Activity Report from International Strategic Advisor
POLAR ENVIRONMENTAL DATA SCIENCE CENTRE (PEDSC)

20th November 2023 – 18th December 2023

Prepared For: Joint Support – Centre for Data Science Research (ROIS-DS)

Prepared By: KASSIM S. MWITONDI (PhD)

January 2024

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Table of Contents
List of Figures 2
Executive Summary .............................................................................................................................. 3
Section 1

Introduction ................................................................................................................. 4

Section 1.1:

Polar Environment Data Science Centre (PEDSC) ..................................................... 5

Section 1.1.1:

Data deluge at PEDSC ................................................................................................ 6

Section 1.1.2:

Staffing Constraints..................................................................................................... 6

Section 1.2:

Centre for Data Assimilation Research & Applications (CDARA) ............................. 7

Section 1.3:

Centre for Open Data in the Humanities (CODH)....................................................... 7

Section 2

Recommendations ....................................................................................................... 7

Section 2.1:

Identifying Strengths and Opportunities ...................................................................... 9

Section 2.2:

Industrial Placement schemes for students ................................................................ 10

Section 2.3:

Cloud-based Data Sharing & Modelling ................................................................... 10

Section 2.4:

Harnessing untapped internal potential ..................................................................... 11

Section 2.5:

International Collaboration and Benchmarking ......................................................... 11

Section 2.5.1:

Forging Data Intensive Partnerships ......................................................................... 12

Section 2.5.2:

Some Benchmark Institutions ................................................................................... 13

Section 3

Concluding Remarks ................................................................................................. 14

References

16

List of Figures
Figure 1: ROIS’ structure for data-driven and knowledge generation initiatives .................................. 4
Figure 2: Integrated imagery and chemical composition data for meteorites and ice cores ................... 8
Figure 3: Potential cross-centre data-intensive activities .................................................................... 10
Figure 4: Antarctica monitoring stations by country. .......................................................................... 12Page 2 of 17

Executive Summary
This report is prepared for the President of Research Organization of Information and Systems(ROIS) and the Director of the Joint Support-Centre for Data Science Research (ROIS-DS). It
outlines a number of advisory activities that the author accomplished as an invited visitor to
ROIS-DS’ Polar Environment Data Science Centre (PEDSC) at the Tachikawa campus
between 20th Nov and 10th Dec 2023. It provides inputs to the management, administration andfuture direction of PEDSC/ROIS-DS focusing on the current evaluation and how it potentiallyleads to future research directions within the research network. Based at the PEDSC, the author
had interactive sessions and gained familiarity with research activities at the following centres.
1) Polar Environment Data Science Centre (PEDSC)
2) Centre for Data Assimilation Research and Applications (CDARA)
3) Centre for Open Data in the Humanities (CODH)
4) Database Centre for Life Science (DBCLS)
5) Centre for Social Data Structuring (CSDS)
The main objective of those sessions was to gain insights into the research activities at eachcentre, assess inherent potential within each, evaluate the current status and makerecommendations for future directions on ROIS-DS’ enhancement of data science applications
across the network. This objective leads to the main recommendation made by this report namely, aligning with global trends of data-intensive research. The recommendation is basedon a relevant SWOT analysis (Stewart & Benepe, 1965) from an international point of view.
It highlights key steps to be taken to consolidate existing international collaborations that isalready taking place across within the network and identifies PEDSC, CDARA, CODH,
DBCLS and CSDS as non-orthogonal drivers of data-driven research transformation. The
report also identifies a number of benchmarking institutions for PEDSC’s unifying role in the
ROIS-DS network, based on which it makes the following specific recommendations.
1) Industrial placement schemes for higher education students: This will help address
time-consuming issues relating to data-deluge challenges such as data harmonisation.
The scheme is also a great way for imparting knowledge on young researchers and it
helps alleviate the impact of data science skills that the centres face.
2) Cloud-based data sharing: Inspired by the AMIDER database, currently being
developed at the PEDSC, this initiative provides a potential for sharing structured andunstructured data in an integrated cloud and Internet of Things (IoT) environment.
3) Tapping into the CDARA potential: It was noted CDARA’s modelling potential wasn’t
fully utilised across ROIS-DS and that in combination with recommendations 1 and 2
the network can achieve a well-organised environment for sharing tools, data and skills.
4) ROIS-DS research centres to draw lessons from benchmarking institutions that conduct
related research and adapt novel technologies from data-intensive practitioners.
The report confines itself to the foregoing recommendations, providing generic comments
about ROIS-DS’ management and future plans. That means, the report may be used, in the
future, as input to any relevant work with full access to ROIS-DS’ strategic and/or budget plans.

