The Educational CAD Model Library; An Analysis of the FAIR Guiding Principles and Data Security using Actor Network Theory

Mukherjee, Rishi, School of Engineering and Applied Science, University of Virginia
Nguyen, Rich, University of Virginia
Forelle, MC, University of Virginia

The focus of my technical report was my work contributing to the creation of the Educational CAD Model Library, the website component of a larger initiative to create an ecosystem for open source hardware models with pedagogical applications. Throughout my time spent in the implementation of the CAD Library, the primary investigator, Professor Bull, and the CAD Library team made very deliberate design choices to best abide by the principles of open-source hardware that inspired our project in the first place. Among these choices was the use of the Dataverse as the CAD Library's central repository system. The Dataverse is open-source database software developed by the Institute for Quantitative Social Science at Harvard University, and one of the core values that the Dataverse embodies is its support for FAIR Data Principles. Since their inception in 2016, the FAIR guiding principles for scientific data management have become one of the commonly accepted standards across scientific disciplines. My interest in the Dataverse's emphasis on the FAIR Guiding Principles compelled me to learn more about the Principles, which, in turn, led me to wonder how the Principles reconcile the seemingly contradictory principles of data privacy and security with openness and accessibility. This motivated my STS research question: “how do the FAIR Guiding Principles intersect and find a balance with data privacy and security concerns?”

My technical project was an implementation of technical infrastructure for an Educational CAD Model Library of curated, peer reviewed educational models designed to address K-12 instructional objectives. Most of my work for this came in the form of familiarizing myself with the backend system and exploring its technical capabilities so the CAD Library team could best utilize it. First, my responsibilities began with reassessing the original proof-of-concept implementation, “Educational Manufacturing,” in WordPress. Next, I began experimenting with LibraData, the University of Virginia's local instance of Harvard University's Dataverse software, as the backend of the system. This included familiarizing myself with the software's web graphical user interface and experimenting with the Dataverse's application programming interface to demonstrate the feasibility of using it in our project. Once such feasibility was demonstrated, I began the implementation of the system's frontend to allow non-technical users to view objects stored in the Dataverse. This technical project seeks to enhance the application of open source “maker” technologies in K-12 education, providing educators with resources to better teach their students. These results will not only lead to better student engagement and understanding in the curriculum, but also improve students' fluency in rapidly developing modern technologies.

My STS research paper explores the intersection of accessibility and security in the world of academic data, specifically governed by the FAIR Guiding Principles, to better inform the decisions made by those who engineer large data systems. The results give insight into methods and practices that balance stakeholder needs without exacerbating popular privacy concerns. This could ultimately lead to more robust and inclusive data-driven systems that are more sustainable to use in the long term. The aim of this paper is to show that the FAIR Guiding Principles' distribution of competencies of privacy and security responsibilities shows faith in the implicit delegation of authorization and authentication to a third party. The paper first uses articles that set the context for data security and privacy. Then, after delving into the details of the FAIR principles themselves, it explores interpretations and criticisms of the principles. The paper then uses Actor Network Theory to study existing implementation choices and show how the FAIR Principles reflect a distribution of competences between stakeholders regarding data privacy considerations. In this sociotechnical framework, systems are developed through the interaction between people and institutions, but artifacts are also considered an integral part of these negotiations, equal “actors” in the networks. These networks are complex webs between human and nonhuman actors, and each of their behaviors and actions contribute to the behavior and actions of other actors.

I have gained a lot of skills and perspective pursuing these two projects together. Specifically, my experience implementing the CAD Library helped me learn how to approach the implementation of a system with a variety of stakeholders, how to explore and experiment with novel softwares, and how academic data is stored and accessed. This led into my STS project quite nicely; the analysis using Actor Network Theory emphasized the role of various stakeholders, and my work on the CAD Library helped inform my understanding of both human and non-human actors in a way that simply reading about them would not. My exploration of academic data stewardship practices and implementation in the research was also supplemented by my firsthand experience dealing with the Dataverse in my technical project.

BS (Bachelor of Science)
CAD, FAIR, Privacy

School of Engineering and Applied Science

Bachelor of Science in Computer Science

Technical Advisor: Rich Nguyen

STS Advisor: MC Forelle

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