This interdisciplinary research experience is a composite of (i) critical evaluation of relevant scientific literature, (ii) application and extension of of methods to interrogate large graphs, and (iii) scientific discovery. A core component is extending the state of the art in clustering methods to detect research communities that assemble around related scientific questions. Particular attention will be paid to evaluating open source data as an alternative to  expensive commercial offerings that currently provide research-grade bibliographies that are typically used. The spectrum of methods used will range across data mining, curation, clustering and any results generated will be evaluated using both theoretical and qualitative criteria. Enrolled students will leverage prior work and tacit knowledge from collaborations that combine discovery-driven interests with methods development, e.g., Wedell et al (2022) https://doi.org/10.1162/qss_a_00184. An aspirational product of this course is an experimental artifact that could become a part of a manuscript submitted to a respectable journal. Emphasis will be placed on self-critical evaluation, and a path to independent research activities.   

Course Calendar: 5/16/2022 – 8/4/2022