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Section 1 Introduction
The report outlines a number of advisory activities that the author accomplished as an invited
visitor to ROIS-DS’ Polar Environment Data Science Centre (PEDSC) at the Tachikawa
campus (20th Nov-10th Dec 2023). The visit was planned to coincide with the International
Symposium on Data Science 2023 (DSWS-2023): Building an Open-Data Collaborative
Network in the Asia-Oceania Area, that took place in Tokyo from 11th to 15th December 2023
on which the author was a member of its International Advisory Committee (IAC). The
symposium was part of PEDSC’s – hence ROIS-DS’ continuum of data science related
research activities – a key motivation for its establishment (ROIS-DS, 2016).

Figure 1: ROIS’ structure for data-driven and knowledge generation initiatives
Figure 1 graphically illustrates ROIS’ as a research hub for different academic societies inJapan, in which it can be seen that the following five national institutes fall under ROIS’s remit:
1) National Institute of Polar Research (NIPR)
2) National Institute of Informatics (NII)3) Institute of Statistical Mathematics (ISM)
4) National Institute of Genetics (NIG)
5) Joint Support-Centre for Data Science Research (ROIS-DS)
ROIS-DS’s main objective is to support academic and industrial research, including that ofstudents, to conduct data-driven scientific research. The report is prepared for the President ofResearch Organization of Information and Systems (ROIS) and the Director of the Joint
Support-Centre for Data Science Research (ROIS-DS). It outlines a number of advisory
activities that the author accomplished as an invited visitor to ROIS-DS’ Polar Environment
Data Science Centre (PEDSC) at the Tachikawa campus between 20th Nov and 10th Dec 2023.
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It is designed to provide inputs to the management, administration and future direction of the
network, focusing on the current evaluation and how it potentially leads to the network’s future
research directions. It particularly emphasises research management, education andinternational collaboration with external research communities, and it makes logistical andstrategic recommendations. During the time spent at the Tachikawa campus, the author hadinteractive sessions with the following research centres, associated with the institutes above.
1) Polar Environment Data Science Centre (PEDSC)
2) Centre for Data Assimilation Research and Applications (CDARA)
3) Centre for Open Data in the Humanities (CODH)
4) Database Centre for Life Science (DBCLS)
5) Centre for Social Data Structuring (CSDS)
The main objective of those sessions was to gain understanding of the research activities at
each centre, assess inherent potential within each, evaluate the current status and makerecommendations for ROIS-DS’ future directions on data science applications across the
network. This objective leads to the main recommendation made by this report - namely,aligning with global trends of data-intensive research. The recommendation is based on arelevant SWOT analysis (Stewart & Benepe, 1965) from an international point of view.
It highlights key steps to be taken to consolidate existing international collaborations that isalready taking place across within the network and identifies PEDSC, CDARA, CODH,
DBCLS and CSDS as non-orthogonal drivers of data-driven research transformation. The
report also identifies a number of benchmarking institutions for PEDSC’s unifying role in the
ROIS-DS network, based on which it makes the following specific recommendations.
1) Industrial placement schemes for higher education students: This will help address
time-consuming issues relating to data-deluge challenges such as data harmonisation.
The scheme is also a great way for imparting knowledge on young researchers and it
helps alleviate the impact of data science skills that the centres face.
2) Cloud-based data sharing: Inspired by the AMIDER database, currently being
developed at the PEDSC, this initiative provides a potential for sharing structured andunstructured data in an integrated cloud and Internet of Things (IoT) environment.
3) Tapping into the CDARA potential: It was noted CDARA’s modelling potential wasn’t
fully utilised across ROIS-DS and that in combination with recommendations 1 and 2
the network can achieve a well-organised environment for sharing tools, data and skills.
4) ROIS-DS research centres to draw lessons from benchmarking institutions that conduct
related research and adapt novel technologies from data-intensive practitioners.
The next exposition provides an outline of the interactions the visitor had with each of theforegoing research centres. It is worth noting that research activities at each of the centres are
far broader than those addressed in this report, which are confined to specific research activities
that were either discussed, presented or demonstrated during the visits.

