UMD Data Collection
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Item 2021 Slavery Law and Power XML Transcriptions(2024) Brewer, HollySLP (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.Item 2022-2023 Slavery Law and Power XML Transcriptions(2024) Brewer, HollySLP (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.Item 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 signs and symptoms, including tremor, bradykinesia, and rigidity. This overlap in the clinical presentation across the different parkinsonian disorders creates a diagnostic challenge, underscoring the need for precise and 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 an accuracy that closely aligns with the diagnostic precision of a movement disorder expert. Moreover, we found that enhanced diagnostic performance can be attained through severity-based partitioning of participants. 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 high yield 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 with a movement disorder specialist, and changes in clinical diagnosis by the treating neurologist 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.Item 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.Item Data for Effects of Strong Capacitive Coupling Between Meta-Atoms in rf SQUID Metamaterials(2024) Cai, Jingnan; Anlage, StevenThe 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.Item Magnetic Susceptibility Instrument for Magnetically Inhomogeneous Granular Mixtures(Review of Scientific Instruments, 2024) Charles T. PettWe 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.Item K-2 Place Value Concepts Smart Errors Study (v.1)(2024-01-19) Bower, Corinne, Mix, Kelly S., Smith, Linda B.Item 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, NicholasThe 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.Item 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.Item Supplementary materials for characterization of high-yield mobility features to identify Parkinson’s disease with a wearable sensor(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 has potential to identify, characterize and manage patients with movement disorders, including Parkinson’s disease (PD). Nonetheless, such sensors are not yet part of routine clinical examinations, in large part because it is still unclear which mobility tasks and which sensor-derived features per task should be analyzed to optimize/maximize the yield of this type of mobility analysis. To address this gap of knowledge, data from 262 participants with PD and 50 controls performing a series of motor tasks with a single wearable sensor on the lower back were analyzed using ensembles of heterogeneous machine learning models incorporating a wide range of classifiers and trained on a large set of features calculated from triaxial accelerometer and triaxial gyroscope signals. Our data show that sensor data analyzed with an ensemble of models effectively differentiate between participants with PD and controls. Furthermore, feature importance analysis revealed that a small number of more complex mobility tasks contribute the most informative features for accurate predictions, suggesting potential simplifications in wearable sensor-based mobility testing without sacrificing predictive performance.Item 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.Item 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.Item 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.Item 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).Item 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, DaleThe 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.Item Crenshaw transcripts 2023(0023-08-24) Crenshaw, Kenyatta; Elby, AndrewItem 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, CodyWe 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.Item Supplementary materials for positive-unlabeled learning identifies vaccine candidate antigens in the malaria parasite Plasmodium falciparum(2023) Chou, Renee Ti; Ouattara, Amed; Adams, Matthew; Berry, Andrea A.; Takala-Harrison, Shannon; Cummings, Michael P.Malaria vaccine development is hampered by extensive antigenic variation and complex life stages of Plasmodium species. Vaccine development has focused on a small number of antigens identified prior to availability of the P. falciparum genome. In this study, we implement a machine learning-based reverse vaccinology approach to predict potential new malaria vaccine candidate antigens. We assemble and analyze P. falciparum proteomic, structural, functional, immunological, genomic, and transcriptomic data, and use positive-unlabeled learning to predict potential antigens based on the properties of known antigens and remaining proteins. We prioritize candidate antigens based on model performance on reference antigens with different genetic diversity and quantify the protein properties that contribute the most to identifying top candidates. Candidate antigens are characterized by gene essentiality, gene ontology, and gene expression in different life stages to inform future vaccine development. This approach provides a framework for identifying and prioritizing candidate vaccine antigens for a broad range of pathogens.Item 9M Parent Number Talk(2023-04-10) Mix, Kelly; Cabrera, NatashaThe dataset contains parent math talk scores derived from coding of videorecorded home visits (Cabrera & Reich, 2017) completed when children were 9 months of age, as well as numeracy outcome scores collected when children were 42 months old. Coding was completed between June 2021 and December, 2022.