data
standard（Servicestandard
standardarchiving（Metadata,
content, service
standard
（Data
（Data
data element,
quality, service
collection and （scientific data preservation,
classification
procedures,production, datadataarchiving ）and codes, dataservice modes,
processing andmaintenance,serviceidentification）
sorting）
data update）
capabilities）

related scientific data management.

ęę ęęAgricultural science
Earth science

Basic standard
1 Definition and guide（Standardization guidelines, reference
models, terminology, general requirements）

Acknowledgments.

A

specialacknowledgement should be expressed toSpecial Foundation of National Science &Technology Infrastructure Center “Survey andanalysis of global typical databases in key fields”

8 Scientific data security standard（Physical security, system security,
network security, data usage security）（ No. 2020WT22 ） and the 13th Five-year

General standard

Figure 1. Ref erence model of scientific data

Informatization Plan of Chinese Academy ofstandard system

Sciences (No.XXH13505-07).

129

Introduction of FUXI Platform——
Global Integrated Observation Data Management and Service
SystemYuanyuan Wang1,2*, Zhenhong Du1* School of Earth Sciences, Zhejiang University, 38 Zheda Road, Hangzhou 310027, China;
2
Ocean Academy, Zhejiang University, 1 Zheda Road, Zhoushan 316021, China
Email: wangyuanyuanxy@zju.edu.cn

1

Summary. Global Integrated Observation Data Management and Sharing Service Platform——FUXI is a
new generation of intelligent platform for remote sensing observation data, which integrates super-large-scale remote sensing comprehensive observation data results, massive artificial intelligencemodels and algorithms, and online high-performance computing and analysis services in one. FUXI

has the capabilities which were independently innovated of efficient management of EB-level

observation results, artificial intelligence-driven knowledge modelling, and constructing online
template chains for application scenarios supported by microservices. It will support platforms forinternational shared services, such as the Cooperation on the Analysis of carbon Satellite data (CASA)
and Global Ecosystems and Environment Observation Analysis Research Cooperation.
Keywords. Remote Sensing, Data Management, Data Service

130

WDS-led Activities on Data in the Asia-Oceania Area
Takashi Watanabe1*,Juanle Wang2,Toshihiko Iyemori3, Yasuhiro Murayama4* WDS International Programme Office, Koganei, Tokyo, 184-8795, Japan
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Science
1A, Datun Road, Chaoyang District, Beijing, 100101, China
3
Kyoto University, Kyoto, 606-8501, Country
4
National Institute of Communications Technology, Koganei, Tokyo, 184-8795, Japan
Email: takashi.watanabe@worlddatasystem.org
1

2

Summary. As a part of the activities of the World Data System (WDS) of the International Science
Council (ISC), two WDS Asia–-Oceania Conferences were held in Japan in 2017 and in China in 2019respectively, These Conferences were co-organized by the collaboration of the WDS National
Committee of Science Council of Japan, the WDS community of China, and the WDS International

Programme Office. The principal objective of the conferences was to build a WDS-oriented network of

research-data repositories in Asia–Oceania comprising of existing WDS Members and other datafocused organizations. Collaboration with the committees of ISC’s Committee on Data (CODATA) andthe ISC Regional Office for Asia–-Pacific (ROAP) will also be important to progress such activities, as wellas including the involvement of government data repositories, particularly in Southeast Asian countries.
Keywords. Data Network, WDS, Asia, Oceania

1. Background(2)

preservation and open provision of researchresearchers working in universities or othercurrent data-oriented activities, mainly in the(3) Various observational data are being

was performed by searching for data portals oninternational research programmes. However,and governmental repositories. As of result of

shared by data repositories in more advanced

Only

very

limitednumberofdata

To identify current problem on long-term

repositories were found to be operated bydata in the Asia–Oceania area, a survey ofresearch institutions.

field of Environmental Sciences in Asia-Oceania

obtainedthe Internet and by visiting several institutional

the majority of these data are hosted andthis survey, the following problems shown

countries.

below were discovered.

inSoutheast

Asia

as

part

of

2 Past and future WDS Asia–-Oceania
Conferences(1) The majority of well-established datarepositories, especially in the Southeast AsianTo ascertain the current status and thecountries, are governmental in nature; forproblems of Research Data Management

example, they manage satellite remote sensing(RDM) in Asia–-Oceania, two WDS Asia-Oceania

topological images or environmental data. The

Conferences focusing on the region werevalues of Open Science and Open Data are notorganized through a collaboration among thehigh on the agenda of such repositories at this

WDS National Committee of the Sciencestage because they are funded at the national

Council of Japan, the WDS Community of China,level by mandated programmes.

