FAIR Data, Workflows, and Research
These lessons will teach you how to make your research objects more FAIR with practical, hands-on advice.
Material
You can view the tutorial materials in different languages by clicking the dropdown icon next to the slides (slides) and tutorial (tutorial) buttons below.FAIR Data Management
The FAIR (Findable, Accessible, Interoperable, Reusable) data stewardship created the foundation for sharing and publishing digital assets. These lessons will apply to machine accessibility and emphasize that all digital assets should share data in a way that will enable maximum use and reuse.
Make workflows FAIR with RO-Crate
This section brought to you by the BY-COVID project, and will teach you how to make your research objects more FAIR with practical, hands-on advice.
| Lesson | Slides | Hands-on | Recordings | Input dataset | Workflows |
|---|---|---|---|---|---|
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RO-Crate - Introduction
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| Workflow Run RO-Crate Introduction | |||||
| Exporting Workflow Run RO-Crates from Galaxy | |||||
| RO-Crate in Python | |||||
| Best practices for workflows in GitHub repositories | |||||
| Submitting workflows to LifeMonitor |
FAIR Pointers
This series of lessons will tackle FAIR from the perspective of the 15 FAIR Principles published in 2016. You will learn about: FAIR and its origins The FAIR Principles and its basic characteristics.
| Lesson | Slides | Hands-on | Recordings | Input dataset | Workflows |
|---|---|---|---|---|---|
| FAIR and its Origins | |||||
| Metadata | |||||
| Data Registration | |||||
| Access | |||||
| Persistent Identifiers |
DataPLANT ARCs
This set of tutorials will introduce you to DataPLANT ARCs, and all the relevant tools to create your own ARCs
| Lesson | Slides | Hands-on | Recordings | Input dataset | Workflows |
|---|---|---|---|---|---|
| DataPLANT ARCs |
Other
Assorted Tutorials
| Lesson | Slides | Hands-on | Recordings | Input dataset | Workflows |
|---|---|---|---|---|---|
| An earth-system RO-Crate |
Learning Pathways
Or have a look at one of our learning pathways involving this topic. Learning pathways are sets of tutorials curated for you by community experts to form a coherent set of lessons around a topic, building up knowledge as you go. We always recommend to follow the tutorials in the order they are listed in the pathway.
Frequently Asked Questions
Common questions regarding this topic have been collected on a dedicated FAQ page . Common questions related to specific tutorials can be accessed from the tutorials themselves.
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Community Resources
Community Home Maintainer HomeEditorial Board
This material is reviewed by our Editorial Board:
Simone Leo
Luca Pireddu
Stian Soiland-Reyes
Paul De Geest
Katarzyna Kamieniecka
Krzysztof Poterlowicz
Are you on the editorial board or want to help out with this topic? View the topic maintainer page for ways to improve this topic!
Contributors
This material was contributed to by:
Cristina Martins Rodrigues
Kellie Snow
Nicola Soranzo
Marco Enrico Piras
Dominik Brilhaus
Beatriz Serrano-Solano
Yvan Le Bras
Sandra Ng
Xenia Perez Sitja
Martin Carrere
Martin Kuhl
Kevin Frey
Stian Soiland-Reyes
Munazah Andrabi
Bruno P. Kinoshita
David López
Björn Grüning
Julia Jakiela
Paul De Geest
Robert Andrews
Teresa Müller
Martin Čech
Eli Chadwick
Saskia Hiltemann
Saskia Lawson-Tovey
Laura Cooper
Andrew Mason
Philippe Rocca-Serra
Krzysztof Poterlowicz
Khaled Jum'ah
Gabriela Lopez-Gonzalez
Simone Leo
Armin Dadras
Tristan Reynolds
Leonid Kostrykin
Wendi Bacon
Sabrina Zander
Luca Pireddu
Laura Cooper
Stella Eggels
Kevin Rue-Albrecht
Gareth Price
Branka Franicevic
Nick Juty
Katarzyna Kamieniecka
Sonal Henson
Helena Rasche
Emily Angiolini
Korneel Hens
Sara Morsy
Peter Sefton
Tim Beck
Marie Josse
Funding
These individuals or organisations provided funding support for the development of this resource
