# Sustainable computational science: the ReScience initiative

arXiv: https://arxiv.org/abs/1707.04393 · HTML: https://mlsanity.com/paper/1707.04393/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2017.

## Built on by (most cited first, 10 of 10)

- [A Systematic Review of Reproducibility Research in Natural Language Processing](https://mlsanity.com/paper/2103.07929/) (2021)
- [Reproducibility in Machine Learning-based Research: Overview, Barriers and Drivers](https://mlsanity.com/paper/2406.14325/) (2024)
- [Reproducibility in Machine Learning-Driven Research](https://mlsanity.com/paper/2307.10320/) (2023)
- [From Reproduction to Replication: Evaluating Research Agents with Progressive Code Masking](https://mlsanity.com/paper/2506.19724/) (2025)
- [SciCoQA: Quality Assurance for Scientific Paper--Code Alignment](https://mlsanity.com/paper/2601.12910/) (2026)
- [Quantified Reproducibility Assessment of NLP Results](https://mlsanity.com/paper/2204.05961/) (2022)
- [NLP Reproducibility For All: Understanding Experiences of Beginners](https://mlsanity.com/paper/2305.16579/) (2023)
- [Quantifying Reproducibility in NLP and ML](https://mlsanity.com/paper/2109.01211/) (2021)
- [LLM-Assisted Replication for Quantitative Social Science](https://mlsanity.com/paper/2602.18453/) (2026)
- [Make Literature-Based Discovery Great Again through Reproducible Pipelines](https://mlsanity.com/paper/2502.16450/) (2025)

## Abstract (arXiv)

Computer science offers a large set of tools for prototyping, writing, running, testing, validating, sharing and reproducing results, however computational science lags behind. In the best case, authors may provide their source code as a compressed archive and they may feel confident their research is reproducible. But this is not exactly true. James Buckheit and David Donoho proposed more than two decades ago that an article about computational results is advertising, not scholarship. The actual scholarship is the full software environment, code, and data that produced the result. This implies new workflows, in particular in peer-reviews. Existing journals have been slow to adapt: source codes are rarely requested, hardly ever actually executed to check that they produce the results advertised in the article. ReScience is a peer-reviewed journal that targets computational research and encourages the explicit replication of already published research, promoting new and open-source implementations in order to ensure that the original research can be replicated from its description. To achieve this goal, the whole publishing chain is radically different from other traditional scientific journals. ReScience resides on GitHub where each new implementation of a computational study is made available together with comments, explanations, and software tests.