Terminologies for Reproducible Research
Not on the map. Why?
Abstract (arXiv)
Reproducible research---by its many names---has come to be regarded as a key concern across disciplines and stakeholder groups. Funding agencies and journals, professional societies and even mass media are paying attention, often focusing on the so-called "crisis" of reproducibility. One big problem keeps coming up among those seeking to tackle the issue: different groups are using terminologies in utter contradiction with each other. Looking at a broad sample of publications in different fields, we can classify their terminology via decision tree: they either, A---make no distinction between the words reproduce and replicate, or B---use them distinctly. If B, then they are commonly divided in two camps. In a spectrum of concerns that starts at a minimum standard of "same data+same methods=same results," to "new data and/or new methods in an independent study=same findings," group 1 calls the minimum standard reproduce, while group 2 calls it replicate. This direct swap of the two terms aggravates an already weighty issue. By attempting to inventory the terminologies across disciplines, I hope that some patterns will emerge to help us resolve the contradictions.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Feb 2018.
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Citing papers per year: 2021: 3; 2022: 1; 2023: 2; 2024: 3; 2025: 0; 2026: 2.
11 papers.
- 2021HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPOcited by 111
- 2021Randomness In Neural Network Training: Characterizing The Impact of Toolingcited by 57
- 2024Position: Why We Must Rethink Empirical Research in Machine Learningcited by 19
- 2022Towards Better User Studies in Computer Graphics and Visioncited by 11
- 2024What is Reproducibility in Artificial Intelligence and Machine Learning Research?cited by 11
- 2023Reproducibility of Machine Learning: Terminology, Recommendations and Open Issuescited by 8
- 2026Automating Computational Reproducibility in Social Science: Comparing Prompt-Based and Agent-Based Approachescited by 4
- 2021Reproducible and Portable Big Data Analytics in the Cloudcited by 1
- 2024Predicting Parkinson's disease trajectory using clinical and functional MRI features: a reproduction and replication studycited by 1
- 2026Coding-agents can replicate scientific machine learning paperscited by 1
- 2023Defining Replicability of Prediction Rulescited by 0
- 2026Coding-agents can replicate scientific machine learning papers2 Jul 2026
- 2026Automating Computational Reproducibility in Social Science: Comparing Prompt-Based and Agent-Based Approaches9 Feb 2026
- 2024Position: Why We Must Rethink Empirical Research in Machine Learning3 May 2024
- 2024What is Reproducibility in Artificial Intelligence and Machine Learning Research?29 Apr 2024
- 2024Predicting Parkinson's disease trajectory using clinical and functional MRI features: a reproduction and replication study20 Feb 2024
- 2023Defining Replicability of Prediction Rules30 Apr 2023
- 2023Reproducibility of Machine Learning: Terminology, Recommendations and Open Issues24 Feb 2023
- 2022Towards Better User Studies in Computer Graphics and Vision23 Jun 2022
- 2021Reproducible and Portable Big Data Analytics in the Cloud17 Dec 2021
- 2021HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO14 Sep 2021
- 2021Randomness In Neural Network Training: Characterizing The Impact of Tooling22 Jun 2021