Search Results

Search Constraints

Search Results

  • Dataset for: Solar wind heavy ions and alpha particles within Earth’s magnetosphere and their variability with upstream conditions

    work
    Creator:
    Colón-Rodríguez, Stephanie, Liemohn, Michael, Raines, Jim, and Lepri, Susan T.
    Description:
    During its trajectory, Wind spent a significant amount of time in the magnetotail, where its SupraThermal Ion Composition Spectrometer (STICS) measured the mass and mass per charge of protons, alpha particles, and heavy ions with an energy/charge ratio up to 226 keV/e. Although STICS originally aimed to measure the abundance of these ion species in the solar wind, its measurements within the magnetosphere from 1995 to 2002 help us identify preferential entry between the different solar wind ion species. This study statistically analyzes how the ratio between solar wind heavy ions and alpha particles (Heavies Solar Wind / He2+) varies for different upstream conditions and locations within the magnetosphere: northward vs. southward Interplanetary Magnetic Field (IMF), low vs. high solar wind density (Nsw), low vs. high solar wind dynamic pressure (PDyn), slow vs. fast solar wind (Vsw), and dawn vs. dusk. Our results indicate that the HeaviesSolar Wind enter the magnetosphere more efficiently than He2+ during northward IMF and that the Heavies Solar Wind / He2+ ratios decrease during high PDyn. In addition, the Heavies Solar Wind / He2+ ratios exhibit a dawn-dusk asymmetry, highly skewed towards the dawn side for all upstream cases likely due to charge-exchange processes.
    Keyword:
    Magnetosphere, Wind STICS, Solar wind heavy ions, Alpha particles, and dawn-dusk asymmetry
    Citation to related publication:
    Colón-Rodríguez, S., Liemohn, M. W., Raines, J. M, & Lepri, S.T. (2024). Solar wind heavy ions and alpha particles within Earth’s magnetosphere and their variability with upstream conditions. Journal of Geophysical Research Space Physics. In preparation.
    Discipline:
    Science

  • Data For: Achieving Reproducibility and Replicability of Molecular Dynamics and Monte Carlo Simulations Using the Molecular Simulation Design Framework (MoSDeF)

    work
    Creator:
    Craven, Nicholas C, Singh, Ramanish, Quach, Co D, Gilmer, Justin B, Crawford, Brad, Marin-Rimoldi, Eliseo, Smith, Ryan, DeFever, Ryan, Dyukov, Maxim, Fothergill, Jenny, Jones, Chris, Moore, Timothy, Butler, Brandon L, Anderson, Joshua A, Iacovella, Christopher, Jankowski, Eric, Maginn, Eric, Potoff, Jeffrey, Glotzer, Sharon C, McCabe, Clare, Cummings, Peter T, and Siepmann, Ilja J
    Description:
    Data are collected in 5 separate workspace, one for the main density data calculations across the space and 4 for the subproject simulations that were performed to validate and dive deeper into specific engine implementations. In order to copy the simulation trajectory and calculated averages used to generate figures, these workspace folders must be downloaded and pointed to the correct place in the GitHub Project Structure, which can be found at  https://github.com/mosdef-hub/reproducibility_study and Each compressed file contains the data for a single workspace.
    Keyword:
    molecular dynamics, monte carlo, reproducibility, and replicability
    Citation to related publication:
     https://doi.org/10.1021/acs.jced.5c00010
    Discipline:
    Science

