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Research Data Management Workshop 4: Understanding FAIR Data Principles
October 26, 2021 @ 2:30 pm - 3:30 pm
Research Data Management Workshop #4: Understanding FAIR Data Principles
Participants will be introduced to FAIR Data Principles, examples, and develop FAIR data competencies-. FAIR data is Findable, Accessible, Interoperable, and Reusable. The FAIR term developed from the Data FAIRport initiative resulting from a Lorentz Workshop in Leiden, Netherlands in 2014. In 2018, the NIH NIEHS Superfund Hazardous Substance Research and Training Program (SRP) P42 (RFA-ES-18-002) solicitation required a new Data Management and Analysis Core (DMAC) to support NIH data sharing policies and promote best principles so data is Findable, Accessible, Interoperable, and Reusable (FAIR).FAIR is now a community standard for best practices for data.
In this workshop, participants will learn:
- What are 15 guiding FAIR Data Principles?
- How are FAIR Data Principles part of data management planning?
- How to explore a FAIR data example as guide in understanding FAIR data principles?
Instructor: Plato Smith, Ph.D. (UF George A. Smathers Libraries)
Date and Time: Tuesday, October 26, 2021, 2:30 pm – 3:30 pm
Duration: 60 minutes
Location: Virtual via Zoom
Audience: Anyone interested in and/or responsible for creating data management plans
Pre-requisite knowledge: Participants must have at least basic IT skills
Workshop is free but registration is required.
RSVP below. Instructions to attend talk via Zoom will be emailed to you.
Nov. 9, 2021: Funder Data Management Policies
Nov. 23, 2021: Data Storage
For any questions regarding the workshops, please contact Dr. Plato Smith
Plato L. Smith II, Ph.D.
Data Management Librarian
University of Florida, George A. Smathers Libraries, USA
Office: + 1 352-294-1077 | Cell: + 1 850-319-7924 | Email: email@example.com
Data FAIRPort. (2014). Find, Access, Interoperable & Re-use Data. https://www.datafairport.org/index.html.
Lamprecht, Anna-Lena et al. (2020). Towards FAIR Principles for Research Software. Data Science, vol. 3, no. 1, pp. 37 – 59. https://content.iospress.com/articles/data-science/ds190026.
Mons, Barend et al. (2017). Cloudy, Increasingly FAIR; Revisiting the FAIR Data Guiding Principles for the European Open Science Cloud. Information Services & Use, vol. 37, no. 1, pp. 49 – 56. https://content.iospress.com/articles/information-services-and-use/isu824.
The University of Edinburgh. (2021). MANTRA: Research Data Management Training. FAIR sharing and access learning unit. https://mantra.ed.ac.uk/.
Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016). https://doi.org/10.1038/sdata.2016.18.