131

mission-oriented activities. The realization by

and the WDS International Programme office inthe 2019 Conference was that building theTokyo, Japan.

envisaged network to involve more people

2-1. WDS Asia–-Oceania Conference 2017,from more countries would be a gradualprocess; especially, when it comes from

Kyoto, Japan

1

Southeast Asia. With the 20th Meeting of the

The first WDS Asia–-Oceania Conference was

WDS Scientific Committee held in conjunction

held on 27–29 September 2017 at Kyoto
University, Japan, hosted by the WDC forwith the Conference, this was a notable

related people from 16 countries attended theConference participants on the projects of WDS,

opportunity to impart information to the

Geomagnetism, Kyoto. More than 110 data-

including the procedure to become certified as2017 Conference (Figure 1), The Conference

trustworthy by obtaining the CoreTrustSeal3 forbrought together data practitioners, datarepositories

managers,

andData Repositories.

data-oriented

researchers to identify data issues and discuss afuture plan to help strength data activities, andbuild a platform to connect this al activity to theinternational data community. The resulting
network was considered valuable also in

helping to support other international research
programmes under ISC, namely, Future Earth.

Figure 2. Participants of the WDS Asia–-

Oceania Conference 20192-3. Future Activities

It will be important to continue our activity

having meetings regularly, at least biannually.
Collaborations

Figure 1. Participants of the WDS Asia–-

with

ROAP

and

CODATA

National committees in the Asia-Oceania area

Oceania Conference 2017.

will be important to involve governmental
repositories in this are to our activity.

2-2. WDS Asia–-Oceania Conference 2019,
Beijing, China2

References

The second WDS Asia-Oceania Conference was

http://wdc2.kugi.kyotou.ac.jp/wds2017/progra

1held on 6–7 May 2019 at the Institute ofm20170917.html

Geographic Sciences and Natural Resources2

Beijing, China. On this occasion, we had more3

http://www.wds-

Research, Chinese Academy of Science, inchina.org/meeting201905.htm

https://www.coretrustseal.org/

than 120 participants from 14 countries were inattendance (Figure 2), and about 50 papers
were presented. The main objective of the 2019

Conference was to reinforce the collaborationsin Asia–-Oceania that began in the first
Conference, and at the same time progress

activities of strategic importance to the WDS

132

Importance of a Multi-disciplinary Study of Peculiar
Environmental and Socio-economic Movements in 18/19 Century
Takashi Watanabe1**World Data System International Programme Office, Koganei, Tokyo, 184-8795, Japan
Email: takashi.watanabe@worlddatasystem.org

1

Summary. The 18/19 Century was an interesting period in terms of environmental and socio-economic
aspects. The period is known to have been a relatively cold period in the last phase of the Little Ice Agestarting in 16 Century. The level of solar activity was generally low in the period of the Mounder

Minimum (1500-1700) and the Dalton Minimum (1790-1830). Global influences of extensive volcanic
activities were also important. It is found that this interval will be important to promote a multi-

disciplinary data analysis to study environmental influences on socio-economic activities. To do this,collaborations between data providers and users (scientists) will be important to promote
multidisciplinary usages of data.

Keywords. Data Analysis, Space Weather, Environmental Science, Social Sciencecalled Dalton Minimum (DM), was taken place

1. Introductionin 1790-1830. Since considerable depression ofsocio-economic activities, e.g., the Industrial

The importance of multidisciplinary usage ofRevolutions in England, the French Revolution

data has been stressed in various disciplines but(1789-1799) and the Napoleonic Wars (1799-

there still exists a lot of obstacles to do. To

1815) were taken place in Europe under theidentify problems in multi-disciplinary usage ofrelatively cold environment and high volcanic

data, a trial is performed on environmental andactivities. A collaborative study of this interval

economic data during the 18th-19th Centuries

by scientists in a wide range of disciplines will

because this interval will be interesting to manybe important to know the socio-economic

scientists in various research fields including the

movements in the advent of the industrial worldspace weather, the atmospheric sciences, theunder the “basic” natural environment of thehistory, the economics, the agriculture, etc.

Earth,

Socio-economic movements in this period were

just

beforetheanthropologicalenvironmental changes have been clearlytaken place under the peculiar environmental

recognized. Such collaborative works will besituation. This period was located in the last half

useful to create a strong motivation to improveof the so-called Little Ice Age (LIA) which was a

the accessibility and usability of research data

period of generally lower global temperaturestoexpanding from 16th to the mid of 19th

meet

the

FAIR

(Findable,

Accessible,

Interoperative, and Reusable) Guiding Principle

centuries. In the LIA, the Maunder Minimum

research data for scientific data management(MM) of solar activity (Eddy 1976) expanding

and stewardship.

from 1645 to 1715 was a remarkable period of

2. Why the 18th/19th century?

globally cold climate. Following the MM, arelatively short minimum of the solar activity,133

Time changes of the sunspot number and theprocedure has been employed in several

Centuries are shown in Figure 1. A depression

between solar activity and precipitation in

annual wheat price in England in 18 /19
thcorrelationth

Europe.

of amplitude of the 11-year sunspot cycle in the

the

the

correlation

The notable tendencies found bydata for the summertime (JJA) show a

Minimum (DM). It is difficult to find a clear
betweene.g.

