DRUM - Digital Repository at the University of Maryland
DRUM collects, preserves, and provides public access to the scholarly output of the university. Faculty and researchers can upload research products for rapid dissemination, global visibility and impact, and long-term preservation.
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The University of Maryland Equitable Access Policy provides equitable, open access to the University's research and scholarship. Faculty can learn more about what is covered by the policy and how to deposit on the policy website.
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Recent Submissions
Watergate: A Legacy
(2024-10-10) Lewis, Irene M.; Hughes-Watkins, Lae'l; Mayfield, Catherine Dayrit
Watergate: A Legacy is a presentation that looks into the political and cultural history of the Watergate scandal from the early 1970s in the United States and how it led the House of Representatives to initiate impeachment proceedings against President Richard M. Nixon in 1973. This presentation was given at the University of Maryland Libraires' Living Democracy Symposium held on October 10, 2024, at the College Park campus. The presentation examines how President Nixon and his administration undermined democracy, truth, and the rule of law through their activities to cover up the Watergate break-in and how public officials such as Maryland Representative Lawrence J. Hogan, Sr., other members of the House Judiciary Committee, members of the Senate Watergate Committee, the FBI, and many others worked hard to uphold justice in order to preserve democracy and the welfare of the country. Throughout the presentation, documents from Lawrence Joseph Hogan, Sr. papers held at the University of Maryland Archives are highlighted to illustrate the political and cultural impact this historic event had upon the American people during an era of great social and political change.
Supporting Cultural Rights and Indigenous Sovereignty through Archival Repatriation
(PoLAR: Political and Legal Anthropology Review, 2024-09-21) Sorensen, Amanda H.; Bull, Ia; Marsh, Diana; Lee, Samantha
Primary source materials are irreplaceable cultural resources for the communities in which they originated, particularly when they derive from Native and Indigenous communities (Parezo 1999). These communities have been disenfranchised from their own information, data, and knowledge through the evidentiary and collecting practices of historical anthropological researchers, as well as the actions of archives, museums, and other collecting institutions. Knowledge extraction, wherein practitioners collect data for their own uses without appreciation of originating community perspectives or needs to access the data, was frequent in the early years of the discipline (First Archivists Circle 2007; Christen and Anderson 2019, 92-3). This localized information (regarding religious or ceremonial practices, for example) was dispersed to archives worldwide via what scholars have called an “archival diaspora” (Punzalan 2014a), effectively removing archives from the hands of originating communities. Furthermore, anthropologists have at times created field records in the context of assimilation and genocide, or through imbalanced and unethical power relations (O’Neal 2014). These historical factors underscore the ethical responsibility of archivists and data curators to provide community access to archival and unpublished information. There is a strong need for political and legal anthropologists, cultural heritage professionals, and policy writers to not only center human rights in ongoing research, but also to place Indigenous Knowledge Systems at the core of their efforts (O’Neal 2019, 50). We argue that the repatriation of archival materials (including physical repatriation but also encompassing ownership transfer or shared stewardship) is crucial to protecting “moral and material interests” embedded in community knowledge, language, storytelling, survivance, and the wider “cultural life of the community” (United Nations 2007).
You're Only as Good as Your Customs Holdings: Optimizing Interlibrary Loan Borrowing
(2024-06-29) Norton, Brynne; Weible, Cherié
Faster shipping, cheaper fees, and longer loan periods are the buzzwords when it comes to interlibrary borrowing, but how do you effectively gather data to identify lending libraries that meet these factors? One way is to take advantage of customization and automations in WorldShare ILL to leverage smart lender strings in combination with partner libraries. Information from lending libraries is available in the policies directory, however, sometimes other factors, such as shipping times, impact turnaround time. Reviewing real data periodically helps to identify misconceptions around which libraries meet that criteria. Two large academic libraries will show how they have effectively optimized their customs holdings and how you can replicate the process in order to meet these goals as applicable to your local environment. Graphs of turnaround times, maps, and screenshots showing customizations and workflows will demonstrate to the audience how easy it is to implement changes in their library setting and the positive impact these changes have on patron satisfaction as well as the reduction of staff time.
