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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Now showing 1 - 20 of 170

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/10.1111/1750-3841.17657
Measurements of boulders ejected in the Double Asteroid Redirection Test (DART) impact
(2025-01-17) Farnham, Tony
This 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.
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, William
GEODES San Francisco Volcanic Field Geochemical Sampling
(2023) Shubham, Sourabh; Farcy, Benjamin; Wright, Shawn; Schmerr, Nicholas; Whelley, Patrick
In 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.
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.
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, Lyle
This dataset contains the electronic data files that support the publication.
Vertical Column Densities of LNOx*
(2024) Seiler, Madilynn; Bucsela, Eric; Pickering, Kenneth
This 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.
30M Parent Spatial Talk
(2024-11-22) Mix, Kelly
Frequency 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.
OMI Satellite LNOx* VCD June 11, 2012
(2024) Seiler, Madilynn; Buscela, Eric; Pickering, Kenneth
1ºx1º gridded OMI Vertical Column Densities of Lightning NOx without background subtraction for June 11th, 2012
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.
Mobility tasks like the Timed Up and Go test (TUG), cognitive TUG (cogTUG), and walking with turns provide insight into motor control, balance, and cognitive functions affected by Parkinson’s disease (PD). We assess the test-retest reliability of these tasks in 262 PD participants and 50 controls by evaluating machine learning models based on wearable sensor-derived measures and statistical metrics. This evaluation examines total duration, subtask duration, and other quantitative measures across two trials. We show that the diagnostic accuracy for distinguishing PD from controls decreases by a mean of 1.8% between the first and the second trial, suggesting that task repetition may not be necessary for accurate diagnosis. Although the total duration remains relatively consistent between trials (intraclass correlation coefficient (ICC) = 0.62 to 0.95), greater variability is seen in subtask duration and sensor-derived measures, reflected in machine learning performance and statistical differences. Our findings also show that this variability differs not only between controls and PD participants but also among groups with varying levels of PD severity, indicating the need to consider population characteristics. 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.
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.
Dataset for "Resistance of Boron Nitride Nanotubes to Radiation-Induced Oxidation" as published in The Journal of Physical Chemistry C
(2024) Chao, Hsin-Yun (Joy); Nolan, Adelaide M.; Hall, Alex T.; Golberg, Dmitri; Park, Cheol; Yang, Wei-Chang David; Mo, Yifei; Sharma, Renu; Cumings, John
St. Dunstan editions prices, 1903-2024
(2024) Hovde, Sarah
This is a dataset of auction and sale prices between 1903 and 2024 for the St. Dunstan Illuminated Editions, a set of de luxe editions published by George D. Sproul between 1901-1904.
Interface Diagnostics Platform for Thin-Film Solid-State Batteries
(2024-08-28) Ferrari, Victoria Castagna; Stewart, David Murdock; Rubloff, Gary
This dataset comprises electorchemical impedance spectroscopy measurements from thin film batteries comprised of LiV2O5, LiPON, and Si. The data is associated with a manuscript that describes the methodology and analysis of the data and conclusions we draw from it in complete detail. (At the time of submission, the manuscript was set to be submitted to a peer reviewed journal.) The data herein is intended to be used to model equivalent circuits for each material and the charge transfer interfaces throughout the device in order to construct the model of the full battery. The demonstrated methods to build from simple materials to a complex device are novel in the field and we hope this data and process will be used by other researchers to develop more robust analysis of batteries across academic labs and industry.
Code and Data for 'Generalized Time-Series Analysis for In-Situ Spacecraft Observations: Anomaly Detection and Data Prioritization using Principal Components Analysis and Unsupervised Clustering'
(2024) Finley, Matthew G.; Martinez-Ledesma, Miguel; Paterson, William R.; Argall, Matthew R.; Miles, David M.; Dorelli, John C.; Zesta, Eftyhia
Neural correlates of perceptual plasticity in the auditory midbrain and thalamus
(2024-08-28) Ying, Rose; Stolzberg, Daniel J; Caras, Melissa L
Hearing is an active process in which listeners must detect and identify sounds, segregate and discriminate stimulus features, and extract their behavioral relevance. Adaptive changes in sound detection can emerge rapidly, during sudden shifts in acoustic or environmental context, or more slowly as a result of practice. Although we know that context- and learning-dependent changes in the spectral and temporal sensitivity of auditory cortical (ACX) neurons support many aspects of perceptual plasticity, the contribution of subcortical auditory regions to this process is less understood. Here, we recorded single- and multi-unit activity from the central nucleus of the inferior colliculus (ICC) and the ventral subdivision of the medial geniculate nucleus (MGV) of Mongolian gerbils under two different behavioral contexts: as animals performed an amplitude modulation (AM) detection task and as they were passively exposed to AM sounds. Using a signal detection framework to estimate neurometric sensitivity, we found that neural thresholds in both regions improved during task performance, and this improvement was driven by changes in firing rate rather than phase locking. We also found that ICC and MGV neurometric thresholds improved as animals learn to detect small AM depths during a multi-day perceptual training paradigm. Finally, we reveal that in the MGV, but not the ICC, context-dependent enhancements in AM sensitivity grow stronger during perceptual training, mirroring prior observations in the ACX. Together, our results suggest that the auditory midbrain and thalamus contribute to changes in sound processing and perception over rapid and slow timescales.
