Reusability in the FAIR principles aims to enable workflows that can be reused in multiple contexts. This includes reproducibility of the same test data used for the development of the workflow, reuse of the workflow for similar or dissimilar data to analyse the usage for different types of data, and lastly, extending the functionalities of software to incorporate multiple data types and projects. Workflows can evolve and develop over time, with each component changing differently. So, enabling reusability on all levels is essential by providing detailed provenance about the workflow and its execution with different datasets. Provenance is metadata about data entities, activities or steps, and agents that generate the data using the workflow and the relational connection between research artefacts. Provenance could either be "Prospective", which captures an abstract specification of the workflow, serving as a blueprint for future reuse of the workflow, or "Retrospective", which captures information about the execution of the workflow with different data, and the derivation of different data entities . FAIR workflows should have associated prospective provenance and ideally, there is also retrospective provenance available for a typical usage of the workflow. The workflow should also be provided with a F/OSS licence for the subsequent users or developers to self-determine how to build upon, change, use, access, or distribute the workflow.