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

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.


Please direct questions regarding the UMD Data Collection or assistance in preparing and depositing data to the Data Services Librarian:


Recent Submissions

Now showing 1 - 5 of 135
  • 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.
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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 (markdown file).