Abstract
In recent years, the FAIR guiding principles and the broader concept of open science has grown in importance in academic research, especially as funding entities have aggressively promoted public sharing of research products. Key to public research sharing is deposition of datasets into online data repositories, but it can be a chore to transform messy unstructured data into the forms required by these repositories. To help generate Metabolomics Workbench depositions, we have developed the MESSES (Metadata from Experimental SpreadSheets Extraction System) software package, implemented in the Python 3 programming language and supported on Linux, Windows, and Mac operating systems. MESSES helps transform tabular data from multiple sources into a Metabolomics Workbench specific deposition format. The package provides three commands, extract, validate, and convert, that implement a natural data transformation workflow. Moreover, MESSES facilitates richer metadata capture than is typically attempted by manual efforts. The source code and extensive documentation is hosted on GitHub and is also available on the Python Package Index for easy installation.
Document Type
Article
Publication Date
7-2023
Digital Object Identifier (DOI)
https://doi.org/10.3390/metabo13070842
Funding Information
The research was funded by the National Institutes of Health, grant number P42 ES007380 (University of Kentucky Superfund Research Program Grant; PI Pennell), and by the National Science Foundation, grant number 2020026 (PI Moseley). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Environmental Health Sciences nor the National Science Foundation.
Repository Citation
Thompson, P. Travis and Moseley, Hunter N. B., "MESSES: Software for Transforming Messy Research Datasets into Clean Submissions to Metabolomics Workbench for Public Sharing" (2023). Markey Cancer Center Faculty Publications. 367.
https://uknowledge.uky.edu/markey_facpub/367
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Notes/Citation Information
© 2023 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 (https:// creativecommons.org/licenses/by/ 4.0/).