correlation analyses are: (1) The precipitationinterval of 1790-1830 is called the Daltoncorrelation

analysessignificant positive correlation (the correlation

sunspot 11-year

coefficient is +0.6) with the long-term (>11

Schwabe Cycle and the British wheat price inyears) variations of sunspot numbers. The

this figure but the price showed a decadaltemperature shows no significant correlation

enhancement during the DM. The rise of wheatwith the sunspot number in the period ofprice in question is suggested to have beenpresent analysis (1770-1860), although a week

caused by poor harvesting known to havecorrelation (+0.2) is seen in the wider interval

happened in England during the period of the(1700-1900), (2) The wheat price has a

Napoleonic Wars (1799-1815), accidentallysignificant negative correlation (-0.7) with thehappened in the DM, and several spiky

wheat yield. This suggest that the mostenhancements of the price were mainly caused

important factor to control the wheat price inby shocks of the war, including blockages ofthis period was the wheat yield, (3) The wheat

British and French seaports. A general increasein wheat price in 1815-1846 was partly caused

yield in the DM era was largely controlled by theperform our study to find environmental

with the averaged JJA temperature but an

precipitation (+0.4). No significant correlationby the influence of the British Corn Laws.We will

enduring cold summer in the interval of 1809-

influence on the price history because similar

1817 is suggested to partly contribute to theincreases in prices of crops can be found also inotherEuropeancountries(e.g.,

reduction of yield, (4) The JJA precipitation in

France,England-Wales showed a negative correlation

Germany, and Austria), North America, and in(-0.5) with the positive (westerly) PLWI (fullAsian countries including India and China. Thismeans that the rise of the crop price isnames of datasets are given in Figure 2)

manner by some common environmental

temperature

throughout the period of the DM, (5) The JJA

suggested to have been taken place in a global

did

not

show

a

significant

correlation with the sunspot number but verychanges.

weak correlation with PLWI is seen, (6) Major

volcanic activities in the interval of analysis (e.g.
Tambola in1815) produced clear short-term

depressions of temperature. Weak correlations
seen between TEM and VOI were produced bythese volcanic events.

Figure 1. Time series of yearly sunspot

numbers (x 0.1) and the wheat price in EnglandOn the data issue, efforts to convert data on(shillings per bushel) in the interval of 1700-

papers to machine-readable formats and to1910.

standardize reconstructed data are highly

desirable. The collaborative multi-disciplinary

3. Data Analysis

study of the 18th/19th century will be pertinent

to stimulate the efforts and useful to predict

A provisional correlation analysis is performed

consequences of the current anthropologicalfor the parameters shown in Figure 2. In thisenvironmental change.

analysis, no time lag is applied although this134

Figure 2. Time series of economic andenvironmental data in the 18th-19th Centuries:(a) wheat price at London (PRICE), deviation of

the wheat yield (YELD) in England from a linear
regression line in the interval, , yearly sunspot

number (SSN), yearly mean temperature (deg.
C) in the central England (TEM), Paris-London

Westerly Index (PLWI) as a measure of theNorth-Atlantic Oscillation, and the Dust Vail
Index (DVI) which is a measure of the effect ofmajor volcanic eruptions in the world.

135

COVID-19 Data Sharing Initiative by WDCM and NMDC
Linhuan Wu1*, Juncai Ma1* Institute of Microbiology, Chinese Academy of Sciences, NO.1-3 Beichen West Road,
Chaoyang District, Beijing 100101, China
Email: wulh@im.ac.cn

1

Summary: As the COVID-19 virus are widely spreading on the world, the World Data centre forMicroorganisms (WDCM) and NMDC (National Microbiology Data Center), as national scientific data
center in China which is responsible for data collections, data sharing and data service in the field of

microbiology, are making efforts on provide public available data sharing fighting for COVID-19
including resources and efforts for SARS-CoV-2 data sharing and promoting global cooperation.

NMDC established the Novel Coronavirus National Science And Technology Resource Service Systemreleasing the first electron microscope picture of the virus and strain information at Jan 24, 2020. And
online data analysis tools were developed in the system including Phylogenetic analysis and display and
COVID-19 multidimensional analysis platform. A total of 237077 users from 176 countries and regions

worldwide made 10.27 million visits for the system, 834 thousand of which were by 28372 overseas
users.

Keywords. COVID-19, genome sequences, data sharing
1.

136

Information platform promoting global collaboration in microbialcommunity
Linhuan Wu1*
* Microbial Resource and Big Data Center, Institute of Microbiology,

1Chinese Academy of Sciences, Beijing 100101, China
Email:wulh@im.ac.cn

Summary: World Data Centre for Microorganisms (WDCM) is the most important physical resource
data platform of microorganisms worldwide, which was hosted by Institute of Microbiology, Chinese
Academy of Sciences including several databases - Culture Collections Information Worldwide, Global

Catalogue of Microorganisms, Analyzer of Bio-resource Citations and Reference Strain Catalogue,

GCM2.0 Type Strain Genome Database and gcMeta Microbiome Research Platform. GCM international

cooperation program was established for providing effective data support for all aspects of physicalresources of microorganisms. GCM2.0 Global Microbial Type Strain Genome and Microbiome

Sequencing Project is expected to complete genome sequencing of more than 10,000 microbial typestrains, and set up a globally authoritative reference database and data analysis platform of microbiome.
As the COVID-19 virus are widely spreading on the world, WDCM are making some efforts on fighting
for COVID-19.

