“Behind every complex system lies a complex network that codifies the interactions between the system's components”, writes Albert-László Barabási in his book “Network science”. Society, for example, requires the cooperation of billions of individuals, while communication networks integrate billions of cell phones, computers and satellites... In line with this idea, Marc Santolini explores the how collective phenomena emerge from complex networks of interactions between elementary parts. After a doctorate in physics at ENS Paris, he did his postdoc at the University of Paris. Barabási Laboratory in Boston. It was in this laboratory, a pioneer in network science, that he began to ask himself: “How could network science be used to better connect people and improve the way we learn and solve problems together?” One of his sources of inspiration was Michael Nielsen's book Reinventing discovery. In it, the author discusses the advent of citizen science and how massive collaborations would require “serendipity”, in other words, to encourage the chance meeting of people whose collaboration will accelerate knowledge. ” If we allow people to share their projects, results and profile on a digital platform, then we could use this information to help them connect with the resources and people they need to take their projects forward.”, continues Marc Santolini.
These questions led him to create JOGLor Just One Giant Lab, a non-profit initiative hosted by CRI. Co-founded with Thomas Laindrain (The bench) and Léo Blondel (Harvard) in 2016, JOGL is a platform that allows everyone to share results, find collaborators and contribute to cracking challenges. The aim is to build a decentralized research institute that enables everyone to learn and collaborate together in the digital age, overcoming the barriers of traditional institutions. Many projects have already been submitted to the platform: a mobile health platform to improve the health of refugees, for example, or an algorithm measuring vaccine hesitancy using social media data. Behind the scenes, there is a large database of networks where users are connected to the projects they participate in, the skills they have declared or the users they follow. This information is then used to provide recommendations on who they might interact with or what projects they might be useful for.
In parallel with his work on the design of this open scientific platform, Marc Santolini has launched several research projects to better understand how collaborations work. As a long-term researcher at CRI, he leads the project iGEM TIES (Team interactions study) with a team of postdocs, developers and interns. At the annual iGEM science and engineering competition, around 300 multidisciplinary teams of students work together to design projects using synthetic biology. How do these students work together, and how do these interactions lead to better team performance? To answer these questions, Marc's team extracted data from the open Wiki pages of over 2,000 teams who had participated in the competition over the last ten years. From these wikis, they were able to reconstruct team interactions and study their structure and dynamics. In addition, they are currently recording the actual interactions of team members using a Bluetooth smartphone application they have developed. The network characteristics measured have been associated with team success (medal, prize, finalists) to explore the types of collaboration that underpin team performance. “What distinguishes high-performance teams is their propensity to collaborate collectively on a large number of tasks, with a dynamic of rapid interactions.“explains Marc Santolini, who adds: “In a way, we're doing quantitative scientific anthropology. It's a modern version of what Bruno Latour did in the 80s, when he sat in laboratories as an anthropologist to study how social structures affect scientific production”.” While the iGEM study focuses on small teams (10-20 members), Marc asked himself what used to happen with large teams of 100, 500 or 1000 people His team approached the question of massive collaborations by studying open-source communities on GitHub, a platform where developers create and design software. “When a community exceeds 100-150 people, all projects adopt a “fractal” work-sharing structure, where leadership - defined by the greatest number of contributions - over tasks is nested at different scales. For example, at the global level, someone oversees the whole project, but if you zoom in on the local level of a sub-task, for example the user interface part of the project, you observe the same leadership structure with someone overseeing all the elements of that specific part. This fractal behavior is typical of the self-organization phenomena observed in physics since the 80″.
In addition to cross-team collaboration, our teams are also interested in learning and innovation. For example, in partnership with Orange Labs, they are studying how knowledge spreads through learners using a data set of fine-grained telephone calls collected during a 6-month training course in Madagascar. Using models borrowed from epidemiology, they describe a contagion process where engagement and performance in training are “transmitted” through social interactions. Another line of research focuses on the dynamics of knowledge production. By analyzing scientific publications in arXiv, an open archive of electronic preprints of scientific articles, they explore the universal models underlying scientific innovation. “We have observed that all fields of research follow a similar, saw-tooth life-curve. By studying the early stages of a given field, we discovered facets common to scientific innovators. For example, they tend to be at the start of their career, work in small teams or have a highly multidisciplinary profile.”. Taken together, these different projects address the fundamental questions of how people work together, innovate and learn, with the ultimate aim of guiding the design of the JOGL platform for open scientific innovation.
As a young scientist and team leader, Marc compares a young research team to a start-up: “You're suddenly drawn into a world where you have to wear an increasing number of hats: executive, administrative, hiring process, operational, managerial, strategic, communication...”While he has found at CRI the freedom to work on subjects he's passionate about, he admits that “.“building a team was an incredible challenge“.
