# Dataset search: a survey

arXiv: https://arxiv.org/abs/1901.00735 · HTML: https://mlsanity.com/paper/1901.00735/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jan 2019.

## Built on by (most cited first, 30 of 36)

- [Annotating Columns with Pre-trained Language Models](https://mlsanity.com/paper/2104.01785/) (2021)
- [CHORUS: Foundation Models for Unified Data Discovery and Exploration](https://mlsanity.com/paper/2306.09610/) (2023)
- [TabLib: A Dataset of 627M Tables with Context](https://mlsanity.com/paper/2310.07875/) (2023)
- [Jellyfish: A Large Language Model for Data Preprocessing](https://mlsanity.com/paper/2312.01678/) (2023)
- [Column Type Annotation using ChatGPT](https://mlsanity.com/paper/2306.00745/) (2023)
- [DataFinder: Scientific Dataset Recommendation from Natural Language Descriptions](https://mlsanity.com/paper/2305.16636/) (2023)
- [Tabular Data Augmentation for Machine Learning: Progress and Prospects of Embracing Generative AI](https://mlsanity.com/paper/2407.21523/) (2024)
- [RACOON: An LLM-based Framework for Retrieval-Augmented Column Type Annotation with a Knowledge Graph](https://mlsanity.com/paper/2409.14556/) (2024)
- [ChatPD: An LLM-driven Paper-Dataset Networking System](https://mlsanity.com/paper/2505.22349/) (2025)
- [Metadata-based Data Exploration with Retrieval-Augmented Generation for Large Language Models](https://mlsanity.com/paper/2410.04231/) (2024)
- [Compliance Cards: Automated EU AI Act Compliance Analyses amidst a Complex AI Supply Chain](https://mlsanity.com/paper/2406.14758/) (2024)
- [Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation](https://mlsanity.com/paper/2501.01367/) (2025)
- [Cracking Vector Search Indexes](https://mlsanity.com/paper/2503.01823/) (2025)
- [Model Lakes](https://mlsanity.com/paper/2403.02327/) (2024)
- [SynQuE: Estimating Synthetic Dataset Quality Without Annotations](https://mlsanity.com/paper/2511.03928/) (2025)
- [AgentExpt: Automating AI Experiment Design with LLM-based Resource Retrieval Agent](https://mlsanity.com/paper/2511.04921/) (2025)
- [Prompting Datasets: Data Discovery with Conversational Agents](https://mlsanity.com/paper/2312.09947/) (2023)
- [A Survey on Extractive Knowledge Graph Summarization: Applications, Approaches, Evaluation, and Future Directions](https://mlsanity.com/paper/2402.12001/) (2024)
- [A Principled Approach for Data Bias Mitigation](https://mlsanity.com/paper/2405.12312/) (2024)
- [ML-Asset Management: Curation, Discovery, and Utilization](https://mlsanity.com/paper/2509.23577/) (2025)
- [A Reference Architecture for Agentic Hybrid Retrieval in Dataset Search](https://mlsanity.com/paper/2604.16394/) (2026)
- [LakeQA: An Exploratory QA Benchmark over a Million-Scale Data Lake](https://mlsanity.com/paper/2606.10460/) (2026)
- [A Recommender System for Scientific Datasets and Analysis Pipelines](https://mlsanity.com/paper/2108.09275/) (2021)
- [An Initialization Schema for Neuronal Networks on Tabular Data](https://mlsanity.com/paper/2311.03996/) (2023)
- [OpenConstruction: A Systematic Synthesis of Open Visual Datasets for Data-Centric Artificial Intelligence in Construction Monitoring](https://mlsanity.com/paper/2508.11482/) (2025)
- [From keywords to semantics: Perceptions of large language models in data discovery](https://mlsanity.com/paper/2510.01473/) (2025)
- [ModelTables: A Corpus of Tables about Models](https://mlsanity.com/paper/2512.16106/) (2025)
- [SeDa: A Unified System for Dataset Discovery and Multi-Entity Augmented Semantic Exploration](https://mlsanity.com/paper/2603.07502/) (2026)
- [Paper2Data: Large-Scale LLM Extraction and Metadata Structuring of Global Urban Data from Scientific Literature](https://mlsanity.com/paper/2604.16317/) (2026)
- [PIPER: Content-Based Table Search via profiling and LLM-Generated Pseudoqueries](https://mlsanity.com/paper/2605.18199/) (2026)

## Abstract (arXiv)

Generating value from data requires the ability to find, access and make sense of datasets. There are many efforts underway to encourage data sharing and reuse, from scientific publishers asking authors to submit data alongside manuscripts to data marketplaces, open data portals and data communities. Google recently beta released a search service for datasets, which allows users to discover data stored in various online repositories via keyword queries. These developments foreshadow an emerging research field around dataset search or retrieval that broadly encompasses frameworks, methods and tools that help match a user data need against a collection of datasets. Here, we survey the state of the art of research and commercial systems in dataset retrieval. We identify what makes dataset search a research field in its own right, with unique challenges and methods and highlight open problems. We look at approaches and implementations from related areas dataset search is drawing upon, including information retrieval, databases, entity-centric and tabular search in order to identify possible paths to resolve these open problems as well as immediate next steps that will take the field forward.