Open Research Data Management: policies and tools May 24-25 , Milano, Università Statale

1. Open Research Data Management: policies and tools May 24-25, Milano, Università Statale Paolo Budroni The LEARN Project Using the LEARN RDM Policy & Guidance This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 654139.

2. The LEARN Project This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 654139.

3. 5 partners • UCL (lead) • University of Barcelona • University of Vienna • LIBER • ECLAC – UN Commission for Latin America and the Caribbean • June 2015 – May 2017 LEaders Activating Research Networks http://www.learn-rdm.eu

4. 10 421 31 Twitter followers Workshops & Events blog posts 500 65 At A Glance average monthly website users presentations

5. 6 main Workshops • 3 mini Workshops • 1 Open Science Café Audience: Policymakers, librarians, funders, publishers, SMEs and more… Attendance: 70+ people on average attended each main workshop vs KPI of 50 Workshops

6. 65 Presentations to Policy Makers, Higher Education Professionals, Librarians, Data Specialists Presentations

7. Audiences Reached

8. 23 Best Practice Case Studies in 8 sections • Policy and Leadership • Advocacy • Subject approaches • Open Data • Research Data Infrastructure • Costs • Roles, Responsibilities, Skills • Tool development LEARN Toolkit http://www.learn-rdm.eu

9. Take the survey - http://learn-rdm.eu/en/rdm-readiness-survey/ Case Study 23:Paul Ayris & Ignasi Labastida: Surveying your level of preparation for research data management

10. Toolkit Part 3: LEARN Executive Briefing http://www.learn-rdm.eu

11. RDM Policies and Policy Alignement About Research Data Understanding Policies From Taboos to Policies Model Policy for Research Data Management (RDM) at Research Institutions/Institutes Guideline, Further Developments and Outreach This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 654139.

12. World ofdata Raw data (primarydata) Different levels of processing of dataModel for digital archiving Processed Data Negative Results  Strata of research data  Open Data  Restricted Data  Published Data Processed Data Processed Data Inconclusive Results Processed Data Processed Data Processed Data Positive results Shared Data Positive results Shared Data Pub. Data Shared Data Pub. Data Released Data OA Ensuring legal and ethical compliance is key issue in this context

13. Produce a Policy and a Guidance which can be tailored by any University or Research Institution to meet their needs • Enhance Policy Coordination & Alignment Mission

14. 2 Understanding Policies: From Taboos to Policies

15. Looking for an Ontology From Taboos to Policies Starting with some Taboos 1 Going over to related Principles 2 Going over to the creation of aPolicy 3 Going over to Rules, Legislations and Regulations (canons, norms, guidelines) 4

16. A taboo is something, which is forbidden or disapproved of, or placed under a social prohibition. “Thou shalt not delete scientific data“ “Thou shalt not destroy infrastructures” Usually a negative assertion. In society and academic environment taboos are accepted only if they are just a few. Taboo

17. A principle is a fundamental truth or proposition that serves as the foundation for a system of belief or behaviour or for a chain of reasoning. Research data are to be preserved Format: positive assertion: Derivation for an academic institution or an academic service provider: beliefs governing the organization’s (body) behaviour. Research data are to be kept FAIR - Findable, Accessible, Interoperable, Reusable. Research data infrastructures are to be kept accessible Principle

18. A policy is… • a course or principle of action adopted or proposed by an organization (or individual); “The Institution [name XY] will preserve its research data infrastructure always accessible and free to its members according to the FAIR principles” - a development generated from the bottom (resulting from the action of individuals); - a development generated from the top (resulting from the action of an executive); N.B.: the original Greek ideal of “the projection of the volition of an individual” is expressed through the politeia and therefore included in this principle of action. Policies/ 1

19. General assumptions concerning policies: • A single Policy: the policy is a single entity, it should not be in competition with other policies • Policy offers the frame for the generation of Rules • Policy is usually accepted after a while • Creators of Policy do not want to modify it • “Policies lag behind” (usually policies are oriented to the past. Most Policies are reflections of existing conventions) • Valid for long periods of time – and there is an end (expiry date) Policies /2

