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DataAtlas: automatic generation of data dictionaries using large language models.

Raffaele Giancotti^1,2, Rajna Fani^1,3, Rafi Al Attrach^1,3

¹Laboratory for Computational Physiology, MIT Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, United States.

|June 29, 2026

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This summary is machine-generated.

DataAtlas automates data dictionary creation for tabular datasets, improving data interpretation and reuse. This system enhances data accessibility and analytical performance for clinical data.

Area of Science:

Data Science
Bioinformatics
Clinical Informatics

Background:

Dataset reuse is hampered by poor documentation, limiting secondary analysis.
Automated data dictionaries are needed to improve data interpretability and accessibility.

Purpose of the Study:

Develop DataAtlas, an open-source system for automated data dictionary generation from tabular datasets.
Enhance the accessibility, reproducibility, and reuse of clinical data through improved documentation.

Main Methods:

DataAtlas integrates structural profiling and large language model (LLM)-based semantic inference.
It generates descriptions using column metadata, statistical summaries, and sample values.
The system was evaluated on clinical datasets using validation, expert review, and task performance.

Keywords:

clinical data data dictionary generative AI large language models

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Last Updated: Jun 30, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Main Results:

Generated descriptions were often preferred over official documentation, especially when existing ones were incomplete.
Human expert review confirmed high accuracy and low hallucination rates for LLM-generated descriptions.
Augmenting database schemas with generated data dictionaries significantly improved text-to-SQL execution accuracy (0.52 to 0.88).

Conclusions:

Automated data dictionary generation enhances dataset interpretability and downstream analytical performance.
Column-level metadata, particularly sample values, is crucial for grounding LLM descriptions.
DataAtlas offers a practical solution for generating structured data dictionaries, boosting clinical data reuse.