Practical Support

The FAIR Toolkit is designed to provide support for management of the FAIR data life cycle as illustrated in Figure 1 below. It places emphasis on the practical aspects of FAIR data management through the leverage of existing resources that are most relevant to the needs of Life Science industry.

Figure 1: The FAIR Toolkit supports management of the FAIR data life cycle.

Use Cases

The FAIR Toolkit brings together a set of use cases from large enterprises in pharmaceuticals, agrifood, veterinary healthcare and smaller technology companies. These use cases show the benefits of FAIR data management as a key operational process to gain maximum value from data as a corporate digital asset.

Methods

The FAIR Toolkit also provides a set of Tools, Training and Change methods as shown in Figure 2 below.

  • Methods for 1) Data Management Plans, 2) Data granularity and context, 3) BYOD datathon workshops and 4) Readiness for change can be used to prepare FAIR objectives for data management.
  • Methods for 5) Data stewardship and¬† 6) Change agents are complementary which can be considered together to support the generic 7) FAIRification workflow method.
  • Initial FAIRness of data is determined with the Maturity Indicator methods for 8) Findability 9) Accessibility 10) Interoperability and 11) Reusability¬† to understand what improvements in FAIRness can be made and aligned to the FAIR objectives as part of the 7) FAIRification workflow method.
  • Finally, at the organisational level, FAIR data management can benefit from building a 12) Data Capability Maturity Model to understand the optimal level of FAIRness required to realise the full value of corporate data assets in a particular enterprise.

Figure 2: The FAIR Toolkit includes a set of Tools, Training and Change methods