20. Rules are prescribing conducts or actions. They are generated by the founder of “orders”. Characteristics of rules are: - There may exist “lots of rules”: the number of rules can be „endless“. - Rules are not always clear (they often need interpretation according to the situation). - Rules are usually accepted, but often imposed procedures. - It is allowed to modify Rules by definition. - Rules are only valid during a specified period of time. - The Law is an expression of rules - Law (usually written order or direction or legal precept or doctrine) Rules, Regulations/ 1

21. Example: “Our University will maintain accessible our infrastructure each day from 9:00 a.m. to 12:00 a.m and offer support only on Friday from 7:00 a.m. to 8:00 a.m. The research data, that are publicly funded are to be kept free and accessible to all members of our University each Sunday, from 9:00 to 12:00 a.m.“ Rules, Regulations /2

22. From Taboos to Policies

23. A few words about KPIs

24. Conclusion: the use and adoption of KPIs is related to future developments that are depending on policies. KPIs will then offer a “screenshot” of existing situations, may also be used as a regulative instrument for future developments. About KPIs/2

25. It is important to identify the different semantic levels • Understand the differences between Taboos, Principles, Policies, Rules and Regulations • Understanding of the semantic hierarchy is useful in order to produce appropriate guidelines Why these differentiations?

26. 3 The Model Policy

27. Creation of first model policy and guidance • Continuous involvement of LEARN Partners • Discussion of policy insights and results at 5 Partner Workshops in London, Vienna, Helsinki, Santiago de Chile and Barcelona • Co-operation in Mini-Workshops in the Latin America area to compare and standardise terminology and to foster policy alignment • 12/2016 – 02/2017: Peer review process of Model Policy and Guidance How we continued

29. Preamble • Jurisdiction • Intellectual Property Rights • Handling Research Data • Responsibilities, Rights, Duties • 5.1. Researchers are responsible for:... • 5.2. The [name of research institution] is responsible for:… • 6. Validity

30. Preamble • The [name of research institution] recognizes the fundamental importance of research data and the management of related administrative records in maintaining quality research and scientific integrity, and is committed to pursuing the highest standards. The [name of research institution] acknowledges that correct and easily retrievable research data are the foundation of and integral to every research project. They are necessary for the verification and defence of research processes and results. RDM policies are highly valuable to current and future researchers. Research data have a long-term value for research and academia, with the potential for widespread use in society.

31. Jurisdiction • This policy for the management of research data applies to all researchers active at the [name of research institution]. The policy was approved by the [dean/commission/authority] on [date]. In cases when research is funded by a third party, any agreements made with that party concerning intellectual property rights, access rights and the storage of research data take precedence over this policy.

32. 3. Intellectual Property Rights Intellectual property rights (IPR) are defined in the work contract between a researcher and his or her employer. IPRs might also be defined through further agreements (e.g. grant or consortial agreements). In cases where the IPR belong to the institution that employs the researcher, the institution has the right to choose how to publish and share the data.

33. Handling research data (1/2) Research data should be stored and made available for use in a suitable repository or archiving system, such as [name of institutional repository/archiving system, if applicable]. Data should be provided with persistent identifiers. It is important to preserve the integrity of research data. Research data must be stored in a correct, complete, unadulterated and reliable manner. Furthermore, they must be identifiable, accessible, traceable, interoperable, and whenever possible, available for subsequent use. In compliance with intellectual property rights, and if no third-party rights, legal requirements or property laws prohibit it, research data should be assigned a licence for open use.

34. Handling research data (2/2) Adherence to citation norms and requirements regarding publication and future research should be assured, sources of subsequently-used data explicitly traceable, and original sources can be acknowledged. Research data and records are to be stored and made available according to intellectual property laws or the requirements of third-party funders, within the parameters of applicable legal or contractual requirements, e.g. EU restrictions on where identifiable personal data may be stored. Research data of future historical interest and the administrative records accompanying research projects should also be archived. The minimum archive duration for research data and records is 10 years after either the assignment of a persistent identifier or publication of a related work following project completion, whichever is later. In the event that research data and records are to be deleted or destroyed, either after expiration of the required archive duration or for legal or ethical reasons, such action will be carried out only after considering all legal and ethical perspectives. The interests and contractual stipulations of third-party funders and other stakeholders, employees and partner participants in particular, as well as the aspects of confidentiality and security, must be taken into consideration when decisions about retention and destruction are made. Any action taken must be documented and be accessible for possible future audit.

