Talking datasets: Understanding data sensemaking behaviours
This addresses the problem of inefficient data reuse for researchers and data practitioners, though it is incremental in building understanding of sensemaking behaviors.
The study investigated how researchers make sense of datasets for reuse, identifying common patterns and necessary attributes across inspecting data, engaging with content, and placing data in context, and proposed design recommendations to facilitate this process.
The sharing and reuse of data are seen as critical to solving the most complex problems of today. Despite this potential, relatively little is known about a key step in data reuse: people's behaviours involved in data-centric sensemaking. We aim to address this gap by presenting a mixed-methods study combining in-depth interviews, a think-aloud task and a screen recording analysis with 31 researchers as they summarised and interacted with both familiar and unfamiliar data. We use our findings to identify and detail common activity patterns and necessary data attributes across three clusters of sensemaking activities: inspecting data, engaging with content, and placing data within broader contexts. We conclude by proposing design recommendations for tools and documentation practices which can be used to facilitate sensemaking and subsequent data reuse.