Science of Science Research

Predictability of Scientific Success

Developing measures and models that offer actionable information towards a quantitative evaluation and prediction of scientific success — from individual publications to entire research institutions.

Open-access PDF · Published in Science, Vol. 354, Issue 6312 (2016)

About the project

Scientific discovery is the key driver for technological and cultural innovation, and a foundational pillar of society.

Despite the extraordinary impact science has on our life, little is known about the patterns that lead to scientific excellence. Is the success of a particular work predictable? Can future individual performance be predicted? Are scientific discoveries more likely in certain countries and/or institutions?

By combining the tools of statistical physics and information science, we aim to uncover the predictability and the limits of predictability of scientific success. Our focus is on scientific performance captured at increasing levels of complexity: individual publications, scientists' careers, and research institutions. The final goal of our research is to provide theory and tools of potential utility to policy making, from funding decisions to discovering scientific talent.

New publication in Science

Quantifying the evolution of individual scientific impact

Preview of the published paper figures in Science magazine
As published in Science, Vol. 354, Issue 6312 (2016)

Read the paper that introduces the Q-model for predicting a scientist's most impactful work, or go straight to the PDF.

Download PDF

Who's involved

Team

Researchers from the Center for Complex Network Research (CCNR) and the MOBS Lab collaborating on this work.

  • Portrait of László Barabási

    László Barabási

    Director, CCNR

  • Portrait of Yifang Ma

    Yifang Ma

    Researcher, CCNR

  • Portrait of Roberta Sinatra

    Roberta Sinatra

    Researcher, CCNR

  • Portrait of Michael Szell

    Michael Szell

    Researcher, CCNR

  • Portrait of Alex Vespignani

    Alex Vespignani

    Director, MOBS Lab

  • Portrait of Matteo Chinazzi

    Matteo Chinazzi

    Researcher, MOBS Lab

  • Portrait of Junming Huang

    Junming Huang

    Researcher, CCNR

  • Portrait of Yasamin Khorramzadeh

    Yasamin Khorramzadeh

    Researcher, CCNR

  • Kaiyuan Sun

    Researcher, MOBS Lab

Publications

Papers

Peer-reviewed work from the project, spanning individual impact, institutional funding, and the physics of citation dynamics.

Research category: Individual scientific impact

Quantifying the evolution of individual scientific impact

Roberta Sinatra, Dashun Wang, Pierre Deville, Chaoming Song, Albert-László Barabási

Science, Vol. 354, Issue 6312 (2016) · DOI: 10.1126/science.aaf5239

✓ Peer reviewed Open PDF Citation available

Featured in: New York Times, Science News, Nature News, The Scientist, Wired, Chronicle of Higher Education, Scientific American, Forbes. View the full list of press coverage here.

Talks & slides

Presentations

Science of Science: The Fundamentals of Predictability of Scientific Success

Roberta Sinatra, Alessandro Vespignani, Albert-László Barabási

Download slides (PDF)