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

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
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Roche embeds data standards and quality checks to harmonize, automate and integrate very heterogeneous and complex processes.

  • Self-contained micro services deliver performance and scalability
  • Scalable and flexible for data models in clinical and non-clinical
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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
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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
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Discover how to apply the FAIR Maturity Indicators to measure the REUSABILITY of the data and metadata.

  • Reusability of data is compared with your FAIR objectives to identify and make improvements in an iterative manner
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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
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CREATED BY LEADING LIFE SCIENCE ORGANISATIONS