# paperswithcode/releasing-research-code

Tips for releasing research code in Machine Learning (with official NeurIPS 2020 recommendations)

Repository: https://github.com/paperswithcode/releasing-research-code
Canonical: https://ross.abutalabs.com/products/releasing-research-code
License: MIT
License Family: permissive
Topics: machine-learning, awesome-list, neurips, neurips-2020
Last push: 2023-05-19T13:22:56+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2372, "days_push": 1202, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2960, forks 744 (observed 2026-08-28T04:07:32.311475+00:00)

## What it is
A curated guide with best practices and templates for releasing machine learning research code, based on analysis of 200+ popular ML repositories. It includes the ML Code Completeness Checklist and a README template adopted as official NeurIPS recommendations.

## Use cases
- how to release research code for a machine learning paper
- make my ML paper repository reproducible
- what should a research code README include
- NeurIPS code submission guidelines
- ML code completeness checklist
- template for publishing deep learning code on GitHub
- tips for getting stars on a research repository

## When to choose
- you are publishing code accompanying an ML research paper
- you want to improve reproducibility of your research repository
- you need a README template for a NeurIPS or similar conference submission

## When to avoid
- you need executable tooling rather than guidelines and templates
- you are releasing production or non-research software
- you need up-to-date tooling advice, as the resource is in maintenance mode

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: documentation, developer-tools
- domain: machine-learning, tutorials, awesome-lists, developer-tools
- platform: cross-platform
- tags: reproducibility, research-code, neurips, best-practices, checklist

## Member repositories
- paperswithcode/releasing-research-code (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:32.311475+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:32:10.427109+00:00, confidence not recorded.
  - readme: https://github.com/paperswithcode/releasing-research-code (fetched 2026-08-28T04:07:32.311475+00:00, sha 918d96538ecd)
- Data as of 2026-08-30T08:39:29.467469+00:00.