Cartographie des pratiques du Vélo’v : le regard de physiciens et d’informaticiens

L'étude des données de location du système de vélos en libre service Vélo'v, installé dans Lyon et Villeurbanne, amène à poser des questions de méthodes quand il s'agit, en tant qu'informaticien ou physicien et non de géographe ou de cartographe, de trouver comment faire des cartes représentant ces données. Partant d'une discussion sur les masses de données numérisées accessibles aux scientifiques, tout particulièrement pour des études en sciences sociales, des outils utiles pour manipuler sont évoqués. L'exemple de l'analyse des déplacements en Vélo'v permet d'illustrer pourquoi et comment réaliser des cartes qui décrivent des résultats sur les types de déplacements effectués ou sur la disponibilité des vélos ou des places aux stations. Une conclusion traite des pratiques, en partie nouvelles, liées aux masses de données.From the study of the data of Vélo'v, the Bicycle Sharing System of Lyon and Villeurbanne, several issues are discussed about how researchers from computer science and physics, and not from geography or cartography, were lead to draw maps representing these data. First, the diversity of available digital data is shown, especially for studies related to social or human studies. Some tools to manipulate them are discussed. The example of the Vélo'v data of trips illustrates why and how to propose maps describing features about the movements made with these bikes, or about the availability of bikes or free stands at stations. This practice gives way to a conclusion that suggests a whole new meaning related to the mass of data

Graph analysis of functional brain networks: practical issues in translational neuroscience

The brain can be regarded as a network: a connected system where nodes, or units, represent different specialized regions and links, or connections, represent communication pathways. From a functional perspective communication is coded by temporal dependence between the activities of different brain areas. In the last decade, the abstract representation of the brain as a graph has allowed to visualize functional brain networks and describe their non-trivial topological properties in a compact and objective way. Nowadays, the use of graph analysis in translational neuroscience has become essential to quantify brain dysfunctions in terms of aberrant reconfiguration of functional brain networks. Despite its evident impact, graph analysis of functional brain networks is not a simple toolbox that can be blindly applied to brain signals. On the one hand, it requires a know-how of all the methodological steps of the processing pipeline that manipulates the input brain signals and extract the functional network properties. On the other hand, a knowledge of the neural phenomenon under study is required to perform physiological-relevant analysis. The aim of this review is to provide practical indications to make sense of brain network analysis and contrast counterproductive attitudes

Bootstrapping under constraint for the assessment of group behavior in human contact networks

International audienceThe increasing availability of time --and space-- resolved data describing human activities and interactions gives insights into both static and dynamic properties of human behavior. In practice, nevertheless, real-world datasets can often be considered as only one realisation of a particular event. This highlights a key issue in social network analysis: the statistical significance of estimated properties. In this context, we focus here on the assessment of quantitative features of specific subset of nodes in empirical networks. We present a method of statistical resampling based on bootstrapping groups of nodes under constraints within the empirical network. The method enables us to define acceptance intervals for various Null Hypotheses concerning relevant properties of the subset of nodes under consideration, in order to characterize by a statistical test its behavior as ''normal'' or not. We apply this method to a high resolution dataset describing the face-to-face proximity of individuals during two co-located scientific conferences. As a case study, we show how to probe whether co-locating the two conferences succeeded in bringing together the two corresponding groups of scientists