An integrated analysis platform for efficiently achieving computational reproducibility
- Updated
Nov 15, 2022 - Python
An integrated analysis platform for efficiently achieving computational reproducibility
A structured guide to computational reproducibility reviews
Teaching computational reproducibility for neuroimaging
Eindhoven University of Technology (TU/e) course "Improving Your Statistical Questions" by Daniël Lakens on Coursera (completed Dec 2022).
Dockerfile to build an image for use in the repliCATS pipeline
Distributed integrity infrastructure for scientific reproducibility. Blind peer validation of any verifiable claim — computational, experimental, or physical — recorded as tamper-evident Harmony Records. Built in Rust on Holochain.
2019 Computational Reproducibility Workshop Series, led by Nelle Varoquaux
Gemelo Digital para Simulación Sanitaria: Impacto de la Promoción y Prevención (PyP) en la demanda y costos.
Repository for the "Computational Reproducibility in Machine Learning" workshop, containing a setup guide, slides, and files to support hands-on reproducibility exercises.
This repository contains the short talk "Computational Reproducibility & Docker" given by Ross Gayler to the ReproducibiliTea Melbourne group on 2026-03-26. The target paper is “Longitudinal Study of the Software Environments Produced by Dockerfiles from Research Artifacts: Initial Design” (Guilloteau et al., 2025, DOI:10.1145/3736731.3746146)
Scripts and files related to my Master's thesis.
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