# What Features of Datasets Make Reuse More Likely?

Throughout this toolkit we'll be highlighting the practices that facilitate reuse. Empirical research has shown that this includes:

<mark style="color:orange;">**Publishing data to a well-known repository.**</mark> There is a relationship between the size of the repository and the likelihood of reuse, so if possible, publish data to the largest relevant repository.&#x20;

What's a repository? [Learn about some common ones](https://github.com/theodi/data-publish-list?tab=readme-ov-file#data-publishing-platforms).\
<mark style="color:purple;">**Go to**</mark>: [How do I decide where to publish my data?](../../publication/where-to-publish)

<mark style="color:orange;">**Providing an informative short textual summary of the dataset**</mark>. Summarising data as text helps people make sense of it. It also improves data discovery, as search algorithms can match this text against keyword queries.

[Best practices in data summarisation](../../publication/dataset-summarisation)

[Using AI for data descriptions](../../using-ai-in-data-preparation)

<mark style="color:orange;">**Providing a comprehensive README file**</mark> with plenty of detail in a structured form, and links to further information. This should include information on the creation and use of the dataset. One way to do this is with datasheets.&#x20;

[What's a README file? Find out how to make one here](https://www.makeareadme.com/)

[What's a datasheet? Find out what should be included he](https://arxiv.org/pdf/1803.09010)re.

<mark style="color:orange;">**Keeping datasets to a manageable size**</mark><mark style="color:orange;">.</mark> It's not always true that the biggest data is most useful. Many users will be working with standard laptops and computers - datasets should not exceed standard processable file sizes

<mark style="color:orange;">**Making machine learning datasets easy to access.**</mark> Similarly - datasets should be possible to open with a standard configuration of a common library (such as Pandas).



[How do we know this? Read the original research here](https://www.sciencedirect.com/science/article/pii/S2666389920301847#sec8)
