This module covers making data for AI/ML FAIR (Findable, Accessible, Interoperable, and Reusable).
Development of these FAIR AI/ML training modules was supported by a supplement to NIH/NIGMS T34GM118272 (D. Julian, PI; 08/01/2021-05/31/2023).
Before the session
Before starting this session, please take this quick pre-assessment.
Exercise 1: A Hands-on exploration of data in spreadsheets.
Students recognize the importance of organizing data so that others can understand them by experiencing the challenges of reviewing data sheets that are organized in a common but inappropriate way.
- Students can explain why standardizing descriptions and organization of data is important.
- Students can strategize about the best ways to organize data.
Before the session
Prior to session 2, students should complete the activity for Exercise 2: Searching the literature for published datasets.
Students recognize the value of accessibly archived data, by experiencing the challenges of accessing data from published papers.
- Students can explain why accessible data archiving is valuable.
- Students can provide strategies for getting data from published papers, and anticipate challenges to accessing the data.
- Students can define FAIR and the identify the components FAIR data
- Students can summarize steps involved in FAIR data collection, management, and deposition.
- Students can define metadata summarize key components of metadata
- For a closer look at the Cardiovascular Disease Ontology, check out the
.obofile here: https://github.com/OpenLHS/CVDO
- For a nice, graphical interface to an ontology, check out the Disease Ontology.
- Another good example is the Plant Phenology Ontology.
Exercise 3 will introduce you to data repositories.
- Students recognize the value of shared data, and develop some skills at searching for data, by searching data repositories for datasets.
- Students learn about domain-specific and generalist repositories.
- Students evaluate metadata associated with data found in repositories.
- DataOne Data Sharing and Research Data Management handout.
- Higman, R., Bangert, D. and Jones, S., 2019. Three camps, one destination: the intersections of research data management, FAIR and Open. Insights, 32(1), p.18. DOI: http://doi.org/10.1629/uksg.468
- European Commission, Directorate-General for Research and Innovation, Turning FAIR into reality : final report and action plan from the European Commission expert group on FAIR data, Publications Office, 2018, https://data.europa.eu/doi/10.2777/54599
View the source repository on GitHub
Instructors wishing to use these resources can apply for access to the instructor repositories which contain additional lesson plans and discussion prompts.