R Code and Output Supporting: Computational reproducibility in The Wildlife Society's flagship journals

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R Code and Output Supporting: Computational reproducibility in The Wildlife Society's flagship journals

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ArchMiller, Althea A


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Survey Data-Quantitative
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The goal of this study was to gauge the level of computational reproducibility, which is the ability to reach the same results using the same data and analysis methods, in the field of wildlife sciences. We randomly selected 80 papers published in the Journal of Wildlife Management and Wildlife Society Bulletin between 1 June 2016 and 1 June 2018. Of those for which we could obtain data, we attempted to reproduce their quantitative results using the original methods and data. The dataset shared in this repository is the de-identified results of our review, and the code provided here produces the results and figures in our published manuscript.


A full description of the data and code files is provided in the attached README.txt. Briefly, each html file uses the raw data (or processed data) to create results and figures for the manuscript. The script in 01_processing_data.html should be run first before any other subsequent scripts can be run. The zipped folder (item K) contains all of the Program R files (.R extension) for each of the html files.

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ArchMiller, Johnson, Nolan, Edwards, Elliot, Ferguson, Iannarilli, Velez, Vitense, Johnson and Fieberg. Computational reproducibility in The Wildlife Society's flagship journals, Journal of Wildlife Management.

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Concordia College, Moorhead, Minnesota (Centennial Scholars Program)




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File View/OpenDescriptionSize
README.txtReadme file14.03 KB
google_form_data_raw.csvRaw data from Google form survey25.78 KB
01_data_processing.htmlData processing script47.69 KB
02_study_overview_figure.htmlResults for figure 1 in manuscript40.28 KB
03_scores_by_journal_figure.htmlProduces figure 2 in manuscript35.65 KB
04_reproducibility_criteria_figure.htmlProduces figure 3 in manuscript71.98 KB
R_scripts.zipR Programs10.3 KB

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