Metadata is richly described with a plurality of accurate and relevant attributes (R1) Resubility R1 can be linked to Findability category F2, where the metadata is provided for the workflow to be discovered. However, in the case of R1, it is also important to provide contextual metadata aligning with the workflow concept and its usage. This metadata can be as rich as possible, as the developers are encouraged to provide metadata from every conceivable aspect of the workflow reusability, such as the advantages and limitations, hardware requirements for execution, self-explanatory function/modules names or their descriptions, and the versions. The metadata can surpass the above-given list based on individual projects. Reusability (R1) refers to the following: (1) Licence associated with the workflow and the data should be mentioned in metadata, (2) Associated provenance for reuse of the workflow and data, (3) Following domain relevant standards and rules to allow easy exchange of knowledge and reuse of the digital objects. MAW provides detailed prospective provenance in the GitHub README.md archived on Zenodo. The prospective provenance is also generated with the Workflow RO-Crate in an automated way during the WorkflowHub submission process. Table details the different FAIR principles and the specific tools, registries, or ontologies that ensure adherence to each principle. Table : FAIR supporting tools, ontologies, and registries and the respective FAIR principles 3 Results and Discussion: Towards Reproducibility in FAIR Metabolomics Data, Software, and Workflows Metabolomics provides a global "small molecules" content within a biological sample. It is an ever-evolving analytical approach to comprehend the chemical dark space. A metabolomics experiment starts with sample preparation. The metabolites extracted from the sample are then subjected to the appropriate analytical tools such as Tandem Mass Spectrometry (MS 2 ) or Nucleic Magnetic Resonance (NMR). The chemical data is acquired as spectra, which are analysed with various downstream analysis steps. Execution of each stage in the metabolomics experiment can introduce variations hindering reproducibility . The replicates of the same sample under different instruments, data acquisition modes, and data processing tools, can give varying results even with the same parameters . Despite ongoing efforts to standardise the entire metabolomics pipeline (including experimental steps, and data acquisition and processing steps), obtaining reproducible data from metabolomics experiments remains one of the biggest challenges, as it depends on the analytical aspects, such as instrument operator, run time, etc., and biological replicates' differences . Determining the specific step in the workflow that has the most impact on reproducibility is crucial.