EDISON Project Overview:
Building the Data Science Profession
for Research and Industry
Yuri Demchenko, University of Amsterdam

EDISON – Education for Data Intensive
Science to Open New science frontiers
Grant 675419 (INFRASUPP-4-2015: CSA)

2nd EDISON Data Science Champions
Conference
15-16 March 2017
Universidad Carlos III de Madrid, MadridOutline•

Background and motivation––•

EDISON Data Science Framework (EDSF)–••

From Data Science Competences to Body of Knowledge and Model Curriculum

Data Science Competence Framework: Essential competences and skills
Education and training focus
–
–
–

•

European initiatives related to Digital Single Market and Digital Skills Agenda
EDISON building network to promote establishing Data Science profession

Data Science Body of Knowledge (DS-BoK)
Data Science Model Curriculum (MC-DS)
Example Research Data Management Literacy curriculum

Further developments to formalise EDSF and Data Science profession establishment

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EDISON Objectives, Impact and Actions

IMPACT

Objectives
andActions

Increase the number of Data Scientists andMarket for establishing Data Science Profession
Create a Data
Science profession

Services to
education andtraining

Engage stakeholder
communities

Data Science professional
profilesDefine Model Curriculumand design tools

Sustain platforms ofcommunities of practiceInteract with demand andsupply sides

Support for accreditationand certification

Create community of“champion” universities

Career path building andskills transferability

Collaborating and sharing
expertise and materials

Interact with Expert Liaison
GroupsData Science Competence Framework and Body of Knowledge

Visionaries and Drivers:
Seminal works, High level reports, Activities
The Fourth Paradigm: Data-Intensive Scientific Discovery.
By Jim Gray, Microsoft, 2009. Edited by Tony Hey, Kristin Tolle, et al.
http://research.microsoft.com/en-us/collaboration/fourthparadigm/
Riding the wave: How Europe can gain fromthe rising tide of scientific data.
Final report of the High Level Expert Group on
Scientific Data. October 2010.
http://cordis.europa.eu/fp7/ict/einfrastructure/docs/hlg-sdi-report.pdf

The Data Harvest: Howsharing research data
can yield knowledge,
jobs and growth.
An RDA Europe Report.
December 2014

https://www.rd-alliance.org/
HLEG report on European
Open Science Cloud(October 2016)

DSci ChampConf March 2017https://rd-alliance.org/dataharvest-report-sharing-dataknowledge-jobs-and-growth.html

Emergence of Cognitive Technologies(IBM Watson and others)
EDISON Data Science Framework4Recent European Commission Initiatives 2016
Digitising European Industry: Reaping the full benefits of a Digital Single Market.
COM(2016) 180 final, Brussels, 19.4.2016•

The need for new multidisciplinary and digital skills in particular Data Scientist–

Expected rapidly growing demand will lead to more than 800 000 unfilled vacancies by 2020

European Cloud Initiative - Building a competitive data and knowledge economy inEurope, COM(2016) 178 final, Brussels, 19.4.2016•

European Open Science Cloud (EOSC) and European digital research and data
infrastructure–•

To offer 1.7 million European researchers and 70 million professionals in science and technology
open and seamless services for storage, management, analysis and re-use of research data

Address growing demand and shortage of data-related skills

A New Skills Agenda for Europe, COM(2016) 381 final Brussels, 10.6.2016••

Addresses the need for digital and complementary skills, ensure young talents flow into
data driven research and industry
Launch Digital Skills and Jobs Coalition (1st December 2016, Brussels) to develop
comprehensive national digital skills strategies by mid-2017

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HLEG report on European Open Science Cloud(October 2016) – Demand for Data Scientists/Stewards
Realising the European Open Science Cloud. First report andrecommendations of the Commission High Level Expert Group on theEuropean Open Science Cloud, October 2016
https://ec.europa.eu/research/openscience/pdf/realising_the_european_open_science_cloud_2
016.pdf•

Definition of the Data Steward as a distinctive role and profession–•

Estimation: More than 80,000 data stewards to serve 1.7 mln scientists in Europe(1 per every 20 scientists)–•