Section 1.1:

Polar Environment Data Science Centre (PEDSC)

The Polar Environment Data Science Centre (PEDSC) promotes access and sharing of thescientific data that is routinely collected via scientific observations and research activities inPage 5 of 17

the Antarctic and arctic regions by the polar science research community. The PEDSC wasformally launched in 2017 and it has since worked in close collaboration with universities andother external communities, based on the standard academic ranking staffing structure. As partof ROIS-DS, the centre supports archiving, processing, analysing and sharing of polar
environment data collected by scientific missions and observatories in the Antarctic and Arctic
regions. The following are the visitor’s comments on PEDSC and related centres.

Section 1.1.1:

Data deluge at PEDSC

The author visited the space and upper atmospheric sciences, the meteorology and glaciology
and the geosciences centres. While those three constituted only part of a much larger data
storage capacity, it was evident that the PEDSC generates and has access to an awful lot of
data, far beyond the centre’s capacity to process it. For instance, the upper atmospheric sciences
centre receives live data transmission from NIPR’s monitoring centre, in the Antarctica and at
the PEDSC head office there is live streaming of seismological data. Apparently, PEDSC is
not sufficiently staffed to optimally manage and utilise the data inflow. The author understands
that such data is readily available to scientists and researchers across Japan and to allcollaborating cold region partners. There is potential for that practice to be extended to include
data assimilation applications run from within ROIS-DS, using for instance, postgraduate
students affiliated with the Institute of Statistical Mathematics (ISM).
Other examples include the meteorite storage room that has over 20,000 stored meteorites,
collected from the Antarctica and the ice cores at the low temperature room where samples
collected mainly from the Antarctica are stored. Both the meteorites and the ice cores containhighly useful chemical composition data, with potentially highly useful information, much ofwhich remains buried in the samples. For instance, ice cores contain air bubbles some datingback to more than 10,000 years, which may be quite informative about current surface
temperature levels (Buizert, 2021). Classification of meteorites presents another challenge andopportunity as they are currently identified and classified after complex and slow chemical
analyses processes. The centre currently stores them into three categories: achondrites, ordinarychondrites, and carbonaceous chondrites. One way of enhancing performance at the meteorites
and the ice cores centres would be to automate analytical processes by adopting Machine
Learning (ML) techniques, for instance. In discussions with staff at both centres, the need forintensive data analysis skills was evident but so were staffing constraints.

Section 1.1.2:

Staffing Constraints

Staff at both centres were fully aware of the potentially useful data that remains buried in thesamples but, much as they would have liked to get out and share data assimilation results, they
were seriously constrained on staffing. Different options of addressing this issue werediscussed – including deploying Machine Learning (ML) models for automatic detection offeatures in samples. It was noted that some of these ideas had previously been explored. At themeteorites centre, for instance, the author was advised that a third member of staff, who wasaway on a scientific mission in Antarctica during that visit, had been very keen in deploying
such techniques. Both centres expressed great eagerness to pursue the idea further.
Recommendations on how the institute can proceed in this respect are made in Section 2.