COMSOL fracture model
(2024-08-06) Jung, Taeho; Carmona, Eric A; Song, Yueming; Albertus, Paul
Solid-electrolyte (SE) fracture initiation model at the lithium/SE interface during lithium plating.
A Better Understanding of Atmospheric Methane Sources Using 13CH3D and 12CH2D2 Clumped Isotopes
(2024-09) Haghnegahdar, Mojhgan A.
We evaluate the use of clumped isotopes of methane (CH4) to fingerprint local atmospheric sources of methane. We focus on a regenerative stormwater conveyance (RSC) stream wetland site running through the University of Maryland campus, which emits methane due to its engineering. Air samples in the RSC were collected at different heights above the surface and at different times of the day including both early in the morning, after methane accumulated below the nocturnal boundary layer, and late in the afternoon when convection mixed air to the cloud layer. Measured Δ12CH2D2 values of air samples record mixing between locally produced methane with low D/H and ambient air. The Δ12CH2D2 of the near surface air collected at the RSC during the early morning ranges from ~+23‰ to ~+35‰ which is lower than the ~+50‰ values of tropospheric air. Mixing between background air (with Δ12CH2D2 ~+50‰) and methane captured from chamber and bubble samples, as well as produced in incubation (all with negative Δ12CH2D2), explains the observed values of Δ12CH2D2 and Δ13CH3D of near surface RSC air samples. The effect of mixing with biogenic sources on Δ13CH3D is much smaller. The findings demonstrate how methane isotopologues can be used as a tool not only to fingerprint local contributions to these greenhouse gas emissions but also to identify sources of near-surface methane hot spots.
Code and Data for "Sparse high-dimensional decomposition of non-primary auditory cortical receptive fields"
(2024) Mukherjee, Shoutik; Babadi, Behtash; Shamma, Shihab A.
Characterizing neuronal responses to natural stimuli remains a central goal in sensory neuroscience. In auditory cortical neurons, the stimulus selectivity of elicited spiking activity is summarized by a spectrotemporal receptive field (STRF) that relates neuronal responses to the stimulus spectrogram. Though effective in characterizing primary auditory cortical responses, STRFs of non-primary auditory neurons can be quite intricate, reflecting their mixed selectivity. The complexity of non-primary STRFs hence impedes understanding how acoustic stimulus representations are transformed along the auditory pathway. Here, we focus on the relationship between ferret primary auditory cortex (A1) and a secondary region, dorsal posterior ectosylvian gyrus (PEG). We propose estimating receptive fields in PEG with respect to a well-established high-dimensional computational model of primary-cortical stimulus representations. These ``cortical receptive fields'' (CortRF) are estimated greedily to identify the salient primary-cortical features modulating spiking responses and in turn related to corresponding spectrotemporal features. Hence, they provide biologically plausible hierarchical decompositions of STRFs in PEG. Such CortRF analysis was applied to PEG neuronal responses to speech and temporally orthogonal ripple combination (TORC) stimuli and, for comparison, to A1 neuronal responses. CortRFs of PEG neurons captured their selectivity to more complex spectrotemporal features than A1 neurons; moreover, CortRF models were more predictive of PEG (but not A1) responses to speech. Our results thus suggest that secondary-cortical stimulus representations can be computed as sparse combinations of primary-cortical features that facilitate encoding natural stimuli. Thus, by adding the primary-cortical representation, we can account for PEG single-unit responses to natural sounds better than bypassing it and considering as input the auditory spectrogram. These results confirm with explicit details the presumed hierarchical organization of the auditory cortex.
30 Parent Number Input
(2024-07-15) Mix, Kelly; Cabrera, Natasha; not applicable
This dataset contains codes of parent numeracy input including number word utterances, other quantitative words, and quantitative actions or gestures based on a set of video recorded home visits conducted for a separate study (Cabrera & Reich, 2017) when children were 30 months old. The dataset also includes demographic information and children's scores on a numeracy outcome measure collected when children were 43 months on average. The parent number input codes were collected in 2022-2023 and the children’s numeracy outcome scores collected between 2020-2021.

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