@article{35,
  keywords = {comparative research, data sharing, database, Macaca, primates, repository, social networks, team science},
  author = {Delphine De Moor and Macaela Skelton and MacaqueNet and Federica Amici and Malgorzata Arlet and Krishna Balasubramaniam and Sébastien Ballesta and Andreas Berghänel and Carol Berman and Sofia Bernstein and Debottam Bhattacharjee and Eliza Bliss-Moreau and Fany Brotcorne and Marina Butovskaya and Liz Campbell and Monica Carosi and Mayukh Chatterjee and Matthew Cooper and Veronica Cowl and Claudio De la O and Arianna De Marco and Amanda Dettmer and Ashni Dhawale and Joseph Erinjery and Cara Evans and Julia Fischer and Iván García-Nisa and Gwennan Giraud and Roy Hammer and Malene Hansen and Anna Holzner and Stefano Kaburu and Martina Konečná and Honnavalli Kumara and Marine Larrivaz and Jean-Baptiste Leca and Mathieu Legrand and Julia Lehmann and Jin-Hua Li and Anne-Sophie Lezé and Andrew MacIntosh and Bonaventura Majolo and Laëtitia Maréchal and Pascal Marty and Jorg Massen and Risma Maulany and Brenda McCowan and Richard McFarland and Pierre Merieau and Hélène Meunier and Jérôme Micheletta and Partha Mishra and Shahrul Sah and Sandra Molesti and Kristen Morrow and Nadine Müller-Klein and Putu Ngakan and Elisabetta Palagi and Odile Petit and Lena Pflüger and Eugenia di Sorrentino and Roopali Raghaven and Gaël Raimbault and Sunita Ram and Ulrich Reichard and Erin Riley and Alan Rincon and Nadine Ruppert and Baptiste Sadoughi and Kumar Santhosh and Gabriele Schino and Lori Sheeran and Joan Silk and Mewa Singh and Anindya Sinha and Sebastian Sosa and Mathieu Stribos and Cédric Sueur and Barbara Tiddi and Patrick Tkaczynski and Florian Trebouet and Anja Widdig and Jamie Whitehouse and Lauren Wooddell and Dong-Po Xia and Lorenzo von Fersen and Christopher Young and Oliver Schülke and Julia Ostner and Christof Neumann and Julie Duboscq and Lauren Brent},
  title = {MacaqueNet: Advancing comparative behavioural research through large-scale collaboration},
  abstract = {There is a vast and ever-accumulating amount of behavioural data on individually recognised animals, an incredible resource to shed light on the ecological and evolutionary drivers of variation in animal behaviour. Yet, the full potential of such data lies in comparative research across taxa with distinct life histories and ecologies. Substantial challenges impede systematic comparisons, one of which is the lack of persistent, accessible and standardised databases. Big-team approaches to building standardised databases offer a solution to facilitating reliable cross-species comparisons. By sharing both data and expertise among researchers, these approaches ensure that valuable data, which might otherwise go unused, become easier to discover, repurpose and synthesise. Additionally, such large-scale collaborations promote a culture of sharing within the research community, incentivising researchers to contribute their data by ensuring their interests are considered through clear sharing guidelines. Active communication with the data contributors during the standardisation process also helps avoid misinterpretation of the data, ultimately improving the reliability of comparative databases. Here, we introduce MacaqueNet, a global collaboration of over 100 researchers (https://macaquenet.github.io/) aimed at unlocking the wealth of cross-species data for research on macaque social behaviour. The MacaqueNet database encompasses data from 1981 to the present on 61 populations across 14 species and is the first publicly searchable and standardised database on affiliative and agonistic animal social behaviour. We describe the establishment of MacaqueNet, from the steps we took to start a large-scale collective, to the creation of a cross-species collaborative database and the implementation of data entry and retrieval protocols. We share MacaqueNet's component resources: an R package for data standardisation, website code, the relational database structure, a glossary and data sharing terms of use. With all these components openly accessible, MacaqueNet can act as a fully replicable template for future endeavours establishing large-scale collaborative comparative databases.},
  year = {2025},
  journal = {Journal of Animal Ecology},
  volume = {n/a},
  month = {02/2025},
  isbn = {1365-2656},
  url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/1365-2656.14223},
  doi = {10.1111/1365-2656.14223},
  language = {en},
}