Research Data Management Workshop #4: Understanding FAIR Data Principles

Workshop Summary:

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.

Course contents:

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?

Workshop Details:

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.

Upcoming Workshops:

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: plato.smith@ufl.edu
ORCiD: http://orcid.org/0000-0003-1814-0151

References:

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.