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Section 1.2: Centre for Data Assimilation Research & Applications (CDARA)The visit to CDARA highlighted both challenges and opportunities as one would normallyencounter in the data modelling world – that of merging computer generated information andreal-life observations. CDARA adopts the historical application patterns by focusing on
atmospheric and weather research (Zhang et. al, 2020), as evidenced by the presentation at thecentre and at the Centre for Open Data in the Humanities (CODH). In both presentations, theunderlying problem was detection of natural structures in data, for which finite mixturesmodels (McLachlan and Peel, 2000) were applied. This section highlights the key points thatemerged from the discussions at CDARA, the CODH application is discussed in Section 1.3:
Apparently, CDARA’s major strength lies in its modelling skills capacity – particularly theapplication of statistical and data science tools and techniques that can readily be adapted toother centres. While there is a well-established working relationship between CDARA and
CODH and between CDARA and the National Institute of Genetics (NIG), it looks like suchcollaboration has not been extended to other centres. Given the amount of data that PEDSC
generates, a sustained relationship with CDARA would lead to profound scientific researchoutcomes. The same can be said about links to DBCLS and CSDS. Section 2 makes
recommendations on how CDARA can significantly contribute to other centres.

Section 1.3:

Centre for Open Data in the Humanities (CODH)

The CODH seeks to promote data-driven research and external collaboration in various areasof humanity research. It is embarking upon developing science-driven, digitised humanities
research across organizations within Japan and beyond. The centre promotes open access todata providing access to humanities data using state-of-the-art technologies and modelling
techniques. It was quite intriguing to see the large volume of real-life applications CODH has
its eyes on, which is great in promoting interdisciplinary research. However, that also implies
that the centre requires a diverse range of skills and staffing not only of data professionals, butalso of relevant underlying domain knowledge areas. CODH’s current direction is in deepening
Machine Learning (ML) applications with an increased Citizen Science (CS) content, and they
have been looking for partners both in terms of technology and problems space (in humanities).
The CODH staff showcased some of their on-going research work and three research
application tools stood out – historical Big Data (BGD), citizen science and open access. The
importance of harnessing the vast wealth of historical data alongside modern data sets cannot
be overemphasised. Recorded weather data dating back to the 17th century unlocks the potentialof historical data for contemporary applications. The data comes in imagery format and has tobe digitised and converted to interpretable attributes. It provides scope for validation with datafrom other sources – e.g., from ice cores (see Section 1.1: Through these applications the centre
seeks to deliver humanities knowledge within Japan and beyond through collaborative
initiatives. CODH research aspirations fit nicely with Sustainable Development Goals (SDG)
and which lead to Big Data Modelling of Sustainable Development Goals (BDSDG)(Mwitondi, K. and Munyakazi, I. and Gatsheni, B., 2020). CODH’s activities align with thoseof the International Research Centre for Sustainable Development Goals (CBAS, 2024).

Section 2 Recommendations
The report makes a number of general and specific recommendations based on the interactive
conversations the author had with scientists and researchers at different research centres. The
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recommendations hinge on how ROIS-DS can optimally utilise the resources to harness thepotential that is currently buried in different data archives at the centre. One obvious example
that emerged was the on-going development of the AMIDER database at the PEDSC. The
development, still its initial stages, has the potential to address some of the challenges otherdivisions of the institute faces. For instance, the meteorite storage room currently stores both
imagery and chemical composition data in separate filing systems. While the files areappropriately coded for unambiguous identification of each meteorite, matching each image toits chemical composition is a tedious and lengthy process. Apparently, the same applies to theice cores data. Both data types are crucial in classification of the meteorites and indeed in any
other relevant analyses. Getting the files linked together will provide not only easy access, butalso a much better way of validating analyses based on either data type.

