Abstract

Background: The Metabolomics Workbench (MW) is a public scientific data repository consisting of experimental data and metadata from metabolomics studies collected with mass spectroscopy (MS) and nuclear magnetic resonance (NMR) analyses. Although not as rapidly as in the past, MW has steadily evolved, updating its mwTab and JSON deposition text file formats and its web-based infrastructure. However, the growth of MW has been exponential since its inception in 2013 and continues to be exponential, with the number of datasets hosted on the repository increasing by 50% since April 2024. As part of regular maintenance to keep up with changes to the mwTab file format and an earnest effort to use MW datasets in meta-analyses, the mwtab Python package has been updated.

Methods: Updates include better error handling for batch processing, better parsing to read more files without error, and extensive improvements to the validation capabilities of the package. These updates also required our mwFileStatusWebsite to be updated and improved.

Results: We used the enhanced validation features of the mwtab package to evaluate all available datasets in MW to facilitate improved curation, FAIRness of the repository, and reuse for meta-analyses.

Conclusions: Version 2.0.0 of the mwtab Python package is now officially released and freely available on GitHub and the Python Package Index (PyPI) under a Clear Berkeley Software Distribution (BSD) license, with documentation available on GitHub. The updated mwFileStatusWebsite is also officially in its 2.0.0 version.

Document Type

Article

Publication Date

2026

Notes/Citation Information

© 2026 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.

Digital Object Identifier (DOI)

https://doi.org/10.3390/metabo16010076

Funding Information

This research was funded by NIH/NIEHS, grant number P42ES007380 (UK Superfund Research Center); NSF, grant number 2020026 (PI Moseley); and NIH, grant number 1R03LM014928-01 (PI Moseley).

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