UMD Data Collection
Permanent URI for this collectionhttp://hdl.handle.net/1903/27670
University of Maryland faculty and researchers can upload their research products in DRUM for rapid dissemination, global visibility and impact, and long-term preservation. Depositing data in DRUM can assist in compliance with data management and sharing requirements from the NSF, NIH, and other funding agencies and journals. You can also deposit code, documents, images, supplemental material, and other research products. DRUM tracks views and downloads of your research, and all DRUM records are indexed by Google and Google Scholar. Additionally, DRUM assigns permanent DOIs for your items, making it easy for other researchers to cite your work.
Submissions to the Data Collection
To add files to the UMD Data Collection, submit a new item through your associated department or program's DRUM collection and check the box indicating your upload contains a dataset.
Find more information and guidelines for depositing into the Data Collection on the University of Maryland Libraries' DRUM for Data page.
Assistance
Please direct questions regarding the UMD Data Collection or assistance in preparing and depositing data to: lib-research-data@umd.edu.
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Item Active Seismic Exploration of Planetary Subsurfaces via Compressive Sensing(2025) Wang, Jingchuan; Schmerr, Nicholas; Lekic, VedranThe software supports the following study: We present a method for improving seismic data collection on planetary surfaces such as the Moon and Mars. This approach is based on recent advances in compressive sensing technology to reduce the number of data collection points required compared to conventional methods without sacrificing the quality of the resulting subsurface images. We demonstrate its effectiveness using both synthetic and field data from locations with similarities to planetary surface environments. The method is then applied to reanalyze seismic data collected by the crew of the Apollo 14 and 16 missions. Our study has implications for mission planning, as this method can make space missions more efficient by reducing the equipment and time to collect geophysical data on planetary surfaces. It also makes it possible to reconstruct missing or damaged data, improving the quality of imagery and enhancing our understanding of the interior of other worlds.Item Dataset for Constraining Wetland and Landfill Methane Emission Signatures Through Atmospheric Methane Clumped Isotopologue Measurements" [Paper #2024JG008249-T](2025) Sun, Jiayang; Farquhar, JamesFrom Primary Paper: Microbial methane emissions are associated with a wide range of isotopic signatures, providing information about the sources and sinks of methane. Methods of directly sampling methane from environments such as wetlands may fail to capture the temporal and spatial variations in emissions at a specific site and time. The Keeling plot method is commonly used to infer the overarching isotopic signatures of methane sources. In this study, we have expanded the application of the Keeling plot from conventional stable isotope ratios to include novel clumped isotopologue compositions of methane. This advancement aims to provide more robust constraints on regional methane emission signatures. We analyzed methane isotopologue compositions from air samples collected above wetlands and landfills across Maryland, USA, and determined the endmember compositions for background air, wetland, and landfill sources. Our findings indicate that the isotopologue compositions of methane from regional wetland emissions exhibit seasonal variations — δ13C and δD values become less positive as winter approaches, reflecting changes in methane oxidation and production rates. The continuous monitoring of air methane isotopologue signatures will deepen our understanding of the seasonal patterns in methane emissions and contribute to refining the global methane budget, as valuable insights can be extracted from these measurements.Item Experimental data for Efficacy of sodium hypochlorite and peracetic acid in reducing cross‐contamination during washing of baby spinach at different water quality levels(2025-01-06) Gao, Zhujun; Tikekar, Rohan V.This is the dataset for manuscript titled Efficacy of sodium hypochlorite and peracetic acid in reducing cross‐contamination during washing of baby spinach at different water quality levels that has the DOI of https://doi.org/10.1111/1750-3841.17657Item Measurements of boulders ejected in the Double Asteroid Redirection Test (DART) impact(2025-01-17) Farnham, TonyThis data collection contains tables that provide measurements of the positions and brightnesses of meter-sized boulders that were ejected when the Double Asteroid Impact Test (DART) spacecraft crashed into Dimorphos, the moon of asteroid (65803) Didymos on September 26, 2022. The measurements come from the LICIACube