# Elements of effective machine learning datasets in astronomy

arXiv: https://arxiv.org/abs/2211.14401 · HTML: https://mlsanity.com/paper/2211.14401/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 24 references that are in the map.
- First posted on arXiv 25 Nov 2022.

## Builds on (most cited first, 6 of 6)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Rotation-invariant convolutional neural networks for galaxy morphology prediction](https://mlsanity.com/paper/1503.07077/) (2015)
- [Galaxy Zoo DECaLS: Detailed Visual Morphology Measurements from Volunteers and Deep Learning for 314,000 Galaxies](https://mlsanity.com/paper/2102.08414/) (2021)
- [DeepMerge: Classifying High-redshift Merging Galaxies with Deep Neural Networks](https://mlsanity.com/paper/2004.11981/) (2020)
- [Predicting galaxy spectra from images with hybrid convolutional neural networks](https://mlsanity.com/paper/2009.12318/) (2020)

## Built on by (most cited first, 2 of 2)

- [GalaxiesML: a dataset of galaxy images, photometry, redshifts, and structural parameters for machine learning](https://mlsanity.com/paper/2410.00271/) (2024)
- [AquiLLM: Evaluating Faithfulness in Open-Weight RAG-LLM Systems for Scientific Research](https://mlsanity.com/paper/2609.16519/) (2026)

## Abstract (arXiv)

In this work, we identify elements of effective machine learning datasets in astronomy and present suggestions for their design and creation. Machine learning has become an increasingly important tool for analyzing and understanding the large-scale flood of data in astronomy. To take advantage of these tools, datasets are required for training and testing. However, building machine learning datasets for astronomy can be challenging. Astronomical data is collected from instruments built to explore science questions in a traditional fashion rather than to conduct machine learning. Thus, it is often the case that raw data, or even downstream processed data is not in a form amenable to machine learning. We explore the construction of machine learning datasets and we ask: what elements define effective machine learning datasets? We define effective machine learning datasets in astronomy to be formed with well-defined data points, structure, and metadata. We discuss why these elements are important for astronomical applications and ways to put them in practice. We posit that these qualities not only make the data suitable for machine learning, they also help to foster usable, reusable, and replicable science practices.