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LEARN Final Conference: Tutorial Group | Implementing the LEARN RDM Toolkit

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This presentation was given at the Final Conference of the LEARN Project, 5 May 2017. www.learn-rdm.eu

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LEARN Final Conference: Tutorial Group | Implementing the LEARN RDM Toolkit

  1. 1. UCL LIBRARY SERVICES How to implement the LEARN Toolkit and Executive Briefing Dr Paul Ayris Pro-Vice-Provost (UCL Library Services) Co-Chair of the INFO LERU community Adviser to the LIBER Board Chair Jisc Content Strategy Group e-mail: p.ayris@ucl.ac.uk
  2. 2. UCL LIBRARY SERVICES Content  LEARN  Project Objectives  LEARN Toolkit  LEARN Executive Briefing and Recommendations  Conclusions 2 Plaster Relief by John Flaxman, Flaxman Gallery, UCL
  3. 3. UCL LIBRARY SERVICES LEARN  5 partners  UCL (University College London) – lead partner  University of Barcelona  University of Vienna  LIBER  ECLAC – UN Commission for Latin America and the Caribbean  Started in June 2015; runs for 24 months  €497,000 budget  100% funded 3 Wilkins Building, UCL, 1826
  4. 4. UCL LIBRARY SERVICES LEARN 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 six languages) 4 Wilkins Building, UCL, 1826
  5. 5. UCL LIBRARY SERVICES What is the problem LEARN seeks to address? How prepared are you and your institution for RDM? 5 Plaster Relief by John Flaxman, Flaxman Gallery, UCL
  6. 6. UCL LIBRARY SERVICES UCL survey by Research Data Advocacy Officer  130 research departments, institutes, centres and units represented in survey  Response rate – 306 completed surveys out of 619  Respondents  18% early career researchers  39% experienced researchers  30% research students 6
  7. 7. UCL LIBRARY SERVICES  45% of respondents used a personal computer for storage  Choices included Cloud services; others used paper...  Central UCL facility used by only 5% 7 17 196 130 116 145 112 64 60 22 178 112 109 122 79 36 21 21 19 105 68 50 94 45 50 22 22 29 0 50 100 150 200 250 300 350 400 External hard drive or USB stick Hard drive of personal PC or laptop Cloud service (e.g. Dropbox, Google Docs, iCloud) Shared drive / UCL server Hard drive of UCL PC or laptop On paper, on UCL premises On paper, off-site Hard drive of instrument / sensor Institutional repository (e.g. Research Data Storage; Discovery) for long-term archiving to back them up while working on them Qu.34 In your most recent project, where did you keep your data? (433 respondents; multiple choice)
  8. 8. UCL LIBRARY SERVICES 8 20 29 30 33 49 52 57 74 84 89 94 111 143 175 0 50 100 150 200 Other Commercial questions related to your data No help needed Citing data Ethical questions related to your data Finding data and publications to re-use Creating metadata Legal questions related to your data Sharing data Open Access to your publications Project budget and costing data management Storage & preservation of data (MOSTLY personal and sensitive) Data Management Plans Storage & preservation of data (NOT personal or sensitive) Qu.64 Would you be interested in some help with data management? Please tick up to FIVE preferred elements from the list below. (308 respondents; multiple choice)
  9. 9. UCL LIBRARY SERVICES 9 Beginning of the project ("very early on", "straightaway", "pre- protocol", "at the outset", etc.) 51% "Always" ("all the time", "throughout") 16% Project development ("proposal writing", "for ethical review", "planning", etc.) 14% Before or after "data collection" ("questionnaire design", "fieldwork preparation", etc.) 4% During the project ("periodically", "halfway through", "1st year of PhD") 4% "Never" 4% "Late" / "too late" 2% End of the project ("at the end", "towards the end") 1% Project completion ("ready for publication", "database completion") 1% "Ad-hoc" 1% When a problem occurred 0,5% "Not until I took this survey" 0,5% Qu.61 At what stage of the project did you think about data management? (217 respondents; free text)
  10. 10. UCL LIBRARY SERVICES Content  LEARN  Project Objectives  LEARN Toolkit  LEARN Executive Briefing and Recommendations  Conclusions 10 Plaster Relief by John Flaxman, Flaxman Gallery, UCL
