e-learning
FAIR Galaxy Training Material
Abstract
Encouraging computational reproducibility in research, we will present a variety of data stewardship recommendations that we have found useful in the process of training development. As part of that process, we are exploring the application of the FAIR (Findable, Accessible, Interoperable, Reusable) guidelines to the Galaxy Training Network (GTN) materials, in order to improve their secondary use and adaptation.
About This Material
This is a Hands-on Tutorial from the GTN which is usable either for individual self-study, or as a teaching material in a classroom.
Questions this will address
- What are the FAIR training materials?
- How to test, reproduce and share your content?
- How to collaborate and don’t duplicate?
Learning Objectives
- Learn about metadata and findability
- Learn how to support system and content curation
DOI: https://gxy.io/GTN:T00350
Licence: Creative Commons Attribution 4.0 International
Keywords: FAIR Data, Workflows, and Research, fair, gtn, training
Target audience: Students
Resource type: e-learning
Version: 5
Status: Active
Prerequisites:
FAIR in a nutshell
Learning objectives:
- Learn about metadata and findability
- Learn how to support system and content curation
Date created: 2023-05-30
Date modified: 2024-03-27
Date published: 2023-05-30
Contributors: Helena Rasche
Activity log
