# DataHub: Collaborative Data Science & Dataset Version Management at Scale

arXiv: https://arxiv.org/abs/1409.0798 · HTML: https://mlsanity.com/paper/1409.0798/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2014.

## Built on by (most cited first, 10 of 10)

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- [Reusable Templates and Guides For Documenting Datasets and Models for Natural Language Processing and Generation: A Case Study of the HuggingFace and GEM Data and Model Cards](https://mlsanity.com/paper/2108.07374/) (2021)
- [Ember: No-Code Context Enrichment via Similarity-Based Keyless Joins](https://mlsanity.com/paper/2106.01501/) (2021)
- [Data Collection and Labeling Techniques for Machine Learning](https://mlsanity.com/paper/2407.12793/) (2024)
- [Data Science Methodologies: Current Challenges and Future Approaches](https://mlsanity.com/paper/2106.07287/) (2021)
- [EdnaML: A Declarative API and Framework for Reproducible Deep Learning](https://mlsanity.com/paper/2211.06783/) (2022)
- [AI for Open Science: A Multi-Agent Perspective for Ethically Translating Data to Knowledge](https://mlsanity.com/paper/2310.18852/) (2023)
- [Experimental Insights Towards Explainable and Interpretable Pedestrian Crossing Prediction](https://mlsanity.com/paper/2312.02872/) (2023)
- [SAVeD: Semantic Aware Version Discovery](https://mlsanity.com/paper/2511.17298/) (2025)

## Abstract (arXiv)

Relational databases have limited support for data collaboration, where teams collaboratively curate and analyze large datasets. Inspired by software version control systems like git, we propose (a) a dataset version control system, giving users the ability to create, branch, merge, difference and search large, divergent collections of datasets, and (b) a platform, DataHub, that gives users the ability to perform collaborative data analysis building on this version control system. We outline the challenges in providing dataset version control at scale.