UMD Data Collection

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http://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.

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Find more information and guidelines for depositing into the Data Collection on the University of Maryland Libraries' DRUM for Data page.

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Please direct questions regarding the UMD Data Collection or assistance in preparing and depositing data to the Data Services Librarian: lib-research-data@umd.edu.

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Now showing 1 - 20 of 162

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.; 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.
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. The data accompanies a forthcoming article introducing the St. Dunstan volumes (this record will be updated upon publication).
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.
Replication Code for "Should We Expect Merger Synergies To Be Passed Through to Consumers?"
(2024-07-01) Sweeting, Andrew; Lecesse, Mario; Tao, Xuezhen
When reviewing horizontal mergers, antitrust agencies balance anticompetitive incentives, resulting from market power, with procompetitive incentives, created by efficiencies, assuming complete information and static, simultaneous move Nash equilibrium play. These models miss how a merged firm may prefer not to pass through efficiencies when rivals would respond by lowering their prices. We use an asymmetric information model, where rivals do not observe the size of the realized cost efficiency, to investigate how this incentive could affect post-merger prices. We highlight how the strength of this incentive will depend on the market structure of non-merging rivals and discuss alternative settings where similar issues arise.
Replication code for Dynamic Oligopoly Pricing with Asymmetric Information: Implications for Horizontal Mergers
(2024-07-01) Sweeting, Andrew; Yao, Xinlu; Tao, Xuezhen
We model repeated pricing by differentiated product firms when each firm has private information about its serially-correlated marginal cost. In a fully separating equilibrium of the dynamic game, signaling incentives can lead equilibrium prices to be signif icantly above those in a static, complete information game, even when the possible variation in the privately-observed state variables is very limited. We calibrate our model using data from the beer industry, and show that, without any change in conduct, our model can explain increases in price levels and changes in price dynamics and cost pass-through after the 2008 MillerCoors joint venture. The software in this repository allows all of the simulated numbers to be recalculated. It provides information on where the IRI dataset used in the empirical work can be found. Code to process the data is included.
Data for: Competing adaptations maintain non-adaptive variation in a wild cricket population
(2024) Rayner, Jack; Eichenberger, Franca; Bainbridge, Jessica; Zhang, Shangzhe; Zhang, Xiao; Yusuf, Leeban; Balenger, Susan; Gaggiotti, Oscar; Bailey, Nathan
Orbitofrontal Cortex Modulates Auditory Cortical Sensitivity and Sound Perception in Mongolian gerbils
(Current Biology, 2024) Macedo-Lima, Matheus; Hamlette, Lashaka Sierra; Caras, Melissa L.
Sensory perception is dynamic, quickly adapting to sudden shifts in environmental or behavioral context. Though decades of work have established that these dynamics are mediated by rapid fluctuations in sensory cortical activity, we have a limited understanding of the brain regions and pathways that orchestrate these changes. Neurons in the orbitofrontal cortex (OFC) encode contextual information, and recent data suggest that some of these signals are transmitted to sensory cortices. Whether and how these signals shape sensory encoding and perceptual sensitivity remains uncertain. Here, we asked whether the OFC mediates context-dependent changes in auditory cortical sensitivity and sound perception by monitoring and manipulating OFC activity in freely moving Mongolian gerbils of both sexes under two behavioral contexts: passive sound exposure and engagement in an amplitude modulation (AM) detection task. We found that the majority of OFC neurons, including the specific subset that innervate the auditory cortex, were strongly modulated by task engagement. Pharmacological inactivation of the OFC prevented rapid context-dependent changes in auditory cortical firing, and significantly impaired behavioral AM detection. Our findings suggest that contextual information from the OFC mediates rapid plasticity in the auditory cortex and facilitates the perception of behaviorally relevant sounds.
Experimental data for Development and evaluation of a modified most probable number (MPN) method for enumerating rifampicin-resistant Escherichia coli in agricultural, food, and environmental samples
