FAIR applied: a practical FAIRification guide for life science data from FAIRplus
- This workshop session is sponsored by ELIXIR
- Date: Sunday, September 18th
- Time: 9-13h
- Format: Face-to-face
- Tony Burdett, EMBL-EBI (United Kingdom)
- Ibrahim Emam, Imperial College (United Kingdom)
- Nick Juty, University of Manchester (United Kingdom)
- Philippe Rocca-Serra, University of Oxford (United Kingdom)
- Danielle Welter, University of Luxembourg (Luxembourg)
- Fuqi Xu, EMBL-EBI (United Kingdom)
We will demonstrate the FAIRplus approach to improving the FAIRness of
life science data through systematic, practical and easy to adopt
processes, tools and recommendations. The FAIRplus approach has been
defined and verified through real-world FAIR implementations developed to
enable and support FAIR Data stewardship in life science research projects
(IMI) and pharmaceutical research environments.
The FAIR principles were established in 2016 and have seen widespread adoption across the life sciences, being seen as a set of guidelines to promote good data management and stewardship. However, despite wide community adoption of the principles themselves, practical details on how to implement the principles to become FAIR are often too generic and lack the level of domain-specificity that facilitates actual execution of practical steps towards achieving real value-added FAIRified data. Within FAIRplus, we have used the FAIR principles to develop a practical and systematic framework for data FAIRification. Our approach consists of a set of general processes that can be adopted by any life-science-based data-generating
projects, a FAIR maturity model for establishing the current state of FAIRness and a target state with practical domain-specific requirements to achieve the desired level of FAIR maturity, the FAIR cookbook containing practical guidance on FAIR best practices through specific recipes, and recommended tools that can be used within these recipes. In this workshop, participants will learn how to assess their own level of FAIRness against the FAIR principles and how to practically and easily apply FAIRplus methodology and tools to improve their own FAIR maturity
Researchers and data managers who are interested in promoting the reuse of their life sciences datasets through the FAIR principles. Expected audience range from 30-60 participants.
Data managers and data stewards who are interested in learning about hands-on practices and tools to support and enable the implementation of FAIR principles to life sciences datasets.
To get the most out of the workshop, participants are expected to be familiar with concepts and techniques involved in biocuration, preferably with experience in generating or managing life science projects’ datasets.
Maximum number of attendees
Material required (for participants)
No materials are required, but attendees wishing to apply the processes and tools presented in the workshop will benefit most if they come with a project in mind that has clearly identified datasets.
|09:00 – 10:30||Part I: Introductions to FAIRplus FAIRification process and tools|
Introductions to the FAIRification process, FAIR wizard, FAIR Dataset Maturity model and the FAIR Cookbook
|10:30 – 11:15||Part II: Goal Driven FAIRification|
Practical – Setting a FAIRification Goal using the FAIR wizard: a tool that guides users towards generating an actionable FAIRification goal
|11:15 – 11:30||Break|
|11:30 – 12:15||Part III: Data Driven FAIRification|
Practical: using the FAIR Dataset Maturity (FAIR-DSM) model: a hands-on FAIR maturity guidance and assessment tool
|12:15 – 13:00||Part IV: Develop a FAIRification task plan + Feedback and Review|
– Feedback review – what worked well, what could be improved.
– How to develop a FAIRification task plan – a walk-away actionable plan based on the outputs from the previous sessions that provides guidance on the next steps to take towards achieving the FAIRification goal.
Workshop Expected Outcomes for the attendees
Develop a concrete actionable FAIRification goal to motivate and drive the FAIRification process
Run an assessment of the current state of their project dataset
Receive guidance on which components could be improved from a FAIR maturity perspective
Produce a list of tasks (Process diagram) to reach this goal