A Guide to Using GitHub for Developing and Versioning Data Standards and Reporting Formats

Crystal‐Ornelas, Robert; Varadharajan, Charuleka; Bond‐Lamberty, Ben; Boye, Kristin; Burrus, Madison; Cholia, Shreyas; Crow, Michael; Ball-Damerow, Joan E.; Devarakonda, Ranjeet; Ely, Kim; Goldman, Amy; Heinz, Susan; Hendrix, Valerie; Kakalia, Zarine; Pennington, Stephanie C.; Robles, Emily; Rogers, Alistair; Simmonds, Maegen; Velliquette, Terri; Weierbach, Helen; Weisenhorn, Pamela; Welch, Jessica; Agarwal, D.

doi:10.1029/2021ea001797

Cited by 18 publications

(18 citation statements)

References 34 publications

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“…Such studies are likely to call for more collaboration and team science (Camerer et al, 2016 ; O’Dea et al, 2021 ), and the use of large‐scale ecosystem research infrastructures (Roy et al, 2021 ). Moreover, researchers should strive for open and transparent science practices (Gallagher et al, 2020 ), such as controlling for magnitude and sign errors when planning field experiments (i.e., an extension of power analysis; Lemoine et al, 2016 ), archiving and sharing data, following the FAIR guideline (i.e., findable, accessible, interoperable, and reusable data; Wilkinson et al, 2016 ; see also, Crystal‐Ornelas et al, 2021 ), increasing transparent reporting (T. H. Parker et al, 2016 ), embracing preregistrations and registered reports (T. Parker et al, 2019 ), and implementing more replication projects (Fraser et al, 2020 ). Adopting these practices will not only aid further meta‐analytical syntheses but also make ecological findings more reproducible and reliable in general (Nakagawa & Parker, 2015 ; O’Dea et al, 2021 ).”…”

Section: Conclusion and Future Perspectivesmentioning

confidence: 99%

Low statistical power and overestimated anthropogenic impacts, exacerbated by publication bias, dominate field studies in global change biology

Yang

Hillebrand

Lagisz

et al. 2021

Global Change Biology

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Field studies are essential to reliably quantify ecological responses to global change because they are exposed to realistic climate manipulations. Yet such studies are limited in replicates, resulting in less power and, therefore, potentially unreliable effect estimates. Furthermore, while manipulative field experiments are assumed to be more powerful than non-manipulative observations, it has rarely been scrutinized using extensive data. Here, using 3847 field experiments that were designed to estimate the effect of environmental stressors on ecosystems, we systematically quantified their statistical power and magnitude (Type M) and sign (Type S) errors. Our investigations focused upon the reliability of field experiments to assess the effect of stressors on both ecosystem's response magnitude and variability. When controlling for publication bias, single experiments were underpowered to detect response magnitude (median power: 18%-38% depending on effect sizes). Single experiments also had much lower power to detect response variability (6%-12% depending on effect sizes) than response magnitude. Such underpowered studies could exaggerate estimates of response magnitude by 2-3 times (Type M errors) and variability by 4-10 times. Type S errors were comparatively rare. These observations indicate that low power, coupled with publication bias, inflates the estimates of anthropogenic impacts.Importantly, we found that meta-analyses largely mitigated the issues of low power and exaggerated effect size estimates. Rather surprisingly, manipulative experimentsThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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Section: Conclusion and Future Perspectivesmentioning

confidence: 99%

Low statistical power and overestimated anthropogenic impacts, exacerbated by publication bias, dominate field studies in global change biology

Yang

Hillebrand

Lagisz

et al. 2021

Global Change Biology

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“…Further details on these guidelines are described on the ESS-DIVE Community Space on GitHub (https://github.com/ess-dive-community/essdive-model-data-archiving-guidelines). The GitHub site also allows for users of these guidelines to provide feedback, and for tracking any future revisions to the guidelines (Crystal-Ornelas et al, 2021).…”

Section: Optional Files -mentioning

confidence: 99%

“…In comparison to pre-existing model data guidelines (EarthCube-RCN, NSF Arctic Data Center, ORNL-DAAC), our recommendations strike a balance between the complexity of considerations needed to properly archive the various components of model data and a need for the guidelines to be practical and useful for scientists. We have created additional user-friendly documentation using the GitBooks feature of GitHub (Crystal-Ornelas et al, 2021) to enable adoption of these guidelines (https://ess-dive.…”

