WHAT IS IN THE FAIR TOOLKIT?
- Why FAIR data matters for Life Science industry
- Use cases to exemplify the benefits of FAIR implementation by Life Science industry
- How-to methods for FAIR tools, training and change management
- Tips for Life Science industry and links to relevant resources
WHO IS THE FAIR TOOLKIT FOR?
- Data Stewards
- Laboratory Scientists
- Business Analysts
- Science Managers
The Hyve present an approach to making new collaborative scientific research data FAIR in a real time manner on the internet.
- Rapid development of a semantic model expressed as subset of schema.org
- Reusable static web site generator code
Hear more about Roche’s ‘learning-by-doing’ FAIRification efforts
- Lessons learned from FAIRification of clinical data for Ophthalmology, Autism, Asthma and COPD
- Set up integrated end-to-end process for curation workflows for prospective studies
How to unlock the value of RNA sequencing (RNA-Seq) and microarray data from a public repository Gene Expression Omnibus (GEO)
- Using machine-learning assisted curation, guided by the FAIR principles
- Leverage ontologies and controlled vocabularies for annotation
A Data Management Plan documents the specific attributes expected for your FAIR objectives.
- Prepare the Data Management Plan as early as possible
Find out more >
Consider how the granularity and context of data and associated metadata to help to inform your FAIR objectives.
- Understand the granularity and context of the data as early as possible
Find out how to apply the FAIR Maturity Indicators to measure the FINDABILITY of the data and metadata.
- Findability of data is compared with your FAIR objectives to identify and make improvements in an iterative manner
CREATED BY LEADING LIFE SCIENCE ORGANISATIONS
