UMD Data Collection

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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

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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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Recent Submissions

Now showing 1 - 20 of 147
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    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.
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    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
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    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.
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    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.
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    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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    Characterizing Low-Lying Coastal Upland Forests to Predict Future Landward Marsh Expansion
    (Ecological Society of America, 2024) Powell, Elisabeth; Dubayah, Ralph; Stovall, Atticus, E.L.
    Sea level rise (SLR) is causing vegetation regime shifts on both the seaward and landward sides of many coastal ecosystems, with the Eastern coast of North America experiencing accelerated impacts due to land subsidence and the weakening of the Gulf Stream. Tidal wetland ecosystems, known for their significant carbon storage capacity, are crucial but vulnerable blue carbon habitats. Recent observations suggest that SLR rates may exceed the threshold for elevation gain primarily through vertical accretion in many coastal regions. Therefore, research has focused on mapping the upslope migration of marshes into suitable adjacent lands, as this landward gain may be the most salient process for estimating future wetland resiliency to accelerated rates of SLR. However, our understanding of coastal vegetation characteristics and dynamics in response to SLR is limited due to a lack of in-situ data and effective mapping strategies for delineating the boundaries, or ecotones, of these complex coastal ecosystems. In order to effectively study these transitioning ecosystems, it is necessary to employ reliable and scalable landscape metrics that can differentiate between marsh and coastal forests. As such, integrating vegetation structure metrics from Light detection and ranging (Lidar) could enhance traditional mapping strategies compared to using optical data alone. Here, we used terrestrial laser scanning (TLS) to measure changes in forest structure along elevation gradients that may be indicative of degradation associated with increased inundation in the Delaware Bay estuary. We analyzed a set of TLS-derived forest structure metrics to investigate their relationships with elevation, specifically seeking those that showed consistent change from the forest edge to the interior. Our findings revealed a consistent pattern between elevation and the Plant Area Index (PAI), a metric that holds potential for enhancing the delineation of complex coastal ecosystem boundaries, particularly in relation to landward marsh migration. This work provides support for utilizing lidar-derived forest structural metrics to enable a more accurate assessment of future marsh landscapes and the overall coastal carbon sink under accelerated sea-level rise conditions.
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    Data for Effects of Strong Capacitive Coupling Between Meta-Atoms in rf SQUID Metamaterials
    (2024) Cai, Jingnan; Anlage, Steven
    The raw data for the publication Effects of Strong Capacitive Coupling Between Meta-Atoms in rf SQUID Metamaterials is available here. Both the data from the numerical calculation and experiments are reproduced.
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    Magnetic Susceptibility Instrument for Magnetically Inhomogeneous Granular Mixtures
    (Review of Scientific Instruments, 2024) Charles T. Pett
    We introduce an instrument and novel method for characterizing the bulk magnetic susceptibility of granular mixtures by submerging an inductor coil in a bed of metallic beads and gauging changes in self-inductance. The resonance frequency of the coil was measured to determine its inductance and evaluate the magnetic permeability of ferrous mixtures relative to air. In air, our coil was accurate to within 0.1\% of the permeability of free space. The range of magnetic susceptibility values for magnetically inhomogeneous granular mixtures is poorly constrained, but our coil uniquely quantifies bulk effects that other surface meters are not designed to resolve. Compared to both a commercial Terraplus Inc. KT-10 meter and theoretical approximations, we report similar trends in susceptibility values measured as a function of mass of ferromagnetic material per volume.
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    K-2 Place Value Concepts Smart Errors Study (v.1)
    (2024-01-19) Bower, Corinne, Mix, Kelly S., Smith, Linda B.
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    Mesozoic subduction shaped lower mantle structures beneath the East Pacific Rise
    (2024) Wang, Jingchuan; Lekic, Vedran; Schmerr, Nicholas; Gu, Yu Jeffrey; Guo, Yi; Lin, Rongzhi; Lekic, Vedran; Schmerr, Nicholas
    The morphology of the Large Low Shear Velocity Provinces (LLSVPs) has been a subject of debate for decades. Large-scale features of the Pacific LLSVP, as revealed by cluster analysis of global tomographic models, suggest three distinct portions. Notably, the East Pacific Anomaly and the Superswell Anomaly are characterized by a ~20 deg wide gap. The cause of the structural gap remains unclear, and there has been no direct evidence for a subduction episode beneath the region. In this study, we take advantage of an up-to-date SS precursor data set that samples the Nazca Plate and investigate the high-resolution seismic structure at mantle transition zone (MTZ) depths. We find that much of the southern East Pacific Rise is underlain by a thin MTZ due to the depressed 410 by up to 15 km, which suggests along-ridge temperature variations extending into the MTZ. East of the East Pacific Rise, the MTZ is characterized by anomalous thickening and fast seismic velocities from seismic tomography, consistent with the presence of cold subducted slab material intersecting the MTZ. Furthermore, recent global tomographic models reveal a slab-like structure throughout the MTZ and lower mantle, which is also evidenced by tomographic vote maps, albeit with less visibility. The observations reconcile with Mesozoic intraoceanic subduction beneath the present-day Nazca Plate, which is predicted by an earlier plate reconstruction model of proto-Pacific Ocean. The subduction initiated ~250 Myr ago and ceased before 120 Myr ago. The implications of this discovery are that the shape of the eastern portion of the Pacific LLSVP was separated by downwelling associated with this ancient subducted slab.
