# Position Paper on Dataset Engineering to Accelerate Science

arXiv: https://arxiv.org/abs/2303.05545 · HTML: https://mlsanity.com/paper/2303.05545/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 54 references that are in the map.
- First posted on arXiv 9 Mar 2023.

## Builds on (most cited first, 7 of 7)

- [Uncertainty Quantification Using Neural Networks for Molecular Property Prediction](https://mlsanity.com/paper/2005.10036/) (2020)
- [Towards Explainable Deep Neural Networks (xDNN)](https://mlsanity.com/paper/1912.02523/) (2019)
- [Human-in-the-loop Artificial Intelligence](https://mlsanity.com/paper/1710.08191/) (2017)
- [Interactive AI with a Theory of Mind](https://mlsanity.com/paper/1912.05284/) (2019)
- [Big Data Systems Meet Machine Learning Challenges: Towards Big Data Science as a Service](https://mlsanity.com/paper/1709.07493/) (2017)
- [Sample-Efficient Generation of Novel Photo-acid Generator Molecules using a Deep Generative Model](https://mlsanity.com/paper/2112.01625/) (2021)
- [Keeping Track of User Steering Actions in Dynamic Workflows](https://mlsanity.com/paper/1905.07167/) (2019)

## Abstract (arXiv)

Data is a critical element in any discovery process. In the last decades, we observed exponential growth in the volume of available data and the technology to manipulate it. However, data is only practical when one can structure it for a well-defined task. For instance, we need a corpus of text broken into sentences to train a natural language machine-learning model. In this work, we will use the token textitdataset to designate a structured set of data built to perform a well-defined task. Moreover, the dataset will be used in most cases as a blueprint of an entity that at any moment can be stored as a table. Specifically, in science, each area has unique forms to organize, gather and handle its datasets. We believe that datasets must be a first-class entity in any knowledge-intensive process, and all workflows should have exceptional attention to datasets' lifecycle, from their gathering to uses and evolution. We advocate that science and engineering discovery processes are extreme instances of the need for such organization on datasets, claiming for new approaches and tooling. Furthermore, these requirements are more evident when the discovery workflow uses artificial intelligence methods to empower the subject-matter expert. In this work, we discuss an approach to bringing datasets as a critical entity in the discovery process in science. We illustrate some concepts using material discovery as a use case. We chose this domain because it leverages many significant problems that can be generalized to other science fields.