TY - JOUR
KW - AnnualReport2024-25
KW - comparative research
KW - data sharing
KW - database
KW - Macaca
KW - primates
KW - repository
KW - social networks
KW - team science
AU - Delphine De Moor
AU - Macaela Skelton
AU - MacaqueNet
AU - Federica Amici
AU - Malgorzata Arlet
AU - Krishna Balasubramaniam
AU - Sébastien Ballesta
AU - Andreas Berghänel
AU - Carol Berman
AU - Sofia Bernstein
AU - Debottam Bhattacharjee
AU - Eliza Bliss-Moreau
AU - Fany Brotcorne
AU - Marina Butovskaya
AU - Liz Campbell
AU - Monica Carosi
AU - Mayukh Chatterjee
AU - Matthew Cooper
AU - Veronica Cowl
AU - Claudio De la O
AU - Arianna De Marco
AU - Amanda Dettmer
AU - Ashni Dhawale
AU - Joseph Erinjery
AU - Cara Evans
AU - Julia Fischer
AU - Iván García-Nisa
AU - Gwennan Giraud
AU - Roy Hammer
AU - Malene Hansen
AU - Anna Holzner
AU - Stefano Kaburu
AU - Martina Konečná
AU - Honnavalli Kumara
AU - Marine Larrivaz
AU - Jean-Baptiste Leca
AU - Mathieu Legrand
AU - Julia Lehmann
AU - Jin-Hua Li
AU - Anne-Sophie Lezé
AU - Andrew MacIntosh
AU - Bonaventura Majolo
AU - Laëtitia Maréchal
AU - Pascal Marty
AU - Jorg Massen
AU - Risma Maulany
AU - Brenda McCowan
AU - Richard McFarland
AU - Pierre Merieau
AU - Hélène Meunier
AU - Jérôme Micheletta
AU - Partha Mishra
AU - Shahrul Sah
AU - Sandra Molesti
AU - Kristen Morrow
AU - Nadine Müller-Klein
AU - Putu Ngakan
AU - Elisabetta Palagi
AU - Odile Petit
AU - Lena Pflüger
AU - Eugenia di Sorrentino
AU - Roopali Raghaven
AU - Gaël Raimbault
AU - Sunita Ram
AU - Ulrich Reichard
AU - Erin Riley
AU - Alan Rincon
AU - Nadine Ruppert
AU - Baptiste Sadoughi
AU - Kumar Santhosh
AU - Gabriele Schino
AU - Lori Sheeran
AU - Joan Silk
AU - Mewa Singh
AU - Anindya Sinha
AU - Sebastian Sosa
AU - Mathieu Stribos
AU - Cédric Sueur
AU - Barbara Tiddi
AU - Patrick Tkaczynski
AU - Florian Trebouet
AU - Anja Widdig
AU - Jamie Whitehouse
AU - Lauren Wooddell
AU - Dong-Po Xia
AU - Lorenzo von Fersen
AU - Christopher Young
AU - Oliver Schülke
AU - Julia Ostner
AU - Christof Neumann
AU - Julie Duboscq
AU - Lauren Brent
AB - 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.
BT - Journal of Animal Ecology
DA - 2025/02/11/
DO - 10.1111/1365-2656.14223
DP - Wiley Online Library
IS - n/a
LA - en
N2 - 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.
PY - 2025
SN - 1365-2656
ST - MacaqueNet
T2 - Journal of Animal Ecology
TI - MacaqueNet: Advancing comparative behavioural research through large-scale collaboration
UR - https://onlinelibrary.wiley.com/doi/abs/10.1111/1365-2656.14223
VL - n/a
Y2 - 2025/02/18/07:05:24
ER -