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Research data support: a growth area for academic libraries?


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Research data support: a growth area for academic libraries?

  1. 1. Research data support: a growth area for academic libraries? Robin Rice University of Edinburgh Future of Libraries Conference Indian Institute of Management Bangalore 26-Feb-2019
  2. 2. On Future of Libraries theme…
  3. 3. Overview 1. Open Science (Open Research) as a current and future driver for Research Data Management (RDM) 2. The impetus for RDM policy and culture change within Higher Education Institutions – libraries as leaders for research data support 3. What do research data management services look like? University of Edinburgh as example 4. Changing skills and priorities in academic libraries to adapt to the data revolution
  4. 4. The four pillars of Open Science Image from Foster - What is open science online training course
  5. 5. Open Science: a definition • Open Science has been defined as the combination of “Open Source, Open Data, Open Access, Open Notebook”, which together signify the goals of: – Transparency in experimental methodology, observation, and collection of data; – Public availability and reusability of scientific data; – Public accessibility and transparency of scientific communication; – Using web-based tools to facilitate scientific collaboration [Dan Gezelter,] With acknowledgement to Martin Donnelly and the Digital Curation Centre
  6. 6. FAIR paradigm: Open Data 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.”
  7. 7. FAIR Data visualised From: European Commission Horizon 2020 infographic; July 2016
  8. 8. Benefits of Open Data - Journal of Open Archaeology Data, CC-BY 3.0
  9. 9. Not all data can be open: “As open as possible, as closed as necessary” From: European Commission Horizon 2020 infographic; July 2016
  10. 10. How can research libraries drive culture change? e.g. LIBER 2018-22 strategy • Open Access is the predominant form of publishing; • Research Data is Findable, Accessible, Interoperable and Reusable (FAIR); • Digital Skills underpin a more open and transparent research life cycle; • Research Infrastructure is participatory, tailored and scaled to the needs of the diverse disciplines; • The cultural heritage of tomorrow is built on today’s digital information.
  11. 11. RDM policy and culture change within Higher Education Institutions (HEIs) • Research data management  FAIR data • BUT culture change can’t be forced and academic norms are resistant to change • HEIs need to create Open Science/RDM policies in response to pressure from funders, publishers, the public, laws • How can librarians lead their institutions if their job is to provide support?...
  12. 12. Provide user-centric support /service! • Data-driven researchers may not value libraries; a chance to change their mind • Researchers suffering from information overload (data deluge), lacking skills • Start by finding RDM champions; collaborate to pilot new services & evaluate • Gather evidence; do a needs assessment • Focus on advice and guidance first; learn how to tailor services that suit through engagement
  13. 13. 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
  14. 14. Pragmatic pointers for libraries to get started in RDM • A “top ten” list of recommendations for libraries to get started with research data management from LIBER, • Research Data Alliance (RDA) 23 things • LEARN RDM Toolkit including a model policy
  15. 15. About UoE Information Services • Applications • IT Infrastructure • Learning, Teaching & Web • Library & University Collections • User Services • Information Security • EDINA • Digital Curation Centre 16 Argyle House © CoStar
  16. 16. Univ. of Edinburgh: RDM Policy (2011) as framework for building services “The University will provide mechanisms and services for storage, backup, registration, deposit and retention of research data assets in support of current and future access, during and after completion of research projects.” “All new research proposals must include research data management plans or protocols that explicitly address data capture, management, integrity, confidentiality, retention, sharing and publication.” “Research data of future historical interest, and all research data that represent records of the University, including data that substantiate research findings, will be offered and assessed for deposit and retention in an appropriate national or international data service or domain repository, or a University repository.” data-policy
  17. 17. UoE Research Data Service = Tools and support for working across the data lifecycle 18 -data-service
  18. 18. 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 19
  19. 19. 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 Research in progress 20
  20. 20. 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 Approaching completion 21
  21. 21. Tools and Support Description General RDM support Answer enquiries by email, phone or appointment; track through central 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 RDM Forum & Sharepoint site Regular meetings for school support staff plus access to shared resources Dealing with Data event Attend an annual conference of researchers talking about their data challenges and solutions Training and support throughout your project 22
  22. 22. Changing skills and priorities in academic libraries? (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
  23. 23. What skills do data librarians find important? (1 of 3 slides) • 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. • Methods: Librarians who do data-related work were surveyed about their work and educational backgrounds and asked to rate the relevance of a set of data-related skills and knowledge to their work.
  24. 24. • “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.” Surprise! Soft skills highly rated
  25. 25. “Data generalists vs subject specialists” • Federer found both of these types of data professionals in her study. • Items that subject specialists rated as more important than the data generalists reflected the more specialized areas that these respondents likely supported. • For example, subject specialists rated “Bioinformatics support,” “Support for clinical data management,” and “Support for data resources.
  26. 26. Librarians can skill themselves up on open science /data - • Data, software, & library carpentry • FOSTER open science online training • Research Data MANTRA • Research Data and Sharing MOOC • Other data analysis, analytics and data science MOOCs • When all else fails, read a book
  27. 27. Many thanks!

Editor's Notes

  • Open notebook = sharing methods and workflows to enable replication
  • “The principles refer to three types of entities: data (or any digital object), metadata (information about that digital object), and infrastructure. For instance, principle F4 defines that both metadata and data are registered or indexed in a searchable resource (the infrastructure component).”
  • Powering Sustainable Knowledge in the Digital Age