  • Data and Analysis for Global Probabilistic Geomagnetic Perturbation Forecasting Using the Data-Driven Model GeoDGP

    work
    Creator:
    Chen, Hongfan, Chen, Yang, Huang, Zhenguang, Zou, Shasha, Huan, Xun, and Toth, Gabor
    Description:
    Accurately predicting the horizontal component of the ground magnetic field perturbation (dBH), which can be used to calculate the Geomagnetically Induced Currents (GICs), is crucial for estimating the space weather impact of geomagnetic disturbances. In this work, we develop a new data-driven model GeoDGP using deep Gaussian process (DGP), which is a Bayesian non-parametric approach. The model provides global probabilistic forecasts of dBH at 1-minute time cadence and with arbitrary spatial resolutions. We evaluate the model comprehensively on a wide range of geomagnetic storms, including the 2024 Gannon extreme storm. The results show that GeoDGP significantly outperforms the state-of-the-art physics-based first-principles Space Weather Modeling Framework (SWMF) Michigan Geospace model and the data-driven DAGGER model.
    Keyword:
    Space Weather, Uncertainty Quantification, Machine Learning, and Bayesian Inference
    Citation to related publication:
    Chen, H., et al. (2024). GeoDGP: One-Hour Ahead Global Probabilistic Geomagnetic Perturbation Forecasting using Deep Gaussian Process.
    Discipline:
    Science and Engineering

  • Self-oscillating Gel Simulation Snapshot Dataset

    work
    Creator:
    Aksoy, Doruk and Kim, Donghak
    Description:
    This dataset contains snapshots from simulations of a hexagonal self oscillating gel sheet defined via a triangular lattice. The lattice has stretching springs between neighboring vertices and bending springs with energy proportional to the square of the angle between neighboring traingular faces. The motion of the lattice is driven by time- and space-varying distributions of the rest lengths of the stretching springs. In the motivating experiments on thin gel sheets, there are chemical waves, radial or spiral in form, that induce local swelling of the sheets. As a simple model, this dataset considers radial or planar (unidirectional) traveling waves in the simulations. The sheet is modeled as a flat hexagon of radius 1 with an equilateral triangular triangle lattice mesh, with initially uniform mesh spacing of 1/33, resulting in 3367 mesh points. A small out-of-plane perturbation is applied and the motion evolves over the sheet over time. The sheet is modeled to have damped dynamics. However for large enough wave amplitudes, the sheet rapidly buckles into shapes with time-varying distributions of curvature, large in magnitude. For more information on the simulation that generated the data, please refer to "Semi-implicit methods for the dynamics of elastic sheets,” at Journal of Computational Physics by Alben et al. For an example SciML application that considers this dataset, please refer to "Inverse design of self-oscillatory gels through deep learning." Neural Computing and Applications by Aksoy et al.
    Keyword:
    Soft robotics, Partial Differential Equations, Scientific Simulations, and Chaotic Systems
    Citation to related publication:
    Alben, Silas, et al. "Semi-implicit methods for the dynamics of elastic sheets." Journal of Computational Physics 399 (2019): 108952., Aksoy, Doruk, et al. "Inverse design of self-oscillatory gels through deep learning." Neural Computing and Applications 34.9 (2022): 6879-6905., Aksoy, Doruk, et al. "An incremental tensor train decomposition algorithm." SIAM Journal on Scientific Computing 46.2 (2024): A1047-A1075., and Aksoy, Doruk, and Alex A. Gorodetsky. "Incremental Hierarchical Tucker Decomposition." arXiv preprint arXiv:2412.16544 (2024).
    Discipline:
    Engineering and Science

  • Data for Discovery and characterization of a novel telomerase alternative splicing isoform that protects lung cancer cells from chemotherapy induced cell death.