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    Supplementary materials for statistical and machine learning analyses demonstrate test-retest reliability assessment is misled by focusing on total duration of mobility tasks in Parkinson's disease
    (2023) 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 evaluate the test-retest reliability of these tasks by assessing the performance of machine learning models based on quantitative sensor-derived measures, and statistical measures to examine 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 from the first to the second trial of our mobility tasks, suggesting that mobility testing can be simplified by not repeating tasks without losing relevant information. Although the total duration remains relatively consistent between trials, there is more variability in subtask duration and sensor-derived measures, evident in the differences in machine learning model performance and statistical metrics. Relying solely on total task duration and conventional statistical metrics to gauge the reliability of mobility tasks overlooks the nuanced variations in movement captured by other quantitative measures.
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    Supplementary material for machine learning analysis of data from a simplified mobility testing procedure with a single sensor and single task accurately differentiates Parkinson's disease from controls
    (2023) Khalil, Rana M.; Shulman, Lisa M.; Gruber-Baldini, Ann L.; Shakya, Sunita; von Coelln, Rainer; Cummings, Michael P.; Fenderson, Rebecca; van Hoven, Maxwell; Hausdorff, Jeffrey M.; Cummings, Michael P.
    Quantitative mobility analysis using wearable sensors, while promising as a diagnostic tool for Parkinson's disease (PD), is not commonly applied in clinical settings. Major obstacles include uncertainty regarding the best protocol for instrumented mobility testing and subsequent data processing, as well as the added workload and complexity of this multi-step process. To simplify sensor-based mobility testing in diagnosing PD, we analyzed data from 262 PD participants and 50 controls performing several motor tasks wearing a sensor on the lower back containing a triaxial accelerometer and a triaxial gyroscope. Using ensembles of heterogeneous machine learning models incorporating a range of classifiers trained on a large set of sensor features, we show that our models effectively differentiate between participants with PD and controls, both for mixed-stage PD (92.6% accuracy) and a group selected for mild PD only (89.4% accuracy). Omitting algorithmic segmentation of complex mobility tasks decreased the diagnostic accuracy of our models, as did the inclusion of kinesiological features. Feature importance analysis revealed Timed Up & Go (TUG) tasks to contribute highest-yield predictive features, with only minor decrease in accuracy for models based on cognitive TUG as a single mobility task. Our machine learning approach facilitates major simplification of instrumented mobility testing without compromising predictive performance.
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    Supplementary materials for Plasmodium vivax antigen candidate prediction improves with the addition of Plasmodium falciparum data
    (2023) Chou, Renee Ti; Ouattara, Amed; Takala-Harrison, Shannon; Cummings, Michael P.
    Intensive malaria control and elimination efforts have led to substantial reductions in malaria incidence over the past two decades. However, the reduction in Plasmodium falciparum malaria cases has led to a species shift in some geographic areas, with P. vivax predominating in many areas outside of Africa. Despite its wide geographic distribution, P. vivax vaccine development has lagged far behind that for P. falciparum, in part due to the inability to cultivate P. vivax in vitro, hindering traditional approaches for antigen identification. In a prior study, we have used a positive-unlabeled random forest (PURF) machine learning approach to identify P. falciparum antigens for consideration in vaccine development efforts. Here we integrate systems data from P. falciparum (the better-studied species) to improve PURF models to predict potential P. vivax vaccine antigen candidates. We further show that inclusion of known antigens from the other species is critical for model performance, but the inclusion of unlabeled proteins the other species can result in misdirection of the model toward predictors of species classification, rather than antigen identification. Beyond malaria, incorporating antigens from a closely related species may aid in vaccine development for emerging pathogens having few or no known antigens.
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    Tracing Sources of Atmospheric Methane Using Clumped Isotopes
    (PNAS, 2023) Haghnegahdar Mojhgan A.
