Interdisciplinary research centers are typically presented as a means for exploiting opportunities in science where the complexity of the research problem calls for sustained interaction among multiple disciplines. This study analyzed the effects of an interdisciplinary research center (NIMBioS) on the publication and collaboration behaviors of faculty affiliated with the center. The study also sought to determine what factors contributed to these effects for participants whose publication and collaboration behaviors were changed the most after affiliation.
The study employed a mixed-method case study approach, using quantitative bibliometric data along with qualitative data collected from interviews. Publication data for each participant in the study was collected from Web of Science (WOS) and analyzed by year against several demographic control variables to understand what effect affiliation with NIMBioS had on publication behaviors of participants. In addition to bibliometrics, a selection of study participants who demonstrated the most change in publication and collaboration behaviors since their affiliation with NIMBioS were interviewed to determine (a) what benefits (if any) participants felt they achieved as a result of participating in their working group, and (b) what factors (if any) participants felt may have contributed to the impact of NIMBioS affiliation on their publication and collaboration behavior.
Results of the study indicate that affiliation with a NIMBioS working group has a significant positive effect on participant collaboration activities (i.e. number of co-authors, number of international co-authors, number of cross-institutional co-authors), and a moderate effect on publication activities (i.e. publishing in new fields). Qualitative analysis of interdisciplinarity showed a shift in publication WOS subject categories (SCs) toward mathematical fields. Factors contributing to success cited by interviewees included organized leadership, a positive atmosphere, breaking into sub-groups, and the ability to collaborate with researchers with whom they would not have interacted outside of the group.
Doctoral candidate: Pamela Rene Bishop
University: University of Tennessee, Knoxville
Committee Members: Schuyler W. Huck, Jennifer K. Richards, Bonnie H. Ownley
Degree program: Doctor of Philosophy – Educational Psychology and Research
Because the existing applications of Technology Opportunity Analysis (TOA) text data mining framework developed by Alan Porter and other researchers used small datasets, previous research never pushed the limits of the methodology and failed to identify areas for future research associated with using larger datasets. This research developed extensions to the TOA framework to improve its performance and scalability and proved that the Technology Opportunity Analysis text data mining framework could be successfully scaled to analyze large datasets. The work included the development of a comprehensive set of new or significantly improved data extraction filters and data cleaning thesauruses, a data model and architecture based on relational database and online analytical processing technologies that provides an open platform provides easy, standards-compliant access to browsing, reporting, and data mining software that support either SQL or MDX queries, and a report distribution framework that does not require the end-users of the output of Technology Opportunity Analysis to use any specialized or prohibitively expensive client applications beyond the standard Microsoft Office applications and Adobe Acrobat Reader. In addition, it demonstrated that the time necessary to complete the data acquisition, cleaning, and transformation tasks can be reduced by at least 75% by creating libraries of import filters for commonly used data sources, eliminating unnecessary steps, using 64-bit native databases and extraction filters, improving the data model and architecture, and using significantly better data cleaning thesauruses. This work is significant because it enables a variety of research paths applying alternative statistical or data mining algorithms that previously would have been impossible to undertake. Thesauruses and fuzzy logic routines to clean and group the data are presented and their accuracy is tested on gene expression, energy storage, photovoltaics, smart materials, bioinformatics, quantum computing, wind turbine, nanotube, global warming, and data fusion data sets and benchmarked against existing thesauruses and fuzzy logic routines. A database on photovoltaic solar cell research that integrates data from 116,240 records from thirteen bibliographic, patent, and funding abstract databases was used to illustrate the concepts developed and tested in this dissertation.
Doctoral candidate: Richard Peyton George
University: Capella University
Degree program: Doctor of Philosophy – Due Diligence / Data Mining
Water scarcity in the World today is growing faster than expected and it is
among the main problems of 21st century to be faced by the World.
Central Asia is considered like a region with enough water resources, however,
an ineffective use of water, it’s allocation, rapidly growing population, and lack
of knowledge in sharing common basin among riparian countries could lead to
serious consequences. In this work we wanted to analyse literature about water
issues with the aim to understand when and how water disputes were
occurring. For this purpose, we have used bibliometric analysis to define and
look for better and reliable dates for the Thesis. All the dates and articles which
were used for this work were taken from the Web of Science. Furthermore,
thanks to VantagePoint and Social Networks Analysis we have got specific
articles which were sorted and divided according to your preference, calculating
measures of centrality to determine the importance of each keyword.
In following chapters we have also presented small research on the Central
Asian example, and the role of Kyrgyzstan in water sharing is illustrated.
Masters candidate: Meerim Avazbekova
Thesis Supervisor: Professor Blanca de Miguel
University: Polytechnic University of Valencia
Degree program: Master Degree in Business Management, Products and Services
Full-text available here https://riunet.upv.es/bitstream/handle/10251/44346/TFM.pdf?sequence=2
In this thesis, a framework based on text mining techniques is proposed to discover useful intelligence implicit in large bodies of electronic text sources. This intelligence is a prime requirement for successful R&D management. This research extends the approach called “Technology Opportunities Analysis” (developed by the Technology Policy and Assessment Center, Georgia Institute of Technology, in conjunction with Search Technology, Inc.) to create the proposed framework. The commercialized software, called VantagePoint, is mainly used to perform basic analyses. In addition to utilizing functions in VantagePoint, this thesis also implements a novel text association rule mining algorithm for gathering related concepts among text data. Two algorithms based on text association rule mining are also implemented. The first algorithm called “tree-structured networks” is used to capture important aspects of both parent-child (hierarchical structure) and sibling relations (non-hierarchical structure) among related terms. The second algorithm called “concept-grouping” is used to construct term thesauri for data preprocessing. Finally, the framework is applied to Thai xvi S&T publication abstracts toward the objective of improving R&D management. The results of the study can help support strategic decision-making on the direction of S&T programs in Thailand.
Doctoral candidate: Alisa Kongthon
Committee: Alan L. Porter, Xiaoming Huo, Donghua Zhu, Jye-Chyi Lu, and Susan E. Cozzens
University: Georgia Tech
Degree program: Doctor of Philosophy in Industrial Engineering
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