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Description
The quality and quantity of talented members of the US STEM workforce has

been a subject of great interest to policy and decision makers for the past 40 years.

Recent research indicates that while there exist specific shortages in specific disciplines

and areas of expertise in the private sector and the federal government,

The quality and quantity of talented members of the US STEM workforce has

been a subject of great interest to policy and decision makers for the past 40 years.

Recent research indicates that while there exist specific shortages in specific disciplines

and areas of expertise in the private sector and the federal government, there is no

noticeable shortage in any STEM academic discipline, but rather a surplus of PhDs

vying for increasingly scarce tenure track positions. Despite the seeming availability

of industry and private sector jobs, recent PhDs still struggle to find employment in

those areas. I argue that the decades old narrative suggesting a shortage of STEM

PhDs in the US poses a threat to the value of the natural science PhD, and that

this narrative contributes significantly to why so many PhDs struggle to find career

employment in their fields. This study aims to address the following question: what is

the value of a STEM PhD outside academia? I begin with a critical review of existing

literature, and then analyze programmatic documents for STEM PhD programs at

ASU, interviews with industry employers, and an examination the public face of value

for these degrees. I then uncover the nature of the value alignment, value disconnect,

and value erosion in the ecosystem which produces and then employs STEM PhDs,

concluding with specific areas which merit special consideration in an effort to increase

the value of these degrees for all stakeholders involved.
ContributorsGarbee, Elizabeth (Author) / Maynard, Andrew D. (Thesis advisor) / Wetmore, Jameson (Committee member) / Anderson, Derrick (Committee member) / Arizona State University (Publisher)
Created2018
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Description
Visualizations are an integral component for communicating and evaluating modern networks. As data becomes more complex, info-graphics require a balance between visual noise and effective storytelling that is often restricted by layouts unsuitable for scalability. The challenge then rests upon researchers to effectively structure their information in a way that

Visualizations are an integral component for communicating and evaluating modern networks. As data becomes more complex, info-graphics require a balance between visual noise and effective storytelling that is often restricted by layouts unsuitable for scalability. The challenge then rests upon researchers to effectively structure their information in a way that allows for flexible, transparent illustration. We propose network graphing as an operative alternative for demonstrating community behavior over traditional charts which are unable to look past numeric data. In this paper, we explore methods for manipulating, processing, cleaning, and aggregating data in Python; a programming language tailored for handling structured data, which can then be formatted for analysis and modeling of social network tendencies in Gephi. We implement this data by applying an algorithm known as the Fruchterman-Reingold force-directed layout to datasets of Arizona State University’s research and collaboration network. The result is a visualization that analyzes the university’s infrastructure by providing insight about community behaviors between colleges. Furthermore, we highlight how the flexibility of this visualization provides a foundation for specific use cases by demonstrating centrality measures to find important liaisons that connect distant communities.
ContributorsMcMichael, Jacob Andrew (Author) / LiKamWa, Robert (Thesis director) / Anderson, Derrick (Committee member) / Goshert, Maxwell (Committee member) / Arts, Media and Engineering Sch T (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05