(2024) Gao, Zhujun; Tikekar, Rohan V.
This is the dataset for manuscript titled Development and evaluation of a modified most probable number (MPN) method for enumerating rifampicin-resistant Escherichia coli in agricultural, food, and environmental samples that has a DOI of https://doi.org/10.1111/jfs.13127
2021 Slavery Law and Power XML Transcriptions
(2024) Brewer, Holly
SLP (Slavery, Law, and Power) is a project dedicated to bringing the many disparate sources that help to explain the long history of slavery and its connection to struggles over power in early America, particularly in the colonies that would become the United States. Going back to the early English Empire, this project traces the rise of the slave trade along with the parallel struggles between monarchical power and early democratic institutions and ideals. We are creating a curated set of documents that help researchers and students to understand the background to the fierce struggles over both slavery and power during the American Revolution, when questions of monarchical power, consent to government, and hereditary slavery were all fiercely debated. After America separated from Britain, the United States was still deeply influenced by this long history, especially up to the Civil War. The colonial legacies of these debates continued to affect the course of politics, law, and justice in American society as a whole. This dataset covers transcriptions from our 2021 document selection on various curated documents related to slavery, law, and power. The purpose of this set it too make these transcriptions accessible for future scholars as well as store these transcriptions in long term digital storage.
2022-2023 Slavery Law and Power XML Transcriptions
(2024) Brewer, Holly
SLP (Slavery, Law, and Power) is a project dedicated to bringing the many disparate sources that help to explain the long history of slavery and its connection to struggles over power in early America, particularly in the colonies that would become the United States. Going back to the early English Empire, this project traces the rise of the slave trade along with the parallel struggles between monarchical power and early democratic institutions and ideals. We are creating a curated set of documents that help researchers and students to understand the background to the fierce struggles over both slavery and power during the American Revolution, when questions of monarchical power, consent to government, and hereditary slavery were all fiercely debated. After America separated from Britain, the United States was still deeply influenced by this long history, especially up to the Civil War. The colonial legacies of these debates continued to affect the course of politics, law, and justice in American society as a whole. This dataset covers transcriptions from our 2022-2023 document selection on various curated documents related to slavery, law, and power. The purpose of this set it too make these transcriptions accessible for future scholars as well as store these transcriptions in long term digital storage.
Supplementary material for Machine learning analysis of wearable sensor data from mobility testing distinguishes Parkinson's disease from other forms of parkinsonism
(2024-03-13) Khalil, Rana M.; Shulman, Lisa M.; Gruber-Baldini, Ann L.; Hausdorff, Jeffrey M.; von Coelln, Rainer; Cummings, Michael P.; Cummings, Michael P.
Parkinson's Disease (PD) and other forms of parkinsonism share characteristic motor symptoms, including tremor, bradykinesia, and rigidity. This overlap in the clinical presentation creates a diagnostic challenge, underscoring the need for objective differentiation tools. In this study, we analyzed wearable sensor data collected during mobility testing from 260 PD participants and 18 participants with etiologically diverse forms of parkinsonism. Our findings illustrate that machine learning-based analysis of data from a single wearable sensor can effectively distinguish idiopathic PD from non-PD parkinsonism with a balanced accuracy of 83.5%, comparable to expert diagnosis. Moreover, we found that diagnostic performance can be improved through severity-based partitioning of participants, achieving a balanced accuracy of 95.9%, 91.2% and 100% for mild, moderate and severe cases, respectively. Beyond its diagnostic implications, our results suggest the possibility of streamlining the testing protocol by using the Timed Up and Go test as a single mobility task. Furthermore, we present a detailed analysis of several case studies of challenging scenarios commonly encountered in clinical practice, including diagnostic uncertainty at the initial visit, and changes in clinical diagnosis at a subsequent visit. Together, these findings demonstrate the potential of applying machine learning on sensor-based measures of mobility to distinguish between PD and other forms of parkinsonism.

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