    work
    Creator:
    Ludlow, Andrew
    Description:
    Single molecule long read RNA/cDNA sequencing of TERT revealed 45 TERT mRNA variants including 13 known and 32 novel variants. Among the variants, TERT Delta 2-4, which lacks exons 2-4 but retains the original open reading frame, was selected for further study. Induced pluripotent stem cells and cancer cells express higher levels of TERT Delta 2-4 compared to primary human bronchial epithelial cells. Overexpression of TERT Delta 2-4 enhanced clonogenicity and resistance to cisplatin-induced apoptosis. Knockdown of endogenous TERT Delta 2-4 in Calu-6 cells reduced clonogenicity and resistance to cisplatin. Our results suggest that TERT Delta 2-4 enhances cancer cells’ resistance to intrinsic apoptosis. RNA sequencing following knockdown of Delta 2-4 TERT indicates that translation is downregulated and that mitochondrial related proteins are upregulated compared to controls.
    Keyword:
    TERT, Alternative splicing, Telomere, and Telomerase
    Citation to related publication:
    Kim, J.J., Ahn, A., Ying, J.Y. et al. Discovery and characterization of a novel telomerase alternative splicing isoform that protects lung cancer cells from chemotherapy induced cell death. Sci Rep 15, 6787 (2025).  https://doi.org/10.1038/s41598-025-90639-3
    Discipline:
    Science

  • Supporting data: Domain-agnostic predictions of nanoscale interactions in proteins and nanoparticles

    work
    Creator:
    Saldinger, Jacob, Raymond, Matt, Elvati, Paolo, and Violi, Angela
    Description:
    The accurate and rapid prediction of generic nanoscale interactions is a challenging problem with broad applications. Much of biology functions at the nanoscale, and our ability to manipulate materials and purposefully engage biological machinery requires knowledge of nano-bio interfaces. While several protein-protein interaction models are available, they leverage protein-specific information, limiting their abstraction to other structures. Here, we present NeCLAS, a general, and rapid machine learning pipeline that predicts the location of nanoscale interactions, providing human-intelligible predictions. Two key aspects distinguish NeCLAS: coarse-grained representations, and the use of environmental features to encode the chemical neighborhood. We showcase NeCLAS with challenges for protein-protein, protein-nanoparticle and nanoparticle-nanoparticle systems, demonstrating that NeCLAS replicates computationally- and experimentally-observed interactions. NeCLAS outperforms current nanoscale prediction models, and it shows cross-domain validity, qualifying as a tool for basic research, rapid prototyping, and design of nanostructures., Software: - To reproduce all-atom molecular dynamics (MD) NAMD is required (version 2.14 or later is suggested). NAMD software and documentation can be found at  https://www.ks.uiuc.edu/Research/namd/, - To reproduce coarse-grained MD simulations, LAMMPS (version 29 Sep 2021 - Update 2 or later is suggested). LAMMPS software and documentation can be found at  https://www.lammps.org, - To rebuild free energy profiles, the PLUMED plugin (version 2.6) was used. PLUMED software and documentation can be found at  https://www.plumed.org/ , and - To generate force matching potentials, the was used the OpenMSCG software was used. OpenMSCG software and documentation can be found at  https://software.rcc.uchicago.edu/mscg/
    Keyword:
    Neural Networks, Proteins, Dimensionality Reduction, Nanoparticles, and Coarse-Graining
    Citation to related publication:
     https://www.biorxiv.org/content/10.1101/2022.08.09.503361v2
    Discipline:
    Science

  • Data and Code Investigating the Dimensional Dependence of Molecular-Polariton Mode Number

    work
    Creator:
    Lydick, Nathanial and Deng, Hui
    Description:
    The included python scripts and Jupyter notebook generate and analyze the data.
    Keyword:
    Molecular Polariton, Dimensional Dependence, Tavis-Cummings Model, Strong Coupling, and Disorder
    Citation to related publication:
    N. Lydick, J. Hu, and H. Deng, "Dimensional dependence of a molecular-polariton mode number," J. Opt. Soc. Am. B 41, C247-C253 (2024).  https://doi.org/10.1364/JOSAB.524026
    Discipline:
    Science