Core Data Experts need to be trained and their career perspective improved

Based on 5% grant funding for Data management and preservation

Clash of cultures between domain specialists and e-Infrastructure specialists(i.e. IT/Computer Science)

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EDISON Data Science Framework6OECD and UN on Digital Economy and Data Literacy
OECD• Demand for new type of “dynamic self-re-skilling workforce”• Continuous learning and professional development to become ashared responsibility of workers and organisations[ref] SKILLS FOR A DIGITAL WORLD, OECD, 25-May-2016
http://www.oecd.org/officialdocuments/publicdisplaydocumentpdf/?cote=DSTI/ICCP/IIS(2015)10/FINAL&docLanguage=EnUN• Data Revolution Report "A WORLD THAT COUNTS"
Presented to Secretary-General (2014)
http://www.undatarevolution.org/report/• Data Literacy is defined as key for digital revolution• Data literacy = critically analyse data collected anddata visualised
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Approach• Task is not for one project – Need collaboration• Task is not for science only in isolation from industry• Needs strong conceptual approach– Use science to solve the problems of science• Standardisation is an important factor of sustainability anddevelopment

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EDISON Network and Engagement Activity (1)• Cooperative relations and exchange of developments with RI
projects– ELIXIR, CORBEL/RItrain, EUDAT, ENVRI– FOSTER2, EOSCpilot• Cooperation with Big Data and Data Science projects– EDSA, BDVA

• Active contribution to the Research Data Alliance (RDA)
activities– RDA IG on Education and Training on Handling Research Data (IGETHRD)– BoFs and proposed WG on Certification and accreditation,– Proposed WG on Text Data Mining– Proposed WG on Research Data Management Curriculum– BoF on Data Champions
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EDISON Network and Engagement Activity (2)•Workshops to promote a common approach towards addressing growing demandfor Data Science and critical data competences and skills as required by
European Research Infrastructures (RI), future European Open Science
Cloud (EOSC) and generally European Digital Single Market (DSM).
– Joint EDISON and EC workshop “Data Infrastructure Competences andSkills Framework: a European and Global Challenge” (Brussels, 9th
February, 2016)– Joint IEEE, STC CC and RDA Workshop on Curricula and Teaching Methods(DTW2015 and DTW2016 collocated with IEEE CloudCom)•

EDISON initiated a set of national action meetings to address Data
Science and digital skills by bringing together key stakeholders fromuniversities, employer associations, and government– A first workshop jointly organised by the EDISON project and Dutch Ministryof Education, Culture and Science in June 2016 (during Netherlands
Presidency in EU)

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EDISON Network and Engagement Activity (3)•European and international standardisation bodies and professionalorganisations– CEN TC426 Committee (former e-Competence Framework e-CFv3.0 workshop)– ESCO (European Skills, Competence, Occupations)– CEPIS and association ICT Professionalism Europe (co-signed 21 Nov 2016,
Amsterdam)•

EDISON Booth at the Launch event of the Digital Skills and Jobs
Coalition: Boosting Europe's Digital skills, 1 December 2016, Brussels– Part of actions toward European Digital Single Market (DSM) and•Contribution to International standardization bodies, professionalorganisations and initiatives– Business Higher Education Forum (BHEF) in USA– DARE project for APEC (Asia Pacific Economic Cooperation) to develop a Data
Analytics checklist for APEC countries– Data Science Curriculum Meeting of Professional and Academic Societies in
USA (4 March 2017, Alexandria) including ACM

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EDISON Data Science Framework (EDSF)
Release 1, October 2016
DS-BoK

MC-DS

EDISON Online
Educational Environ

CF-DS
Taxonomy andVocabulary

Data Science
Framework

Edu&Train Marketpltz
and Directory

DS Prof Profiles

Foundation & Concepts

Services

Roadmap &Sustainability• Community
Portal (CP)• Professional
certification• Data Science
career & prof
development

Biz Model

EDISON Framework components

Methodology

–
–
–
–
–

•

Job market study, existing practices inacademic, research and industry.

