Structured data is becoming critical in every domain and its availability on the web is increasing rapidly. Despite its abundance and variety of applications, we know very little about how people find data, understand it, and put it to use. This work aims to inform the design of data discovery tools and technologies from a user centred perspective. We aim to better understand what type of information supports people in finding and selecting data relevant for their respective tasks. We conducted a mixed-methods study looking at the workflow of data practitioners when searching for data. From that we identified textual summaries as a key element that supports the decision making process in information seeking activities for data. Based on these results we performed a mixed-methods study to identify attributes people consider important when summarising a dataset. We found text summaries are laid out according to common structures, contain four main information types, and cover a set of dataset features. We describe follow-up studies that are planned to validate these findings and to evaluate their applicability in a dataset search scenario.

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