sp_primary_school — Primary school dynamic contacts (2009)

Description

Networks of face-to-face proximity contacts among the children and teachers of a primary school in Lyon, France, measured by the SocioPatterns collaboration with wearable sensors during two days (Thursday 1 and Friday 2 October 2009). The school has five grades, each divided in two classes, and the study included 232 children and 10 teachers. The following networks are provided:

contacts: temporal network of all contacts. Nodes are persons, and each edge is a contact between two persons active during a 20-second interval of the data collection, so that a pair of persons is connected by one edge per interval in which they were in contact.
day_1, day_2: the cumulative contact networks of the first and second day, published separately upstream. Nodes are the persons present on that day, and an edge connects two persons that were in contact at least once during the day.

The networks have the following properties:

(Node) id: the anonymous identifier of the person (the same in all networks).
(Node) class: the class of the child (1A to 5B, where the digit is the grade), or Teachers.
(Node) gender: the gender (M, F or Unknown).
(Edge) time (contacts): the end of the 20-second interval $[t - 20\,\text{s}, t]$ during which the contact was active, in seconds from an unspecified reference time.
(Edge) duration (day_1, day_2): the cumulative time the two persons spent in contact during the day, in seconds (a multiple of 20 seconds).
(Edge) count (day_1, day_2): the number of times the contact between the two persons was established during the day.

Tags

Social Offline Weighted Temporal Metadata

Added

2020-08-11

Citation

Juliette Stehle et al., "High-Resolution Measurements of Face-to-Face Contact Patterns in a Primary School." PLoS ONE 6(8), e23176 (2011). 10.1371/journal.pone.0023176 Sci-Hub

Upstream URL OK

https://sociopatterns.org/datasets/primary-school-temporal-network-data/

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
contacts	242	125,773	1039.45	517.65	79.14	13.15	0.35	0.75	3	1.00	Undirected	Unipartite	id class gender	time	178 KiB	457 KiB	412 KiB	350 KiB
day_1	236	5,899	49.99	18.91	57.40	2.87	0.17	0.44	3	1.00	Undirected	Unipartite	id class gender	duration count	19 KiB	40 KiB	38 KiB	27 KiB
day_2	238	5,539	46.55	19.85	55.84	3.84	0.22	0.47	3	1.00	Undirected	Unipartite	id class gender	duration count	19 KiB	39 KiB	37 KiB	27 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.