Cross-disciplinary Dynamics and Scalable Visualization of Academic Careers

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dc.contributor.advisorPavlidis, Ioannis T.
dc.contributor.committeeMemberJohnsson, Lennart
dc.contributor.committeeMemberEick, Christoph F.
dc.contributor.committeeMemberAkleman, Ergun
dc.creatorMajeti, Dinesh 1989-
dc.creator.orcid0000-0001-6257-7911
dc.date.accessioned2019-05-23T14:22:12Z
dc.date.createdAugust 2018
dc.date.issued2018-08
dc.date.submittedAugust 2018
dc.date.updated2019-05-23T14:22:13Z
dc.description.abstractBorne out of the Human Genome Project (HGP), the field of genomics evolved into a dominant scientific and business force. While other efforts were intent on estimating the economic impact of the genomics revolution, we focus on the social and cultural capital generated by bridging together biology and computing – two of genomics’ constitutional disciplines. We measured the impact of cross-disciplinarity from three interlocking perspectives: interpersonal collaboration over time, mixed authorship in scholarly products, and mixed methods in premium articles. Our results show: First, research featuring cross-disciplinary (XD) collaborations has higher citation impact than other disciplinary research – both at the career and individual article level. Second, genomics articles featuring XD methods tend to have higher citation impact than epistemically pure articles. Third, XD researchers of computing pedigree are drawn to the biology culture. This statistical evidence acquires deeper meaning when viewed against the organizational and knowledge transfer mechanisms revealed by the data analysis. With cross-disciplinary initiatives set to dominate the agenda of funding agencies, our case study provides a framework for evaluating their long-term effects on science and its standard-bearers. While these curated datasets are valuable for advanced analytics, they also provide an opportunity to create a unique front-end interface for academic careers. In this context, we made an effort to develop a compact visualization for academics. The ability to fairly assess academic performance is critical to rewarding academic merit, charting academic policy, and promoting science. Quintessential to performing these functions is the ability to collect valid and current data through increasingly automated online interfaces. Moreover, it is crucial to remove disciplinary and other biases from these data, presenting them in ways that support insightful analysis at various levels. Existing systems fail in some of these respects. Here we present Scholar Plot (SP), an interface that harvests bibliographic and research funding data from online sources, unbiases the collected data, and combines them synergistically in a plot form for expert appraisal, and a pictorial form for broader consumption.
dc.description.departmentComputer Science, Department of
dc.format.digitalOriginborn digital
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/10657/3993
dc.language.isoeng
dc.rightsThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).
dc.subjectCross-disciplinary
dc.subjectScience of science
dc.subjectGenomics
dc.subjectAcademic performance
dc.subjectVisualization
dc.subjectInterfaces
dc.titleCross-disciplinary Dynamics and Scalable Visualization of Academic Careers
dc.type.dcmiText
dc.type.genreThesis
local.embargo.lift2020-08-01
local.embargo.terms2020-08-01
thesis.degree.collegeCollege of Natural Sciences and Mathematics
thesis.degree.departmentComputer Science, Department of
thesis.degree.disciplineComputer Science
thesis.degree.grantorUniversity of Houston
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

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