As some culture collections are temporarily shut down, WDCM has host several

international online conference and workshop to support culture collections to work and fight the
epidemic.

Keywords. A list of up to 5 words describing the main concepts in the abstract, separated by commas
1.

137National Cross-Domain Activities on Research Data in Australia
and Connections with International Activities
Lesley Wyborn1*, Danny Kingsley 2, Adrian Burton3, Simon Cox4,
Steven McEachern5, Ginny Barbour6* Australian National University, 56 Mills Road, Acton, ACT, 2601, Australia
2Australian National University, 42A Linneaus Way, Acton ACT 2601
3
Australian Research Data Commons, 9 Liversidge Street, Acton, ACT, 2601, Australia
4
CSIRO Land and Water, Address, Private Bag 10, Clayton South, Victoria, 3169, Australia
5
Australian Data Archive, 146 Ellery Crescent, Acton, ACT, 2601, Australia
6
AOASG, UNSW Library, UNSW Sydney, NSW 2052, Australia
1

Email:lesley.wyborn@anu.edu.au

Summary. In the past decade, many data collections of value to Australian research have been madeaccessible as a result of significant, targeted investments in digital data infrastructures, data standards

and protocols, as well as new funding of physical infrastructures to store and process data. In Australia
the FAIR principles are becoming accepted, as is ‘Intelligent Openness’. But to date, investments in data

tend to be siloed with respect to discipline and sector, and it is now timely to focus on enabling crossdomain data infrastructures. To realise the full potential of data-intensive research, any new data

initiatives now must be done within the context and predicted capabilities of next generation
computational infrastructures, which by 2030 will be at exascale.

Keywords. FAIR data, metadata, transdisciplinary research, 2030 computing, research ecosystem

1. Introduction

academic projects as well as by initiatives ofOver the last decade, Australia has had many

government research agencies: it is also

persistence of publicly funded research data.

generated by industry and citizen science.

Reusable

becoming

date have mostly focussed on domains withfunding schemes. ‘Intelligent Openness’ isterabytes and petabytes, which need specificvalid reasons for withholding data or limiting its

geophysics, astronomy). Heterogenous, small

identifiers (PIDs, e.g., DOI, Handle, ORCiD, IGSN,

communities are not as well managed. Data is

implemented and there are agreed minimal

institutional/domain silos, and sector silos aregovernment data. Data of value to the research

capacities

successful data initiatives tobecoming easier to gain access to relevant data

ensure theBut there are noticeable gaps. Initiatives to

The Findable, Accessible, Interoperable and(FAIR)

principles

areaccepted and are now a requirement of somelarger volume collections, particularly those inrecognised, acknowledging that there may be

storagereuse.

scale

Metadata

Grant-ID,

etc)

are

metadata

standards

catalogues,

persistent

progressively(<1GB)

more

being

infrastructures
datasets

commonly(e.g.,

intheorganisedclimate,

long

tail

withinandpresent mainly due to varying capabilities/

community can be collected by publicly fundedstakeholder/community needs. Many onlineforresearch138

anddifferingdata delivery mechanisms are biased towards

International science agenda and supports thedata products accessible: rawer versions of[1], which is also the National Committee for

only making highly processed/ downscaled

National Committee for Data in Science (NCDiS)

datasets can be hard to access.

CODATA. The NCDIS is one of 22 National

In 2020 as physical infrastructures grow in

Committees (NCs) [2], many of which havecapacity (including both instruments to collectconnections toas well as computational power to process andUnions of the International Science Council

store data) traditional scientific paradigms are(ISC).

being transformed in varying ways, to produce

3. Australia

or use increasingly large or complex streams ofdata that can only be explored and analysed

Australian Government through the Nationalquestions and create greater impact, but at the

Collaborative Research Infrastructure Strategy.

same time, these developments are posing
that

need

to

ARDC seeks to enable the Australian researchbecommunity access to nationally significant,addressed to fully realise the undoubted

leading edge data intensive eInfrastructure,potential of data-intensive research. As theplatforms, skills and collections of high-quality

science paradigm changes, we need to ensure

data. It works with both the research sector andthat data storage and management systems arewith government and industry partners to build

developed in the context of 2030 sciencea coherent national and collaborative research

infrastructures and societal drivers (e.g., the UNdata commons. In 2020 it launched the National

Sustainable Development Goals).

Data Assets [3] program to leverage existingThis paper will outline some national cross-investments to ensure ongoing persistence,

domain activities in research data led by thesustainability

Australian Academy of Science (AAS) and thealsohighlight

some

key

Australian

4. Australianconnections to international data infrastructure

of

data

Participationin

In addition to the AAS connections with
CODATA

2. Australian Academy of Science

Australians are

Research Data Alliance, in the development ofscientific engagement. The AAS is currently

PID systems (e.g., DataCite, IGSN, RAiD) and indeveloping a paper on ‘Advancing Data

data standards bodies (e.g., W3C and OGC). The

Intensive Research in Australia’ that is exploring

key challenge for any participant in theseissues, challenges and opportunities affordedgroups is in being able to bring internationalby ‘Big Data’ in Australian research. The AAS
to

many

Australians in Working/ Interest Groups of thescientific advice and promotes international

linkages

ISC,

example, there is active engagement of

The AAS provides independent, authoritativestrongandconnected to international data initiatives. For(AAS)

providesstewardshipInternational Data Initiatives

initiatives.

also

and

collections of value to Australian research.