  • Data in support of the study “Sleep loss diminishes hippocampal reactivation and replay”

    work
    Creator:
    Giri, Bapun, Kinsky, Nathaniel, and Diba, Kamran
    Description:
    The research that produced this data tested how sleep loss impacted the phenomena of reactivation and replay, which occurs when recently-learned information is reactivated/replayed during post-learning sleep/rest.
    Keyword:
    Hippocampus, Memory, Sleep, Sleep Deprivation, and Electrophysiology
    Citation to related publication:
    Giri, B., Kinsky, N.R., Kaya, U., Maboudi, K., Abel, T., Diba, K. (2024). Sleep loss diminishes hippocampal reactivation and replay. Nature, (2024).  https://doi.org/10.1038/s41586-024-07538-2
    Discipline:
    Science

  • Data of the paper "Sulfur Outgassing and In-gassing in Lunar Orange Glass Beads and Implications for 33S “Anomaly” in the Moon "

    work
    Creator:
    Su, Xue, Zhang, Youxue, and Liu, Yang
    Description:
    Our recent investigations have discovered inward diffusion (in-gassing) of moderately volatile elements (MVEs; e.g., Na, K and Cu) from volcanic gas into volcanic beads/droplets. In this work, we examine the distribution of sulfur in lunar orange glass beads. Our analyses reveal that sulfur exhibits a non-uniform distribution across the beads, forming "U" or "W" shaped profiles typical of in-gassing. A model developed to assess sulfur contributions from different sources (original magmatic sulfur versus atmospheric in-gassed sulfur) in the orange beads indicates that atmospheric sulfur in-gassed during eruption contributes approximately 9–24% to the total sulfur content of an orange bead, averaging around 16%. This in-gassed sulfur is derived from the eruption plume, where atmospheric sulfur could undergo photochemical reactions induced by UV light, leading to mass independent fractionation and a distinct sulfur isotope signature. Interestingly, a recent study discovered a small mass independent isotope fractionation of sulfur in lunar orange glass beads in drive tube 74002/1 and a lack of such mass independent isotope fractionation in black glass beads in the same lunar sample. This finding contrasts with sulfur in lunar basalts, which typically exhibit mass dependent fractionation. With our work, the observed mass independent fractionation signal in sulfur isotopes of orange beads can be attributed to the in-gassing of photolytic sulfur in the optically thin part of the eruption plume where UV light can penetrate. Using the sulfur isotope data of lunar orange beads, we estimate that the Δ33S value of atmospheric sulfur is approximately −0.18‰. Our study provides new insights into the complex dynamics of volatile elements in lunar volcanic processes, highlighting the role of in-gassing in shaping sulfur isotope signatures in volcanic glass beads.
    Keyword:
    Moon, Lunar orange glass beads, Sulfur, Sulfur isotope, Diffusion, Outgassing and in-gassing, Mass independent fractionation, and Eruption plume
    Citation to related publication:
    Su, X., Zhang, Y., Liu, Y. (2024) Sulfur Outgassing and In-gassing in Lunar Orange Glass Beads and Implications for 33S “Anomaly” in the Moon. (under review)
    Discipline:
    Science

  • A Dataset of Great Lakes Extratropical Cyclone Tracks and Composites

    work
    Creator:
    Hutson, Abby , Fujisaki-Manome, Ayumi, and Glassman, Ryan
    Description:
    The data herein resulted from a study documenting the characteristics of extratropical cyclones that pass through the Great Lakes Region (GLR) and how the cyclones are trending with time. All scripts used to create these data can be found in the Github repository  https://github.com/abkenyon/GLStormTrends_2024. storm_track_slp_xxxx.npz - Structured numpy files containing all storm tracks identified over one cold season, regardless of whether the storm encountered the GLR, with the file name indicating the year on which the season ended. storm_composite_xxxx-xxxx.nc - NetCDF files containing one seasonal cyclone composite with different atmospheric variables. A composite is storm-centered, and covers a 20 degree square area.
    Keyword:
    Extratropical Cyclones, Climate Trends, Great Lakes Climate
    Citation to related publication:
    Hutson A, Fujisaki-Manome A, Glassman R.: Historical Trends in Cold-Season Mid-Latitude Cyclones. Geophysical Research Letters. In press..
    Discipline:
    Science