Figure 2: Integrated imagery and chemical composition data for meteorites and ice cores
Figure 2 graphically illustrates integration of data from the meteorites and ice cores centres aspart of piloting the AMIDER database. The recommendation is for the AMIDER development
to embed a pilot project on only the meteorite centre, as there is already development in that
direction. Further, imagery and chemical composition data for meteorites is already available
and well-documented, which minimises both back and front-end development efforts.
Upscaling the project to include other centres listed in Figure 2, as well as providing access toexternal collaborators will be much easier once the pilot is successful.
The interactive meetings we had with staff at different research centres revealed that there wasunlimited potential for more cross-centre engagement across the institute. In many cases it wasnoted that there were constraints on staff to carry out what are, sometimes, basic but timeconsuming tasks – for instance, mapping thousands of images to their chemical compositions.
The report therefore makes specific recommendations as outlined below.

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Section 2.1:

Identifying Strengths and Opportunities

Timeliness, accuracy and comprehensiveness in uncovering knowledge hidden in the data iswhat would, typically, determine the usefulness of the data and related project initiatives.
However, like in all fields, data generation capacity far outweighs its processing capacity andas such ROIS-DS needs to take a multi-dimensional assessment of the way its centres generate
and utilise data. Table 1 provides a synopsis of a standard SWOT analysis.

Table 1: Selected SWOT analyses aspects of ROIS-DS’ data challenges and opportunities
Among the institute’s greatest strengths is its access to data – PEDSC being a typical example,
wealth of modelling skills at CDARA, links to Japanese Universities and establishedinternational collaborations. These strengths tie in nicely with the opportunities they create,such as the ease with which research centres can adapt new data technologies. A typicalexample are the meteorites and ice cores centres that already have access and continue to amassmassive amounts of data from Antarctica. Likewise, near-real-time data from the monitoring
station and the seismological observatories present great opportunities.
It is evident, in many cases, that ROIS-DS’ well-established data capturing infrastructure canachieve greater results than it does now. There is also the risk of duplication of data repositories
and so it is imperative to ensure that key steps are taken to consolidate research data initiatives
to avoid internal competition. Figure 3 exhibits a couple of examples of cross-institutional andcross-centre collaborations. Most tools and skills applied at ISM are readily applicable at otherinstitutes, such as NIPR. ROIS-DS stands to benefit from promoting such collaborations.
Obviously, such engagement requires planning, managing and funding and can be achieved byaddressing a major bottleneck that became evident at each centre – i.e., constrained staffing.

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Figure 3: Potential cross-centre data-intensive activities
Clearly, addressing staffing issues can be challenging but the institute can create aninterdisciplinary working environment that draws knowledge and skills from its surrounding
environment – such as industrial placement, enhancing inter-research centres potential andadapting new technologies to existing data harnessing infrastructure, as summarised below.

Section 2.2:

Industrial Placement schemes for students

One way of dealing with data deluge when faced with time-consuming and repetitive tasks,
such as mapping thousands of images to their chemical compositions, would be to engage
short-term, non-contract staff. Industrial placement schemes are known for providing not only
good value for money but also a great way for imparting knowledge on young scientists andresearchers. Further, evidence from literature suggests a relationship between knowledge
sharing and organizational innovation performance (Zhao, Jiang, & Peng, 2020).

Section 2.3: Cloud-based Data Sharing & Modelling
Using the meteorites example, images captured can instantly be shared with AMIDER via
cloud. Again, this will still require manual intervention – but likely to be minimal, as it may
only require linking camera tools to cloud and an AMIDER connection to the cloud. Potential
future developments of this scheme may include Internet of Things (IoT) applications forsharing images with a host computer and/or cloud. Such applications may also be feasible using
IoT sensors fitted to the chemical analysing equipment for instant sharing analyses with a hostcomputer and/or cloud. It must be emphasised that such a process is initially costly, as it may

Page 10 of 17require adaptation to the data gathering tools, but it is quite cost-effective in the long-run, andit will generate massive datasets that are readily available for scientific analysis.