•

Compliance with related standards•

Review and feedback from the ELG, expert
community, domain experts.

•

Input from the champion universities andcommunity of practice.

–

CF-DS – Data Science Competence Framework
DS-BoK – Data Science Body of Knowledge
MC-DS – Data Science Model Curriculum
DSP – Data Science Professional profiles
Data Science Taxonomies and Scientific
Disciplines Classification
EOEE - EDISON Online Education Environment

Data Scientist definition
Based on the definitions by NIST Big Data WG (NIST SP1500 - 2015)•

A Data Scientist is a practitioner who has sufficientknowledge in the overlapping regimes of expertise inbusiness needs, domain knowledge, analytical skills,
and programming and systems engineering expertise to
manage the end-to-end scientific method process
through each stage in the big data lifecycle– … Till the delivery of expected scientific and business value
to science or industry•

Other definitions to admit such features as
–
–
–

•
•

[ref] Legacy: NIST BDWG
definition of Data Science

Ability to solve variety of business problems
Optimize performance and suggest new services for the organisation
Develop a special mindset and be statistically minded, understandraw data and “appreciate data as a first class product”

Data science is the empirical synthesis of actionable knowledge and technologies required tohandle data from raw data through the complete data lifecycle process.
Big Data is the technology to build system and infrastructures to process large volume ofstructurally complex data in a time effective way

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13Data Science Competence Groups - Research
Data Science Competence
includes 5 areas/groups
•
•
•
•
•

Data Analytics
Data Science Engineering
Domain Expertise
Data Management
Scientific Methods (or Business
Process Management)

Scientific Methods
•
•
•
•
•
•

Design Experiment
Collect Data
Analyse Data
Identify Patterns
Hypothesise Explanation
Test Hypothesis

Business Operations
•
•
•
•
•

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Operations Strategy
Plan
Design & Deploy
Monitor & Control
Improve & Re-design

14

Data Science Competences Groups – Business
Data Science Competence
includes 5 areas/groups
•
•
•
•
•

Data Analytics
Data Science Engineering
Domain Expertise
Data Management
Scientific Methods (or Business
Process Management)

Scientific Methods
•
•
•
•
•
•

Design Experiment
Collect Data
Analyse Data
Identify Patterns
Hypothesise Explanation
Test Hypothesis

Business Process
Operations/Stages
•
•
•
•
•
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DesignModel/Plan
Deploy & Execute
Monitor & Control
Optimise & Re-design15

Identified Data Science Competence Groups
Data Science Analytics Data Management(DSDA)(DSDM)

Data Science Engineering Research/Scientific Methods(DSENG)(DSRM)

0

Use appropriate
statistical techniques
and predictive
analytics on available
data to deliverinsights and discover
new relations

Develop andimplement data
management
strategy for datacollection, storage,
preservation, andavailability forfurther processing.

Use engineering
principles to research,
design, develop andimplement new
instruments andapplications for datacollection, analysis andmanagement1

DSDA01
Use predictive
analytics to analyse big
data and discover new
relationsDSBPM01
Understand business and provide
insight, translate unstructured
business problems into an abstract
mathematical framework

DSDA02
Use statistical techniq
to deliver insights

DSENG01
Use engineering principles
to design, prototype data
analytics applications, ordevelop instruments,
systems
DSENG02
Develop and apply
computational solutions

DSRM01Create new understandings andcapabilities by using scientific/
research methods or similar domain
related development methods2

DSDM01Develop andimplement data
strategy, in particular,
Data Management
Plan (DMP)DSDM02Develop data models
including metadata

DSRM02
Direct systematic study toward afuller knowledge or understandingof the observable facts

