Active Maintenance

A Proposal for the Long-Term Computational Reproducibility of Scientific Results

Datos Bibliográficos

ID12379020
AutoresLimor Peer (0000-0002-3234-1593, Yale University, autor de correspondencia), Lilla V Orr (0000-0002-9250-7831, Yale University), Alexander Coppock (0000-0002-5733-2386, Yale University)
Año2021
Volumen54
Número3
Páginas462-466
Fecha de publicación2021-04-23
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaPS Political Science & Politics (JOURNAL)
Identificadores de la revistaISSN: 1049-0965 • E-ISSN: 1537-5935
EditorialCambridge University Press (CUP) (PUBLISHER)
DOI10.1017/s1049096521000366
OpenAlexW3175388084
IdiomaEN
Referencias citadas18

Computational reproducibility, or the ability to reproduce analytic results of a scientific study on the basis of publicly available code and data, is a shared goal of many researchers, journals, and scientific communities. Researchers in many disciplines including political science have made strides toward realizing that goal. A new challenge, however, has arisen. Code too often becomes obsolete within only a few years. We document this problem with a random sample of studies posted to the Institution for Social and Policy Studies (ISPS) Data Archive; we encountered nontrivial errors in seven of 20 studies. In line with similar proposals for the long-term maintenance of data and commercial software, we propose that researchers dedicated to computational reproducibility should have a plan in place for “active maintenance” of their analysis code. We offer concrete suggestions for how data archives, journals, and research communities could encourage and reward the active maintenance of scientific code and data

Code (set theory · Data science · Geography · Institution · Plan (archaeology · Programming language · Sample (material · Social science · Sociology · Term (time · Computer Science · Data Quality and Management · Research Data Management Practices · Scientific Computing and Data Management