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. 2025 Apr;94(4):519-534.
doi: 10.1111/1365-2656.14223. Epub 2025 Feb 11.

MacaqueNet: Advancing comparative behavioural research through large-scale collaboration

Delphine De Moor  1 Macaela Skelton  1 MacaqueNetFederica Amici  2   3 Malgorzata E Arlet  4 Krishna N Balasubramaniam  5   6 Sébastien Ballesta  7   8 Andreas Berghänel  9 Carol M Berman  10 Sofia K Bernstein  11 Debottam Bhattacharjee  12 Eliza Bliss-Moreau  13 Fany Brotcorne  14 Marina Butovskaya  15 Liz A D Campbell  16 Monica Carosi  17 Mayukh Chatterjee  18   19 Matthew A Cooper  20 Veronica B Cowl  21 Claudio De la O  22   23   24 Arianna De Marco  25 Amanda M Dettmer  26 Ashni K Dhawale  27 Joseph J Erinjery  28 Cara L Evans  29 Julia Fischer  30   31   32 Iván García-Nisa  33 Gwennan Giraud  14 Roy Hammer  34 Malene F Hansen  35   36   37   38 Anna Holzner  39   40   41 Stefano Kaburu  42 Martina Konečná  43 Honnavalli N Kumara  44 Marine Larrivaz  45 Jean-Baptiste Leca  46 Mathieu Legrand  8 Julia Lehmann  47 Jin-Hua Li  48   49 Anne-Sophie Lezé  50 Andrew MacIntosh  51 Bonaventura Majolo  52 Laëtitia Maréchal  52 Pascal R Marty  53 Jorg J M Massen  12   54 Risma Illa Maulany  55 Brenda McCowan  6 Richard McFarland  56   57 Pierre Merieau  50 Hélène Meunier  7   8 Jérôme Micheletta  58 Partha S Mishra  44   59 Shahrul A M Sah  41 Sandra Molesti  60 Kristen S Morrow  61 Nadine Müller-Klein  62 Putu Oka Ngakan  55 Elisabetta Palagi  63 Odile Petit  64 Lena S Pflüger  34   54 Eugenia Polizzi di Sorrentino  65 Roopali Raghaven  19 Gaël Raimbault  8 Sunita Ram  19   66 Ulrich H Reichard  67 Erin P Riley  68 Alan V Rincon  58 Nadine Ruppert  41 Baptiste Sadoughi  7   69   70   71 Kumar Santhosh  44 Gabriele Schino  65 Lori K Sheeran  72 Joan B Silk  73 Mewa Singh  74 Anindya Sinha  19 Sebastian Sosa  40 Mathieu S Stribos  12 Cédric Sueur  75 Barbara Tiddi  76 Patrick J Tkaczynski  77 Florian Trebouet  78 Anja Widdig  39   40   79 Jamie Whitehouse  56 Lauren J Wooddell  80 Dong-Po Xia  48   81 Lorenzo von Fersen  82 Christopher Young  56 Oliver Schülke  32   70   71 Julia Ostner  32   70   71 Christof Neumann  30   32 Julie Duboscq  71   83 Lauren J N Brent  1
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MacaqueNet: Advancing comparative behavioural research through large-scale collaboration

Delphine De Moor et al. J Anim Ecol. 2025 Apr.

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.

Keywords: Macaca; comparative research; data sharing; database; primates; repository; social networks; team science.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

FIGURE 1
FIGURE 1
Summary of database content at the time of publication. (a) The MacaqueNet database currently contains data for 14 out of 25 recognised macaque species (note that the depicted phylogenetic tree (Arnold, Matthews & Nunn, 2010) does not include relatively newly described species: Macaca selai, Macaca leucogenys and Macaca siberu). (b) Overview of the number of group‐periods (i.e. a given study period for a given group), individuals and sociometric matrices for aggressive and affiliative behaviours for each species. As some individuals have been observed over multiple group‐periods, the number of individuals represents the number of unique individual datapoints but not necessarily the number of unique individuals. The dot plot on the right illustrates grooming network densities (the proportion of dyads that were observed grooming at least once), with each dot representing the density for one grooming sociometric matrix.
FIGURE 2
FIGURE 2
Geographical distribution of research sites in which the data currently present in the MacaqueNet database have been collected. Populations in America and Europe (with the exception of Gibraltar) have been introduced. Living conditions are classified as wild, free‐ranging or captive (see glossary at https://macaquenet.github.io/documentation/ for definitions). Group‐periods represent the total number of study periods for all groups at a given research site.
FIGURE 3
FIGURE 3
Example of the data of one dataset as stored in the MacaqueNet database. Top left: A sociometric matrix documenting pairwise interactions in a given group‐period. For directed matrices, the actors—the individuals who initiate the behaviour—are listed in the rows, while the receivers—the individuals towards whom the behaviour is directed—are listed in the columns. The matrix entries represent either the total number of times (counts, as depicted here) or the total duration (in seconds) for which an individual in the row performed the behaviour towards the individual in the column for a given study period. Depicted here: Counts of dyadic grooming events in a group of Barbary macaques (Macaca sylvanus) from La Montagne des Singes in 2017. Top right: A subject table listing all individuals observed in a given group‐period, along with individual attributes: Sex, age and observation effort, here duration of observation (in hours). Bottom left: An illustration of the network representing the data in the sociometric matrix. Each blue circle represents a subject, green lines between circles represent dyadic affiliation strength, here quantified as the dyadic rate of grooming. Bottom right: A picture of Barbary macaques grooming (photo credit: Anthony Poynton).
FIGURE 4
FIGURE 4
Set‐up of the relational MacaqueNet database. All variables are defined in the glossary (available at https://macaquenet.github.io/documentation/). Arrows indicate how each table is linked to at least one other table in the database through unique identifiers (the ‘‐id’ columns in each table). Each entry in the ‘datakey’ table links to a sociometric matrix with all instances of a specific behaviour category for one group period (as depicted in Figure 3 top left). Part of the ‘datakey’ table is only relevant for the data import pipeline. This figure was made using dbdiagram (https://www.dbdiagram.io/).
FIGURE 5
FIGURE 5
Overview of the workflow from primary data submission, over the data checks and standardisation pipeline to data requests. All contributed data contain several sociometric matrices along with corresponding subject data and metadata for at least one group‐period for a specific species.

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