DSBPM02
Participate strategically andtactically in financial decisions

3

DSDA03Develop specialized …

DSDM03
Collect integrate data

DSENG03
Develops specialized tools

DSRM03
Undertakes creative work

DSBPM03
Provides support services to other

4

DSDA04
Analyze complex data

DSDM04Maintain repository

DSENG04
Design, build, operate

DSRM04
Translate strategies into actions

DSBPM04
Analyse data for marketing

5

DSDA05Use different analytics

DSDM05Visualise cmplx data

DSENG05
Secure and reliable data

DSRM05Contribute to organizational goals

DSBPM05
Analyse optimise customer relatio

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Data Science Domain
Knowledge, e.g. Business
Processes (DSDK/DSBPM)
Create new understandings andUse domain knowledge
capabilities by using the scientific (scientific or business) tomethod (hypothesis,
develop relevant data analytics
test/artefact, evaluation) orapplications, and adopt generalsimilar engineering methods toData Science methods todiscover new approaches todomain specific data types andcreate new knowledge andpresentations, data and processachieve research ormodels, organisational roles
organisational goals
and relations

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Identified Data Science Skills/Experience Groups•

Group 1: Skills/experience related to competences
–
–
–
–
–
–

•

Group 2: Big Data (Data Science) tools and platforms
–
–
–
–
–
–

•

Big Data Analytics platforms
Mathematics & Statistics applications & tools
Databases (SQL and NoSQL)
Data Management and Curation platform
Data and applications visualisation
Cloud based platforms and tools

Group 3: Programming and programming languages and IDE–•

Data Analytics and Machine Learning
Data Management/Curation (including both general data management and scientific data
management)
Data Science Engineering (hardware and software) skills
Scientific/Research Methods or Business Process Management
Application/subject domain related (research or business)
Mathematics and Statistics

General and specialized development platforms for data analysis and statistics

Group 4: Soft skills or Social Intelligence–

Personal, inter-personal communication, team work, professional network

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Practical Application of the CF-DS• Basis for the definition of the Data Science Body of Knowledge(DS-BoK) and Data Science Model Curriculum (MC-DS)– CF-DS => Learning Outcomes (MC-DS) => Knowledge Areas (DS-BoK)– CF-DS => Data Science taxonomy of scientific subjects and vocabulary• Data Science professional profiles definition– Extend existing EU standards and occupations taxonomies: e-CFv3.0,
ESCO, others• Professional competence benchmarking– For customizable training and career development– Including CV or organisational profiles matching• Professional certification– In combination with DS-BoK professional competences benchmarking• Vacancy construction tool for job advertisement (for HR)– Using controlled vocabulary and Data Science Taxonomy

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Individual Competences Benchmarking
M AT C H I N G – C O M P E T E N C E P R O F I L E S
DSP04 - Data Scientist
DSBPM06
DSBPM05
DSBPM04
DSBPM03
DSBPM02
DSBPM01
DSRM06

Individual Education/Training Pathbased on Competence benchmarking

Candidate - Data Scientist

DSDA01
10
9
8
7
6
5
4
3
2
1
0

DSDA02
DSDA03
DSDA04
DSDA05
DSDA06
DSDM01– DSDA01 – DSDA06 Data Science Analytics– DSRM01 – DSRM05 Data Science Research
Methods

DSDM02

DSRM05

DSDM03

DSRM04

DSDM04

DSRM03

DSDM05

DSRM02

DSDM06

DSRM01• Red polygon indicates the chosen
professional profile: Data Scientist(general)• Green polygon indicates the candidate orpractitioner competences/skills profile• Insufficient competences (gaps) arehighlighted in red•

Can be use for team skills match marking andorganisational skills management[ref] For DSP Profiles definition and for enumerated
competences refer to EDSF documents CF-DS and DSP
Profiles.

DSENG01

DSENG06
DSENG05

DSENG02
DSENG03
DSENG04

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DSP Profiles mapping to ESCO Taxonomy
High Level Groups•

DSP Profiles mapping to corresponding CF-DS Competence Groups– Relevance level from 5 – maximum to 1 – minimum

