The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Recommendations for utilizing and reporting population genetic analyses: the reproducibility of genetic clustering using the program structure
Abstract
Reproducibility is the benchmark for results and conclusions drawn from scientific studies, but systematic studies on the reproducibility of scientific results are surprisingly rare. Moreover, many modern statistical methods make use of ‘random walk’ model fitting procedures, and these are inherently stochastic in their output. Does the combination of these statistical procedures and current standards of data archiving and method reporting permit the reproduction of the authors' results? To test this, we reanalysed data sets gathered from papers using the software package structure to identify genetically similar clusters of individuals. We find that reproducing structure results can be difficult despite the straightforward requirements of the program. Our results indicate that 30% of analyses were unable to reproduce the same number of population clusters. To improve this, we make recommendations for future use of the software and for reporting structure analyses and results in published works.
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Cites
Reproducible research in computational science
Peng, R. D. (2011) · Science · doi:10.1126/science.1213847 · Cited by 1,449 · cited 1×
“Full experimental replication provides the most thorough means of verifying published empirical results, but this approach can be impractical due to the difficulty in obtaining identical samples and large financial and time commitments (Peng 2011).”
MethodRepeatability of published microarray gene expression analyses
Ioannidis, J. P. A., Allison, D. B., Ball, C. A., Coulibaly, I., Cui, X., Culhane, A. C., Falchi, M., Furlanello, C., Game, L., Jurman, G., et al. (2009) · Nature Genetics · doi:10.1038/ng.295 · Cited by 551
Advances in global change research require open science by individual researchers
Wolkovich, E. M., Regetz, J., & O'Connor, M. I. (2012) · Global Change Biology · doi:10.1111/j.1365-2486.2012.02693.x · Cited by 111
Cited by
An introduction to Docker for reproducible research
Boettiger, C. (2015) · ACM SIGOPS Operating Systems Review · doi:10.1145/2723872.2723882 · Cited by 1,059
An empirical analysis of journal policy effectiveness for computational reproducibility
Stodden, V., Seiler, J., & Ma, Z. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1708290115 · Cited by 369
Public Data Archiving in Ecology and Evolution: How Well Are We Doing?
Roche, D. G., Kruuk, L. E. B., Lanfear, R., & Binning, S. A. (2015) · PLOS Biology · doi:10.1371/journal.pbio.1002295 · Cited by 303
Nonindependence and sensitivity analyses in ecological and evolutionary meta‐analyses
Noble, D. W. A., Lagisz, M., O'dea, R. E., & Nakagawa, S. (2017) · Molecular Ecology · doi:10.1111/mec.14031 · Cited by 267
Petzold, A., & Hassanin, A. (2020) · PLOS ONE · doi:10.1371/journal.pone.0217956 · Cited by 60
Metadata
- Authors
- Gilbert, K. J., Andrew, R. L., Bock, D. G., Franklin, M. T., Kane, N. C., Moore, J., Moyers, B. T., Renaut, S., Rennison, D. J., Veen, T., & Vines, T. H.
- Venue
- Molecular Ecology
- Publisher
- Wiley
- Published
- 2012-09-24
- Volume
- 21
- Issue
- 20
- Pages
- 4925-4930
- ISSN
- 0962-1083 (ISSN-L)
- 1365-294X
- OpenAlex
- W2115148249
- Cited by
- 290
- References
- 3
- FWCI
- 11.16
- Type
- journal article
- Language
- en
- Open access
- closed
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- Gilbert, K. J., Andrew, R. L., Bock, D. G., Franklin, M. T., Kane, N. C., Moore, J., Moyers, B. T., Renaut, S., Rennison, D. J., Veen, T., & Vines, T. H. (2012). Recommendations for utilizing and reporting population genetic analyses: the reproducibility of genetic clustering using the program structure. Molecular Ecology. https://doi.org/10.1111/j.1365-294x.2012.05754.x
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