# Unreflected Use of Tabular Data Repositories Can Undermine Research Quality

arXiv: https://arxiv.org/abs/2503.09159 · HTML: https://mlsanity.com/paper/2503.09159/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2025.

## Built on by (most cited first, 9 of 9)

- [Representation Learning for Tabular Data: A Comprehensive Survey](https://mlsanity.com/paper/2504.16109/) (2025)
- [TabSTAR: A Tabular Foundation Model for Tabular Data with Text Fields](https://mlsanity.com/paper/2505.18125/) (2025)
- [Overtuning in Hyperparameter Optimization](https://mlsanity.com/paper/2506.19540/) (2025)
- [Unveiling the Role of Data Uncertainty in Tabular Deep Learning](https://mlsanity.com/paper/2509.04430/) (2025)
- [Explaining Tabular Foundation Model Differences Through Meta-Features](https://mlsanity.com/paper/2605.28418/) (2026)
- [Why Isn't Relational Learning Taking Over the World?](https://mlsanity.com/paper/2507.13558/) (2025)
- [An open dataset of neural networks for hypernetwork research](https://mlsanity.com/paper/2507.15869/) (2025)
- [Best Practices For Empirical Meta-Algorithmic Research: Guidelines from the COSEAL Research Network](https://mlsanity.com/paper/2512.16491/) (2025)
- [Lost in Aggregation: How Benchmarks Overlook Irreplaceable Model Strengths](https://mlsanity.com/paper/2608.18919/) (2026)

## Abstract (arXiv)

Data repositories have accumulated a large number of tabular datasets from various domains. Machine Learning researchers are actively using these datasets to evaluate novel approaches. Consequently, data repositories have an important standing in tabular data research. They not only host datasets but also provide information on how to use them in supervised learning tasks. In this paper, we argue that, despite great achievements in usability, the unreflected usage of datasets from data repositories may have led to reduced research quality and scientific rigor. We present examples from prominent recent studies that illustrate the problematic use of datasets from OpenML, a large data repository for tabular data. Our illustrations help users of data repositories avoid falling into the traps of (1) using suboptimal model selection strategies, (2) overlooking strong baselines, and (3) inappropriate preprocessing. In response, we discuss possible solutions for how data repositories can prevent the inappropriate use of datasets and become the cornerstones for improved overall quality of empirical research studies.