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LEARN Webinar

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Slides for the LEARN Webinar on our RDM Toolkit. For more information, please see www.learn-rdm.eu

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LEARN Webinar

  1. 1. The LEARN Tookit: an armoury of best practice for all research performing organisations The Webinar will start at 1500 CET This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 654139.
  2. 2. SPEAKERS Martin Moyle University College London Wouter Schallier UN ECLAC Paolo Budroni University of Vienna
  3. 3. Leaders Activating Research Networks Martin Moyle – University College London WP1 Stakeholder Engagement This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 654139.
  4. 4. LEARN – LEaders Activating Research Networks • To develop the LERU Roadmap for Research Data • 5 partners • UCL (lead) • University of Barcelona • University of Vienna • LIBER • ECLAC – UN Commission for Latin America and the Caribbean • June 2015 – May 2017 http://www.learn-rdm.eu
  5. 5. •Identifies how policy development and leadership are undertaken Policy and Leadership •Who undertakes advocacy and what is the message? Advocacy •Technical issues around collection and curation Selection, Collection, Curation, Description, Citation, Legal Issues •Where is it stored and by whom? Research Data Infrastructure •How much does it cost? Costs •What skills are required by which communities? Roles, Responsibilities, Skills •Who does what? Recommendations to different stakeholder groups http://www.leru.org/files/publications/AP14_LERU_Roadmap_for_Research_data_final.pdf LERU Roadmap for Research Data
  6. 6. LERU Roadmap: Key Messages • Research-performing organisations are not ready for the challenges of research data management • Universities should have Research Data Management Policies • Researchers should have Research Data Management Plans • Benefits of Open Data for sharing and re-use should be advocated http://www.leru.org/files/publications/AP14_LERU_Roadmap_for_Research_data_final.pdf
  7. 7. Deliverables • Model Research Data Management Policy • Fed by a study of RDM policies and input from Workshop attenders • Toolkit to support implementation • Issues identified in Workshops and in literature • Surveys and self assessment tools • Executive Briefing (in several languages) http://www.learn-rdm.eu
  8. 8. http://www.learn-rdm.eu
  9. 9. 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
  10. 10. Case Study 10: James Bulley and Andrew Gray: RDM in the Performing Arts http://www.learn-rdm.eu
  11. 11. Case Study 11: Professor Geoffrey Boulton: Why Open Data? http://www.learn-rdm.eu
  12. 12. Case Study 17: Robin Rice and David Fergusson: RDM at the University of Edinburgh: How is it done, what does it cost? http://www.learn-rdm.eu
  13. 13. Case Study 23: Paul Ayris & Ignasi Labastida: Surveying your level of preparation for research data management Take the survey - http://learn-rdm.eu/en/rdm- readiness-survey/
  14. 14. Toolkit Part 3: LEARN Executive Briefing http://www.learn-rdm.eu
  15. 15. Leaders Activating Research Networks Paolo Budroni – University of Vienna WP3 Policy Development and Alignment This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 654139.
  16. 16. Outline 1. LEARN - The Mission 2. Understanding Policies From Taboos to Policies 3. Model Policy for Research Data Management (RDM) at Research Institutions/Institutes 4. 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.
  17. 17. 1 LEARN - The Mission
  18. 18. Mission • Produce an exemplar Research Data Management Policy (agreed by all Partners) • Produce a Policy and a Guidance which can be tailored by any University or Research Institution to meet their needs • Enhance Policy Coordination & Alignment
  19. 19. 2 Understanding Policies: From Taboos to Policies
  20. 20. Going over to related Principles Going over to the creation of a Policy Starting with some Taboos Looking for an Ontology From Taboos to Policies 1 2 3 Going over to Rules, Legislations and Regulations (canons, norms, guidelines)4
  21. 21. Taboo 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.
  22. 22. Principle 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
  23. 23. Policies/ 1 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.
  24. 24. Policies /2 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)
  25. 25. Rules, Regulations/ 1 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)
  26. 26. Rules, Regulations /2 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.“
  27. 27. From Taboos to Policies Taboos Principles Policies Rules Negative assertion few “You shall not delete scientific data” “Youl shall not destroy infrastrcutures” Positive assertion more than „few“ “Research data are to be kept FAIR - Findable, Accessible, Interoperable, Reusable.” “Research data infrastructures are to be kept accessible” A course or principle of action. Policy offers the frame for the generation of Rules, should not be in competition with other policies “The Institution [name XY] will preserve its research data infrastructure always accessible and free to its members according to the FAIR principles” Rules prescribe conducts or actions; define who what when and where should be done according to the Policy “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. “
  28. 28. Why these differentiations? • 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
  29. 29. 3 The Model Policy
  30. 30. How we started 1. Identification of Funders in the German speaking countries (DACH) 2. Collection of European RDM policies and first analysis 3. Production of 2 grid documents for policies: • Formal Elements • Content Elements of policies
  31. 31. How we continued • 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
  32. 32. 1. Preamble 2. Jurisdiction 3. Intellectual Property Rights 4. Handling Research Data 5. Responsibilities, Rights, Duties 5.1. Researchers are responsible for:... 5.2. The [name of research institution] is responsible for:… 6. Validity
  33. 33. 1. 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.
  34. 34. 2. 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.
  35. 35. 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.
  36. 36. 4. 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.
  37. 37. 4. 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.
  38. 38. 5. 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.
  39. 39. 5.1. Researchers are responsible for: a)Management of research data and data sets in adherence with principles and requirements expressed in this policy; b)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. c)Compliance with the general requirements of the funders and the research institution; special requirements in specific projects should be described in the DMP; d)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]; e)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). f)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].
  40. 40. 5.2. The [name of research institution] is responsible for: a)Empowerment of organisational units, providing appropriate means and resources for research support operations, the upkeep of services, organizational units, infrastructures, and employee education; b)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; c)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; d)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.
  41. 41. 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].
  42. 42. Published in LEARN Toolkit in April 2017 http://learn-rdm.eu/wp- content/uploads/RDMToolki t.pdf?pdf=RDMToolkit
  43. 43. 4 What else?
  44. 44. Guidance Document for Policy Development Published in LEARN Toolkit: http://learn-rdm.eu/wp- content/uploads/RDMToolkit.pdf?pdf =RDMToolkit
  45. 45. Outreach to Continental Europe: AUSTRIA • 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
  46. 46. Outreach to Continental Europe: ITALY • Expansion of policy activities to Italy (mainly in Venice, Padua, Milan and through CINECA) • Validation of Policy in Italian language
  47. 47. Outreach to LATIN AMERICA • ECLAC study on RDM policies in LAC • Mini-Workshops with ECLAC
  48. 48. Policy Evaluation Grid July 2015-August 2016: Collection and analysis of over 40 European RDM policies with the use of an analysis grid with 25 criteria Results available for download at: http://phaidra.univie.ac.at/o:459219
  49. 49. UNIVIE Team Paolo Budroni Katharina Flicker Imola Dora Riehle-Traub Raman Ganguly Barbara Sánchez Solís name.surname@univie.ac.at
  50. 50. Paolo Budroni Paolo.budroni@univie.ac.at THANK YOU!
  51. 51. Questions? • Type your questions in the chat box • Wouter Schallier (moderator) will select and pose questions to the speakers More Information? • Please see our website, www.learn-rdm.eu • Download a free copy of the Toolkit • Test your RDM Readiness with our survey We’ll share a recording of the webinar shortly!

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