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

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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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
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Discover how Bayer builds a harmonised FAIR data asset based on Health Care Professional partners.

  • A knowledge graph from federated data integration
  • Enables reuse by different consumers
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Find out how the data stakeholders can prepare for the changes necessary to make data FAIR.

  • Readiness for change provides the means to engage a wider group of staff throughout an organisation.
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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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Learn how to apply the FAIR Maturity Indicators to measure the ACCESSIBILITY of the data and metadata.

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