# Croissant: A Metadata Format for ML-Ready Datasets

arXiv: https://arxiv.org/abs/2403.19546 · HTML: https://mlsanity.com/paper/2403.19546/

## Key facts

- Cited by 81 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 62 references that are in the map.
- First posted on arXiv 28 Mar 2024.

## Builds on (most cited first, 16 of 16)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://mlsanity.com/paper/1602.07332/) (2016)
- [MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI](https://mlsanity.com/paper/2311.16502/) (2023)
- [MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts](https://mlsanity.com/paper/2310.02255/) (2023)
- [OpenML: networked science in machine learning](https://mlsanity.com/paper/1407.7722/) (2014)
- [MLS: A Large-Scale Multilingual Dataset for Speech Research](https://mlsanity.com/paper/2012.03411/) (2020)
- [The Dataset Nutrition Label: A Framework To Drive Higher Data Quality Standards](https://mlsanity.com/paper/1805.03677/) (2018)
- [PASS: An ImageNet replacement for self-supervised pretraining without humans](https://mlsanity.com/paper/2109.13228/) (2021)
- [Navigating Dataset Documentations in AI: A Large-Scale Analysis of Dataset Cards on Hugging Face](https://mlsanity.com/paper/2401.13822/) (2024)
- [The Principles of Data-Centric AI (DCAI)](https://mlsanity.com/paper/2211.14611/) (2022)
- [Packaging research artefacts with RO-Crate](https://mlsanity.com/paper/2108.06503/) (2021)
- [Plumber: Diagnosing and Removing Performance Bottlenecks in Machine Learning Data Pipelines](https://mlsanity.com/paper/2111.04131/) (2021)
- [DMLR: Data-centric Machine Learning Research -- Past, Present and Future](https://mlsanity.com/paper/2311.13028/) (2023)
- [Emotion Ratings: How Intensity, Annotation Confidence and Agreements are Entangled](https://mlsanity.com/paper/2103.01667/) (2021)

## Built on by (most cited first, 30 of 81)

- [Are We Done with MMLU?](https://mlsanity.com/paper/2406.04127/) (2024)
- [AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents](https://mlsanity.com/paper/2406.13352/) (2024)
- [The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning](https://mlsanity.com/paper/2412.00568/) (2024)
- [DrivAerNet++: A Large-Scale Multimodal Car Dataset with Computational Fluid Dynamics Simulations and Deep Learning Benchmarks](https://mlsanity.com/paper/2406.09624/) (2024)
- [TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs](https://mlsanity.com/paper/2406.09639/) (2024)
- [Dataset and Lessons Learned from the 2024 SaTML LLM Capture-the-Flag Competition](https://mlsanity.com/paper/2406.07954/) (2024)
- [BenchCAD: A Comprehensive, Industry-Standard Benchmark for Programmatic CAD](https://mlsanity.com/paper/2605.10865/) (2026)
- [BenchmarkCards: Standardized Documentation for Large Language Model Benchmarks](https://mlsanity.com/paper/2410.12974/) (2024)
- [Benchmark Data Repositories for Better Benchmarking](https://mlsanity.com/paper/2410.24100/) (2024)
- [In the Picture: Medical Imaging Datasets, Artifacts, and their Living Review](https://mlsanity.com/paper/2501.10727/) (2025)
- [ResPlan: A Large-Scale Vector-Graph Dataset of 17,000 Residential Floor Plans](https://mlsanity.com/paper/2508.14006/) (2025)
- [Datasheets Aren't Enough: DataRubrics for Automated Quality Metrics and Accountability](https://mlsanity.com/paper/2506.01789/) (2025)
- [GenoTEX: An LLM Agent Benchmark for Automated Gene Expression Data Analysis](https://mlsanity.com/paper/2406.15341/) (2024)
- [Machine Learning Models Have a Supply Chain Problem](https://mlsanity.com/paper/2505.22778/) (2025)
- [ProactBench: Beyond What The User Asked For](https://mlsanity.com/paper/2605.09228/) (2026)
- [WirelessMathLM: Teaching Mathematical Reasoning for LLMs in Wireless Communications with Reinforcement Learning](https://mlsanity.com/paper/2509.23219/) (2025)
- [A Sustainable AI Economy Needs Data Deals That Work for Generators](https://mlsanity.com/paper/2601.09966/) (2026)
- [ELCC: the Emergent Language Corpus Collection](https://mlsanity.com/paper/2407.04158/) (2024)
- [DiNO-Diffusion. Scaling Medical Diffusion via Self-Supervised Pre-Training](https://mlsanity.com/paper/2407.11594/) (2024)
- [CheMixHub: Datasets and Benchmarks for Chemical Mixture Property Prediction](https://mlsanity.com/paper/2506.12231/) (2025)
- [OR-Space: A Full-Lifecycle Workspace Benchmark for Industrial Optimization Agents](https://mlsanity.com/paper/2605.28158/) (2026)
- [DoTime: A Synthetic Benchmark Generator for Interventional and Counterfactual Time Series](https://mlsanity.com/paper/2607.27263/) (2026)
- [AgentDrift: A Step-Labeled Benchmark of Injection-Hijacked LLM Agent Trajectories](https://mlsanity.com/paper/2609.06972/) (2026)
- [TIDMAD: Time Series Dataset for Discovering Dark Matter with AI Denoising](https://mlsanity.com/paper/2406.04378/) (2024)
- [Mitigating Downstream Model Risks via Model Provenance](https://mlsanity.com/paper/2410.02230/) (2024)
- [FinML-Chain: A Blockchain-Integrated Dataset for Enhanced Financial Machine Learning](https://mlsanity.com/paper/2411.16277/) (2024)
- [Mutual Understanding between People and Systems via Neurosymbolic AI and Knowledge Graphs](https://mlsanity.com/paper/2504.11200/) (2025)
- [LCDB 1.1: A Database Illustrating Learning Curves Are More Ill-Behaved Than Previously Thought](https://mlsanity.com/paper/2505.15657/) (2025)
- [Open and Sustainable AI: challenges, opportunities and the road ahead in the life sciences (October 2025 -- Version 2)](https://mlsanity.com/paper/2505.16619/) (2025)
- [MObyGaze: a film dataset of multimodal objectification densely annotated by experts](https://mlsanity.com/paper/2505.22084/) (2025)

## Abstract (arXiv)

Data is a critical resource for machine learning (ML), yet working with data remains a key friction point. This paper introduces Croissant, a metadata format for datasets that creates a shared representation across ML tools, frameworks, and platforms. Croissant makes datasets more discoverable, portable, and interoperable, thereby addressing significant challenges in ML data management. Croissant is already supported by several popular dataset repositories, spanning hundreds of thousands of datasets, enabling easy loading into the most commonly-used ML frameworks, regardless of where the data is stored. Our initial evaluation by human raters shows that Croissant metadata is readable, understandable, complete, yet concise.