Australian Research Data Commons (ARDC): it
will

Data

The ARDC is an initiative supported by theallowing research to ask new and bigger

challenges

Research

Commonswith modern data analytic methods. This issignificant

the relevant International

perspectives back into national initiatives.

the

139

5. Conclusions

Australia is moving from a more siloed,discipline-

and/orsector-based

approachtowards a national research ecosystem for data
generation,

stewardshipmanagement,
that

also

curationrecognises

and

the

importance of linking data to any otherresearch outputs. Wherever possible, national

activities are integrated with international

initiatives. Any new data infrastructures willneed to be planned/developed in the context of2030 science infrastructures to ensure that

future generations of research can contribute to

the societal challenges of the next decade.

Acknowledgments. All authors of this paperaremembers

acknowledges

of

the

the

NCDiS

support

of

and

both

each
the

Academy of Science and of our homeinstitutions in undertaking this work.

References
1.

2.

3.

AAS National Committee for Data in
Science,https://www.science.org.au/supportingscience/national-committeesscience/national-committee-data-science[accessed September 2020].
AAS
National
Committees,
https://www.science.org.au/supportingscience/national-committees-science[accessed September 2020]
ARDC
National
Data
Assets,https://ardc.edu.au/collaborations/strategi
c-activities/national-data-assets/ [accessed
on: September 2020].

140

Disaster Rapid Damage Mapping with Remote Sensing:
benchmarking datasets and methods
Junshi Xia1**RIKEN, Chuo City, Tokyo, 103-0027, Japan
Email: junshi.xia@riken.jp

1

Summary. Disasters such as earthquakes, hurricanes, and flooding are responsible for large-scale

infrastructure damages and loss of human lives. Immediately after disaster strikes, one of the mostcritical and challenging tasks is accurately assessing the extent and severity of the disaster. Due to thedevelopment of deep convolutional neural networks (CNN), it is urgent to develop or construct thebenchmarking datasets for damage mapping. This work first brief introduces the xView2 datasets andthen presents a damage mapping framework using remote sensing imagery.
Keywords. Building damage mapping, xView2, Remote Sensing, CNN

1. Introduction

In this work, we will briefly introduce theWhen disaster strikes, accurate situationalxView2 datasets and which kinds of methods

critical to saving lives. Remote Sensing is one ofusing xView2 datasets.

damage mapping for natural disasters. The

2. xView2 datasets

to obtain building damage mapping [1].

damage mapping and covers 19 disaster events,

networks (CNN) architectures have shown

hurricanes, tornados, wildfires, and volcanic

can be used for building damage mapping

information and fast, effective response are
the most effective tools to provide rapid

xView2 dataset is designed for the building

main issue is how to develop a robust method

such

Recently, deep convolutional neuralastsunamis,earthquakes,

floods,significant advantages for damage mapping

eruptions [3]. All the datasets are derived fromstudies, the researchers often analyze thecontain the pairs of pre- and post-events with atsunami, earthquake. In this context, deep

footprint of the pre-event and the damage

the Worldview image of Maxar. The datasets

using remote sensing imaging [2]. In previous

size of 1024*1024 pixels. Besides, the building

damage mapping of one disaster type, such aslevels (i.e., no-damage, minor-damage, major-

learning techniques demand a vast amount ofdamage, and destroyed) are provided. The

training data for proper generalization ability.

objective of xView2 datasets is to provide not

This essential factor hampers the applicability of
CNN-based

frameworks

for

only the buildings but also the building damage

emergency

levels. Figure 1 has shown examples of xView2

response. Due to the rapid development of

datasetsmachine learning and the increasing number of(including

pre-event,post-event,building footprints, and damage grading).

natural disasters, a large number of remotesensing datasets, including all kinds of disasters,
are urgently needed.

141

Figure 1 Examples of xView2 datasets.

142

3. Building damage mapping

Figure 2 Unet for building footprint extraction
Figure 5 Results of building damage mapping

attention [4-5] for the building footprint

For the building damage mapping, we
proposed to use Siamese Unet. The two-stream
Unet shared the same weights. We used theprvious Unet for building segmentation to finetuning the Siamese Unet.The two-stream
features produced by the U-Net of pre-eventand post-event are stacked together, and thenfed to the final layer for the final semantic
segmentation. Due to the unbalanced samplesof four damage levels, we added the classspecific weights for the loss functions. Finally,
we got the F1 score of 0.7594 (see the results in
Figure 5).

we selected the three powerful encoders:

4. Conclusions

Figure 3 Results of building footprint
extraction

First, we use the widely used Unet withextraction (as shown in Figure 2). In this case,

Resnext50_32x4d, Densenet, Efficientnet B7,

In this paper, we present the ensemble of SiamUNetself-attention for building segmentationand damage mapping. Experinmental results
have shown the proposed model accomplishes
both damage classification and building
segmentation more accurately than otherapproaches with the xView2 dataset

and ensemble their outputs. The F1 score is
0.8613 (as shown in Figure 3), which is better
than the individual ones of 0.8562.

