DOI: 10.7910/dvn/o6mqb4
title: Data and programs used for the article "Reputation shortcoming in academic publishing"

description: This set of data and programs were used to analyze the relationship between authors’ ties with professional editors and the publications of these authors in the editors’ journals. We assessed whether this relationship was related to three aspects of authors’ reputation in the eyes of the editor: a past collaboration with the editor, an affiliation to one of the editor’s former research affiliations, and whether the author had already published in the journal of the editor. We collected all published articles recorded in the PubMed database up to December 2020; job offers for editorial positions at Nature journals between December 2020 and December 2021 published on the Nature website; editors' scientific and editorial experiences from LinkedIn complemented by Google searches. Data extraction was built of three steps to identify the past collaborators of editors, whether these collaborators published in the editor’s journal before or after the editor’s appointment at the journal, and whether an author had a track record in the journal at a given date. Statistical analyses were performed with the output of these steps. The study was complemented by the analysis of job offers for editorial positions issued by the editors’ journal and by retracted publications in the editors' journal.&lt;br&gt;
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Content description:
The file entitled "Neveu et al additional methods.pdf" provides additional details of the method described in the main text of the article and the Supplementary online published alongside the article.&lt;br&gt;
The file entitled "Neveu et al additional results.pdf" provides results of the robustness analyses. These analyses aimed at checking that the results reported in the main text and the Supplementary online of the article were not biased by the parameters mentioned in the Method section of the article.&lt;br&gt;
The compressed file "DataAndPrograms.zip" contains all programs used to extract the data used in the analyses of the article.&lt;br&gt;
These programs are in the folder "Preprocessing of data". Inside this folder, the folders "1st level", "2nd Level", "3rd level" and "4th level" contain programs which must be run in the order suggested by the names of the folders. In each of these folders, .sh programs are for running the Matlab program of the folder on grid computing. The Matlab programs which are in the "Functions" folder are all the functions which are called by the programs which are in the other folders of the folder "Preprocessing of data". The Matlab programs which are in the folder "Output analysis" are all the functions which are used once the four levels have been run. The Matlab programs which are in the "Tools" folder are a set of functions used for the management of the output of each of the four levels especially when the grid computation crashed.
Data are in the folders "Raw data" and "Extracted data". Data in the "Extracted data" are the output of the aforementioned four levels of the preprocessing.&lt;br&gt;
The programs used for the statistical analyses are in the folder "Statistical analyses". They are sorted according to the sets of results reported in the article. This folder includes Excel files of the data used in the linear mixed models used. The programs are R and Matlab ones. The programs of the folder "Robustness analyses" are for the robustness analyses of the results obtained with programs of the "Statistical analyses" folder. The folder "Supplementary analyses" contains data and programs which were used to run analyses. Results of these analyses are reported in the Supplementary materials of the article.&lt;br&gt;
The compressed file “Supplementary prints of websites referenced in the article.zip” contains screenshots of the websites cited in the main text of the article. These screenshots were renewed in 2024. The first version is already included in the file “Prints of websites referenced in the article.zip”.&lt;br&gt;
The PubMed database up to December 2020 used in this study is available on another Dataverse public repository (https://entrepot.recherche.data.gouv.fr) at https://doi.org/10.57745/3QL466.