# Scientific Dataset Discovery via Topic-level Recommendation

arXiv: https://arxiv.org/abs/2106.03399 · HTML: https://mlsanity.com/paper/2106.03399/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2021.

## Built on by (most cited first, 3 of 3)

- [Making Sense of Data in the Wild: Data Analysis Automation at Scale](https://mlsanity.com/paper/2502.15718/) (2025)
- [SciTopic: Enhancing Topic Discovery in Scientific Literature through Advanced LLM](https://mlsanity.com/paper/2508.20514/) (2025)
- [Large language model-assisted discovery of cohorts from scientific literature](https://mlsanity.com/paper/2608.15909/) (2026)

## Abstract (arXiv)

Data intensive research requires the support of appropriate datasets. However, it is often time-consuming to discover usable datasets matching a specific research topic. We formulate the dataset discovery problem on an attributed heterogeneous graph, which is composed of paper-paper citation, paper-dataset citation, and also paper content. We propose to characterize both paper and dataset nodes by their commonly shared latent topics, rather than learning user and item representations via canonical graph embedding models, because the usage of datasets and the themes of research projects can be understood on the common base of research topics. The relevant datasets to a given research project can then be inferred in the shared topic space. The experimental results show that our model can generate reasonable profiles for datasets, and recommend proper datasets for a query, which represents a research project linked with several papers.