Instructors: George Chacko & Tandy Warnow
|Computational Scientometrics||GGC||43665||S10||4||0930 – 1045||M W||3117 Everitt Laboratory|
Objectives: This graduate course is centered around applying quantitative analytical techniques to problems in scientometrics that concern research metadata, particularly citations. Participating students will explore scientific questions, method development, and datasets through presentations, critical discussions, and research projects.
Overview: The term scientometrics, while accommodating a plurality of perspectives, generally refers to quantitative science studies. One definition (and there are others) can be found here. A history of the field can be found in the essays and articles in the Garfield Library at UPenn. As examples, five research articles in scientometrics are listed below. All five will be critically discussed during this course.
- Center–periphery structure in research communities (2022)
- Large teams develop and small teams disrupt science and technology (2019)
- Atypical Combinations and Scientific Impact (2013)
- A new methodology for constructing a publication-level classification system of science (2012)
- Co-citation in the scientific literature: A new measure of the relationship between two documents (1973)
Emphasis will be placed on interdisciplinary perspectives, the use of open source computing tools, and publicly available data. The course will also feature guest speakers. Critical discussion of research literature will be coupled to designing and executing a required research project.
Students will be evaluated with respect to their level of engagement in the class, satisfactory completion of homework assignments, and the quality of their presentations, draft, and final project reports. Students will be encouraged to publish results from these projects. Examples of publications that resulted from projects with envisioned levels of effort can be found here. For those students interested in expanding their course project into a publication, the instructors will help them develop and improve their work, and finally to submit and publish research findings in journals or conferences.
- Class Presentation: 20%
- Course Homework Assignments: 40%
- Course Project: 40%
Who should take the course: The course is designed for graduate students in Computer Science, ECE, or Statistics. However, it is open, with permission of the instructors, to graduate students from other programs as well as advanced undergraduates. Interested students are urged to contact the instructors before registering.
Minimum Required Skills: Programming and statistical analysis adequate to analyze data in tabular and graph formats. Familiarity with Linux is expected.
Course Schedule: (under construction)
Course Reading List: (steadily growing)
Homework Assignments: (also under construction)