Acknowledgments. The authors thank the

Defense Innovation Unit to provide xView2
dataset.

References

Figure 4 Siamese Unet for building damage

1.

M. Pesaresi, A. Gerhardinger, and F. Haag,
Rapid damage assessment of built-up
structures using vhr satellite data intsunami-affected areas, International
Journal of Remote Sensing, vol. 28, no. 1314, pp. 3013–3036, 2007
2. Y. Bai, E. Mas, and S. Koshimura, Towards
operational
satellite-based
damagemapping using u-net convolutional

mapping

143

3.

4.

network: A case study of 2011 tohoku
earthquake-tsunami, Remote Sensing, vol.
10, no. 10, 2018

R. Gupta et al. Creating xbd: A dataset forassessing building damage from satellite
imagery. Proceedings of the IEEE
Conference on Computer Vision and
Pattern Recognition Workshops, June 2019

O. Oktay et al, Attention u-net: Learningwhere to look for the pancreas, CoRR, vol.
abs/1804.03999, 2018
5. S. Xie, R. Girshick, P. Dollar, Z. Tu, and K. He,
Aggregated residual transformations fordeep neural networks, in 2017 IEEE
Conference on Computer Vision and
Pattern Recognition (CVPR), July 2017, pp.
5987– 5995, 2017144

Official Micro Data, Causal Inference and Evidence-Based
Policy Making
Junchao Zhang1** Center for Social Data Structuring, DS, ROIS

1

Email: zhang@ism.ac.jp

Summary. Detailed personal information (e.g., exact date of birth, community location, etc.) is very

important in identifying causal effects of policies and programs. However, these information are

generally not collected or excluded in public use micro data due to privacy reasons . Without theseinformation, we cannot correctly define the treatment variable indicating whether one was exposed toa particular policy or program. Using high-quality official micro data, this study discusses the

problems in causal inference and offers useful evidence for policy making.
Keywords. micro data, causal inference, EBPM

1. Introduction

reflect correlation rather than causality. As an

making (EBPM) has been of great interest tonaturalIn the last decade, evidence-based policy

alternative method, policy researchers exploit

policy makers and academics. Before EBPM

weather events, natural disasters, etc.) that

do not know whether they are doing the rightgroups for causal inference.

governmental resources, how to quantitatively

information(exact date of birth, community

programs is a fundamental challenge.

causal effects. Without these information, we

trial (RCT) is considered as the gold standard.

whether one was exposed to a specific policy

subjects into treatment and control groups,

not collected or excluded in public use micro

treatment group fares better or worse whenIn this study, we use high-quality official

experiments(e.g.

become widespread, the policy makers actuallyoccasionallythings. To effectively and sufficiently allocate

Second,assignpolicypeoplechanges,

into

different

detailed

personal

evaluate the causal effects of policies andlocation, etc.) are very important in identifying

In hard sciences, a randomized controlled

cannot define the treatment variable indicating

Using RCT, researchers randomly assign test

intervention. These information are generally

and can effectively determine whether thedata due to privacy reasons.

exposed to the intervention. Researchers canmicro data, with huge sample size and detailed

even with a simple OLS estimand if the trial is

study. The huge sample size allows us to

However, we practically meet the following

when our identification strategies rely on rareevaluate the average treatment effect (ATE)

personal information, to perform a causal

properly randomized.

explore people’s heterogenous response evenissues in policy studies. First, RCT may not be aevents. Heterogenous effects may help policy

estimates in observational studies probably

population and improve policy design.

makersfeasible option because of law and ethics. The

145

to

targetpotential

treatmentdifferent across birth parity and over time

2. Modelpassed since last child birth.

According to data restriction, previous

Suppose we are interested in the effect ofnumber of children on maternal labor market

studies can only estimate on unconditioned

specified as follows:

allow us to perform IV estimation using

outcome.

Abenchmarklinear𝑦𝑦𝑖𝑖 = 𝛽𝛽0 + 𝛽𝛽1 𝑥𝑥𝑖𝑖 + 𝑢𝑢𝑖𝑖

model

sample. Huge sample size of official micro dataisdifferent

sub-samples

todetectpeople’svariabledifferent decision-makings. According to Table

participation. 𝛽𝛽1 is the coefficient of interest,

women’s labor supply are only significant for

probably not satisfied as number of children

4. Conclusions

where 𝑦𝑦𝑖𝑖 isindicatingabinarymarriedoutcomewomen’slaborcapturing the effect of number of children 𝑥𝑥𝑖𝑖 .
However,arenotcondition

𝐶𝐶𝐶𝐶𝐶𝐶(𝑥𝑥𝑖𝑖 , 𝑢𝑢𝑖𝑖 ) = 0

randomly

assigned

to1, negative effects of number of children on

force

first parity.

iswomen.