    Here we use a box model to evaluate how much additional data from Δ12CH2D2 and Δ13CH3D may add to understanding the temporal trend in atmospheric methane, and specifically, whether they may differentiate the contributions of fossil fuel and microbial sources. EDGAR (Emissions Database for Global Atmospheric Research) provides high-quality constraints on methane fluxes from major anthropogenic sources, and different versions of EDGAR reflect uncertainty in understanding of the apportionment of these fluxes over the past few decades. We used two versions of EDGAR and also considered another model of fossil fuel flux to build four different scenarios for anthropogenic source fluxes for our box model. EDGAR does not include wetland emissions and those are calculated (a free variable) to close the flux balance needed by the model. Each scenario broadly follows one of four parameterizations of anthropogenic source fluxes to obtain an estimate of the composition and evolution of Δ12CH2D2 and Δ13CH3D through time.
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    Example code and data for "SOFIA FEEDBACK Survey: The Pillars of Creation in [C II] and Molecular Lines"
    (2023-09-07) Karim, Ramsey; Pound, Marc W.; Wolfire, Mark G.; Mundy, Lee; Tielens, Alexander G. G. M.
    We present here the original observations used in the manuscript "SOFIA FEEDBACK Survey: The Pillars of Creation in [C II] and Molecular Lines" (Karim et al., Astronomical Journal, 2023). The data consist of FITS format images and datacubes of the rotational transitions of molecular lines CO, 13CO, C18O, CS, HCN, HCO+, and N2H+ in the 3mm spectral window, and the atomic spectral lines [C II] 158 micron and [O I] 63 micron. We also present a snapshot copy of the scoby (Spectra from Catalogs of OB Stars) software repository, some model data necessary for it, and some examples of how to run it.
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    Example code and data for "Identifying physical structures in our Galaxy with Gaussian Mixture Models: An unsupervised machine learning technique"
    (2023) Tiwari, Maitraiyee; Kievit, Rens; Kabanovic, Slawa; Bonne, Lars; Falasca, F.; Guevara, Cristian; Higgins, Ronan; Justen, M.; Karim, Ramsey; Pabst, Cornelia; Pound, Marc W.; Schneider, Nicola; Simon, R.; Stutzki, Jurgen; Wolfire, Mark; Tielens, Alexander G. G. M.
    We present a python software repository implementing the PyGMMis (Melchior & Goudling 2018) method to astronomical data cubes of velocity resolved line observations. This implementation is described extensively in Tiwari et al. 2023, ApJ. An example is included in /example/ containing the SOFIA data of RCW120 used in Tiwari et al. 2023, ApJ, along with example scripts describing the full implementation of our code. The majority of parameter tweaking can be performed within 'rcw120-params.txt' which is continuously called during the procedure. A full description of the code and how to use it is in README.md (markdown file).
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    A CloudSat and CALIPSO-based evaluation of the effects of thermodynamic instability and aerosol loading on Amazon Basin deep convection and lightning
    (2023-08-14) Allen, Dale
    The Amazon Basin, which plays an important role in the carbon and water cycle, is under stress due to changes in climate, agricultural practices, and deforestation. The Basin includes a rainforest in the northwest and a mix of deforested areas, savannah-type vegetation, and agriculture in the southeast. The effects of instability and aerosol loading on thunderstorms in the Basin (75-45° W, 0-15° S) were examined during mid-August through mid-December, a period with large variations in aerosols, intense convective storms, and plentiful flashes. The analysis used measurements of radar reflectivity, ice water content (IWC), and aerosol type from instruments aboard the CloudSat and CALIPSO satellites, flash rates from the ground-based STARNET network, and aerosol optical depth (AOD) from a surface network and a meteorological re-analysis. After controlling for convective available potential energy (CAPE), a measure of instability, it was found that thunderstorms that developed under dirty (high-AOD) conditions were approximately 1.5 km deeper, had 50% more IWC, and more than two times as many flashes as storms that developed under clean (low-AOD) conditions. Flash rates were also found to be larger during periods when smoke rather than dust was common in the lower troposphere, likely because these periods were less stable.
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    Crenshaw transcripts 2023
    (0023-08-24) Crenshaw, Kenyatta; Elby, Andrew
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    Emotions in Polish and Lithuanian Social Media
    (2023) Paletz, Susannah B. F.; Rytting, C. Anton; Johns, Michael A.; Pandža, Nick B.; Golonka, Ewa M.; Murauskaite, Egle E.; Buntain, Cody
    We applied modern psychology theory of emotions and cross-cultural psychology methods to a range of issues surrounding emotions and social media. We developed an annotation guide for three languages and identified 365 Polish and 188 Lithuanian sociopolitical entities, and we developed a consensus annotated corpus for over 3,000 Polish and over 1,500 Lithuanian Facebook posts for emotional content, primary topic, post shares, and more. This corpus represents data we intend to have as sharable that was used in papers we hope to publish. More detail can be gained by reading the methodology description and by contacting the study PI, Susannah Paletz, at paletz@umd.edu.