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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·2019·PLOS Biology
DOI 10.1371/journal.pbio.3000125PMC PMC6368360
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Fig 1 Data workflow for regularly updated data. 1. All field-collected data are double entered with automated checks to prevent invalid values from being entered. 2. The two versions of the double-entered data are compared using an R script, and mismatches are corrected. 3. A pull request is submitted to the data repository (i.e., GitHub), which triggers data checks run by the continuous integration system (i.e., Travis CI). 4. If the system detects any issues, the update is reviewed again, and corrections are made to the pull request, automatically triggering the data checks to run again. 5. Once the new data pass all automated checks, a data manager reviews the changes and merges the new data into the main data repository. 6. Addition of the new data triggers the continuous integration system to run additional scripts to get data from automated sensors (e.g., weather data) and to check for errors. 7. The system then runs scripts that automatically update the supporting tables (information not collected in the field that helps with data use) and updates the version number. 8. Once all tables have been automatically updated, the updates are automatically merged into the main repository. 9. The system automatically triggers a new release on GitHub. 10. The GitHub–Zenodo integration sends the new data release as a new version to Zenodo for archiving. CI, continuous integration.
Fig 1 Data workflow for regularly updated data. 1. All field-collected data are double entered with automated checks to prevent invalid values from being entered. 2. The two versions of the double-entered data are compared using an R script, and mismatches are corrected. 3. A pull request is submitted to the data repository (i.e., GitHub), which triggers data checks run by the continuous integration system (i.e., Travis CI). 4. If the system detects any issues, the update is reviewed again, and corrections are made to the pull request, automatically triggering the data checks to run again. 5. Once the new data pass all automated checks, a data manager reviews the changes and merges the new data into the main data repository. 6. Addition of the new data triggers the continuous integration system to run additional scripts to get data from automated sensors (e.g., weather data) and to check for errors. 7. The system then runs scripts that automatically update the supporting tables (information not collected in the field that helps with data use) and updates the version number. 8. Once all tables have been automatically updated, the updates are automatically merged into the main repository. 9. The system automatically triggers a new release on GitHub. 10. The GitHub–Zenodo integration sends the new data release as a new version to Zenodo for archiving. CI, continuous integration.CC BY · PubMed Central

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Research works printed but not linked: 35

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Metadata

Authors
Yenni, G. M., Christensen, E. M., Bledsoe, E. K., Supp, S. R., Diaz, R. M., White, E. P., & Ernest, S. K. M.
ORCID
Venue
PLOS Biology
Publisher
Public Library of Science (PLoS)
Published
2019-01-29
Volume
17
Issue
1
Pages
e3000125
ISSN
1545-7885
DOI
10.1371/journal.pbio.3000125
PMC
PMC6368360
Cited by
34
References
4
Language
en
Update notices
new_version (2019-02-08)
Licence
creativecommons.org/licenses/by/4.0/ (vor)

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CoChat. (2026). Citation check: Developing a modern data workflow for regularly updated data. https://app.cochat.ai/papers/10.1371/journal.pbio.3000125
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Yenni, G. M., Christensen, E. M., Bledsoe, E. K., Supp, S. R., Diaz, R. M., White, E. P., & Ernest, S. K. M. (2019). Developing a modern data workflow for regularly updated data. PLOS Biology. https://doi.org/10.1371/journal.pbio.3000125