Section: Public Archival Of Model Data Using Recommended Guidelinesmentioning

confidence: 99%

“…The workflow was captured in Jupyter-based scripts that were cited and archived in Zenodo with a DOI (Koven, 2020). We note that despite the integration between Zenodo and GitHub, many projects hosted on GitHub do not take the extra step to archive their content into long-term data repositories (Crystal-Ornelas et al, 2021), and we highlight this as a key step toward long-term model data reuse and accreditation.…”

Section: Enabling Model Data Intercomparison Workflow Reproducibility...mentioning

confidence: 99%

“…This can be achieved in part by archiving data supporting the results of scientific research in public repositories for long-term preservation and discoverability. In addition, adopting data or metadata standards and reporting formats that specify preferred file formats and variable names will improve reusability (Crystal-Ornelas et al, 2021). In most cases, community engagement and consensus is requisite to adopting these standards and guidelines, and building cohesiveness among archived datasets (Sansone et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Guidelines for Publicly Archiving Terrestrial Model Data to Enhance Usability, Intercomparison, and Synthesis

Simmonds

Riley

Agarwal

et al. 2022

CODATA

Self Cite

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Scientific communities are increasingly publishing data to evaluate, accredit, and build on published research. However, guidelines for curating data for publication are sparse for model-related research, limiting the usability of archived simulation data. In particular, there are no established guidelines for archiving data related to terrestrial models that simulate land processes and their coupled interactions with climate. Terrestrial modelers have a unique set of challenges when publishing data due to the diversity of scientific domains, research questions, and the types and scales of simulations. Researchers in the U.S. Department of Energy's (DOE) projects use a variety of multiscale models to advance robust predictions of terrestrial and subsurface ecosystem processes. Here, we synthesize archiving needs for data associated with different DOE models, and provide guidelines for publishing terrestrial model data components following FAIR (Findable, Accessible, Interoperable, Reusable) principles. The guidelines recommend archiving model inputs and testing data used in final simulation runs along with associated codes, workflow scripts, and metadata in public repositories. Researchers should consider archiving model outputs if they are within the storage limits of the repository. We also provide considerations for how to bundle files into different data publications with citable digital object identifiers. Finally, we identify repository features and tools that would enable storage and reuse of model data. Given the diversity of DOE terrestrial models, these guidelines are transferable to other model types and will enable efficient reuse of simulation data for purposes such as model intercomparisons, initialization, benchmarking, synthesis, and comparisons with field observations.

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Vergleichende Evaluierung lichtgetriebener Katalyse: Ein Rahmenkonzept für das standardisierte Berichten von Daten**

et al. 2022

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Lichtgetriebene homogene und heterogene Katalyse erfordern ein komplexes Zusammenspiel von Lichtabsorption, Ladungstrennung, Ladungstransfer und katalytischem Umsatz. Optische und Bestrahlungsparameter sowie reaktionstechnische Aspekte spielen eine wichtige Rolle bei der Kontrolle der katalytischen Leistung. Diese Vielzahl von Faktoren erschwert den objektiven Vergleich lichtgetriebener Katalysatoren und die unvoreingenommene Leistungsbewertung. Dieser Artikel unterstreicht die Bedeutung der Erfassung und Dokumentation experimenteller Daten in homogener und heterogener lichtgetriebener Katalyse. Dazu wird eine kritische Analyse der Vorteile und Grenzen üblicherweise verwendeter experimenteller Indikatoren vorgenommen. Datenerfassung und -dokumentation nach den FAIR-Prinzipien werden im Zusammenhang mit zukünftiger automatisierter Datenanalyse diskutiert. Wir schlagen einen Mindestdatensatz als Grundlage für eine einheitliche Erfassung und Dokumentation von experimentellen Daten in der homogenen und heterogenen lichtgetriebenen Katalyse vor. Die Forschungscommunity wird ermutigt, die zukünftige Entwicklung dieser Parameterliste im Rahmen eines offenen Online-Repository zu unterstützen.

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A Guide to Using GitHub for Developing and Versioning Data Standards and Reporting Formats

Cited by 18 publications

References 34 publications

Low statistical power and overestimated anthropogenic impacts, exacerbated by publication bias, dominate field studies in global change biology

Low statistical power and overestimated anthropogenic impacts, exacerbated by publication bias, dominate field studies in global change biology

Guidelines for Publicly Archiving Terrestrial Model Data to Enhance Usability, Intercomparison, and Synthesis

Vergleichende Evaluierung lichtgetriebener Katalyse: Ein Rahmenkonzept für das standardisierte Berichten von Daten**

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