NeurIPS 2026 Evaluations & Datasets Hosting Guidelines

Please note: These guidelines will be updated on a rolling basis until April 5, 2026 (OpenReview portal opens for submission). Authors should revisit this page regularly to avoid misunderstandings or missing important updates.

This webpage serves as an instruction guide for authors making a submission to the Evaluations and Datasets Track. Authors of datasets will be required to make their dataset available along with Croissant machine-readable metadata in order to streamline the review process and meet industry standards for high quality documentation, reproducibility, and accessibility. We are recommending use of preferred hosting platforms - Dataverse, Kaggle, Hugging Face, and OpenML - which will make compliance simple for authors and reviewers.

The dataset hosting process as part of submitting to the Evaluations and Datasets Track involves:

Choosing among 4 options to host your dataset: Harvard Dataverse, Kaggle, Hugging Face, and OpenML (or setting up a bespoke data hosting solutions)
Using platform tooling to download the automatically generated Croissant file (or create it manually). This will contain only the core fields (RAI data should be filled in by you below, step 3).
Complete the Croissant file with Responsible AI (RAI) metadata. We will provide additional tooling to facilitate this.
Validate your completed Croissant file using the online Croissant validator tool (see below),
Including a URL to your dataset and uploading the generated Croissant file in OpenReview
If your submission is accepted: making your dataset public by the camera ready deadline

Choose a Preferred Hosting Platform

Harvard Dataverse, Kaggle, Hugging Face, and OpenML platforms are the preferred hosting platforms for datasets. These platforms automatically generate a Croissant file and allow us to perform programmatic metadata verification and dataset assessment, which will streamline and standardize the review process. When you make your dataset accessible via one of these platforms, making a submission will be as simple as providing a URL to the dataset and uploading a generated Croissant file.

The table below outlines key platform features to help authors choose where to host their dataset. Authors may make their dataset accessible via more than one platform at any time.

	Automatically generated Croissant file	Client libraries: Croissant download, data download, data loader	Hosting restrictions	Private preview URL access	Credentialized (gated) access	Paper Linking	DOIs
Harvard Dataverse	✅	✅✅✅	1TB per dataset (2.5GB per file) Any file types	✅	✅	✅	✅
Kaggle	✅	✅✅✅	200GB per dataset Any file types	✅	❌	❌	✅
Hugging Face	✅	✅✅✅	300GB per dataset public Any file types	❌	✅	✅	✅
OpenML	✅	✅✅✅	200GB per dataset Any file types	❌	❌	✅	❌

Authors are responsible for reviewing and complying with the Terms of Service of the platform(s) they choose to use.

Alternative hosting

Self-hosting and Other Data Storage Platforms

If you choose NOT to release your dataset via one of these preferred platforms, you can self-host the data or use other platforms, but you will still be required to make your dataset accessible to reviewers via URL (e.g., to a GitHub repo, public cloud bucket, Zenodo, etc.) and manually generate and upload a Croissant file representation of your dataset as part of the OpenReview submission process.

Generating a Croissant File via Python

This Python tutorial demonstrates how to generate a Croissant file. Find more documentation on the Croissant GitHub repository. You can also try the Croissant editor or run it locally (GitHub).

Responsible AI metadata

It is important that datasets are created and used responsibly. Authors must provide a minimal set of Responsible AI (RAI) metadata, as described below. This information must be clearly identifiable in the submission, and reviewers will check for its presence.

The RAI metadata must be included in the Croissant file, which serves as the canonical, long-term record accompanying the dataset. Wherever possible, authors should provide the full RAI information directly in the corresponding Croissant fields.

If certain information cannot be fully expressed in Croissant (e.g., due to formatting constraints such as LaTeX equations), authors may instead use the Croissant fields to clearly point to the relevant sections of the paper (main text or appendix).

Authors may also include the RAI information in other parts of the paper (e.g., main paper or appendix) for clarity or elaboration. In all cases, we ask that the authors at least touch on these topics at a high level in the main part of the paper, even if only to guide the reader to where more detailed information can be found (e.g. appendix or Croissant).

All submissions that include a dataset as a contribution must provide answers to these RAI fields, at least in the Croissant file. Failure to do so may constitute grounds for rejection.

Minimal RAI data overview

The table below contains a high-level overview of the minimal RAI data. We will provide a more detailed description with examples soon.

