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

Find out how AstraZeneca deploy a policy for identifiers to construct a FAIR infrastructure across the enterprise.

  • A Uniform Resource Identifiers (URI) policy for the enterprise
  • A pilot server for persistent URIs
Find out more >

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
Find out more >

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
Find out more >

Learn how the responsibilities and competencies for data stewardship provides important expert advice and suport for FAIR data management.

  • A growing need for data stewardship competences in life science industry
Find out more >

Learn how change agents, such as data stewards, play an important  role to support FAIR data management and application.

  • A network of change agents coordinate data management across the organisation to support the necessary changes.
Find out more >

Find out how a generic workflow can be deployed by workshops or action team to make important datasets FAIR.

  • FAIRification as a retroactive workflow is common at this time
  • FAIRification by design (data “born” FAIR) is far more desirable for the future
Find out more >

CREATED BY LEADING LIFE SCIENCE ORGANISATIONS