Interaction Data Lab
Research team at CRI Paris
We use network science and data analysis to decipher collective phenomena at biological and social scales. In particular, we study collaborative learning and solving using network approaches on large empirical datasets, with the end goal to develop tools fostering collective intelligence for social impact.
We leverage the power of large datasets to tackle interdisciplinary projects from various fields: social network analyses of scientific teams, dynamics of Twitter communities, collaborative learning using phone call records, dynamics of contribution and collaborations in open-source communities on GitHub, evolution of scientific fields and trajectories of scientists across knowledge domains, design of recommender systems for open science projects, or perturbation spread in technological and biological networks.
In this paper, we explore how we can quantify the rise and fall patterns of scientific fields. In particular we find that the early stage of a field is characterised by small interdisciplinary teams of early career researchers publishing disruptive work, while late phases exhibit the role of specialised, large teams building on the previousContinue reading “New paper on research field evolution”
In this paper, we explore the propagation of perturbations in large-scale engineering projects. Check out the thread below for more information! The paper can be found here.
We are happy to share that we obtained a French National funding ANR “JCJC” (Young researcher): “CORES: Quantitative assessment of open collaborations in science and engineering”. This project will run in the next two years. The abstract can be found below: Understanding how team collaborations underlie team performance is key for the design of organizationalContinue reading “Funding from ANR JCJC”
Journal clubs on open communities and recommender systems
These talks have been organized with the Just One Giant Lab (JOGL) open science community to improve our understanding of crowd science and how to enhance collective intelligence using recommender systems on online platforms.