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NeurIPS 2021 Code and Data Submission Guidelines

If any of the main contributions of your paper depends on an experimental result, you are strongly encouraged to submit code that produces this result. If you are using a new dataset, you are also encouraged to submit the dataset.

If you are submitting your code or data for reviewing, you must anonymize it and include it in a single zip file along with any additional supplementary material (e.g., appendices). Small datasets can also be included in such zip file (which must be <100MB). Large datasets can instead be linked to via an anonymous URL. Reviewers will be asked to keep any submitted code and data in strict confidentiality and use it only for reviewing purposes. The supplementary material deadline is one week after the paper submission deadline.

If you are including code or data with the camera-ready version of your accepted paper, you should de-anonymize it (including any URLs). If any of the main contributions of your accepted paper depends on an experimental result, it’s best practice for responsible research to include code that produces this result.

Code Guidelines

Your code submission should include training and evaluation code, specification of dependencies, etc. See https://github.com/paperswithcode/releasing-research-code for more detailed guidelines.

Your code submission ideally should be self-contained and executable. If this is not the case, you must explain why. Possible reasons might include:

  1. Specialized hardware is required to run the experiment (e.g., specialized accelerators or robotic platforms).

  2. The code depends on non-open-sourced or non-free libraries, which do not include the algorithm that is claimed as the scientific contribution of the paper (e.g., paid-for mathematical programming solvers, commercial simulators, MATLAB).

Data Guidelines

If you are submitting a new dataset, please carefully consider the relevant questions in the NeurIPS 2021 paper checklist. You are also encouraged to conform to the following best practices:

  • Link to the dataset from the paper (anonymized for reviewing, de-anonymized for camera ready).

  • Place the dataset in a repository that ensures long-term preservation of the data.

  • The dataset should have a persistent identifier such as Digital Object Identifier or Compact Identifier.

  • The dataset should adhere to Schema.org or DCAT metadata standards.

  • The license and/or any data access restrictions should be described in the paper.

If it is impossible to conform to the above suggestions, then you should include a justification.