Summarising data as text helps people make sense of it. It also improves data\ndiscovery, as search algorithms can match this text against keyword queries. In\nthis paper, we explore the characteristics of text summaries of data in order\nto understand how meaningful summaries look like. We present two complementary\nstudies: a data-search diary study with 69 students, which offers insight into\nthe information needs of people searching for data; and a summarisation study,\nwith a lab and a crowdsourcing component with overall 80 data-literate\nparticipants, which produced summaries for 25 datasets. In each study we\ncarried out a qualitative analysis to identify key themes and commonly\nmentioned dataset attributes, which people consider when searching and making\nsense of data. The results helped us design a template to create more\nmeaningful textual representations of data, alongside guidelines for improving\ndata-search experience overall.\n