  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.