This study estimates the causality between

Unobserved factors( 𝑢𝑢𝑖𝑖 ) such as preferenceschildren and maternal labor supply. We findregarding childbearing or career ambition

that the effect of fertility varies substantiallycould be correlated with women’s decision

with the time elapsed since the last childbirth.

making. The OLS estimand will be no longer

In contrast to conventional wisdom, the effectunbiased and consistent. 𝛽𝛽1 reflects simpleof fertility on maternal labor suppy is notcorrelation rather than causality.

monotonically decreasing in the number ofInstead, we use a natural experiment thatchildren. These results imply that policy-

induces exogenous variation(𝑧𝑧𝑖𝑖 ) in the number

makers should design accurate policies toof children to estimate the causality. 𝑧𝑧𝑖𝑖

target those who will benefit more fromindicates whether the child is a twin.
𝛽𝛽̂𝐼𝐼𝐼𝐼 = (𝒛𝒛ʹ 𝒙𝒙)−1 𝒛𝒛ʹ 𝒚𝒚policies or programs.

where 𝒛𝒛, 𝒙𝒙, 𝒚𝒚 are 𝑁𝑁 × 1 vectors. Mothers gave

Acknowledgments. This work is supported bybirth to twins and non-twins naturally have

JSPS KAKENHI Grant Number 19K13710.

identification strategy rely on very huge

References

different

numbersofchildren.

Thissample size as twin birth rates are only about 9

1.

to 20 per 1000 deliveries across different

countries. To explore the heterogeneity, we
also estimate the IV estimand by different sub-samples(birth parity and time passed since last2.

child birth).

3. Results

We use Japanese census to estimate the IV3.

estimates. IV estimates are reported in Table 1,
OLS

estimates

are

also

reportedforcomparison. OLS and IV estimates are quite

146

Angrist, J. D., & Evans, W. N., Children andtheir parents’ labor supply: Evidence fromexogenous variation in family size.
American Economic Review, 88(3), 450–
477, 1998
Li, H., Zhang, J., & Zhu, Y., The quantityquality trade-off of children in adeveloping country: Identification using
Chinese twins. Demography, 45(1), 223–
243, 2008
Rosenzweig, M. R., & Wolpin, K. I., Testing
the quantity-quality fertility model: The
use of twins as a natural experiment.
Econometrica, 48(1), 227–240, 1980

Table 1. Effects of number of children on maternal labor supply by birth parity and time since last child birth.
Unconditioned

1
2

No more than 3
years

No more than 1 year

No more than 3months

OLS

IV

OLS

IV

OLS

IV

OLS

IV

Birth parity:-0.0030.000-0.005-0.047-0.027-0.031-0.056

0.004

Birth parity:-0.027-0.002

0.012

0.004

0.015

0.009

-0.008-0.001Birth parity:-0.037

0.050

0.002

0.066

0.012

0.026

0.010

0.072

3

***
***
***

**

***

***

***

**

***
***
**

***

***
***

Notes: All specifications control for age, age squared, education attainment, husband’s education attainment, husband’s labor force
participation, co-residence with elder parents, and prefecture dummies. In all panels, upper bounds on the number of children are notimposed. *** p<0.01, **p<0.05, * p<0.1. Robust standard errors are not reported because of space constraint.

147

Introduction for WDS ECR Network
Lianchong Zhang 1*, Jesse Xiao 2* Aerospace Information Research Institute of the Chinese Academy of Sciences
2
The University of Hong Kong
Email: zhanglc@radi.ac.cn

1

Summary. The World Data System (WDS) is a body of the International Science Council (ISC) thathelps to coordinate and support research data centres and data services worldwide. WDS activities
span all disciplines, and are designed to ensure that research data are preserved and openly

disseminated to safeguard the integrity of science. WDS is also concerned with the availability toscientists and policymakers of the critical information necessary to manage Earth’s resources wisely.
Recognizing the important role of Early Career Researchers in developing and promoting best practicein data management, data analysis and data sharing, WDS establishes a Network of Early Career

Researchers and Scientists (ECRs), to help foster better communication among ECRs, and to design
activities targeting their interests and concerns. WDS-ECR Network members will have an opportunity
to participate in regular webinars and yearly training to develop their skills and share their knowledge

related to the key areas of research data management. The Network will facilitate connections
between the members and more experienced global leaders within WDS and beyond, as well as
regularly profile its members in its quarterly newsletters and other publications.
Keywords.
1.