  11. 11. UCL LIBRARY SERVICES  23 chapters of Best Practice Case Studies in 8 sections  http://learn- rdm.eu/en/about/  Policy and Leadership  Advocacy  Subject approaches  Open Data  Research Data Infrastructure  Costs  Roles, Responsibilities, Skills  Tool development 11
  12. 12. UCL LIBRARY SERVICES 12 Case Study 11: Professor Geoffrey Boulton: Why Open Data?
  13. 13. UCL LIBRARY SERVICES Case Study 18: Paul Ayris & Ignasi Labastida: Training Early Career Researchers 13  LERU (League of European Research Universities) held week-long Doctoral Summer School in July 2016 on research data  UCL (University College London) has begun a Training Programme with the Doctoral School WHO Postgrad/PhD Senior Researcher Librarian Data Scientist WHEN Early stages of postgraduate study As needed, or at beginning of research project/proposal state CPD for subject librarians/during library education Discipline-specific academic courses (doctoral)/CPD WHAT Basics of data management practice, FAIR principles, data citation, data evaluation. Competence in legal and ethical issues. Training on discipline- specific data management practices; an understanding of the FAIR principles; how to write a data management plan (tailored as necessary to funder requirements), data reuse skills. Competence in legal and ethical issues. Data curation. An understanding of the FAIR principles. Some disciplinary- specific e-research methods (TDM)/data collection skills, IT skills. Competence in legal and ethical issues Discipline-specific skills for data management/ exploitation/ interoperability. An understanding of the FAIR principles. Competence in legal and ethical issues HOW Credited models Practical training Accredited CPD/Professional courses Professional (academic) courses and accredited CPD
  14. 14. UCL LIBRARY SERVICES Case Study 23: Paul Ayris & Ignasi Labastida: Surveying your level of preparation for research data management  13 Questions  Answers Red, Amber, Green (RAG)  Score reveals your level of preparation  Survey can be taken iteratively to show progress  350 responses (Mar17) 14
  15. 15. UCL LIBRARY SERVICES 15 Take the survey - http://learn- rdm.eu/en/rdm-readiness- survey/
  16. 16. UCL LIBRARY SERVICES Case Study 22: Fernando-Ariel López: Developing a Data Management Plan: a Case Study from Argentina 16
  17. 17. UCL LIBRARY SERVICES  Main elements of an RDM Policy:  Identify what questions the policy is meant to answer and who owns the policy 1. Preamble, setting policy statements into a local context 2. Scope of the policy – who it covers and how it deals with current legal commitments 3. Intellectual Property Rights 4. Handling research data – curation, destruction, 17
  18. 18. UCL LIBRARY SERVICES  Main elements of an RDM Policy: 5. Responsibilities, Rights, Duties 6. Review period for the policy  Annex of appropriate definitions to help with policy development 18
  19. 19. UCL LIBRARY SERVICES Palo Budroni and the University of Vienna: LEARN Model RDM Policy (drawn from evaluation of 20 European policies) 19
  20. 20. UCL LIBRARY SERVICES Content  LEARN  Project Objectives  LEARN Toolkit  LEARN Executive Briefing and Recommendations  Conclusions 20 Plaster Relief by John Flaxman, Flaxman Gallery, UCL
  21. 21. UCL LIBRARY SERVICES  Executive Briefing in 6 languages which presents strategic issues for decision makers  Presents the RDM challenge, Solutions, the need for an RDM Policy, the benefits of FAIR data, the requirement for RDM stewardship, infrastructure and training and RDM funding needs 21
  22. 22. UCL LIBRARY SERVICES 20 Best Practice Recommendations  20 Recommendations derived from the RDM community attending LEARN Workshops  Policy and Leadership  Open Data  Advocacy  Costs  Roles, Responsibilities and Skills 22
  23. 23. UCL LIBRARY SERVICES Conclusions  Data-driven research changing the way research is undertaken  LEARN has provided  Model RDM policy  Exemplar case studies  Executive Briefings and Recommendations  Self-assessment survey and KPIs  LEARN will help deliver infrastructure for data- driven Scholarship

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