Transparency, usability, and reproducibility: Guiding principles for improving comparative databases using primates as examples

Carola Borries, Aaron A. Sandel, Andreas Koenig, Eduardo Fernandez-Duque, Jason M. Kamilar, Caroline R. Amoroso, Robert A. Barton, Joel Bray, Anthony Di Fiore, Ian C. Gilby, Adam D. Gordon, Roger Mundry, Markus Port, Lauren E. Powell, Anne E. Pusey, Amanda Spriggs, Charles L. Nunn

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

18 Citas (Scopus)

Resumen

Recent decades have seen rapid development of new analytical methods to investigate patterns of interspecific variation. Yet these cutting-edge statistical analyses often rely on data of questionable origin, varying accuracy, and weak comparability, which seem to have reduced the reproducibility of studies. It is time to improve the transparency of comparative data while also making these improved data more widely available. We, the authors, met to discuss how transparency, usability, and reproducibility of comparative data can best be achieved. We propose four guiding principles: 1) data identification with explicit operational definitions and complete descriptions of methods; 2) inclusion of metadata that capture key characteristics of the data, such as sample size, geographic coordinates, and nutrient availability (for example, captive versus wild animals); 3) documentation of the original reference for each datum; and 4) facilitation of effective interactions with the data via user friendly and transparent interfaces. We urge reviewers, editors, publishers, database developers and users, funding agencies, researchers publishing their primary data, and those performing comparative analyses to embrace these standards to increase the transparency, usability, and reproducibility of comparative studies.

Idioma originalInglés
Páginas (desde-hasta)232-238
Número de páginas7
PublicaciónEvolutionary Anthropology: Issues, News, and Reviews
Volumen25
N.º5
DOI
EstadoPublicada - 1 sep. 2016
Publicado de forma externa

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