Section 2.4:

Harnessing untapped internal potential

Another way of striking a balance between NIPR’s data deluge and constrained resources is totap into internal potential, sharing data, resources and skills. For instance, CDARA has
developed several machine learning algorithms for different applications, and these can readily
be adapted to applications in other centres. The main challenge here is how to match problem
situations with potential solutions. Working culture in individual organisations tend to create
silos that are hard to break, unless doors are kept open for sustainable and regular in and out
flows of ideas. Table 2 illustrates different approaches that ROIS-DS can adopt to ensure thatknowledge gaps and potential solutions across the institute are identified in a timely manner.
Initiative
Monthly cross-centre seminars

Description
Many seminars are already taking place at
research-centre levels. This can be enhanced by▪ Regular/monthly ROIS-DS-wide seminars.
▪ Themes are agreed annually by all centres.
▪ Managed advertised months in advance.
▪ Speakers drawn from the ROIS-DS family.
Interdisciplinary research projects
Cross-centre seminars potentially lead tointerdisciplinary research ideas. Interdisciplinary
research projects may range from application ofresearch grants to PhD supervision. There isevidence that some of this is already happening.
Cross-centre peer review
A formalised ROIS-DS-wide peer review ofgrants applications and journal submissions will
make it possible for research centres to share vital
data and insights into potential collaborations.
Table 2: Tapping into ROIS-DS’ internal resources potential.
The initiatives in Table 2 are not exhaustive but they span across the entire spectrum of
collaborative interdisciplinary research, and they can be perceived as one major initiative.
Managing the initiative across the institute may place further staffing strain on already strained
research centres. It is imperative for such a cost to be incurred as it potentially leads toefficiency in utilisation of scarce resources – particularly skills and domain knowledge.

Section 2.5:

International Collaboration and Benchmarking

All research centres that were visited exhibited great non-orthogonality in their research
activities. There are many examples to illustrate this but suffice it to say that natural andphysical phenomena that are investigated at each of the centres aren’t happening in isolation.
It is therefore imperative for ROIS-DS to see itself in the mirror of some “benchmarking
institutions” and to closely collaborate with international institutions that are embarking uponactivities that align with, affect or are affected by, its own research activities.

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Figure 4: Antarctica monitoring stations by country.
To get an insight into the impact of non-orthogonality in data analysis consider the different
locations in Figure 4 (Snels, M. and Colao, F. and Cairo, F. and Shuli, I. and Scoccione, A. and
De Muro, M. and Pitts, M. and Poole, L. and Di Liberto, L., 2021) which are managed bydifferent countries. It is reasonable to assume that as the thickness of the ice layers vary with
distance from the Amundsen-Scott South Pole Station, say, the chemical compositions of the
air bubbles contained in the samples collected may differ. The only way to address thesevariations would be to validate them against samples collected at other stations. The report
proposes the following modes of collaboration based on benchmark institutions and synergies.

Section 2.5.1:

Forging Data Intensive Partnerships

Given its position in the ROIS network, PEDSC plays a unifying role when it comes to data
generation and consumption among all centres. It is imperative that PEDSC develops sustained
partnerships with data-intensive institutions. Partnerships with data practitioners should bedesigned to enhance existing capacities within the centre and tapping into potentials outside it.
There are already established collaborative lines that PEDSC can tap into, including the two
affiliates of the International Science Council (ISC, 2024) – i.e., the Committee on Data
(CODATA, 2024) and the World Data System (WDS, 2024). PEDSC can significantly enhance
its data management, processing and modelling potential by setting up strong a collaboration
with the International Technology Office of WDS (WDS-ITO, 2024) and/or WDS data centres.
The ITO has a well-established working relationship with the polar research community thatseeks to promote a unified research interface for data generators, consumers and managers
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across the community. Thus, forging data-intensive partnerships with such institutions will
provide PEDSC with the potential not only to latch into novel technologies that are developed
and applied across the community, but also to contribute towards the creation of open-source
solutions for researchers. One of the infrastructure settings that can be of mutual benefit isfacilitating access to polar data even under conditions of poor internet connectivity.

