Research Projects
Filtered by: Data Science, Analytics, and Visualization
Additive Manufacturing Digital Curation and Data Management
Principal Investigator(s): Richard Marciano
Funders: DoD-Army
Research Areas: Archival Science Data Science, Analytics, and Visualization
Exploring digital curation, data management, data mining, and the development of a digital asset management system for Additive Manufacturing
Principal Investigator(s): Richard Marciano
Funders: DoD-Army
Research Areas: Archival Science Data Science, Analytics, and Visualization
Exploring digital curation, data management, data mining, and the development of a digital asset management system for Additive Manufacturing
Building a sustainable future for anthropology’s archives: Researching primary source data lifecycles, infrastructures, and reuse
Principal Investigator(s): Diana E. Marsh Katrina Fenlon
Funders: National Science Foundation
Research Areas: Archival Science Data Science, Analytics, and Visualization
This project aims to improve the preservation and accessibility of valuable, unpublished anthropological data, including field notebooks, recordings, and photographs. It investigates barriers to data reusability and seeks sustainable ways to adapt linked data infrastructures. The research involves focus group discussions, open access platforms, training modules, and a virtual symposium to enhance the sharing of primary source cultural research data and support interdisciplinary collaboration in anthropology.
Principal Investigator(s): Diana E. Marsh Katrina Fenlon
Funders: National Science Foundation
Research Areas: Archival Science Data Science, Analytics, and Visualization
This project aims to improve the preservation and accessibility of valuable, unpublished anthropological data, including field notebooks, recordings, and photographs. It investigates barriers to data reusability and seeks sustainable ways to adapt linked data infrastructures. The research involves focus group discussions, open access platforms, training modules, and a virtual symposium to enhance the sharing of primary source cultural research data and support interdisciplinary collaboration in anthropology.
CAREER: API Can Code: Situating Computational Learning Opportunities in the Digital Lives of Students
Principal Investigator(s): David Weintrop
Funders: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization Human-Computer Interaction Youth Experience, Learning, and Digital Practices
This project develops and studies a high school data science curriculum that integrates programming and real-world datasets to engage students in exploring their own questions and interests. Designed in partnership with an urban school district, the research focuses on expanding access to computing for populations historically excluded from the field.
Principal Investigator(s): David Weintrop
Funders: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization Human-Computer Interaction Youth Experience, Learning, and Digital Practices
This project develops and studies a high school data science curriculum that integrates programming and real-world datasets to engage students in exploring their own questions and interests. Designed in partnership with an urban school district, the research focuses on expanding access to computing for populations historically excluded from the field.
CAREER: Socio-Algorithmic Foundations of Trustworthy Recommendations
Principal Investigator(s): Giovanni Luca Ciampaglia
Funders: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization Human-Computer Interaction Social Networks, Online Communities, and Social Media
This project investigates how incorporating audience diversity into content recommendation systems can improve trustworthiness and news quality. It will develop new algorithms, evaluate re-ranking methods, and test impacts on the information diets of news consumers, particularly older audiences.
Principal Investigator(s): Giovanni Luca Ciampaglia
Funders: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization Human-Computer Interaction Social Networks, Online Communities, and Social Media
This project investigates how incorporating audience diversity into content recommendation systems can improve trustworthiness and news quality. It will develop new algorithms, evaluate re-ranking methods, and test impacts on the information diets of news consumers, particularly older audiences.
CHS: Medium: Collaborative Research: Teachable Activity Trackers for Older Adults
Principal Investigator(s): Eun Kyoung Choe
Funders: National Science Foundation
Research Areas: Accessibility and Inclusive Design Data Science, Analytics, and Visualization Health Informatics Human-Computer Interaction
Pushing the boundaries of how personal tracking devices, such as smart watches, can better support older adults---by identifying what health/activities data would be most useful for older adults if tracked, how to collect/track this data, and utilizing this information to develop a new personalized, multimodal activity tracker.
Principal Investigator(s): Eun Kyoung Choe
Funders: National Science Foundation
Research Areas: Accessibility and Inclusive Design Data Science, Analytics, and Visualization Health Informatics Human-Computer Interaction
Pushing the boundaries of how personal tracking devices, such as smart watches, can better support older adults---by identifying what health/activities data would be most useful for older adults if tracked, how to collect/track this data, and utilizing this information to develop a new personalized, multimodal activity tracker.
Computational Treatments to re-member the Legacy of Slavery (CT-LoS)
Principal Investigator(s): Richard Marciano
Funders: Unfunded
Research Areas: Archival Science Data Science, Analytics, and Visualization Information Justice, Human Rights, and Technology Ethics
Using Computational Archival Science to unlock records related to the Legacy of Slavery and provide new point of interaction and analysis.
Principal Investigator(s): Richard Marciano
Funders: Unfunded
Research Areas: Archival Science Data Science, Analytics, and Visualization Information Justice, Human Rights, and Technology Ethics
Using Computational Archival Science to unlock records related to the Legacy of Slavery and provide new point of interaction and analysis.
DataGOAT – Building Counter Structures to Combat Systemic Racism in STEM Education & Sport Through Data Literacy
Principal Investigator(s): Tamara Clegg
Funders: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization Human-Computer Interaction Information Justice, Human Rights, and Technology Ethics Youth Experience, Learning, and Digital Practices
The DataGOAT project integrates data science education with college athletics to promote STEM engagement and racial equity, particularly for Black male athletes. By creating coursework, internships, and technical tools, the project empowers athletes with critical data literacy while challenging stereotypes and linking athletic data practices to meaningful academic and career pathways.