35. Responsibilities, Rights, Duties • This policy for the management of research data applies to all researchers active at the [name of research institution]. The policy was approved by the [dean/commission/authority] on [date]. In cases when research is funded by a third party, any agreements made with that party concerning intellectual property rights, access rights and the storage of research data take precedence over this policy.

36. 5.1. Researchers are responsible for: Management of research data and data sets in adherence with principles and requirements expressed in this policy; Collection, documentation, archiving, access to and storage or proper destruction of research data and research-related records. This also includes the definition of protocols and responsibilities within a joint research project. Such information should be included in a Data Management Plan (DMP), or in protocols that explicitly define the collection, administration, integrity, confidentiality, storage, use and publication of data that will be employed. Researchers will produce a DMP for every research project. Compliance with the general requirements of the funders and the research institution; special requirements in specific projects should be described in the DMP; Planning to enable, wherever possible, the continued use of data even after project completion. This includes defining post-project usage rights, with the assignation of appropriate licences, as well as the clarification of data storage and archiving in the case of discontinued involvement at the [name of university/research institution]; Backup and compliance with all organisational, regulatory, institutional and other contractual and legal requirements, both with regard to research data, as well as the administration of research records (for example contextual or provenance information). To ensure appropriate institutional support, it is required that new research projects are registered at the proposal stage at [name of research institution/central body].

37. 5.2. The [name of research institution] is responsible for: Empowerment of organisational units, providing appropriate means and resources for research support operations, the upkeep of services, organizational units, infrastructures, and employee education; Support of established scientific practices from the beginning. This is possible through the drafting and provision of DMPs, monitoring, training, education and support, while in compliance with regulations, third-party contracts for research grants, university/institutional statutes, codes of conduct, and other relevant guidelines; Developing and providing mechanisms and services for the storage, safekeeping, registration and deposition of research data in support of current and future access to research data during and after the completion of research projects; Providing access to services and infrastructures for the storage, safekeeping and archiving of research data and records, enabling researchers to exercise their responsibilities (as outlined above) and to comply with obligations to third-party funders or other legal entities.

38. 6. Validity This policy will be reviewed and updated as required by the head of/the director of the [name the research institution] every [two years].

39. http://learn-rdm.eu/wp-content/uploads/RDMToolkit.pdf?pdf=RDMToolkithttp://learn-rdm.eu/wp-content/uploads/RDMToolkit.pdf?pdf=RDMToolkit Published in LEARN Toolkit in April 2017

40. 4 What else?

41. Published in LEARN Toolkit: http://learn-rdm.eu/wp-content/uploads/RDMToolkit.pdf?pdf=RDMToolkit Guidance Document for Policy Development

42. Merge of LEARN findings and Use Case in Austria • Adaptation to needs of five Austrian Art Universities and (started) four Medical Universities • Validation of Policy for discipline-specific needs Outreach to Continental Europe: AUSTRIA

43. Expansion of policy activities to Italy (mainly in Venice, Padua, Milan and through CINECA) • Validation of Policy in Italian language Outreach to Continental Europe: ITALY

44. ECLAC study on RDM policies in LAC • Mini-Workshops with ECLAC Outreach to LATIN AMERICA

45. July 2015-August 2016: Collection and analysis of over 40 European RDM policies with the use of an analysis grid with 25 criteria Policy Evaluation Grid Results available for download at: http://phaidra.univie.ac.at/o:459219

46. Paolo Budroni Katharina Flicker Imola Dora Riehle-Traub Raman Ganguly Barbara Sánchez Solís UNIVIE Team name.surname@univie.ac.at