# Navigating Dataset Documentations in AI: A Large-Scale Analysis of Dataset Cards on Hugging Face

arXiv: https://arxiv.org/abs/2401.13822 · HTML: https://mlsanity.com/paper/2401.13822/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 37 references that are in the map.
- First posted on arXiv 24 Jan 2024.

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

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Revisiting Unreasonable Effectiveness of Data in Deep Learning Era](https://mlsanity.com/paper/1707.02968/) (2017)
- [Data and its (dis)contents: A survey of dataset development and use in machine learning research](https://mlsanity.com/paper/2012.05345/) (2020)
- [Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AI](https://mlsanity.com/paper/2204.01075/) (2022)
- [Identifying and Correcting Label Bias in Machine Learning](https://mlsanity.com/paper/1901.04966/) (2019)
- [Reduced, Reused and Recycled: The Life of a Dataset in Machine Learning Research](https://mlsanity.com/paper/2112.01716/) (2021)
- [The Dataset Nutrition Label: A Framework To Drive Higher Data Quality Standards](https://mlsanity.com/paper/1805.03677/) (2018)
- [The Dataset Nutrition Label (2nd Gen): Leveraging Context to Mitigate Harms in Artificial Intelligence](https://mlsanity.com/paper/2201.03954/) (2022)
- [Data Readiness Report](https://mlsanity.com/paper/2010.07213/) (2020)
- [Augmented Datasheets for Speech Datasets and Ethical Decision-Making](https://mlsanity.com/paper/2305.04672/) (2023)
- [An Ethical Highlighter for People-Centric Dataset Creation](https://mlsanity.com/paper/2011.13583/) (2020)
- [Clustering Word Embeddings with Self-Organizing Maps. Application on LaRoSeDa -- A Large Romanian Sentiment Data Set](https://mlsanity.com/paper/2101.04197/) (2021)
- [Design Guidelines for Inclusive Speaker Verification Evaluation Datasets](https://mlsanity.com/paper/2204.02281/) (2022)
- [Datasheet for Subjective and Objective Quality Assessment Datasets](https://mlsanity.com/paper/2305.02142/) (2023)

## Built on by (most cited first, 27 of 27)

- [Croissant: A Metadata Format for ML-Ready Datasets](https://mlsanity.com/paper/2403.19546/) (2024)
- [Benchmark Data Repositories for Better Benchmarking](https://mlsanity.com/paper/2410.24100/) (2024)
- [What do we know about Hugging Face? A systematic literature review and quantitative validation of qualitative claims](https://mlsanity.com/paper/2406.08205/) (2024)
- [Copycats: the many lives of a publicly available medical imaging dataset](https://mlsanity.com/paper/2402.06353/) (2024)
- [DCA-Bench: A Benchmark for Dataset Curation Agents](https://mlsanity.com/paper/2406.07275/) (2024)
- [An Empirical Analysis of Machine Learning Model and Dataset Documentation, Supply Chain, and Licensing Challenges on Hugging Face](https://mlsanity.com/paper/2502.04484/) (2025)
- [Who Evaluates AI's Social Impacts? Mapping Coverage and Gaps in First and Third Party Evaluations](https://mlsanity.com/paper/2511.05613/) (2025)
- [Compliance Cards: Automated EU AI Act Compliance Analyses amidst a Complex AI Supply Chain](https://mlsanity.com/paper/2406.14758/) (2024)
- [A Systematic Review of NeurIPS Dataset Management Practices](https://mlsanity.com/paper/2411.00266/) (2024)
- [Talking About the Assumption in the Room](https://mlsanity.com/paper/2502.13268/) (2025)
- [Beyond Release: Access Considerations for Generative AI Systems](https://mlsanity.com/paper/2502.16701/) (2025)
- [We Need Improved Data Curation and Attribution in AI for Scientific Discovery](https://mlsanity.com/paper/2504.02486/) (2025)
- [Discovering Hidden Gems in Model Repositories](https://mlsanity.com/paper/2601.22157/) (2026)
- [How Hyper-Datafication Impacts the Sustainability Costs in Frontier AI](https://mlsanity.com/paper/2602.00056/) (2026)
- [From Reflection to Repair: A Scoping Review of Dataset Documentation Tools](https://mlsanity.com/paper/2602.15968/) (2026)
- [Ethical and Technical Limits of Deepfake Speech Datasets](https://mlsanity.com/paper/2606.10911/) (2026)
- [Which Models Are Our Models Built On? Auditing Invisible Dependencies in Modern LLMs](https://mlsanity.com/paper/2606.12385/) (2026)
- [Responsible AI in Open Ecosystems: Reconciling Innovation with Risk Assessment and Disclosure](https://mlsanity.com/paper/2409.19104/) (2024)
- [MRM3: Machine Readable ML Model Metadata](https://mlsanity.com/paper/2505.13343/) (2025)
- [TEDI: Trustworthy and Ethical Dataset Indicators to Analyze and Compare Dataset Documentation](https://mlsanity.com/paper/2505.17841/) (2025)
- [No Language Data Left Behind: A Comparative Study of CJK Language Datasets in the Hugging Face Ecosystem](https://mlsanity.com/paper/2507.04329/) (2025)
- [SIEVE: Towards Verifiable Certification for Code-datasets](https://mlsanity.com/paper/2510.02166/) (2025)
- [Towards Contextual Sensitive Data Detection](https://mlsanity.com/paper/2512.04120/) (2025)
- [AdaQE-CG: Adaptive Query Expansion for Web-Scale Generative AI Model and Data Card Generation](https://mlsanity.com/paper/2604.09617/) (2026)
- [ArtifactLinker: Linking Scientific Artifacts for Automatic State-of-the-Art Discovery](https://mlsanity.com/paper/2605.16902/) (2026)
- [A governance horizon for ethical-use constraints in open-weight AI models](https://mlsanity.com/paper/2605.24383/) (2026)
- [Workflow Cards: Structured Summaries of Workflow Executions Using Provenance Data](https://mlsanity.com/paper/2608.11022/) (2026)

## Abstract (arXiv)

Advances in machine learning are closely tied to the creation of datasets. While data documentation is widely recognized as essential to the reliability, reproducibility, and transparency of ML, we lack a systematic empirical understanding of current dataset documentation practices. To shed light on this question, here we take Hugging Face -- one of the largest platforms for sharing and collaborating on ML models and datasets -- as a prominent case study. By analyzing all 7,433 dataset documentation on Hugging Face, our investigation provides an overview of the Hugging Face dataset ecosystem and insights into dataset documentation practices, yielding 5 main findings: (1) The dataset card completion rate shows marked heterogeneity correlated with dataset popularity. (2) A granular examination of each section within the dataset card reveals that the practitioners seem to prioritize Dataset Description and Dataset Structure sections, while the Considerations for Using the Data section receives the lowest proportion of content. (3) By analyzing the subsections within each section and utilizing topic modeling to identify key topics, we uncover what is discussed in each section, and underscore significant themes encompassing both technical and social impacts, as well as limitations within the Considerations for Using the Data section. (4) Our findings also highlight the need for improved accessibility and reproducibility of datasets in the Usage sections. (5) In addition, our human annotation evaluation emphasizes the pivotal role of comprehensive dataset content in shaping individuals' perceptions of a dataset card's overall quality. Overall, our study offers a unique perspective on analyzing dataset documentation through large-scale data science analysis and underlines the need for more thorough dataset documentation in machine learning research.