The ‘FAIR Guiding Principles for scientific data management and stewardship’ provide guidelines to improve the findability, accessibility, interoperability, and reuse of digital assets. These principles emphasize machine-actionability to support the advances in automation and AI. Humans rely on computational support to deal with the exponential growth in the range and volume of biomedical data, making the application of FAIR principles a critical success factor.

There have been a lot of efforts to make data FAIR. FAIR data is crucial, but it doesn’t go far enough -- the processing of data also needs to be FAIR -- thus the applications also need to be FAIR!

A FAIR Application or FAIR App is a collection of computer executable code, as simple as a script or as complex as a fully virtualized multistep analytics pipeline packaged and combined with its metadata description into a FAIR digital object. To ensure that a FAIR App can be executed, all program dependencies must be reliably and consistently externally resolvable or must be included or packed in an App container.

FAIR Apps therefore need to have the following properties:

Findable through a globally unique ID, and digitally signed to allow verification of their authenticity

Accessible because they can be looked up in and pulled from an associated permanent master registry

Interoperable because they rely on standards

Reusable because they are self-contained and fully portable to run on different execution platforms without modification and they can be used with different data and configuration sets without modification

Databiology's CIAO is a toolkit for turning any software into a FAIR App compliant with the above principles.

To learn more about FAIR Apps, please also consider:

Build a FAIR in 10 minutes with the CIAO Tutorial