Research Projects
Filtered by: Data Science, Analytics, and Visualization
Additive Manufacturing Digital Curation and Data Management
Principal Investigator(s): Richard Marciano
Funder: 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
Funder: 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
Funder: 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This project aims to improve the preservation and accessibility of valuable, unpublished anthropological data, including field notebooks, recordings, and photographs.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
Funder: 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This project aims to improve the preservation and accessibility of valuable, unpublished anthropological data, including field notebooks, recordings, and photographs.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
Funder: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization > Human-Computer Interaction > Youth Experience, Learning, and Digital Practices
Principal Investigator(s): David Weintrop
Funder: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization > Human-Computer Interaction > Youth Experience, Learning, and Digital Practices
CAREER: Socio-Algorithmic Foundations of Trustworthy Recommendations
Principal Investigator(s): Giovanni Luca Ciampaglia
Funder: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization > Human-Computer Interaction > Social Networks, Online Communities, and Social Media
Principal Investigator(s): Giovanni Luca Ciampaglia
Funder: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization > Human-Computer Interaction > Social Networks, Online Communities, and Social Media
CHS: Medium: Collaborative Research: Teachable Activity Trackers for Older Adults
Principal Investigator(s): Eun Kyoung Choe
Funder: 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
Funder: 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
Funder: Unfunded Other Non-Federal
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
Funder: Unfunded Other Non-Federal
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.
Digital Curation Fellows Program at the National Agricultural Library 2021-2026
Principal Investigator(s): Katrina Fenlon
Funder: 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
Funder: 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.
FAI: Advancing Deep Learning Towards Spatial Fairness
Principal Investigator(s): Sergii Skakun
Funder: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization > Machine Learning, AI, Computational Linguistics, and Information Retrieval
The project aims to address spatial biases in AI, ensuring spatial fairness in real-world applications like agriculture and disaster management. Traditional machine learning struggles with spatial fairness due to data variations. The project proposes new statistical formulations, network architectures, fairness-driven adversarial learning, and a knowledge-enhanced approach for improved spatial dataset analysis. The results will integrate into geospatial software.fference between habits and behaviors ef
Principal Investigator(s): Sergii Skakun
Funder: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization > Machine Learning, AI, Computational Linguistics, and Information Retrieval
The project aims to address spatial biases in AI, ensuring spatial fairness in real-world applications like agriculture and disaster management. Traditional machine learning struggles with spatial fairness due to data variations. The project proposes new statistical formulations, network architectures, fairness-driven adversarial learning, and a knowledge-enhanced approach for improved spatial dataset analysis. The results will integrate into geospatial software.fference between habits and behaviors ef
III: Small: Bringing Transparency and Interpretability to Bias Mitigation Approaches in Place-based Mobility-centric Prediction Models for Decision
Principal Investigator(s): Vanessa Frias-Martinez
Funder: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization > Health Informatics > Information Justice, Human Rights, and Technology Ethics > Machine Learning, AI, Computational Linguistics, and Information Retrieval
The project focuses on improving the fairness of place-based mobility-centric (PBMC) prediction models, particularly in high-stakes scenarios like public health and safety. By addressing biases in COVID-19 mobility and case data, it aims to make predictions more accurate and equitable. The research introduces novel bias-mitigation and interpretability methods across three technical thrusts, promoting transparency in PBMC models.
Principal Investigator(s): Vanessa Frias-Martinez
Funder: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization > Health Informatics > Information Justice, Human Rights, and Technology Ethics > Machine Learning, AI, Computational Linguistics, and Information Retrieval
The project focuses on improving the fairness of place-based mobility-centric (PBMC) prediction models, particularly in high-stakes scenarios like public health and safety. By addressing biases in COVID-19 mobility and case data, it aims to make predictions more accurate and equitable. The research introduces novel bias-mitigation and interpretability methods across three technical thrusts, promoting transparency in PBMC models.
Launching the TALENT Network to Promote the Training of Archival & Library Educators w. iNnovative Technologies
Principal Investigator(s): Richard Marciano
Funder: Institute of Museum and Library Services
Research Areas: Archival Science > Data Science, Analytics, and Visualization > Library and Information Science
The TALENT Network (Training of Archival & Library Educators with iNnovative Technologies) brings together experts from across the United States (including archivists, librarians, Library and Information Science educators, historians, learning scientists, cognitive scientists, computer scientists, and software engineers) in order to create a durable, diverse, and multidisciplinary national community focused on developing digital expertise and leadership skills among archival and library educators.
Principal Investigator(s): Richard Marciano
Funder: Institute of Museum and Library Services
Research Areas: Archival Science > Data Science, Analytics, and Visualization > Library and Information Science
The TALENT Network (Training of Archival & Library Educators with iNnovative Technologies) brings together experts from across the United States (including archivists, librarians, Library and Information Science educators, historians, learning scientists, cognitive scientists, computer scientists, and software engineers) in order to create a durable, diverse, and multidisciplinary national community focused on developing digital expertise and leadership skills among archival and library educators.
Machine Learning Strategies for FDR Presidential Library Collections (ML-FDR)
Principal Investigator(s): Richard Marciano
Funder: Unfunded Other Non-Federal
Research Areas: Archival Science > Data Science, Analytics, and Visualization > Machine Learning, AI, Computational Linguistics, and Information Retrieval
Demonstrate computational treatments of digital cultural assets using Artificial Intelligence (AI) and Machine Learning (ML) techniques that can help unlock hard-to-reach archival content related to WWII-era records housed at the FDR Presidential Library. This content is under-utilized by scholars examining American responses to the Holocaust.
Principal Investigator(s): Richard Marciano
Funder: Unfunded Other Non-Federal
Research Areas: Archival Science > Data Science, Analytics, and Visualization > Machine Learning, AI, Computational Linguistics, and Information Retrieval
Demonstrate computational treatments of digital cultural assets using Artificial Intelligence (AI) and Machine Learning (ML) techniques that can help unlock hard-to-reach archival content related to WWII-era records housed at the FDR Presidential Library. This content is under-utilized by scholars examining American responses to the Holocaust.
Measuring the Impact of Urban Renewal
Principal Investigator(s): Richard Marciano
Funder: Unfunded Other Non-Federal
Research Areas: Archival Science > Data Science, Analytics, and Visualization
This is a case study focusing on the legacy of urban renewal in Asheville, North Carolina between 1965 and 1980, when housing policies were enacted that ultimately displaced and erased African American businesses and communities with traumatic and lasting effects. The study focuses on designing new access interfaces to tell human stories. Ongoing results were presented to the Racial Reparations Commission of the City of Asheville on May 20, 2023.
Principal Investigator(s): Richard Marciano
Funder: Unfunded Other Non-Federal
Research Areas: Archival Science > Data Science, Analytics, and Visualization
This is a case study focusing on the legacy of urban renewal in Asheville, North Carolina between 1965 and 1980, when housing policies were enacted that ultimately displaced and erased African American businesses and communities with traumatic and lasting effects. The study focuses on designing new access interfaces to tell human stories. Ongoing results were presented to the Racial Reparations Commission of the City of Asheville on May 20, 2023.