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Education and Training• Foundation and methodological base
– Data Science Body of Knowledge (DS-BoK)• Taxonomy and classification of Data Science related scientific subjects– Data Science Model Curriculum (MC-DS)• Set Learning Units mapped to CF-DS Learning and DS-BoK Knowledge
Areas/Units– Instructional methodologies and teaching models• Platforms and environment– Virtual labs, datasets, developments platforms– Online education environment and courses management• Services– Individual benchmarking and profiling tools (competence assessment)– Knowledge evaluation tools– Certifications and training for self-made Data Scientists practitioners– Education and training marketplace: Courses catalog and repository
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21Data Science Body of Knowledge (DS-BoK)
DS-BoK Knowledge Area Groups (KAG)•••

KAG1-DSA: Data Analytics group including
Machine Learning, statistical methods,
and Business Analytics
KAG2-DSE: Data Science Engineering group
including Software and infrastructure engineering
KAG3-DSDM: Data Management group including data curation,
preservation and data infrastructure
KAG4-DSRM: Scientific/Research Methods group
KAG5-DSBP: Business process management group•

Data Science domain knowledge to be defined by related expert groups••

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22Data Science Model Curriculum (MC-DS)
Data Science Model Curriculum includes• Learning Outcomes (LO) definition based on CF-DS– LOs are defined for CF-DS competence groups and for all
enumerated competences• LOs mapping to Learning Units (LU)
– LUs are based on CCS(2012) and universities best practices– Data Science university programmes and courses inventory(interactive)
http://edison-project.eu/university-programs-list• LU/course relevance: Mandatory Tier 1, Tier 2, Elective,
Prerequisite• Learning methods and learning models (in progress)
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EDISON Data Science Framework23Outcome Based Educations and Training Model

Data Science Competence
Framework (CF-DS)

DS Professional Profiles(DSP-P)

Learning Outcomes (LO)

LearningOutcomes
Outcomes (LO)
Learning(LO)(OBE
Learning
Model)(OBE Learning Model)(OBE Learning Model)

Competences
Competences
And
KnowledgeAnd
Units (KU)
Skills- (DS-BoK)
SkillsCompetences
Competences
And (LU)
Learning And
Units
Skills- Skills(MC-DS)

From Competences and DSP
Profilesto Learning Outcomes (LO)andto Knowledge Unites (KU) and
Learning Units (LU)• EDSF allow for customized
educational courses and training
modules design

Tracks/Specialisation
LU/KU/DS-PP
Tracks/Specialisations(basedon DSP-P)
Tracks/Specialisation
LU/KU/DS-PP(LO, LU, KU, courses/modules)

DS Academic Programmes

DS Professional Training

CoursesLO/LU/KU/DS-PP
LO/LU/KU/DS-PP
Courses
Courses LO/KU/LU/DSP-P

CoursesLO/LU/KU/DS-PP
LO/LU/KU/DS-PP
CoursesModules LO/KU/LU/DSP-P

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Example DS-BoK Knowledge Areas definition andmapping to existing BoKs and CCS (2012)•

Mapping suggested to CCS2012
and existing BoKs

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Example MC-DS Mapping Learning Units to DSBoK and CCS (2012)•

Mapping suggested to ACM CCS2012, DS-BoK and other related BoKs

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New courses currently missing• Data Management / Research Data Management– Data Curation, Data Stewardsip• Professional issues in Data Science– + Ethics and responsible use of Data Science

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KAG3-DSDM: Data Management group: data curation,
preservation and data infrastructure
DM-BoK version 2 “Guide for
performing data management”– 11 Knowledge Areas

Other Knowledge Areas motivated by
RDA, European Open Data initiatives,
European Open Data Cloud(1) Data Governance(2) Data Architecture(3) Data Modelling and Design(4) Data Storage and Operations(5) Data Security(6) Data Integration andInteroperability(7) Documents and Content(8) Reference and Master Data
(9) Data Warehousing and BusinessIntelligence(10) Metadata(11) Data Quality

DSci ChampConf March 2017(12) PID, metadata, data registries(13) Data Management Plan
(14) Open Science, Open Data, Open
Access, ORCID(15) Responsible data use•

Highlighted in red: Considered (Research)
Data Management literacy (minimum
required knowledge)