Supplementary material for machine learning and statistical analyses of sensor data reveal variability between repeated trials in Parkinson’s disease mobility assessments
(2024) Khalil, Rana M.; Shulman, Lisa M.; Gruber-Baldini, Ann L.; Shakya, Sunita; Hausdorff, Jeffrey M.; von Coelln, Rainer; Cummings, Michael P.; Cummings, Michael P.
Mobility tasks like the Timed Up and Go test (TUG), cognitive TUG (cogTUG), and walking with turns provide insight into dynamic motor control, balance, and cognitive functions affected by Parkinson’s disease (PD). We assess the test-retest reliability of these tasks in a cohort of 262 PD and 50 controls by evaluating the performance of machine learning models based on quantitative measures derived from wearable sensors, along with statistical measures. This evaluation examines total duration, subtask duration, and other quantitative measures across both trials. We show that the diagnostic accuracy of differentiating between PD and control participants decreases by a mean of 1.1% from the first to the second trial of our mobility tasks, suggesting that mobility testing can be simplified by not repeating tasks without losing diagnostic accuracy. Although the total duration remains relatively consistent between trials (intraclass correlation coefficient (ICC) = 0.62 to 0.95), there is more variability in subtask duration and sensor-derived measures, evident in the differences in machine learning model performance and statistical metrics. Our findings also show that the variability between trials differs not only between controls and participants with PD but also among groups with varying levels of PD severity. Relying solely on total task duration and conventional statistical metrics to gauge the reliability of mobility tasks may fail to reveal nuanced variations in movement captured by other quantitative measures. Additionally, the population studied should be carefully considered, as reliability results differ among and within groups based on disease severity.
A Smart, Connected, and Sustainable Campus Community: Using the Internet of Things (IoT) and low-cost sensors to improve stormwater management at UMD/Greater College Park
(2024) Hendricks, Marccus D.; Si, Qianyao; Alves, Priscila B. R.; Pavao-Zuckerman, Mitchell A.; Davis, Allen P.; Burke, Tara; Bonsignore, Elizabeth M.; Baer, Jason; Peterson, Kaitlyn; Cotting, Jennifer; Gaunaurd, Pierre; Clegg, Tamara; Loshin, David; Fellow, Andrew; Keen, Taylor; Knaap, Gerrit-Jan
This dataset is part of the research project titled “A Smart, Connected, and Sustainable Campus Community: Using the Internet of Things (IoT) and low-cost sensors to improve stormwater management at UMD/Greater College Park”. We use an Internet of Things (IoT) framework along with low-cost sensors to monitor and improve stormwater management on the University of Maryland Campus. This project provides real-time data that can inform both short term responses and longer-term adaptations to stormwater surface runoff. New buildings, the Purple Line, and other developments on the UMD campus will potentially increase the amount of impervious cover and thus increases the amount of surface runoff.
Furthermore, as a result of climate change, the region is expected to experience more frequent and intense rainfall events over shorter periods of time. These two factors have implications for higher quantities of water on campus, pooling water, and potential localized flooding. Stormwater issues can affect the movement of people, goods and services, campus infrastructure, and students as they walk across campus exposing their belongings, and particularly their feet to wetter conditions.
As part of more sustainable development, communities and campuses across the world, are beginning to plan for adaptations within the built campus environment to mitigate both larger scale stormwater issues as well as more practical everyday concerns, including wet pathways, and to meet and evaluate the effectiveness of stormwater permitting requirements.
The research objectives for this project are fourfold:
(1) Install low-cost stormwater sensors that measure water levels at a number of locations across campus that include high pedestrian traffic areas and major campus arterials;
(2) Develop an online database for campus water levels;
(3) Train students to install and read the stormwater sensors, manage the data platform, interpret the data
(4) Use the data to write adaptation plans and designs to better manage stormwater on campus and, perhaps subsequently, downstream from campus.
The dataset contains clean stormwater quality and quantity measurements collected from three different sites, along with processed data that describe runoff behavior during selected rainfall events and corresponding catchment characteristics (imperviousness, slope). The spatial data files provide location information for the outfall locations and the corresponding catchment boundaries. The R code provided includes data processing, statistical analysis, and visualization steps.