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ZENODO
Report . 2021
License: CC BY
Data sources: ZENODO
ZENODO
Report . 2021
License: CC BY
Data sources: Datacite
ZENODO
Report . 2021
License: CC BY
Data sources: Datacite
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Visitor Interaction and Machine Curation in the Virtual Liverpool Biennial: A Towards a National Collection COVID-19 Project Final Report

Authors: Impett, Leonardo; Cetinic, Eva; Krysa, Joasia;

Visitor Interaction and Machine Curation in the Virtual Liverpool Biennial: A Towards a National Collection COVID-19 Project Final Report

Abstract

This report summarises the AHRC-funded Towards a National Collection COVID-19 Project, Visitor Interaction and Machine Curation in the Virtual Liverpool Biennial, which ran from 1 January 2021 to 31 August 2021. The project was based at the Department of Computer Science, Durham University, in collaboration with the Liverpool School of Art and Design, Liverpool John Moores University and the Liverpool Biennial. A summary of our research project and its main questions is provided in the abstract. The aims and objective section outlines our three principal research ambitions: to prototype a different use for machine learning in virtual exhibitions (as co-curators, not search engines); to understand how visitors might interact with such a system; and to look at the bias present in the machine learning algorithms that power it. We then report details of our administrative structures (partnerships, staffing, and timetable). Our research methodologies and results are summarised in the Research Approach, Research Results, and Project Outputs section. We have three principal project outputs, tied to our three research questions: an online machine curation prototype hosted by the Liverpool Biennial, ai.biennial.com, and associated open-source codebase to reproduce it on other collections; a visitor interaction dataset, which is freely viewable inside our online browser, and an associated anonymous survey which is not public; and an open-source toolkit for examining bias in the major machine learning algorithm used in the project. The report concludes with our project’s recommendations for the Towards a National Collection program, in the context of our unusual status as an AHRC project in a computer science department.

Keywords

Machine Learning, Towards a National Collection, Computer Vision

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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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