EDISON Data Science Framework28Research Data Management Model Curriculum –Part of the EDISON Data Literacy Training
A. Use cases for data management and stewardship•
Preserving the Scientific Record
B. Data Management elements (organisational and individual)•
Goals and motivation for managing your data•
Data formats•
Creating documentation and metadata, metadata for discovery•
Using data portals and metadata registries•
Tracking Data Usage•
Handling sensitive data•
Backing up your data•
Data Management Plan (DMP) - to be a part of hands on session

Collaboration with the Research
Data Alliance (RDA) on developing
model curriculum on Research
Data Literacy:• Modular, Customisable,
Localised, Open Access• Supported by the network of
trainers via resource swap
board

C. Responsible Data Use Section (Citation, Copyright, Data Restrictions)
D. Open Science and Open Data (Definition, Standards, Open Data use and reuse, open government data)•
Research data and open access•
Repository and self- archiving services•
ORCID identifier for data•
Stakeholders and roles: engineer, librarian, researcher•
Open Data services: ORCID.org, Altmetric Doughnut, Zenodo
E. Hands on:a) Data Management Plan designb) Metadata and toolsc) Selection of licenses for open data and contents (e.g. Creative Common and Open Database)DSci ChampConf March 2017

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Further Developments and Actions••••

Involve academic and industry experts and professional organisations to thedefinition of DS-BoK following from CF-DS
Work with champion universities to practically validate the proposed EDSF
Formally provide suggestions to ESCO for the definition of the Data Science
professional profiles (occupations) family
Formally provide suggestions for e-CF3.0 extensions for Data Science to
CEN/PC 428– Involve national e-CF bodies and adopters where available•

Suggest required ACM CCS(2012) Classification extensions and proposal for
Data Science curriculum definition

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Discussion• Questions• Comments•

Invitation to contribution and cooperation:– Forum, EDISON Liaisons Groups, Champions Conference (Spring & Summer 2017)•

EDISON project website http://edison-project.eu/•

EDISON Data Science Framework Release 1 (EDSF)
http://edison-project.eu/edison-data-science-framework-edsf
–
–
–
–

•

Data Science Competence Framework
http://edison-project.eu/data-science-competence-framework-cf-ds
Data Science Body of Knowledge
http://edison-project.eu/data-science-body-knowledge-ds-bok
Data Science Model Curriculum
http://edison-project.eu/data-science-model-curriculum-mc-ds
Data Science Professional Profiles
http://edison-project.eu/data-science-professional-profiles-definition-dsp

Survey Data Science Competences: Invitation to participate
https://www.surveymonkey.com/r/EDISON_project_-_Defining_Data_science_profession

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31Definitions (according to e-CFv3.0)•

Competence is a demonstrated ability to apply knowledge, skills andattitudes for achieving observable results.
– Competence vs Competency (e-CF vs ACM)
• Competence is ability acquired by training or education (linked to learning outcome)• Competency is similar to skills or experience (acquired feature of a person)•

Competence is not to be confused with process or technology concepts
such as, ‘Cloud Computing’ or ‘Big Data’. These descriptions represent
evolving technologies and in the context of the e-CF, they may be
integrated as elements within knowledge and skill examples.

•

Knowledge in the context of competence definition is treated as
something to know, to be aware of, familiar with, and obtained as a partof education.

•

Skills is treated as provable ability to do something and relies on the
person’s experience.

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Data Scientist and Subject Domain Specialist•

Subject domain components
–
–
–
–
–

•

Model (and data types)
Methods
Processes
Domain specific data and presentation/visualization methods
Organisational roles and relations

Data Scientist is an assistant to Subject Domain Specialists– Translate subject domain Model, Methods, Processes into abstract data driven form
– Implement computational models in software, build required infrastructure and tools– Do (computational) analytic work and present it in a form understandable to subject
domain– Discover new relations originated from data analysis and advice subject domain
specialist– Present/visualise information in domain related actionable way– Interact and cooperate with different organizational roles to obtain data and deliver
results and/or actionable data

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Data Science and Subject Domains
Data Science domain components
Data structures &databases/storage
Visualisation• Abstract data driven
math&compute models• Data Analytics
methods• Data and Applications
Lifecycle Management

