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Developing a modern data workflow for regularly updated data
Over the past decade, biology has undergone a data revolution in how researchers collect data and the amount of data being collected. An emerging challenge that has received limited attention in biology is managing, working with, and providing access to data under continual active collection. Regula...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6368360/ https://www.ncbi.nlm.nih.gov/pubmed/30695030 http://dx.doi.org/10.1371/journal.pbio.3000125 |
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author | Yenni, Glenda M. Christensen, Erica M. Bledsoe, Ellen K. Supp, Sarah R. Diaz, Renata M. White, Ethan P. Ernest, S. K. Morgan |
author_facet | Yenni, Glenda M. Christensen, Erica M. Bledsoe, Ellen K. Supp, Sarah R. Diaz, Renata M. White, Ethan P. Ernest, S. K. Morgan |
author_sort | Yenni, Glenda M. |
collection | PubMed |
description | Over the past decade, biology has undergone a data revolution in how researchers collect data and the amount of data being collected. An emerging challenge that has received limited attention in biology is managing, working with, and providing access to data under continual active collection. Regularly updated data present unique challenges in quality assurance and control, data publication, archiving, and reproducibility. We developed a workflow for a long-term ecological study that addresses many of the challenges associated with managing this type of data. We do this by leveraging existing tools to 1) perform quality assurance and control; 2) import, restructure, version, and archive data; 3) rapidly publish new data in ways that ensure appropriate credit to all contributors; and 4) automate most steps in the data pipeline to reduce the time and effort required by researchers. The workflow leverages tools from software development, including version control and continuous integration, to create a modern data management system that automates the pipeline. |
format | Online Article Text |
id | pubmed-6368360 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-63683602019-02-22 Developing a modern data workflow for regularly updated data Yenni, Glenda M. Christensen, Erica M. Bledsoe, Ellen K. Supp, Sarah R. Diaz, Renata M. White, Ethan P. Ernest, S. K. Morgan PLoS Biol Community Page Over the past decade, biology has undergone a data revolution in how researchers collect data and the amount of data being collected. An emerging challenge that has received limited attention in biology is managing, working with, and providing access to data under continual active collection. Regularly updated data present unique challenges in quality assurance and control, data publication, archiving, and reproducibility. We developed a workflow for a long-term ecological study that addresses many of the challenges associated with managing this type of data. We do this by leveraging existing tools to 1) perform quality assurance and control; 2) import, restructure, version, and archive data; 3) rapidly publish new data in ways that ensure appropriate credit to all contributors; and 4) automate most steps in the data pipeline to reduce the time and effort required by researchers. The workflow leverages tools from software development, including version control and continuous integration, to create a modern data management system that automates the pipeline. Public Library of Science 2019-01-29 /pmc/articles/PMC6368360/ /pubmed/30695030 http://dx.doi.org/10.1371/journal.pbio.3000125 Text en © 2019 Yenni et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Community Page Yenni, Glenda M. Christensen, Erica M. Bledsoe, Ellen K. Supp, Sarah R. Diaz, Renata M. White, Ethan P. Ernest, S. K. Morgan Developing a modern data workflow for regularly updated data |
title | Developing a modern data workflow for regularly updated data |
title_full | Developing a modern data workflow for regularly updated data |
title_fullStr | Developing a modern data workflow for regularly updated data |
title_full_unstemmed | Developing a modern data workflow for regularly updated data |
title_short | Developing a modern data workflow for regularly updated data |
title_sort | developing a modern data workflow for regularly updated data |
topic | Community Page |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6368360/ https://www.ncbi.nlm.nih.gov/pubmed/30695030 http://dx.doi.org/10.1371/journal.pbio.3000125 |
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