Section 2.5.2:

Some Benchmark Institutions

PEDSC can fulfil its unifying role by drawing lessons from institutions that conduct research
related to any of the research centres in the ROIS-DS network. One outstanding benchmarking
institution is the British Antarctic Survey (BAS, 2024), particularly its affiliate – the UK Polar
Data Centre (UKPDC). The UKPDC is United Kingdom’s focal point for Arctic and Antarctic
environmental data management, and it covers a wide range of interdisciplinary research. Its
research and data activities, highlighted in Table 3, align nicely with ROIS-DS centres.
Selected UKPDC Projects

Description

ROIS-DS RelevanceMarine Metadata

Enhancement of availability and ROIS-DS/DBCLS
accessibility of BAS marine data
Polar Airborne Geophysics Access to airborne survey data
Geophysics apps
Atmospheric Data Access Data visualisation/access for polar
regions’ atmospheric & space weather
Herbarium Collection
Collected dried plant specimens from ROIS-DS/DBCLS
the Antarctic, sub-Antarctic andsurrounding continents
Geological Collection
Over 200,000 individual rock and fossil ROIS-DS/PEDSC
specimens collected from Antarctica meteorites researchand the sub-Antarctic islands andthousands of meters of sediment corefrom the surrounding seabed
BedmapCollaborative community project for Ice cores research at
mapping datasets of Antarctic ice ROIS-DS/PEDSC
thickness and bed topography using adata such as ice-thickness, bathymetry,surface altitude and grounding lines.
Table 3: UKPDC Benchmarking activities for ROIS-DS
The diversity of research activities within the ROIS-DS network entails adaptation of existingdata infrastructure to emerging cloud and IoT computing technologies. ROIS-DS will need tocarry out a comprehensive study as there are many options (Sharma, B. and Obaidat, M., 2020).
The National Centre of Excellence for Food Engineering (NCEFE, 2024) (NCEFE) at Sheffield
Hallam University, for instance, adapts novel AI approaches to food engineering. The centre
attracts a lot of interest from partners globally, working with industry partners and using interns
to fill skills gaps. Apparently, ROIS-DS centres can find adaptations practical and beneficial.
Other relevant benchmarking examples are in the type of research that ROIS-DS centres areembarked upon. For instance, research activities at all centres effectively address one or moreof the 17 Sustainable Development Goals (SDG) (DESA, 2023). Attainment of SDGs varies
enormously across geographical regions, depending on a wide range of factors, many of which
remain unknown (Mwitondi, K. and Munyakazi, I. and Gatsheni, B., 2020). In its quest toenhance its knowledge base and contribute to global scientific achievements, ROIS-DS canPage 13 of 17

draw lessons from research centres with a focus on data science applications ranging fromresearch data validation to cloud-IoT adaptation as shown in selected example in Table 4.
Institution/Centre/Department Action/Core Activities
Data Intensive Research of
Data-driven science
South Africa (DIRISA)
supports to SANAP
United Nations Statistics
Division (UNSTATS)
International Research Centreof Big Data for Sustainable
Development (CBAS)Big Data applications

Relevance to ROIS-DS
Bi-annual datathons incollaboration with South
African Universities
Filling skills gaps

Monitoring of SDGs

Complements the work ofboth ROIS-DS and NIPR.
Its major project. CBAS’major project - Big Earth
Data Science Engineering
Program (CASEarth)
incorporates Big Data andCloud Services
Table 4: Selected examples of research institutions that align with ROIS-DS.

