Better explicating the strengths and shortcomings of these models will help refine projections and improve transparency in the years ahead.
Image credit: Witsawat.S.
Edited by David B. Allison, Indiana University Bloomington, Bloomington, IN, and accepted by Editorial Board Member Susan T. Fiske January 9, 2018 (received for review July 11, 2017)
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- Published in issue March 13, 2018.
- Published first March 12, 2018.
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- aSchool of Information Sciences, University of Illinois at Urbana–Champaign, Champaign, IL 61820;
- bDepartment of Statistics, Columbia University, New York, NY 10027
Edited by David B. Allison, Indiana University Bloomington, Bloomington, IN, and accepted by Editorial Board Member Susan T. Fiske January 9, 2018 (received for review July 11, 2017)
Footnotes
- ↵1To whom correspondence should be addressed. Email: victoria{at}stodden.net.
Author contributions: V.S. designed research; V.S., J.S., and Z.M. performed research; V.S., J.S., and Z.M. analyzed data; and V.S. and J.S. wrote the paper.
The authors declare no conflict of interest.
This paper results from the Arthur M. Sackler Colloquium of the National Academy of Sciences, “Reproducibility of Research: Issues and Proposed Remedies,” held March 8-10, 2017, at the National Academy of Sciences in Washington, DC. The complete program and video recordings of most presentations are available on the NAS website at www.nasonline.org/Reproducibility.
This article is a PNAS Direct Submission. D.B.A. is a guest editor invited by the Editorial Board.
Data deposition: We have created a repository at https://github.com/ReproducibilityInPublishing/Science-2018 containing supplemental material, including the code and email templates that were used in the survey.
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