iGEM TIES (Teams IntEraction Study)

In this project we map the social interaction networks of scientific teams participating to the iGEM scientific competition. We collect high resolution team interaction data from digital traces, collaboration monitoring, and surveys to study the network patterns underlying team performance and learning.
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COVID19 Symptoms Twitter Analysis

We use a citizen-science approach to help analyse how local tweets about symptoms can predict hospital emergencies during a pandemic.
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Collaborative Learning

We reconstruct learner interaction network from phone call data to model the diffusion of knowledge in collaborative learning.
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Analysis of Open Source Communities in GitHub

This project aims to quantify the organizational principles underlying large-scale self-organized open source communities.
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The Rise and Fall of Scientific Fields

We use large datasets of scientific publications to understand the universal patterns underlying the growth and decline of scientific fields and describe the research trajectories of researchers in the knowledge space.
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The biological networks driving ageing

We apply network science approaches to understand and model the dynamics of gene networks driving ageing in Drosophila and Humans. In particular, the project focuses on describing ageing as a propagation of network failures in the multi-layer interactome. The project is supported by a French national ANR JCJC funding.

Quantifying the organizational patterns of Citizen Science projects

In this EU H2020 project, we use digital traces to understand what makes citizen science projects succesful and how citizen scientists can provide reliable data for tracking the progress towards the UN Sustainable Development Goals and how grassroots innovation can help achieve such progress.
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Designing recommender systems to enhance collective intelligence

In this project funded by a NESTA Collective Intelligence grant, we develop a network-based recommender system for the open science social platform Just One Giant Lab to match contributors to needs, and explore its impact on community self-organization.

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