Top 2 Psyarxiv Papers Today in Meta-Science


2.054 Mikeys
#1. Analysis of Open Data and Computational Reproducibility in Registered Reports in Psychology
Pepijn Obels, Daniel Lakens, Nicholas Coles, Jaroslav Gottfried
Ongoing technological developments have made it easier than ever before for scientists to share their data, materials, and analysis code. Sharing data and analysis code makes it easier for other researchers to re-use or check published research. These benefits will only emerge if researchers can reproduce the analysis reported in published articles, and if data is annotated well enough so that it is clear what all variables mean. Because most researchers have not been trained in computational reproducibility, it is important to evaluate current practices to identify practices that can be improved. We examined data and code sharing, as well as computational reproducibility of the main results without contacting the original authors, for Registered Reports published in the in psychological literature between 2014 and 2018. Of the 62 articles that met our inclusion criteria data was available for 40 articles, and analysis scripts for 43 articles. For the 35 articles that shared both data and code and performed analyses in SPSS, R, or...
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Tweets
lakens: New preprint: "Analysis of Open Data and Computational Reproducibility in Registered Reports in Psychology". By Pepijn Obels, me, @coles_nicholas_ and @jargottfried. We try to computationally reproduce Registered Reports to see what can be improved. https://t.co/LB1GtlGDVQ https://t.co/27VgVPsJLv
BrianNosek: "Although the percentage of articles that shared both data and code (61%) and articles that could be computationally reproduced (54%) was relatively high compared to other studies, there is clear room for improvement." https://t.co/QLUEbH2l7z
AnnasJP: Similar, but for computational reproducibility. A cliched take: everyone's stumbling & it's perfectly fine to struggle w/ progress. But let's not take these papers for granted and maybe actually try to implement their recs. (speaking for myself lol) https://t.co/FyLgFr3xBH
ReproFeed: Analysis of Open Data and Computational Reproducibility in Registered Reports in Psychology https://t.co/6kRzOAMFqI
Github

Reproducing Registered Reports in Psychology

Repository: reproducing_registered_reports
User: Lakens
Language: HTML
Stargazers: 2
Subscribers: 2
Forks: 1
Open Issues: 1
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Sample Sizes : None.
Authors: 4
Total Words: 5990
Unqiue Words: 1656

1.997 Mikeys
#2. The importance of standards for sharing of computational models and data
Russell Poldrack, Franklin Feingold, Michael Frank, Padraig Gleeson, Gilles de Hollander, Quentin JM Huys, Bradley C. Love, Christopher Markiewicz, Rosalyn Moran, Petra Ritter
The Target Article by Lee et al. (2019) highlights the ways in which ongoing concerns about research reproducibility extend to model-based approaches in cognitive science. Whereas Lee et al. focus primarily on the importance of research practices to improve model robustness, we propose that the transparent sharing of model specifications, including their inputs and outputs, is also essential to improving the reproducibility of model-based analyses. We outline an ongoing effort (within the context of the Brain Imaging Data Structure community) to develop standards for the sharing of the structure of computational models and their outputs.
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Authors: 10
Total Words: 2529
Unqiue Words: 1089

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Assert is a website where the best academic papers on arXiv (computer science, math, physics), bioRxiv (biology), BITSS (reproducibility), EarthArXiv (earth science), engrXiv (engineering), LawArXiv (law), PsyArXiv (psychology), SocArXiv (social science), and SportRxiv (sport research) bubble to the top each day.

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