Crossorganisational
assistive role

Data Scientist functions is to translate between two domains

Domain specific components
Domain specificdata & presentation(visualization)• Models (and data types)• Methods• Processes

Organisationalroles

Data Scientist role is to maintain the Data Value Chain (domain specific):•

Data Integration => Organisation/Process/Business Optimisation => Innovation

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Demand for Data Science and data related
professions• McKinsey Global Institute on Big Data Jobs (2011)
http://www.mckinsey.com/mgi/publications/big_data/index.asp– Estimated gap of 140,000 - 190,000 data analytics
skills by 2018

• IDC Report on European Data Market (2015)– Number of data workers 6.1 mln (2014) - increase 5.7% from 2013
– Average number of data workers per company 9.5 - increase 4.4%
– Gap between demand and supply 509,000 (2014) or 7.5%

• HLEG report on European Open Science Cloud (October 2016)
identified need for data experts and data stewards– Recommendation: Allocate 5% grant funding for Data management andpreservation– Estimation: More than 80,000 data stewards (1 per every 20 scientists)– Core Data Experts (as defined) need to be trained and their career
perspective improved
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HLEG EOSC Report Essentials – Clash of Cultures• Clash of cultures– between domain specialists and e-Infrastructure specialists (i.e.
IT/Computer Science)• New data experts come from scientific and engineering
cultures– with very different reward systems and incentives,– different jargons and very different skill sets.

• Consequences– Evident a divide between researchers and those that support
research with data processing and software• Two communities that are both essential to Open Science have notclosely co-evolved and do not converge– Lack of data scientists that venture out from classical computer or data
science departments into other scientific fields.
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HLEG EOSC Report Essentials – Core Data Experts•

Core Data Experts is a new class of colleagues with core scientific professional
competencies and the communication skills to fill the gap between the two
cultures.
– Core data experts are neither computer savvy research scientists nor are they hardcore data or computer scientists or software engineers.
– They should be technical data experts, though proficient enough in the content domain
where they work routinely from the very beginning (experimental design, proposal
writing) until the very end of the data discovery cycle– Converge two communities:• Scientists need to be educated to the point where they hire, support and respect Core Data
Experts• Data Scientists (Core Data Experts) need to bring the value to scientific research andorganisations•

Implementation of the EOSC needs to include instruments to help train, retain
and recognise this expertise,– In order to support the 1.7 million scientists and over 70 million people working ininnovation.

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GO FAIR and IFDS
• Global Open FAIR
– Findable – Accessible – Interoperable - Reusable• IFDS – Internet of FAIR Data and Services = EOSC
• GO FAIR implementation approach– GO-BUILD– GO-CHANGE– GO-TRAIN: Training of data stewards capable of providing FAIR dataservices• A critical success factor is availability of expertise in data
stewardship– Training of a new generation of FAIR data experts is urgently needed
to provide the necessary capacity.

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EOSC Report Recommendations –Implementation on training and skills•

I2.1: Set initial guiding principles to kick-start the initiative as quickly as
possible. -> Bridge two cultures/communities––•

A first cohort of core data experts should be trained to translate the needs for data driven science
into technical specifications to be discussed with hard-core data scientists and engineers.
This new class of core data experts will also help translate back to the hard- core scientists thetechnical opportunities and limitations

I3: Fund a concerted effort to develop core data expertise in Europe.
––

Substantial training initiative in Europe to locate, create, maintain and sustain the required core dataexpertise.
By 2022, to train (hundreds of thousands of) certified core data experts with a demonstrable
effect on ESFRI/e-INFRA activities and prospects for long-term sustainability of this criticalhuman resource••

Consolidate and further develop assisting material and tools for Data Management Plans and Data
Stewardship plans (including long-term preservation in FAIR status)

I7: Provide a clear operational timeline to deal with the early preparatory
phase of the EOSC.
–

Define training needs for the necessary data expertise and draw models for the necessary
training infrastructure

DSci ChampConf March 2017

EDISON Data Science Framework39