Supporting Material for “Neural language representations predict outcomes of scientific research”
Abstract: We used the metaBUS data release v2.08 for our corpus of correlate pairs. These data are available for
download at http://www.frankbosco.com/data/CorrelationalEffectSizeBenchmarks.html. More up-to-date
data are searchable using the metaBUS web interface: http://metabus.org. We also used the 300-dimension
(English) word vector representations released by the ConceptNet project called ConceptNet Numberbatch,
specifically version 17.06: https://github.com/commonsense/conceptnet-numberbatch.
The correlate texts have already been curated by the metaBUS team but we performed some further processing to connect the individual words (tokens) to terms in Numberbatch. Nonalphanumeric characters were removed, casing was removed, and text was tokenized on whitespace. Tokens were then mapped to corresponding word vector indices in Numberbatch. A vector “index” of 0 was reserved for tokens in metaBUS not present in Numberbatch. The neural network is able to handle tokens outside the vocabulary of Numberbatch, although predictive performance is likely wose when many tokens are missing than when few or no tokens are missing.
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Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.
Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.
Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.
Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).