Principal Investigator(s): Tamara Clegg
Funders: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization Human-Computer Interaction Information Justice, Human Rights, and Technology Ethics Youth Experience, Learning, and Digital Practices
The DataGOAT project integrates data science education with college athletics to promote STEM engagement and racial equity, particularly for Black male athletes. By creating coursework, internships, and technical tools, the project empowers athletes with critical data literacy while challenging stereotypes and linking athletic data practices to meaningful academic and career pathways.
Detecting and Mapping War-induced Damage to Agricultural Fields in Ukraine using Multi-Modal Remote Sensing Data
Principal Investigator(s): Sergii Skakun
Funders: NASA Other Federal
Research Areas: Data Science, Analytics, and Visualization Human-Computer Interaction Machine Learning, AI, Computational Linguistics, and Information Retrieval Smart Cities and Connected Communities Social Networks, Online Communities, and Social Media
This project advances remote sensing methods to map war-induced damage to Ukraine’s agricultural fields using infrared and visible spectrum satellite data. By developing deep-learning and data fusion techniques, the research will detect artillery craters, burned areas, and abandoned fields to assess the war’s impact on agriculture at scale.
Principal Investigator(s): Sergii Skakun
Funders: NASA Other Federal
Research Areas: Data Science, Analytics, and Visualization Human-Computer Interaction Machine Learning, AI, Computational Linguistics, and Information Retrieval Smart Cities and Connected Communities Social Networks, Online Communities, and Social Media
This project advances remote sensing methods to map war-induced damage to Ukraine’s agricultural fields using infrared and visible spectrum satellite data. By developing deep-learning and data fusion techniques, the research will detect artillery craters, burned areas, and abandoned fields to assess the war’s impact on agriculture at scale.
Developing and Investigating Data Science Interventions Connected to University Athletics to Address Systemic Racism in Undergraduate STEM Education (better known as DataGOAT)
Principal Investigator(s): Tamara Clegg
Funders: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization Future of Work Health Informatics Human-Computer Interaction Information Justice, Human Rights, and Technology Ethics Social Networks, Online Communities, and Social Media Youth Experience, Learning, and Digital Practices
This project, DataGOAT, engages Black male collegiate athletes in data science by connecting their sports performance and health data to STEM learning. It aims to overcome racialized stereotypes, foster STEM identities, and create educational pathways through courses, internships, and data analysis tools, benefiting both participants and the broader educational community.
Principal Investigator(s): Tamara Clegg
Funders: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization Future of Work Health Informatics Human-Computer Interaction Information Justice, Human Rights, and Technology Ethics Social Networks, Online Communities, and Social Media Youth Experience, Learning, and Digital Practices
This project, DataGOAT, engages Black male collegiate athletes in data science by connecting their sports performance and health data to STEM learning. It aims to overcome racialized stereotypes, foster STEM identities, and create educational pathways through courses, internships, and data analysis tools, benefiting both participants and the broader educational community.
Digital Curation Fellows Program at the National Agricultural Library 2021-2026
Principal Investigator(s): Katrina Fenlon
Funders: US Department of Agriculture
Research Areas: Archival Science Data Science, Analytics, and Visualization Library and Information Science
The Digital Curation Fellows program is a partnership with the National Agricultural Library (NAL) to provide students from across all iSchool programs with research and practical experience solving real-world digital curation challenges. Digital curation fellows have contributed to numerous initiatives during this program’s several-year history, such as developing digital preservation plans, researching user experience, evaluating metadata quality, assessing diversity and equity of representation in digital collections, building new digital archives, and creating data analytics dashboards.
Principal Investigator(s): Katrina Fenlon
Funders: US Department of Agriculture
Research Areas: Archival Science Data Science, Analytics, and Visualization Library and Information Science
The Digital Curation Fellows program is a partnership with the National Agricultural Library (NAL) to provide students from across all iSchool programs with research and practical experience solving real-world digital curation challenges. Digital curation fellows have contributed to numerous initiatives during this program’s several-year history, such as developing digital preservation plans, researching user experience, evaluating metadata quality, assessing diversity and equity of representation in digital collections, building new digital archives, and creating data analytics dashboards.
E-VERIFY: For the Human-Machine Teaming for Intelligence Surveillance and Reconnaissance ISR) Analysis (HMT-ISR) Basic IDIQ
Principal Investigator(s): Cody Buntain
Funders: Air Force Research Laboratory - Directorates Other Federal
Research Areas: Data Science, Analytics, and Visualization Human-Computer Interaction
A research endeavor with the College of Information at the University of Maryland, in partnership with the CMNS-Institute for Advanced Computer Studies and funded by Parallax Advanced Research.
Principal Investigator(s): Cody Buntain
Funders: Air Force Research Laboratory - Directorates Other Federal
Research Areas: Data Science, Analytics, and Visualization Human-Computer Interaction
A research endeavor with the College of Information at the University of Maryland, in partnership with the CMNS-Institute for Advanced Computer Studies and funded by Parallax Advanced Research.
E-VERIFY: Task Order 002: Mission Analytics Technology and Research for Innovative eXploitation (MATRIX)
Principal Investigator(s): Cody Buntain
Funders: Air Force Research Laboratory - Directorates Other Federal
Research Areas: Data Science, Analytics, and Visualization Future of Work Machine Learning, AI, Computational Linguistics, and Information Retrieval
This project focuses on developing mathematical and computational methods to advance machine learning and artificial intelligence, with applications that support U.S. Air Force, U.S. Space Force, and Department of Defense personnel.
Principal Investigator(s): Cody Buntain
Funders: Air Force Research Laboratory - Directorates Other Federal
Research Areas: Data Science, Analytics, and Visualization Future of Work Machine Learning, AI, Computational Linguistics, and Information Retrieval
This project focuses on developing mathematical and computational methods to advance machine learning and artificial intelligence, with applications that support U.S. Air Force, U.S. Space Force, and Department of Defense personnel.