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Research Data Service at the University of Edinburgh

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Publicado el

Knowledge Exchange Week
Library & University Collections
University of Edinburgh
12 June, 2019

Publicado en: Educación
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Research Data Service at the University of Edinburgh

  1. 1. RESEARCH DATA SERVICE AT THE UNIVERSITY OF EDINBURGH Robin Rice Data Librarian and Head of Research Data Support r.rice@ed.ac.uk @sparrowbarley (Twitter) www.ed.ac.uk/is/research-data- service http://datablog.is.ed.ac.uk
  2. 2. OVERVIEW • Open Science and FAIR data as rationales for RDM • The University’s Research Data Management Policy • UoE (IS) Research Data Service: a lifecycle approach • Maturity models and strategies • The UoE RDM Roadmap • Skills and staffing • Your experiences / queries / comments
  3. 3. BENEFITS OF OPEN DATA - Journal of Open Archaeology Data, CC-BY 3.0
  4. 4. • Data that contain no personal or disclosive information, e.g. anonymised. • Open data are usually licensed under an open licence such as a Creative Commons Licence (http://wiki.creativecommons.org) and users do not need to register to access the data. • Such data can be shared openly without any restrictions. 5 OPEN DATA
  5. 5. 6 DATA SHARING CONCERNS
  6. 6. • Plan for sharing (via a Data Management Plan). • Don’t collect personal information that’s not needed. • Principle of informed consent: get consent to share data. • Attribute, anonymize, or aggregate individual’s data. • Document all data processing (inside & outside analysis package). 7 WHAT CAN A RESEARCHER DO TO BE ABLE TO SHARE?
  7. 7. FAIR PARADIGM: OPEN BY DEFAULT • FINDABLE: “Metadata and data should be easy to find for both humans and computers. Machine-readable metadata are essential for automatic discovery of datasets and services.” • ACCESSIBLE: “Once the user finds the required data, she/he needs to know how can they be accessed, possibly including authentication and authorisation.” • INTEROPERABLE: “The data usually need to be integrated with other data. In addition, the data need to interoperate with applications or workflows for analysis, storage, and processing.” • REUSABLE: “The ultimate goal of FAIR is to optimise the reuse of data. To achieve this, metadata and data should be well-described so that they can be replicated and/or combined in different settings.”
  8. 8. UNIVERSITY’S RDM POLICY (MAY, 2011) https://www.ed.ac.uk/is/ research-data-policy/ Policy by Nick Youngson CC BY-SA 3.0 Alpha Stock Images • Commitment to research integrity, DMPs, open data • Articulates clear responsibilities of the researcher and of the institution 9
  9. 9. UoE Research Data Service = Tools and support for working across the data lifecycle 10 https://www.ed.ac.uk/is/research -data-service
  10. 10. Tools and Support Description DMPOnline Online tool to create a data management plan, based on University and funders’ templates Support and DMP Review Answer enquiries and review plans, provide advice; in-depth or quick turaround Sample DMPs Library of successful plans to show researchers in different disciplines Before your research project begins 11
  11. 11. Tools and Support Description Finding data ‘Finding data’ portal and data librarian consultancy; help with accessing / purchase of datasets or data subscriptions Active data storage (DataStore) Central, backed up storage for all researchers - individual and shared spaces Sensitive data (Data Safe Haven) New, secure facility for working with sensitive data on remote server. We are pursuing ISO 27001 security certification Code versioning (Subversion, Gitlab) Private or public software code storage and management. Documents all code and allows rollback to prior versions Collaboration and data sync’ing (DataSync) Open source tool to allow external partners to access your research data Electronic Lab Notebook (RSpace) Data management for laboratory based research; interoperable with local systems During your research project 12
  12. 12. Tools and Support Description Open Access data repository (DataShare) Allows researchers to share data publicly and preserve for long-term Long-term retention (DataVault) Deposit datasets for a specified retention period (for example, 10 years), immutable copy Data asset register through the University CRIS (Pure for datasets) Record a description of your dataset along with your publications and research projects After your research project is finished 13
  13. 13. Tools and Support Description General RDM support Answer enquiries by email, phone or appointment; track through helpdesk system Online training (MANTRA and RDMS MOOC) Learn online at your own pace or with a cohort of peers through our open educational resources Scheduled and bespoke training Sign up for a scheduled workshop or request a special training session for your research group Research Data Service website All the tools and support in one place, increasingly self-serve Blog and promotional materials New developments on our Research Data Blog. Service video and brochure Dealing with Data annual event & workshop series Annual conference of researchers talking about their data challenges and solutions Research Data Workshop series in various settings Compact, catered networking events for researchers to engage with the service & each other about challenging topics Training and support throughout your project
  14. 14. A MATURITY MODEL FOR RDM SERVICES Cox, A. et al. “Developments in Research Data Management in Academic Libraries: Towards an Understanding of Research Data Service Maturity” Journal of the Association for Information, Science and Technology - September 2017 p. 2191. DOI: 10.1002/asi
  15. 15. RDM ROADMAP (LIVING DOCUMENT) Frank da Silva on Flickr CC BY- NC-ND 2.0 Academic-led steering group governs the service 1st, August 2012 –May 2015: Rollout and consolidation 2nd, September 2015 – July 2016: Transition, programme to service 3rd, August 2017-July 2020: User journey, filling gaps 32 prioritised objectives with actions and deliverables
  16. 16. STAFF FUNDED BY DEDICATED RDM ALLOCATION • Senior staff: data librarian and two team leaders – librarian or equivalent background, Masters and PhD • 1 p/t Research Data Support Officer – trainer background • 3 (2.5 FTE) Research Data Support Assistants (research backgrounds, subject Masters and PhD) • IT infrastructure manager and 2 IT systems engineers • 1.5 software engineers
  17. 17. VALUED SKILLS AND PRIORITIES IN RDM SERVICES (A. COX, ET AL) Cox, A. et al. “Developments in Research Data Management in Academic Libraries: Towards an Understanding of Research Data Service Maturity” JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY - September 2017 p. 2191. DOI: 10.1002/asi
  18. 18. • “Personal Attributes” - most highly rated category overall (70% respondents ranked Very important +) • “Library Skills” - lowest rated category (40%) • Top 5 items: “Developing relationships with researchers, faculty, etc.”; “Oral communication and presentation skills”; “Teamwork and interpersonal skills”; “Written communication skills”; and “One-on-one consultation or instruction.” • Bottom 5 items: “PhD or doctoral degree”; “Professional memberships”; “Cataloging”; “Graduate degree in a [subject discipline]”; & “Collection dev’t.” SOFT SKILLS HIGHLY RATED IN A STUDY OF LIBRARIANS DOING DATA-RELATED WORK Federer, Lisa. (2018). Defining data librarianship: A survey of competencies, skills, and training. Journal of the Medical Library Association. 106. 10.5195/JMLA.2018.306.
  19. 19. THANKS AND SORRY ABOUT THE RAIN What are your thoughts / questions?

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