sp_baboons — Baboons' interactions (2020)

Description

Network of interactions between a group of 20 Guinea baboons living in an enclosure of a Primate Center in France, between June 13th 2019 and July 10th 2019. The data set contains observational and wearable sensors data. Nodes are baboons. In the observational network, each directed edge $i \to j$ is a behavioral event registered by the observer, in which baboon $i$ (the actor) acts upon baboon $j$ (the recipient), so that a pair of baboons is connected by one edge per event; in the sensor network, each undirected edge is a contact between two baboons recorded by the proximity sensors, one edge per recorded contact.

The observational data contains all the behavioral events registered by an observer, with 8 columns:

DateTime: Time stamp of the event, namely the moment the observed behavior was registered. In case of STATE events (events with duration > 0), it refers to the beginning of the behavior
Actor: The name of the actor
Recipient: The name of the individual the Actor is acting upon
Behavior: The behavior the Actor. 14 types of behaviors are registered:’Resting’, ‘Grooming’, ‘Presenting’,’Playing with’, ‘Grunting-Lipsmacking’, ‘Supplanting’,’Threatening’, ‘Submission’, ‘Touching’, ‘Avoiding’, ‘Attacking’,’Carrying’, ‘Embracing’, ‘Mounting’, ‘Copulating’, ‘Chasing’. In addition two other categories were included: ‘Invisible’ and ‘Other’
Category: The classification of the behavior. It can be ‘Affiliative’, ‘Agonistic’, ‘Other’
Duration: Duration of the observed behavior. POINT events have no duration
Localisation: Zone of the enclosure where the observed behavior takes place
Point: indicates if the event is a POINT event (YES) or a STATE event (NO).

The sensor data contains contacts recorded in the same period by the SocioPatterns infrastructure. The proximity sensors were worn by 13 of the 20 individuals cited above. The data file consists of 4 columns:

t: time of the beginning of the contact in Epoch format (Unix timestamps)
i: Name of the first individual
j: Name of the second individual
DateTime

Tags

Social Animal Offline Unweighted Weighted Temporal Metadata

Added

2021-08-07

Citation

V. Gelardi, J. Godard, D. Paleressompoulle, N. Claidière, A. Barrat, "Measuring social networks in primates: wearable sensors vs. direct observations", Proc. R. Soc. A 476:20190737 (2020) 10.1098/rspa.2019.0737 Sci-Hub

Upstream URL OK

https://sociopatterns.org/datasets/baboons-interactions/

Upstream license

Creative Commons Attribution-NonCommercial-ShareAlike, http://creativecommons.org/licenses/by-nc-sa/3.0/

Networks

Tip: click on a column header to sort the list, or hover over it for a description.

Column legend

$\left<k\right>$: Mean degree
$\sigma_k$: Standard deviation of the degrees
$\lambda_h$: Largest eigenvalue of the Hashimoto (non-backtracking) matrix
$\tau$: Random walk mixing time
$r$: Degree assortativity
$c$: Global clustering coefficient
$\oslash$: (Pseudo-) diameter
$S$: Relative size of the largest component
NPs: Node properties
EPs: Edge properties

Name	Nodes	Edges	$\left<k\right>$	$\sigma_k$	$\lambda_h$	$\tau$	$r$	$c$	$\oslash$	$S$	Kind	Mode	NPs	EPs	gt	GraphML	GML	csv
observational	23	3,197	139.00	207.87	16.00	2.23	0.23	0.85	4	1.00	Directed	Unipartite	name	time behavior category duration localization point	14 KiB	23 KiB	22 KiB	18 KiB
sensor	13	63,095	9706.92	4232.92	11.00	2.39	0.27	0.79	1	1.00	Undirected	Unipartite	name	time	112 KiB	193 KiB	188 KiB	168 KiB

Ridiculograms^*

^* These are automatically generated force-directed visualizations, and can be quite meaningless for networks both big and small. They should not be taken seriously as sources of scientific insight. See here for a discussion.