The Data Intensive Research of South Africa (DIRISA, 2004) provides data service to theSouth African National Antarctic Programme. DIRISA acts as the data hub for polar researchthrough the South African National Antarctic Programme (SANAP, 2024) which monitors thenatural environment and life in the Antarctic & Southern Ocean via data-driven science.
DIRISA also supports space science through the MeerKAT radio telescope Square Kilometre
Array (SKA) telescope (SKA, 2024). In collaboration with the National Integrated Cyber
Infrastructure System (NICIS, 2024) and the Centre for High Performance Computing (CHPC,2024), DIRISA plays a key role in grooming young scientists and researchers via sustained biannual datathon and hackathon schemes. These schemes not only help groom these youngsters,
but they also help fill skills data science and cybersecurity gaps via internships and placements.
With an already established working relationships with Universities across Japan, ROIS-DS
symposia and/or conferences could emulate such schemes by engaging students frompartnering Universities. However, running such schemes will require establishing a dedicatedperson/s within PEDSC to organise and manage the activities. Datathon and hackathon
schemes are also run by the United Nations Statistics Division (UNSTATS, 2024), focusing
mainly on Big Data applications. The institutions in Table 3 and Table 4 provide benchmarking
criteria for guiding ROIS-DS' data and general operational processes. Research centres candevelop their own metrics for measuring the quality and cost of internal activities for liaison
among them. This can be based on internal SWOT to help identify sources of opportunities for
improvement and the data activities, which resonate with each centre. PEDSC’s AMIDER
database is probably best placed to draw lessons from the foregoing benchmarking institutions.

Section 3 Concluding Remarks
This report sought to highlight the potential of ROIS-DS’ individual research centres, as partof a coherent research infrastructure. Apparently, ROIS-DS has its short and long-term
strategies covering the management and future plans. The report is based on information
gathered on short visits to the research centres listed in Section 1. Among the recommendations
made include addressing staffing shortfalls in data-related activities – not anymore from thecollection point of view than from data management, processing and modelling. Apparently,
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the ultimate goal of data modelling is application-specific, which entails internal collaborations
among the centres and externally with institutions outside Japan.
All the recommendations made require proper and careful planning, executing and monitoring.
For instance, all centres typically are embarked upon different projects at any one time and so
one way of ensuring that the recommendations are successful would be to carefully evaluate
individual project proposals before, during and after implementation. From a data science pointof view, any robust monitoring and control will require aligning with the dynamics in the field– that is, adapting to global trends. A few benchmarking examples were identified to sign post
ROIS-DS into the direction of good practice in keeping abreast with new developments. It is
recommended that ROIS-DS keeps pace with recent developments in Artificial Intelligence(AI) and the related Explainable Artificial Intelligence (XAI) and how these are likely to impact
its research centres. For instance, validating numerical analysis using image analysis or viceversa renders itself readily to validation of meteorites chemical compositions with their ML
image classification. Such an application is a manifestation of the so-called Multimodal
Machine Learning (MML) (Zhang, C. and Yang, Z. and He, X and Deng, L., 2020).
Advances in algorithmic computing and automation of complex processes–like the foregoing
mapping of chemical compositions of meteorites to their classified images may require running
ML algorithms on small, low-power devices, such as microcontrollers or other IoT devices thattransmit data from, say, chemical analysis equipment to the AMIDER server. It is important toavoid quick fixes, when dealing with highly dynamic Big Data. For instance, Generative AI(Hacker, P. and Engel, A. and Mauer, M., 2023) is becoming increasingly popular, as staff withminimal training in data science can carefully utilise to generate realistic content. Much as thismay look a blessing, it is also a curse as knowledge gaps in underlying mechanics of data
science techniques may potentially lead to undesirable consequences.
Recursive Big Data Modelling (Mwitondi, K. and Storrar, R., 2023) is another trend that may
empower algorithms to learn by recursively running through previous paths. The model has thepotential to provide robust solutions under highly volatile data conditions or when there arelimitations in training data. Hence, PESDC and other ROIS-DS centres need to pay attention
to Ethical and Explainable Artificial Intelligence (EXAI) (Izumo, T. and Weng, Y., 2022) toprovide fairness, transparency, accountability and trustworthiness. Paying attention to EXAI is
crucial, not least because ROIS-DS research data activities span across the entire spectrum of
humanity – from citizen science applications–conducted by residents of habitable lowlands inthe cold regions, say, to sophisticated research technologies utilising satellites, cloud and IoTs.

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