Supporting Data Scholarship in the College of Arts and Sciences
Data are an ever-growing part of scholarship. This is true across all scholarly domains we serve in the University of Oklahoma College of Arts and Sciences — natural sciences, social sciences, humanities, and professional programs.
The ability to manipulate and analyze data sets, especially “big” data sets, is a high-demand skill. Our aim is to increase support for data scholarship, research, and creative activity in the college by supporting ongoing scholarly activities of faculty and implementing new learning opportunities in the form of classes, certificates and minors. The applications of data science are wide ranging and range from medical imaging, bio-related research and business decision making to the mining of historical and literary texts.
To strengthen scholarship that builds on synergies with existing and new areas of research focus at OU, the college has invested in hiring in Data Scholarship across our areas.
In the humanities, this year we are welcoming Carrie Schroeder as a professor in the Department of Classics and Letters.
Schroeder’s current research involves digital and computational methods for studying the language, literature, religion and culture of Egypt in late antiquity. In addition, she publishes on gender and monasticism in Christianity during the Roman Empire. As part of a collaborative, interdisciplinary team, she has created open-source, open-access natural language processing tools for Coptic (the last phase of the ancient Egyptian language family) as well as a searchable, richly annotated digital corpus of Coptic texts annotated with those tools. Her digital research examines the ways digital methods can expand our understanding of the past as well as how they can address inequalities and injustices of the past, such as the effects of colonialism on manuscripts and artifacts from Egypt.
Professor Schroeder is also interested in the roles and limits of standardization in digital humanities research and in the ways digital and computational research on cultural heritage needs to account for cultural difference. She will be teaching courses on late antiquity and on digital research in the humanities (text analysis, digitization and cultural heritage, digital editions).
In the social sciences, this year we are welcoming Jonathan McFadden as an assistant professor in the Department of Economics.
McFadden comes to us from the U.S. Department of Agriculture and his current research examines the economics of genetically engineered foods, agricultural adaptation to climate change, adoption of information and data-driven inputs and the structure of commodity markets. His work seeks to answer a range of policy-relevant questions related to environmental economics, natural resources and food policy. Necessarily interdisciplinary, some of his work involves the development and application of machine learning techniques to gain traction in problems of optimal resource use.
As part of his interdisciplinary teaching emphasis in conjunction with the college’s Data Scholarship Program, McFadden will offer graduate courses in Applied Bayesian Statistics and Data Science. These classes are designed to increase students’ understanding and practice of big data analytics, while complementing conventional methods of data analysis learned in students’ home disciplines.
In 2018, Chongle Pan joined the natural sciences with a joint appointment as an associate professor in the Department of Microbiology and Plant Biology and the School of Computer Science in the Gallogly College of Engineering.
Pan’s research is focused on analytics of large biological data from metagenomics, metaproteomics, plant genomics and human population genomics. He has developed new bioinformatics algorithms using high-performance computing and machine learning. His work has shed light on the responses of soil microbiota to climate change and environmental perturbations, the roles of human microbiota in diseases, and the prediction of complex disease risks from personal genomes. Pan is interested in leveraging his data analytics expertise in other research fields through interdisciplinary collaboration in the data scholarship program.
He teaches an Introduction to Python Programming for Data Analytics course as one of the gateways for students across many majors to learn data scholarship. He also teaches a Parallel, Distributed and Network Programming course for advanced students who are interested in using supercomputing and cloud computing for big data.