Talking datasets: Understanding data sensemaking behaviours

TL;DR

This study investigates how researchers interpret and understand data during reuse, identifying key behaviors and suggesting design improvements for tools to enhance data sensemaking and reuse.

Contribution

It provides a detailed analysis of data sensemaking behaviors through mixed methods and offers design recommendations for improving data reuse tools and documentation.

Findings

Identified common sensemaking activity patterns

Highlighted key data attributes for effective understanding

Proposed design recommendations for data reuse tools

Abstract

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.