05712nas a2201333   4500000000100000000000100001008004100002260001600043653002500059653001700084653001300101653001100114653001300125653001500138653002000153653001700173100002100190700002000211700001500231700001900246700002100265700002800286700002400314700002300338700001700361700002000378700002700398700002300425700001900448700002200467700001700489700001800506700002200524700001900546700001800565700002000583700002100603700001900624700001800643700002000661700001500681700001800696700002300714700001900737700001500756700001800771700001700789700001900806700002200825700002200847700002000869700002300889700002000912700001800932700001500950700002200965700002100987700002301008700002401031700001701055700001601072700001801088700001901106700002201125700001901147700002101166700002401187700001801211700001601229700001901245700001901264700002501283700001601308700002201324700001601346700001801362700002601380700002101406700002001427700001501447700002001462700001501482700001601497700001901513700002201532700001901554700002001573700001701593700001401610700001501624700001801639700001901657700002001676700001801696700001801714700002301732700002101755700001601776700002101792700002001813700001601833700002301849700002201872700002001894700001701914700002101931700001801952700001701970245009301987856006802080490000802148520220802156020001404364       2025                            d  c2025/02/11/10acomparative research10adata sharing10adatabase10aMacaca10aprimates10arepository10asocial networks10ateam science1 aDelphine De Moor1 aMacaela Skelton1 aMacaqueNet1 aFederica Amici1 aMalgorzata Arlet1 aKrishna Balasubramaniam1 aSébastien Ballesta1 aAndreas Berghänel1 aCarol Berman1 aSofia Bernstein1 aDebottam Bhattacharjee1 aEliza Bliss-Moreau1 aFany Brotcorne1 aMarina Butovskaya1 aLiz Campbell1 aMonica Carosi1 aMayukh Chatterjee1 aMatthew Cooper1 aVeronica Cowl1 aClaudio De la O1 aArianna De Marco1 aAmanda Dettmer1 aAshni Dhawale1 aJoseph Erinjery1 aCara Evans1 aJulia Fischer1 aIván García-Nisa1 aGwennan Giraud1 aRoy Hammer1 aMalene Hansen1 aAnna Holzner1 aStefano Kaburu1 aMartina Konečná1 aHonnavalli Kumara1 aMarine Larrivaz1 aJean-Baptiste Leca1 aMathieu Legrand1 aJulia Lehmann1 aJin-Hua Li1 aAnne-Sophie Lezé1 aAndrew MacIntosh1 aBonaventura Majolo1 aLaëtitia Maréchal1 aPascal Marty1 aJorg Massen1 aRisma Maulany1 aBrenda McCowan1 aRichard McFarland1 aPierre Merieau1 aHélène Meunier1 aJérôme Micheletta1 aPartha Mishra1 aShahrul Sah1 aSandra Molesti1 aKristen Morrow1 aNadine Müller-Klein1 aPutu Ngakan1 aElisabetta Palagi1 aOdile Petit1 aLena Pflüger1 aEugenia di Sorrentino1 aRoopali Raghaven1 aGaël Raimbault1 aSunita Ram1 aUlrich Reichard1 aErin Riley1 aAlan Rincon1 aNadine Ruppert1 aBaptiste Sadoughi1 aKumar Santhosh1 aGabriele Schino1 aLori Sheeran1 aJoan Silk1 aMewa Singh1 aAnindya Sinha1 aSebastian Sosa1 aMathieu Stribos1 aCédric Sueur1 aBarbara Tiddi1 aPatrick Tkaczynski1 aFlorian Trebouet1 aAnja Widdig1 aJamie Whitehouse1 aLauren Wooddell1 aDong-Po Xia1 aLorenzo von Fersen1 aChristopher Young1 aOliver Schülke1 aJulia Ostner1 aChristof Neumann1 aJulie Duboscq1 aLauren Brent00aMacaqueNet: Advancing comparative behavioural research through large-scale collaboration  uhttps://onlinelibrary.wiley.com/doi/abs/10.1111/1365-2656.142230 vn/a3 aThere 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.  a1365-2656