Slides of the keynote at the 3rd Big Data Europe SC6 Workshop co-located at SEMANTiCS2018 in Amsterdam (NL) on: The European Research Data Landscape: Opportunities for CESSDA by Peter Doorn, Director DANS, Chair, Science Europe W.G. on Research Data. Chair, CESSDA ERIC General Assembly

1. The European Research
Data Landscape:
Opportunities for
CESSDA
Peter Doorn, Director DANS
Chair, Science Europe W.G. on Research Data
Chair, CESSDA ERIC General Assembly
@dansknaw @pkdoorn

2. (Infra-)structure
in Cheese & Art

5. Four Themes
1. European Open Science Cloud
2. FAIR Data
3. Research Data Management
4. Privacy: GDPR and Datatags

6. 1. EOSC Pilot
The EOSCpilot project supports the first phase in the
development of the European Open Science Cloud (EOSC).
“EOSC will build on already available resources and capabilities
from research infrastructure and e-infrastructure organisations to
maximise their use across the research community”.

7. 1. EOSCpilot
Objectives:
• Propose governance framework for EOSC and contribute to
European open science policy;
• Demonstrators that integrate services and infrastructures to
show interoperability and its benefits in a number of scientific
domains;
• Engage with a broad range of stakeholders, crossing
borders and communities, to build the trust and skills
required for adoption of open science.
• Reduce fragmentation between data infrastructures by
working across scientific and economic domains, countries
and governance models, and
• Improve interoperability between data infrastructures
by demonstrating how resources can be shared even when
they are large and complex and in varied formats.

9. 2. FAIR Principles
DSA Principles (for data
repositories)
FAIR Principles (for data sets)
data can be found on the internet Findable
data are accessible Accessible
data are in a usable format Interoperable
data are reliable Reusable
data can be referred to (citable)
•
•
•
Resemblance Data Seal of Approval – FAIR principles

10. All data sets in a
Trusted Repository
are FAIR, but some
are more FAIR than
others

11. Operationalize FAIR
• Growing demand for quality criteria for
research datasets and ways to assess their
fitness for use
• Combine the principles of core repository
certification and FAIR
• Use the principles as quality criteria:
• Core certification – digital repositories
• FAIR principles – research data (sets)
• Operationalize the principles as an
instrument to assess FAIRness of existing
datasets in certified TDRs

12. FAIR “light”
badge scheme
• FAIR as proxy for data “quality” or
“fitness for (re-)use”
• Score each FAIR dimension on a
5-point scale
• Prevent interactions among
dimensions to ease scoring
• Assessment tool based on
questionnaire to evaluate any dataset
in any (trusted) repository by
depositors, data specialists and users
• Independent website will collect the
scores and deliver the badges
• Prototype is being tested
F A I R
2 User Reviews
1 Archivist Assessment
24 Downloads

13. 3. Research Data
Management
Science Europe is an association of European Research Funding
Organisations (RFO) and Research Performing Organisations
(RPO), based in Brussels.
The Science Europe Roadmap states that research data should
be permanently, publicly and freely available for re-use.
Access to and sharing of research data are central pillars
of Open Science, a concept that Science Europe members fully
support.
Science Europe is committed to supporting data sharing
by contributing to the definition and use of consistent
data-sharing policies and practices. This includes identifying
legitimate reasons for delayed or restricted access when
necessary. In addition, it is crucial to enable access to and
sharing of data by resolving data management issues.

14. Science Europe WG
Research Data
Until 2016, the SEWGRD worked on
fundamental aspects of research data, such
as:
➢ funding of data management and
infrastructures
➢ legal aspects related to copyright and
Text and Data Mining (TDM)
➢ common data terminology:
http://sedataglossary.shoutwiki.com/wiki/Main_Page
Since summer 2016 the Working Group has
focused on the topic of Research Data
Management Protocols (RDMP)

15. Aligning DMP
requirements
➢ Requirements by RFO’s and RPO’s for Research Data
Management (RDM) and Data Management Plans (DMP)
➢ Currently: RDM policies, requirements, templates have
similar objectives, but differ in details
➢ Science Europe Data Group working towards a common
RDM framework across Europe
➢ Foundation: common core RDM requirements across countries,
funders and domains
➢ Specialized domain data protocols to address different
disciplines and communities
➢ Will be much more suitable to serve community needs
➢ Will get better acceptance/adoption by research communities
➢ Will make the life of all stakeholders easier

16. Proof-of-Concept
Domain Data Protocols

17. Draft Report “Framework Document for Discipline Dependent
Research Data Management” available at:
https://www.rd-alliance.org/ig-domain-repositories-rda-9th-plena
ry-meeting
Or:
https://goo.gl/nMTrhI
Support at RDA
Plenary 9, 2017

18. 4. Privacy: GDPR
and Datatags
• General Data Protection Regulation EU –
Passed 14 April 2016
• New European “Law”:
– Data minimisation required
– Informed consent important
– Data Protection Officer mandatory
– Right to know (e.g. data leakages)
– High fines for trespassing (data leakage!)
• Implications for sharing data on human subjects?
– Researchers don’t know
– Data repositories don’t know
→ Data Tagging Approach, initially developed at Harvard

19. Background
Sweeney & Crosas introduced the notion of a datatags repository
• Stores and shares data files in accordance with different security
levels, access requirements and usage agreements
• American laws and legislations of personal data

20. Step by step 1
1. Identify the relevant articles of GDPR for research and archive
purposes
Example: Article 9(2) sets out the circumstances in which the processing of
sensitive personal data (which is otherwise prohibited) may take place:
• Necessary for archiving purposes in the public interest, or scientific and
historical research purposes or statistical purposes in accordance with Article
89(1).
Article 17 - right to be forgotten
2. Transformation of relevant articles into questions
Were the data processed for archiving in the public interest, scientific or historical
research purposes or statistical purposes?
Would you consider the dataset to contain sensitive personal information? [article 9]

21. Step by step 2
3. Decision tree evolution
– Creating routes for questions, ending with tags
– Deciding on tag options and recommendations following each route
– Tree diagram and feedback

22. Step by step 3

23. CESSDA
Opportunities
1. EOSC: CESSDA to represent social science interests in the
data hurricane
2. FAIR: CESSDA Service Providers already play a key role as
Trusted Digital Repositories: rate FAIRness of datasets within
CESSDA archives
3. Research Data Management: CESSDA to work with funders
and researchers to develop RDM protocol for social sciences
4. Privacy: support implementing Datatags approach by
CESSDA service providers to share personal data under
secure conditions conformant with GDPR