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
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
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
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
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
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