Charting mobility patterns in the scientific knowledge landscape

| Marc Santolini

Our paper on the trajectories of scientists in the knowledge space has been accepted in EPJ Data Science! We explore the connections between interdisciplinary mobility and innovation, building on our previous work on the dynamics of scientific fields in arXiv (see this post). Here, we show that tools for studying human mobility can be applied to the mobility within scientific knowledge. Using low-dimensional embedding techniques, we created a knowledge space composed of 1.5 million articles in physics, computer science, and mathematics. The analysis of individual researchers’ publication histories reveals patterns of knowledge mobility similar to physical mobility. Collectively, these trajectories form mobility flows that follow a gravity model, favoring jumps in high-density areas and making long-distance moves less likely. We identify a dichotomy between two types of researchers based on their individual mobility: interdisciplinary explorers, who venture into new fields, and exploiters, who primarily stay within their specialty areas. This work opens up the possibility of using this space to quantify how interventions (such as funding) modify trajectories and the structure of the space.

Here is a thread summarising its findings:

An epistemology for democratic citizen science

| Marc Santolini

In this new paper published in the Royal Society Open Science, we introduce an epistemological framework for citizen science that values diversity in the discovery process and is built on three philosophical foundations: perspectival realism, a naturalistic process-based epistemology, and deliberative social practices. These foundations shift the focus from immediate outcomes to the cognitive and social processes that foster sustainable scientific innovation and productivity, advocating for an ecological approach to scientific research and assessment.

Introducing iGEM: a model system for team science and innovation

| Marc Santolini

This new preprint is the result of a collaboration initiated during my postdoctoral stay at the Barabasi lab in Boston, which I continued at the LPI as an affiliated professor. In this project, we introduce the synthetic biology competition iGEM as a model system for the Science of Science and Innovation, enabling large-scale “laboratory ethnography.” We present the collection and analysis of laboratory notebooks data from 3,000 teams, which we deposited on the open archive Zenodo. We highlight the organizational characteristics (intra- and inter-team collaboration networks) of teams related to learning and success in the competition. In particular, we emphasize how teams overcome coordination costs as they grow in size, as well as the crystallization of the inter-team collaboration network over time, limiting access to relational capital for peripheral teams. This work is currently funded by an ANR JCJC grant to collect field data and build network models of collaborations and performance.

A multi‑level network analysis to analyse online courses

| Marc Santolini

We just published a new article in Educational Technology Research and Development: “Forum posts, communication patterns, and relational structures: A multi-level view of discussions in online courses” . This article relies on the approach we previously published using the formalism of Exponential Random Graph Models (ERGMs) to model the formation of relational networks from data of online forums used in university courses. Utilizing these models in the context of bipartite networks before projecting them into a weighted network allows for the creation of null models that assume different mechanisms of forum use. The statistical comparison of these null model projections with the actual network enabled us to assess the significance of global characteristics such as density, the number of communities, or clustering, as well as filter links to obtain sparse relational structures whose structural properties can be compared and grouped by similarity.

Collaboration and Performance of Citizen Science Projects Addressing the Sustainable Development Goals

| Marc Santolini

Our paper on measuring collaborations and performance in citizen science projects is out in Citizen Science, Theory and Practice!

This work is the result of the European project Crowd4SDG, where we directed the part on the quality criteria of citizen science. We implemented the measurement of processual criteria based on a perspectivist and deliberative epistemology of citizen science published in the Royal Society Open Science (see this other post). With the help of the CoSo app (that we presented in this post), we monitored interactions within a collaborative ecosystem of citizen science innovation projects, revealing the relational dynamics and their influence on project performance. Our approach, combining digital analyses and self-reports, allowed us to break down interactions into multi-layer social networks, highlighting the importance of social capital and relationship management for the success of initiatives. We identified links between team structures, their communications, and the quality of their projects, emphasizing the impact of engagement and collaboration on producing relevant and innovative outcomes. This approach enriches the evaluation tools in citizen science and offers concrete ways to improve engagement, inclusion, and diversity in these projects.

a frugal community review system for agile resource allocation in open innovation contexts

| Marc Santolini

Resource allocation is crucial for the development of innovative projects in science and technology. In response to the urgent COVID-19 pandemic in 2020, we implemented an agile “community review” system with JOGL to quickly allocate micro-grants for the prototyping of innovative solutions. In this paper published in f1000Research we analyzed the results of 7 review cycles. Implemented across 147 projects, this process is characterized by its speed (median duration of 10 days), scalability (4 reviewers per project regardless of the total number of projects), and robustness, measured by the preservation of the projects’ ranking order after the random removal of reviewers. Including applicants in the review process does not introduce significant bias, showing a correlation of r=0.28 between evaluations, similar to that observed for non-applicants and within traditional funding methods. This system allows for agile improvement of proposals, promoting the implementation of successful early prototypes and the constructive revision of initially rejected projects. This work demonstrates the effectiveness of a frugal community review for agile resource allocation in open innovation contexts.

Quantifying the rise and fall of research fields

| Marc Santolini

In this paper published in PLoS ONE, we leverage field tags metadata from the open access arXiv repository (1.5M articles) to reconstruct the evolution of 175 research fields from Physics, Maths, CS. We show that the observed rise and fall behaviour of fields is well described by a 2-parameters right-tailed Gumbel distribution, allowing us to rescale fields on a universal time scale and compare them on the same terms. Using delineations from the innovation literature, we then distinguish standard evolutionary stages of creation, adoption, peak, early and late decay, and we quantify characteristics of authors and articles at each stage. We find that early stages are 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 previous works in the field.

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A “Collaborative sonar” to reconstruct team networks

| Marc Santolini

In this new article presented at the ACM Conference on Pervasive and Ubiquitous Computing, we introduce the CoSo app (Collaborative Sonar) for large-scale collection of collaboration data within team ecosystems. The acquisition of passive social data through digital traces can be limited and often needs to be supplemented by qualitative approaches such as surveys or self-reported data. However, collecting relational surveys at scale poses challenges both in terms of human deployment and technical issues, as it involves probing the interactions relevant to an individual (for example, their team members). To address this need, we developed the CoSo (Collaborative Sonar) platform.

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