Implementing GitHub Actions continuous integration to reduce error rates in ecological data collection

Abstract

Accurate field data are essential to understanding ecological systems and forecasting their responses to global change. Yet, data collection errors are common, and data analysis often lags far enough behind its collection that many errors can no longer be corrected, nor can anomalous observations be revisited. Needed is a system in which data quality assurance and control (QA/QC), along with the production of basic data summaries, can be automated immediately following data collection. Here, we implement and test a system to satisfy these needs. For two annual tree mortality censuses and a dendrometer band survey at two forest research sites, we used GitHub Actions continuous integration (CI) to automate data QA/QC and run routine data wrangling scripts to produce cleaned datasets ready for analysis. This system automation had numerous benefits, including (1) the production of near real‐time information on data collection status and errors requiring correction, resulting in final datasets free of detectable errors, (2) an apparent learning effect among field technicians, wherein original error rates in field data collection declined significantly following implementation of the system, and (3) an assurance of computational reproducibility—that is, robustness of the system to changes in code, data and software. By implementing CI, researchers can ensure that datasets are free of any errors for which a test can be coded. The result is dramatically improved data quality, increased skill among field technicians, and reduced need for expert oversight. Furthermore, we view CI implementation as a first step towards a data collection and analysis pipeline that is also more responsive to rapidly changing ecological dynamics, making it better suited to study ecological systems in the current era of rapid environmental change.

Wild insects and honey bees are equally important to crop yields in a global analysis

“…We must also keep in mind that the patterns that define the current state of pollination are dependent on many complex variables that overviews such as ours are unable to measure, such as the size of wild insect populations near crop fields and the decisions growers make about honey bee stocking levels, and thus must be monitored closely as future conditions change. By creating a pipeline which automatically updates the main results as more data are added to the database under version control (Kim et al., 2022), we give the first step to obtain robust iterative conclusions on the long term.…”

When things get MESI: The Manipulation Experiments Synthesis Initiative—A coordinated effort to synthesize terrestrial global change experiments

“…Within the MESI initiative (Figure 6), we follow the FAIR (Findable, Accessible, Interoperable, Reusable) and TRUST (Transparency, Responsibility, User focus, Sustainability, Technology) principles for data stewardship and repositories (Kim et al, 2022; Lin et al, 2020; Wilkinson et al, 2016). We host the MESI database on GitHub (http://github.com/MESI-organization/mesi-db), from where versions are managed, tagged, and released to Zenodo (Van Sundert et al, 2022—https://doi.org/10.5281/zenodo.7153253), under open access license CC‐BY‐4, meaning that the database can be freely used and edited, provided that the present study and the database at Zenodo are properly cited.…”

Regional ecological forecasting across scales: A manifesto for a biodiversity hotspot

“…https://github.com/azave a/raste r-vision; Yuan et al, 2020). Similarly, routine data transformations and quality checks can be automated using continuous integration/continuous deployment (CI/CD) tools like Github actions (Kim et al, 2022). While some of these data products may currently not be good enough to drive forecasts on their own, advances in data integration allow them to be used to constrain models in time-steps between more comprehensive data inputs (e.g.…”