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Big Data Europe: SC6 Workshop 3: The European Research Data Landscape: Opportunities for CESSDA by Peter Doorn, Director DANS

  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
  3. Four Themes 1. European Open Science Cloud 2. FAIR Data 3. Research Data Management 4. Privacy: GDPR and Datatags
  4. 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”.
  5. 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.
  6. 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
  7. All data sets in a Trusted Repository are FAIR, but some are more FAIR than others
  8. 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
  9. 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
  10. 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.
  11. 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: Since summer 2016 the Working Group has focused on the topic of Research Data Management Protocols (RDMP)
  12. 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
  13. Proof-of-Concept Domain Data Protocols
  14. Draft Report “Framework Document for Discipline Dependent Research Data Management” available at: ry-meeting Or: Support at RDA Plenary 9, 2017
  15. 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
  16. 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
  17. 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]
  18. 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
  19. Step by step 3
  20. 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