WHAT IS IN THE FAIR TOOLKIT?
- Why FAIR data matters for Life Science industry
- Use cases to exemplify the benefits of FAIR implemention 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
Learn out how SciBite unlock the value of bioassay data through semantic enrichment of metadata to create FAIR annotation.
- Unified annotation from ontologies across an area of business
- Standardized metadata for any application
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
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
Capability Maturity Model applied to data helps an organisation to determine the capability for making data assets FAIR.
- The method defines five levels of maturity for how an organisation makes and maintains FAIR data.
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