148

Modelling the Suitability of Parcel Pick-up Lockers Using theMulti-Source Open Data
Zilai Zheng1*, Takehiro Morimoto2* Graduate School of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai,
Tsukuba, Ibaraki, 305-0006, Japan
2
Faculty of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki,305-0006, Japan
Email: zhengzilai23@gmail.com
1

Summary. Parcel pickup locker (PPL) is an effective way of solving the last-mile problem and becomesa hot topic in logistics geography. In this study, a GIS-based multivariate logistic regression(MLR) model

was developed to modelling the suitability of PPLs using the multi-source data, such as the point-ofinterest(POI) data from a popular daily navigation application named Gaode and the road data fromcrowdsourcing project OpenStreetMap(OSM). A spatial database of 27 potential predictor variables wasconstructed under Geographical Information System (GIS) environment. Eight variables were chosen by

the forward-stepwise selection method of the model, and the most crucial variable was the distance tothe nearest residential quarter. This study proved that conducting the GIS-based MLR model using

multi-source open data to model the suitability of the facility is efficient and with high accuracy.
Furthermore, it gives the data support for decision-makers to decide the new location of PPLs.

Keywords. parcel pick-up locker(PPL), multivariate logistic regression model(MLR), multi-source, point-of-interest(POI),Geographical Information System (GIS)

1. Introduction

PPLs in Guangzhou city, where the amount ofcomplicated issue in the optimization topic of

Suitability problems can be considered as astudies on PPLs focused only on the facility or

regression is regarded as an efficient and

The location planning of PPL is the mostparcel ranks the first in China from 2014 to 2019.

PPL. From the literature review, the micro-scalebinary classification problem, and logistic

location selection in a small area and have notstraightforward way in ML to solve this problem.

[1]. The macro-scale studies focused mainly on

2. Materials and methods

facilities and influencing factors. Few studies

resolution GIS data was collected from different

pointed out how to find suitable places for PPLs

A comprehensive range of detailed, high-the relationships between the number ofopen sources, including topographic data (30have conducted location analyses from them), population density raster data (100 m), land

pixel scale to find the relationship with theprice, road networks, and point of interest(POI)

factors.

data. Comparing the POI data extracted from

This study attempted to conduct a GIS-

Gaode Map [2] by the Python code and thatbased machine learning(ML) model using

downloaded from OSM [3], the data frommulti-source data to model the suitability of149

Gaode Map is more detailed and with higher

nearest bus stop, and the density of commercial

quality. After the data cleaning, the POI databuildings.

layers of the PPL, bus stop, metro exit, parking
lot, commercial office building, commercial-

3.2 The performance of the model

dormitory and community centre contain 679,

evaluated

residential building, residential quarter, villa,

The performance of the MLR model was usually

6778, 808, 9882, 5658, 825, 7619, 280, 2031 and

by

their

discrimination

andcalibration [4]. As shown in table 1, the353 features, respectively. The analysis ofclassification accuracy of the training data in thecalculate the distance from each cell in theminimal, and the AUC value greater than 0.9

Euclidean distance in Esri’s ArcMap was used tomodel was very high. The model’s bias wasraster data to the closest POI, different types of

was considered outstanding [5].

roads, and water area. The kernel density

validation was conducted with the test data,

function was employed to calculate the amountand the performance of the model was slightlyof the feature in one cell. A spatial database of27

potential

predictor

variables

The model

better than the training data. It can be seen thatwas

the suitability model with these coefficients isconstructed at the unified resolution of 100 m.

good-fit the real data.

The methodology is shown in Figure 1.

Table 1. The evaluation of the suitability model
Data

Training
Test

Discrimination
Precision

Recall

Accur
a-cy

90.6%

86.5%

88.2%

FMeasu
re
88.5%

92.0%

89.8%

90.8%

90.9%

Calibration
AUC
0.95
0.96

The results show that 8.7% of the total area in

Guangzhou city is a suitable area for PPLs,
contains 91% of the amount of PPLs.

4. Conclusions

Figure 1. The methodology of the study

This study proved that conducting the GIS-

3. Results

based MLR model using multi-source open

3.1 Selection the significant variables

data to model the suitability of the facility is

the MLR model to choose some significant (p-reference for decision-makers to decide themodel and to make the model optimizationMap, a daily navigation map, is more detailed

The forward-stepwise method was applied inefficient and with high accuracy. It gives a data

value<0.005) predictor variables for the final

new location of PPLs. The POI data of the Gaodewith the least-squares criteria. Eight variables

and higher quality than the crowdsourced OSM.

the Wald-value of each variable in the final

Referenceswere selected by the MLR model. According tomodel, the most significant factor was the1.

distance to the nearest residential quarter,

followed by the land price, the distance to the150

Zheng, Z., Morimoto, T., Murayama, Y.,
Optimal Location Analysis of Delivery
Parcel-Pickup Points Using AHP and
Network Huff Model: A Case Study of

2.
3.

4.

Shiweitang Sub-District in Guangzhou City,
China. ISPRS International Journal of GeoInformation, 9(4), 193,2020
Gaode
Map,
https://ditu.amap.com/[accessed on: August 2020]
OSM
Ontology,
http://wiki.openstreetmap.org/wiki/OSMo
nto [accessed on: August 2020]

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Pearce, J., Ferrier, S, Evaluating thepredictive performance of habitat models
developed using logistic regression.
Ecological modelling, 133(3), 225-245,2000
Hosmer, DW., Lemeshow, S., Applied
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