Athena - Spot species in fine art

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Authors: Thomas van Goethem
Cited by: 0

Abstract

An interdisciplinary approach is necessary to fully understand the impacts and consequences of human-nature dialogues. By collecting, combining and integrating information from different historical sources, scientists, policy makers and the broader public can more easily study and articulate changes in mutual relationships between socio-cultural and ecological systems. To this end a wide variety of historical, archaeological and ecological sources are brought together in ATHENA, from species distributions data to paintings, and from bestiaria to newspaper articles. Rather than concentrating on one specific event in time and space, ATHENA allows to study and understand human-nature interactions across temporal and spatial scales in order to gain a full understanding of its cumulative effects. By working with data from different disciplines information quality can be improved significantly. Aim of the ATHENA project is to develop an internationally unique database allowing researchers from multiple scientific disciplines to study human-nature relationships in an interdisciplinary environment. The resulting database will provide a platform for large-scale, comparative (both in space and time) and multilevel studies of human-nature relationships. The development of the ATHENA database will advance the fields of research involved, and will strengthen high level interdisciplinary research on human – nature interactions. Athena has started a crowdsourcing project to identify animal and plant species in the Rijksmuseum's painting collection. The Zooniverse platform was used for the first phase of the project. We asked volunteers to indicate per painting / print whether species from different "main groups" (mammals, fish, trees, etc.) were depicted. In total, 4104 volunteers classified 105,239 paintings and prints through 528,518 individual interactions.

Publication details

Journal / source
Utrecht University - Yoda
Publication date
6 January 2026
Language
EN
Document type
Dataset