Reproducible Data Science Environments with Nix

Simplifies the creation of reproducible data science environments using the Nix package manager, as described in Dolstra (2006) <ISBN 90-393-4130-3>. The included `rix()` function generates a complete description of the environment as a `default.nix` file, which can then be built using Nix. This results in project specific software environments with pinned versions of R, packages, linked system dependencies, and other tools or programming languages such as Python or Julia. Additional helpers make it easy to run R code in Nix software environments for testing and production.

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Reproducibility & Replicability in Computational Social Science Research

Presentation on "Reproducibility & Replicability in Computational Social Science Research" given at the workshop "Computational Social Science: AI and Society – Exploring Inequality in the Digital Age", Mannheim, Germany, May 15-16, 2025.

Code-sharing policies are associated with increased reproducibility potential of ecological findings

Estimating the replicability of Brazilian biomedical science

Concerns over the replicability and reproducibility of published research have grown in many research fields, but empirical data to inform policies are still scarce. Biomedical research in Brazil expanded rapidly over the last three decades, with no systematic assessment of the replicability of its findings. With this in mind, we set up the Brazilian Reproducibility Initiative, a multicenter replication of published experiments from Brazilian science using three common experimental methods: the MTT assay, the reverse transcription polymerase chain reaction (RT-PCR) and the elevated plus maze (EPM). A total of 56 laboratories performed 143 replications of 56 experiments; of these, 96 replications of 47 experiments were considered valid by an independent committee. Replication rates for these experiments varied between 15 and 45% according to five predefined criteria. In median terms, relative effect sizes were 60% larger in original experiments than in replications, while coefficients of variation were 60% smaller. Effect size decrease was smaller for MTT, cell line experiments and original results with less variability, while t values for replications were positively correlated with researcher predictions about replicability. Deviations from preregistered protocols were very common in replications, most frequently due to reasons inherent to the experimental model or related to infrastructure and logistics. Our results highlight factors that limit the replicability of results published by researchers in Brazil and suggest ways by which this scenario can be improved. ### Competing Interest Statement The authors have declared no competing interest.

Graphics Replicability Stamp Initiative

What fraction of repeat experiments will have an effect size within the 95% confidence interval of the first experiment?

Let's stick to an ideal situation with random sampling, Gaussian populations, equal variances, no P-hacking, etc. Step 1. You run an experiment say comparing two sample means, and compute a 95%