# Netzschleuder network catalogue, repository and centrifuge

Problems with this dataset? Open an issue.
You may also take a look at the source code.
The networks in this dataset can be loaded directly from graph-tool with:
import graph_tool.all as gt
g = gt.collection.ns["sp_baboons/observational"]

(and likewise for the other networks available.)

# 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.

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
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), https://doi.org/10.1098/rspa.2019.0737 [@sci-hub]
Upstream URL OK
http://www.sociopatterns.org/datasets/baboons-interactions/
Networks
Tip: click on the table header to sort the list. Hover your mouse over it to obtain a legend.
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 19 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 116 KiB 223 KiB 204 KiB 168 KiB
Ridiculograms