Eleven quick tips for properly handling tabular data

Proper handling of tabular data, i.e., spreadsheets, is an essential skill for most researchers.Well-curated tabular data has built-in quality control, is machine readable by downstream analysis applications, and ultimately saves researchers' time.The increased expectation of data sharing grounded by funder and publisher policies has made it even more crucial to format and describe tabular data properly to ensure that it is FAIR-findable, accessible, interoperable, and reusable [1].However, many researchers are not formally trained in the data management skills necessary to build strong tabular datasets.They may also struggle to anticipate the future needs and potential applications of their data.This article attempts to demystify tabular data practices and outlines 11 tips presented roughly in the order of use during a research project.While these tips feature examples using the Microsoft Excel application, chosen deliberately because it is commonly used and is often the entry point software for data analysis, they are applicable to similar spreadsheet software programs.

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Publication Details

Journal
PLoS Computational Biology
Published
2024-11-27
DOI
https://doi.org/10.1371/journal.pcbi.1012604
Citations
4
Primary Topic
Research Data Management Practices
Type
article
Field-Weighted Citation Impact
3.12
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article

Eleven quick tips for properly handling tabular data

Marla I. Hertz, Ashley S. McNeill
4 citations
PLoS Computational Biology
Research Data Management Practices
3.12
article

Eleven quick tips for properly handling tabular data

Marla I. Hertz, Ashley S. McNeill
article en
4 citations

Abstract

Proper handling of tabular data, i.e., spreadsheets, is an essential skill for most researchers.Well-curated tabular data has built-in quality control, is machine readable by downstream analysis applications, and ultimately saves researchers' time.The increased expectation of data sharing grounded by funder and publisher policies has made it even more crucial to format and describe tabular data properly to ensure that it is FAIR-findable, accessible, interoperable, and reusable [1].However, many researchers are not formally trained in the data management skills necessary to build strong tabular datasets.They may also struggle to anticipate the future needs and potential applications of their data.This article attempts to demystify tabular data practices and outlines 11 tips presented roughly in the order of use during a research project.While these tips feature examples using the Microsoft Excel application, chosen deliberately because it is commonly used and is often the entry point software for data analysis, they are applicable to similar spreadsheet software programs.

PLoS Computational BiologyVol. 20(11)
University of Alabama at Birmingham (US)
Openalex Percentile: Top 7%
Research Data Management Practices
3.12
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