Summary
Citizen science has become an integral part of biodiversity research with enormous potential for both improving biodiversity monitoring and involving citizens in scientific projects. However, the full integration of data generated with citizen science in professional research programs has not been fully reached. This is largely due to methodological challenges that compromise the value of the data that most citizen science projects have up to date generated. HyperCitizen will create an innovative solution that enables citizen science projects to generate data that are essentially equally informative as data generated by professional scientists. At the same time, HyperCitizen will provide additional value for the citizen scientists themselves, engaging them more profoundly to scientific research than has been earlier possible. HyperCitizen derives from recent results generated by the ERC-Synergy project LIFEPLAN: “A Planetary Inventory of Life – a New Synthesis Built on Big Data Combined with Novel Statistical Methods”. HyperCitizen will develop a mobile phone application for citizen science -based bird monitoring, enabling real-time interaction with users by hyperautomation. It will further develop computationally highly scalable methods for (i) convolutional neural network -based bird vocalization classification and (ii) joint species distribution modelling used produce prediction maps on past, present and future distributions of birds and their vocalizations. The proof of concept will be delivered by teaming up with bird enthusiast organizations and the Finnish national broadcasting company to launch and run a citizen science campaign on Finnish bird monitoring, culminating in the Great Migratory Bird Evening in spring 2024. HyperCitizen aimed to demonstrate how public can be engaged in research in a way that breaks the current wall between citizen science and professional science.
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| Web resources: | https://cordis.europa.eu/project/id/101123091 |
| Start date: | 01-07-2023 |
| End date: | 31-12-2024 |
| Total budget - Public funding: | - 150 000,00 Euro |
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Original description
Citizen science has become an integral part of biodiversity research with enormous potential for both improving biodiversity monitoring and involving citizens in scientific projects. However, the full integration of data generated with citizen science in professional research programs has not been fully reached. This is largely due to methodological challenges that compromise the value of the data that most citizen science projects have up to date generated. HyperCitizen will create an innovative solution that enables citizen science projects to generate data that are essentially equally informative as data generated by professional scientists. At the same time, HyperCitizen will provide additional value for the citizen scientists themselves, engaging them more profoundly to scientific research than has been earlier possible. HyperCitizen derives from recent results generated by the ERC-Synergy project LIFEPLAN: “A Planetary Inventory of Life – a New Synthesis Built on Big Data Combined with Novel Statistical Methods”. HyperCitizen will develop a mobile phone application for citizen science -based bird monitoring, enabling real-time interaction with users by hyperautomation. It will further develop computationally highly scalable methods for (i) convolutional neural network -based bird vocalization classification and (ii) joint species distribution modelling used produce prediction maps on past, present and future distributions of birds and their vocalizations. The proof of concept will be delivered by teaming up with bird enthusiast organizations and the Finnish national broadcasting company to launch and run a citizen science campaign on Finnish bird monitoring, culminating in the Great Migratory Bird Evening in spring 2024. HyperCitizen aimed to demonstrate how public can be engaged in research in a way that breaks the current wall between citizen science and professional science.Status
SIGNEDCall topic
ERC-2023-POCUpdate Date
12-03-2024
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