Journal of Machine Learning Research (JMLR)

Venue URL:

The Percentage of Empirical Papers Documenting Each Reproducibility Variable

Venue Year Papers
Repro. Score Reproducibility Score based on Gundersen et al. (2025)
Doc. Mean Global mean is the average score over the seven reproducibility variables for empirical research papers.
Doc. Median Global median is the median score over the seven reproducibility variables for empirical research papers.
Dataset Doc. Documentation mean is the average score over the Open Datasets and Dataset Splits reproducibility variables for empirical research papers.
Code Doc. Documentation mean is the average score over the Open Source Code reproducibility variables for empirical research papers.
Other Doc. Documentation mean is the average score over the Pseudocode, Hardware Specification, Software Dependencies, and Experiment Setup reproducibility variables for empirical research papers.
% Empirical Percentage of papers that are empirical research vs theoretical research
% Industry Percentage of empirical research papers with at least one author from Industry
Website
JMLR 2025 269 0.46 3.79 4.0 1.16 0.57 2.06 86.99% 17.52%
JMLR 2024 421 0.49 3.73 4.0 1.15 0.57 2.01 84.32% 19.44%
JMLR 2023 400 0.47 3.83 4.0 1.22 0.54 2.08 86.25% 17.97%
JMLR 2022 351 0.52 3.89 4.0 1.34 0.56 2.0 84.05% 21.02%
JMLR 2021 290 0.46 3.74 4.0 1.23 0.46 2.05 85.52% 21.77%
JMLR 2020 252 0.45 3.75 4.0 1.26 0.47 2.03 86.9% 22.37%
JMLR 2019 184 0.4 3.55 4.0 1.14 0.44 1.97 83.15% 24.84%
JMLR 2018 84 0.36 3.35 4.0 1.03 0.36 1.96 89.29% 21.33%
JMLR 2017 234 0.42 3.57 4.0 1.22 0.38 1.97 83.33% 21.03%
JMLR 2016 236 0.41 3.54 4.0 1.3 0.38 1.86 84.75% 16.0%
JMLR 2015 118 0.39 3.65 4.0 1.23 0.43 1.99 69.49% 18.29%
JMLR 2014 120 0.44 3.78 4.0 1.43 0.38 1.97 85.0% 17.65%