DOI: 10.5061/dryad.c53k1d6
title: Data from: Publication reform to safeguard wildlife from researcher harm

description: Despite abundant focus on responsible care of laboratory animals, we argue
 that inattention to the maltreatment of wildlife constitutes an ethical
 blind spot in contemporary animal research. We begin by reviewing
 significant shortcomings in legal and institutional oversight, arguing for
 the relatively rapid and transformational potential of editorial oversight
 at journals in preventing harm to vertebrates studied in the field and
 outside the direct supervision of institutions. Straightforward changes to
 animal care policies in journals, which our analysis of 206 journals
 suggests are either absent (34%), weak, incoherent, or neglected by
 researchers, could provide a practical, effective, and rapidly imposed
 safeguard against unnecessary suffering. The ARROW (Animal Research:
 Reporting on Wildlife) guidelines we propose here, coupled with strong
 enforcement, could result in significant changes to how animals involved
 in wildlife research are treated. The research process would also benefit.
 Sound science requires animal subjects to be physically, physiologically,
 and behaviorally unharmed. Accordingly, publication of methods that
 contravenes animal welfare principles risks perpetuating inhumane
 approaches and bad science.
description: Data preparation
 codeThis is a data
 preparation file. The code incorporates external guidelines scores into
 journal scores to account for journal referral to external guidelines
 using hierarchical approach (see Fig B in S1 Text).Script1_Data-preparation.RLogistic regression analysis and Poisson regression analysisCode to conduct logistic regression analysis to test for associations between the presence of any animal care policy and journal characteristics (impact factor, open-access status, animal welfare legislation in country of the journals’ headquarters, and whether the journal was conservation-oriented). We centered all predictors (subtracted the mean from each observation) and scaled (divided by 2 SDs). This file also includes code for a Poisson regression analysis to test for associations with number of criteria detected among journals that had an animal care policy and the same journal characteristics. For the latter, criteria were considered fulfilled if a journal received any score other than “none”.Script2_Data-analyses.RPlot proportion of journals that fulfill a given criterionThis file contains code to create Fig. 1 in the main text of the manuscript, which plots proportions of journals that fulfill a given criterion based on compliance language (i.e., requirement (3), recommendation (2), suggestion (1) or absent (0)).Script3_Plot-proportions.RCoefficient plotCode for coefficient plot in main text (Fig 2.). Use 'model-rescale-coef-names.csv' to extract label names from external CSV.Script4_Coefficient-plot.RRaw journal dataScores assigned to journals based on compliance language used by journals. Use Script1_Data-preparation.R to incorporate external guideline scores into final journal score. See README file for more information.JournalData.csvExternal and publishing house guidelinesScores assigned to external and publishing house guidelines based on compliance language. Use Script1_Data-preparation_KField.R to incorporate external guideline scores into final journal score. See README file associated with JournalData.csv for more information.ExternalGuidelines.csvLabel names for coefficient plotThis file contains label names, which are called into Script4_Coefficient-plot.R to create coefficient plot.model-rescale-coef-names_submit.csv