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Details of Grant 

EPSRC Reference: EP/V010662/1
Title: Semi-automatic Data Tours to Support Data Exploration and Visualisation Literacy for Novice Analysts
Principal Investigator: Bach, Dr B
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Microsoft Université Aix-en-Provence University of Glasgow
Department: Sch of Informatics
Organisation: University of Edinburgh
Scheme: New Investigator Award
Starts: 01 July 2021 Ends: 30 June 2023 Value (£): 261,061
EPSRC Research Topic Classifications:
Computer Graphics & Visual. Human-Computer Interactions
EPSRC Industrial Sector Classifications:
Information Technologies
Related Grants:
Panel History:
Panel DatePanel NameOutcome
01 Oct 2020 EPSRC ICT Prioritisation Panel October 2020 Announced
Summary on Grant Application Form
Data analysis is key to understanding timely phenomena from climate change to social media, from diseases to political conflicts, from the human brain to migration. In order to complement statistical analysis and modern machine learning approaches for data analysis, visualisation techniques and interactive interfaces support human-in-the-loop control over these systems as well as human sensemaking in cases where data is uncertain, requires greater overview for the generation of hypotheses, and effective communication to larger audiences. While more and more tools, such as Tableau, Gephi or Microsoft's PowerBI are democratising the use of data visualisation, using data visualisations to their full extend requires training novice analysts in tools, techniques, and interactive exploration, as well as communication and presentation.

This project aims to free the analyst from their burden of exploring a data set from the beginning while having to chose among tools, learn their workflows, and create visualisations themselves. Rather, it aims to support novice analysts through a system that automatically displays information about a data set to an analyst while explaining visualisation techniques and findings. In such a "data tour", an analyst starts as a passive reader following a set of visualisations and textual explanations. Respective visualisations will be explained to the analyst. As the analyst becomes familiar with visualisations and their data, they are invited to explore the data by themselves through an interactive interface and communicate the system in which aspects they are most interested in.

Creating effective data tours draws inspiration from previous work on using comics for data-driven storytelling (htttp://datacomics.net), visualisation cheatsheets (http://visualizationcheatsheets.github.io) and approaches to data visualisation literacy, data mining for networks, and human-computer interaction.

To provide for specific data sets and contact with novice analysts for evaluating our tool, this project involves collaborators in history, archeology, sociology and network science and their complex geo-temporal networks including social networks, archeological trading networks, family networks, and Twitter networks.

To create compelling data tours for these data sets we lack significant understanding of

- current exploration strategies employed by analysts and their barriers to analysis,

- ways of automatically extracting and annotating patterns-of-interest in networks, and

- ways of creating meaningful explanatory sequences and high-level structures for data tours.

This research involves a coordinated approach of field studies, visualisation and interface design, implementation, and user-centered evaluation. During a brief first phase, we will closely work with experts in Humanities research to create effective visualisations for their networks; in a second phase we mine and present insights from these data sets, and in the last phase, we investigate ways to structure and present findings in data tours.

Our research will open new questions in how far storytelling and explaining visualisations can be supported by intelligent agents, i.e., computer programs, that partner with humans and engage in a dialogue. Our research may inspire new forms of intelligent interfaces that foresee an analyst's tasks and understand their specific interest in the data. Researchers in the digital humanities, social sciences, and network analysis will benefit from better support for visualising their geo-temporal networks and semi-automatic ways to analyse and lead to a better understanding of their data and new collaborative research agendas using visual analysis. Our project aims to provide impulses for commercial products and recommendation engines and will provide companies with knowledge and techniques to build customised data tours for their clients.

Key Findings
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Potential use in non-academic contexts
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