		Data limitations rai:dataLimitations		Known constraints on the dataset's applicability: distributional gaps, underrepresented populations, data quality issues, or domain restrictions. Also list any uses for which this dataset is explicitly not recommended.
		Data biases rai:dataBiases,		Any known or suspected biases in the data, including selection bias, label bias, or demographic skew. Which population groups or scenarios may be over- or under-represented, and how this may affect model behaviour.
		Personal or sensitive information rai:personalSensitiveInformation		Personal or sensitive information such as Gender, Socio-economic status, Geography, Language, Age, Culture, Experience or Seniority, Health or medical data, Political or religious beliefs.
		Data use cases rai:dataUseCases		Construct validity: what real-world concept the data is intended to measure or represent, and provide evidence that the data reliably captures that construct. List the use cases for which validity has been established (e.g. safety evaluation, fairness auditing, fine-tuning), and for which it has not.
		Social impact rai:dataSocialImpact		The potential positive and negative societal effects of using this dataset, including risks of misuse, fairness implications for specific communities, and any mitigations put in place.
		Synthetic data rai:hasSyntheticData		A boolean indicating the presence of synthetic data. If so, authors are encouraged to describe the synthetic data process in using the data collection and annotation fields.
		Source datasets prov:wasDerivedFrom		The URI of the dataset from which the present dataset is derived. Can be many URIs from different data sources. For synthetic data point to synthetic data seeds used.
		Provenance activities prov:wasGeneratedBy		Preprocessing: cleaning, or filtering steps applied to the data prior to use, to make the data pipeline reproducible. Data collection: collection period, geographic scope, and any instruments or protocols used. In case of synthetic data, document the seeds or prompts used or the synthetic data generator used. Data annotation: labeling schema, instructions provided to annotators, quality control measures, and inter-annotator agreement scores where available. Describe the human teams, synthetic agents and platforms used to collect, annotate or preprocess the data.

Including RAI metadata in the Croissant file

The hosting platforms above currently only populate the “core” Croissant fields, so you need to ensure that you add the RAI fields. There are several options to do this:

Croissant RAI tool
We will provide an online tool where you can easily augment your existing Croissant file with the minimal RAI metadata. We will share this soon and update these guidelines accordingly.

Manually add the RAI fields
Adapt the Croissant file by adding the fields specified in the table above (e.g., rai:dataLimitations). We will provide a document with clear examples on how to do this. Afterwards, use the Croissant validator to check whether it was done correctly.

How to Publish on Preferred Hosting Platforms

This section provides specific guidance and documentation on how to make your dataset and its Croissant metadata file accessible via the preferred hosting platforms: Harvard Dataverse, Kaggle, Hugging Face, and OpenML.

	How to upload	How to download (files, Croissant)	How to get help, e.g., to request additional storage quota
Harvard Dataverse	Upload a Dataset via UI (after login) or CLI) Requirements An email-verified Harvard Dataverse user account Publicly shared (or “Link Sharing” turned on in the “Edit” menu to generate a Preview URL for a private dataset) at time of submission to E&D track Platform restrictions Documentation 1TB per dataset (2.5GB per file)	Download files Click “Access Dataset” and choose an option Download Croissant Click “Metadata”, Click “Export Metadata”, and select “Croissant” Via Python client	Contact support@dataverse.harvard.edu
Kaggle	Upload a Dataset via UI (after login) or Python client Requirements A phone-verified Kaggle user account Publicly shared (or “Link Sharing” turned on in “Settings” to generate a Preview URL for a private dataset) at time of submission to E&D track (Optional) An approved Organization profile to host the data under if preferred, e.g., your research lab Platform restrictions Documentation 200GB per dataset	Download files Click “Download” and choose “Download dataset as zip” Via kagglehub Python client Download Croissant Click “Download” and choose “Export metadata as Croissant” Click “Code”, select “Load via mlcroissant”, and copy Python code	Contact kaggle-datasets@kaggle.com
Hugging Face	Upload a Dataset via UI (after login) or Python client Requirements A Hugging Face user account Publicly shared at time of submission to E&D track Must be a format listed here in order to generate a Croissant file (Optional) An Organization profile to host the data under if preferred, e.g., your research lab Platform restrictions Documentation 300GB per dataset	Download files Click “API” and copy curl code Download Croissant Click “Croissant” and choose “Download Croissant metadata” Via Python client Download files Click “API” and copy curl code Download Croissant Click “Croissant” and choose “Download Croissant metadata” Via Python client	Contact datasets@huggingface.co
OpenML	Upload Dataset via UI (after login) or Python client (recommended) Requirements An email-verified OpenML user account Publicly shared at time of submission to E&D track Platform restrictions Documentation 200GB per dataset	Download files Click “Download” Via Python client Download Croissant In the web UI, click “Croissant”.	Contact openmlhq@openml.org

What to Include in Your Submission

When you submit to Open Review, you will be required to provide:

A URL to your dataset accessible to reviewers
A Croissant metadata file
A confirmation that you verified the validity of your Croissant file.

Please use this online tool to verify your Croissant file. This tool will check whether it is valid, sufficiently complete, and whether the data can be automatically accessed and loaded (the latter may not be possible for all datasets). The same information will be made available to reviewers.

After submissions are closed, if your Croissant file is invalid or if your data is not accessible, your submission may be desk-rejected. Otherwise, reviewers will commence review of your submission paper, dataset, and metadata.

If your dataset is accepted, you will be required to make it public by the camera ready deadline. Failure to do so may result in removal from the conference and proceedings.

FAQ

Please check the Evaluations and Datasets Track FAQs