Unit Key Explorer (LUKE) instrument on board the LICIACube spacecraft that flew by the Didymos system about 3 minutes after the impact event. The positions table provides the pixel locations of each boulder in the images where it was detected, while the photometry table gives the brightness for each boulder in each of the images where it was measured.Item DNA Barcoding Module in Undergraduate Biology Courses: A Comparative Analysis on Student Learning(2025-01-14) Kraemer, Jenna; Craig, Helen; Brucchieri, Amanda; Helbling, Yasmine; Lamp, WilliamItem GEODES San Francisco Volcanic Field Geochemical Sampling(2023) Shubham, Sourabh; Farcy, Benjamin; Wright, Shawn; Schmerr, Nicholas; Whelley, PatrickIn August of 2023, the NASA SSERVI GEODES team conducted a field expedition in the San Francisco Volcanic Field, northern Arizona. The field expedition had a geochemical component, with the objective of characterizing the lithological and geochemical diversity across key volcanic features. Using handheld XRF and VNIR spectrometers, field data and rock samples were collected from O'Leary Peak, Elden Mountain, Schultz Peak, SP Crater, and the Lava River Cave. Sampling strategies included targeting visually distinct lithologies and calibrating instruments with geochemical standards to ensure data accuracy. Preliminary analyses reveal diverse compositions, including rhyolites, basalts, and intermediate magmas. The collected samples will undergo detailed laboratory geochemical analysis to enhance understanding of volcanic processes and evolution in this region.Item Supplementary material for Applying Wearable Sensors and Machine Learning to the Diagnostic Challenge of Distinguishing Parkinson's Disease from Other Forms of Parkinsonism(2025) Khalil, Rana M.; Shulman, Lisa M.; Gruber-Baldini, Ann L.; Reich, Stephen G.; Savitt, Joseph M.; Hausdorff, Jeffrey M.; von Coelln, Rainer; Cummings, Michael P.Parkinson's Disease (PD) and other forms of parkinsonism share motor symptoms, including tremor, bradykinesia, and rigidity. This overlap in the clinical presentation creates a diagnostic challenge, underscoring the need for objective differentiation. However, applying machine learning (ML) to clinical datasets faces challenges such as imbalanced class distributions, small sample sizes for non-PD parkinsonism, and heterogeneity within the non-PD group. This study analyzed wearable sensor data from 260 PD participants and 18 individuals with etiologically diverse forms of non-PD parkinsonism during clinical mobility tasks, using a single sensor placed on the lower-back. We evaluated the performance of ML models in distinguishing these two groups and identified the most informative mobility tasks for classification. Additionally, we examined clinical characteristics of misclassified participants and presented case studies of common challenges in clinical practice, including diagnostic uncertainty at the initial visit and changes in diagnosis over time. We also suggested potential steps to address dataset challenges which limited the models' performance. We demonstrate that ML-based analysis is a promising approach for distinguishing idiopathic PD from non-PD parkinsonism, though its accuracy remains below that of expert clinicians. Using the Timed Up and Go test as a single mobility task outperformed the use of all tasks combined, achieving a balanced accuracy of 78.2%. We also identified differences in some clinical scores between participants correctly and falsely classified by our models. These findings demonstrate the feasibility of using ML and wearable sensors for differentiating PD from other parkinsonian disorders, addressing key challenges in diagnosis, and streamlining diagnostic workflows.Item Acyclic Cucurbit[n]uril Bearing Alkyl Sulfate Ionic Groups - Electronic Supporting Data(Beilstein Journal of Organic Chemistry, 2025-01-09) Akakpo, Christian; Zavalij, Peter Y.; Isaacs, LyleThis dataset contains the electronic data files that support the publication.Item Vertical Column Densities of LNOx*(2024) Seiler, Madilynn; Bucsela, Eric; Pickering, KennethThis dataset was created by Eric Bucsela and contains vertical column densities of LNOx before background contribution was removed (LNOx*). There are 6 values for these column amounts, one for each method used to retrieve vertical column densities. The method used in Seiler et al., (2025) is that of VLNOxhi_cld. There are files for three case studies: June 11th, 2012, August 5th, 2007, August 6th, 2006.Item 30M Parent Spatial Talk(2024-11-22) Mix, KellyFrequency of spatial utterances were coded from videorecorded home visits (Cabrera & Reich, 2017). Spatial talk was coded in both Spanish and English, and for mothers and fathers of the same children, measured when children were 30 months old, on average. The dataset include children's performance on a numeracy outcome measure completed when children were 42 months old, on average.