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      Turing’s Sunflowers: Public research and the role of museums

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      Proceedings of EVA London 2020 (EVA 2020)

      AI and the Arts: Artificial Imagination

      6th July – 9th July 2020

      Alan Turing, Citizen science, Crowdsourcing, Museums, Public engagement, Public research

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          Abstract

          In recent years, public engagement in museums is increasingly being influenced by the paradigm of “citizen science”, that is, active participation in research teams by members of the general public with no formal training in the field of research concerned. This paper provides an overview of citizen science approaches which museums can deploy using online platforms, digital tools and apps. It also aims to highlight challenges and innovations, as well as possible opportunities for cultural organisations to include public participation in research and knowledge creation.

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          Most cited references 30

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          Citizen science as seen by scientists: Methodological, epistemological and ethical dimensions.

          Citizen science as a way of communicating science and doing public engagement has over the past decade become the focus of considerable hopes and expectations. It can be seen as a win-win situation, where scientists get help from the public and the participants get a public engagement experience that involves them in real and meaningful scientific research. In this paper we present the results of a series of qualitative interviews with scientists who participated in the 'OPAL' portfolio of citizen science projects that has been running in England since 2007: What were their experiences of participating in citizen science? We highlight two particular sets of issues that our participants have voiced, methodological/epistemological and ethical issues. While we share the general enthusiasm over citizen science, we hope that the research in this paper opens up more debate over the potential pitfalls of citizen science as seen by the scientists themselves.
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            Taking a ‘Big Data’ approach to data quality in a citizen science project

            Data from well-designed experiments provide the strongest evidence of causation in biodiversity studies. However, for many species the collection of these data is not scalable to the spatial and temporal extents required to understand patterns at the population level. Only data collected from citizen science projects can gather sufficient quantities of data, but data collected from volunteers are inherently noisy and heterogeneous. Here we describe a ‘Big Data’ approach to improve the data quality in eBird, a global citizen science project that gathers bird observations. First, eBird’s data submission design ensures that all data meet high standards of completeness and accuracy. Second, we take a ‘sensor calibration’ approach to measure individual variation in eBird participant’s ability to detect and identify birds. Third, we use species distribution models to fill in data gaps. Finally, we provide examples of novel analyses exploring population-level patterns in bird distributions.
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              Contributions to conservation outcomes by natural history museum-led citizen science: Examining evidence and next steps

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                Author and article information

                Contributors
                Conference
                July 2020
                July 2020
                : 32-39
                Affiliations
                Centre for the Digital Transformation of Health

                University of Melbourne

                Australia
                School of Engineering

                London South Bank University

                United Kingdom
                Article
                10.14236/ewic/EVA2020.5
                © Borda et al. Published by BCS Learning and Development Ltd. Proceedings of EVA London 2020

                This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                Proceedings of EVA London 2020
                EVA 2020
                30
                London
                6th July – 9th July 2020
                Electronic Workshops in Computing (eWiC)
                AI and the Arts: Artificial Imagination
                Product
                Product Information: 1477-9358BCS Learning and Development Ltd
                Self URI (journal page): https://ewic.bcs.org/
                Categories
                Electronic Workshops in Computing

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