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.

Submit to DRUM

Submit to DRUM

To submit an item to DRUM, login using your UMD credentials. Then select the "Submit Item to DRUM" link in the navigation bar. View DRUM policies and submission guidelines.
Equitable Access Policy

Equitable Access Policy

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.
Theses and Dissertations

Theses and Dissertations

DRUM includes all UMD theses and dissertations from 2003 forward.

List of Communities

Collections Organized by Department

UM Community-managed Collections

Recent Submissions

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    Undertrained and Overwhelmed: Adapting to Unexpected Job Changes
    (Journal of New Librarianship, 2025-08-31) Ginsberg, Sharona
    As libraries grapple with declining budgets, shifting priorities, high rates of turnover, vacancies, the “new normal,” and the persistent push to “do more with less,” an increasing number of library workers have found that their roles have deviated from their original job descriptions, sometimes drastically. In many cases, these roles were loosely or confusingly defined and have called upon individuals to perform duties for which they have limited to no experience, training, or guidance. This paper explores the challenges of adjusting to such unanticipated changes and, through exploration of the author’s own experiences, begins a discussion of strategies and techniques that may be helpful to those navigating similar circumstances.
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    Creating Community in the Virtual Study Hall: Lessons from Spring 2021
    (2021-07-28) Hughes, Casey; Ginsberg, Sharona
    The undergraduate learning commons (Terrapin Learning Commons – TLC) at the University of Maryland, College Park serves as a key study space and site for student engagement in the Libraries. In an effort to reach out to students and meet their needs when university activities were restricted to a virtual environment, the TLC began offering virtual study sessions through Zoom. While feedback on these events was positive, the attendance was relatively low. As a result, the TLC partnered with Casey Hughes, a Research & Teaching Fellow and MLIS graduate student, in Spring 2021 to revamp the virtual study session program and shift the focus to student organizations. This poster presents a model for how to assess and learn from less-than-successful projects, while describing the program, detailing key changes made in the Spring 2021 semester, discussing challenges faced, and offer important takeaways moving forward for future virtual programming possibilities.
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    A Sea of Makerspaces: Finding a Niche for a STEM Library Makerspace at an R1 Institution
    (Association of College & Research Libraries, 2023-09-28) Coalter, Jodi H.; Weber, James D.; Scally, S. Eliza; Ginsberg, Sharona
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    Dataset for Figures in Microscopic Investigation of rf Vortex Nucleation in Nb3Sn Films Using a Near-Field Magnetic Microwave Microscope
    (2025) Wang, Chung-Yang; Anlage, Steven
    This is the dataset used to create the figures in the paper: Microscopic Investigation of rf Vortex Nucleation in Nb3Sn Films Using a Near-Field Magnetic Microwave Microscope
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    Deriving Vegetation Variables from Satellite Observations using a Data-driven Approach
    (2025-08-20) Wang, Alan; Wang, Heshun
    This presentation was shared at the virtual conference of the 2025 CISESS Summer Internship Program held on 20 August 2025. The slides were presented by Alan Wang, an undergraduate student at the University of Maryland, under the mentorship of Heshun Wang (CISESS/UMD). Building on previous research in processing Earth Observation data, the presentation detailed the performance of the XGBoost, Cubist, and random forest regression models in deriving in-situ measurements of vegetation cover fraction (fCover) from satellite observations. Ground measurements of fCover from 43 National Ecological Observatory Network (NEON) sites provided labels for training, validation, and testing, which were then upscaled to align with the high spatial resolution land surface reflectance data provided by the Visible Infrared Imaging Radiometer Suite (VIIRS) daily surface reflectance (VNP09GA) product. When evaluated against unseen data, the random forest regression model demonstrated the best agreement (R-squared = 0.912, MAE = 0.043), followed by the XGBoost regressor (R-squared = 0.910, MAE = 0.043) and lastly the Cubist model (R-squared = 0.904, MAE = 0.047). Applying the random forest model on the 2023 VIIRS data for the East Coast produced estimates consistent with the expected annual phenological cycle.