Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
115
datasets available to search
ShareScore release 0.9.0
Dataset results
115 results for “density modeling”
Figure 2 in Density estimations of the Asiatic black bear: application of the random encounter model
Figure 2. Relationship between the fixed interval of the global positioning system (GPS) collar and the distance moved (km/h) as calculated based on the interval.
Figure 1 in Density estimations of the Asiatic black bear: application of the random encounter model
Figure 1. Shirakawa Village, Gifu Prefecture, Japan, where the field study was conducted, including the locations of the sensor cameras.
Figure 4 in Density estimations of the Asiatic black bear: application of the random encounter model
Figure 4. Coefficient of variation of the estimated density and its confidence interval when the number of cameras is varied from 1 to 100. The coefficients of variation are calculated for each session. The black straight line indicates a border at CV = 0.2.
3-D crustal models of S-wave velocity and density around the JPH volcanic area in NE China
<p>The 3-D crustal S-wave velocity and density models around the Jingpohu volcanic area in NE China from the joint inversion of full-waveform ambient noise and gravity data.</p>
Model-based Investigation of Electron Precipitation-driven Density Structures and their Effects on Auroral Scintillation
<p>This folder contains the precipitation modeling results and the camera dataset used for this study. It also includes the details of the supplementary material cited in the text in sections 3 (table params), 3.1 (minimum total energy flux), 4.1 (minimum total energy flux), and 4.2 (density profiles for different characteristic energies). </p>
Supplementary material for "Hybrid-Vlasov modelling of ion velocity distribution functions associated with the Kelvin-Helmholtz instability with a density and temperature asymmetry"
<p>Supplementary material for the article:</p> <p>"Hybrid-Vlasov modelling of ion velocity distribution functions associated with a density and temperature asymmetry" by</p> <p><strong>V. Tarvus</strong>, L. Turc, H. Zhou, T. Nakamura, A. Settino, K.Blasl, G. Cozzani, U. Ganse, Y. Pfau-Kempf, M. Alho, M. Battarbee, M. Bussov, M. Dubart, E. Gordeev, F. Tesema Kebede, K. Papadakis, J. Suni, I. Zaitsev and M. Palmroth</p> <p> </p> <p><strong>Supplementary video A</strong>:</p> <p>The development of the Kelvin-Helmholtz instability (KHI) in a purely transverse geometry (velocity shear perpendicular to the magnetic field), simulated using the hybrid-Vlasov model Vlasiator. The parameters shown are: Proton temperature (panel a), the non-Maxwellianity of the proton velocity distribution function (panel b), proton heat flux (panel c) and vorticity (panel d). A black contour in each panel shows the region where the magnitude of the proton temperature gradient is larger than the maximum gradient at the beginning of the simulation. Arrows in panel d) show the velocity field. The evolution of KHI proceeds from the formation of linear surface waves (t<50 Ω<sub>c,p</sub><sup>-1</sup>, with proton gyroperiod Ω<sub>c,p</sub><sup>-1</sup>) to the waves rolling up into vortices (t>50 Ω<sub>c,p</sub><sup>-1</sup>). Due to the steepening of the velocity shear layer, whose thickness tends towards the thermal proton Larmor radius, finite Larmor radius effects become active at the vortex edges, manifesting as enhanced non-Maxwellianity (panel b) and a heat flux (panel c), which originates from the temperature gradient according to the mechanism described by Braginskii (1965). At the end of the simulation (t=90-100 Ω<sub>c,p</sub><sup>-1</sup>), non-Maxwellianity increases also in the vortex interior, as protons from the two initial regions are mixed together.</p> <p> </p> <p><strong>Supplementary video B</strong>:</p> <p>The same as Supplementary video A, but with an added in-plane magnetic field of the form (<em>B</em><sub>0,z</sub>/5) tanh(x/a) <strong>y</strong>,<strong> </strong>where <em>B</em><sub>0,z</sub> is the magnitude of the background magnetic field perpendicular to the velocity shear. Analogous behavior is found compared to the simulation without an in-plane magnetic field (Supplementary video A), with the exception of the suppression of secondary instabilities by the added magnetic tension. This leads to less irregularities in the vortex structure during the non-linear stage (t>~50 Ω<sub>p</sub><sup>-1</sup>).</p>
The dataset for the manuscript entitled "SUPHRE: A reactive transport model with unsaturated and density-dependent flow"
<p>This is the dataset for the manuscript entitled "SUPHRE: A reactive transport model with unsaturated and density-dependent flow".</p>
A mass-density model can account for the size-weight illusion
<p>Dataset from the following publication:</p> <p>Wolf, C., Bergmann Tiest, W.M., & Drewing, K. (2018). A mass-density model can account for the size-weight illusion. <em>Plos One</em>.</p> <p>Each folder contains the data belonging to the corresponding experiment in the publication. Data sheets are xls files.</p> <p>Each data folder contains a description of the columns, i.e. variables in a separate txt file. In the data, every row corresponds to one trial. If a value is missing not applicable for a given trial, it is labelled as NaN ("not a number").</p> <p><br> For further questions, please contact:<br> chr.wolf[at]uni-marburg.de</p> <p><br> -- Jan 2nd 2018 --</p>
Source files for training the NN-based calibration model of Swarm LP ion densities
<p>Data files used for training the NN-based calibration model for Swarm Langmuir Probe ion densities.</p>
Data Files for P. Mai et al., "Fluctuating charge-density-wave correlations in the three-band Hubbard model" (2024)
<p>These are the data for P. Mai et al., "Fluctuating charge-density-wave correlations in the three-band Hubbard model" (2024)</p> <p>arXiv reference: https://arxiv.org/abs/2405.13164</p> <p>This work was supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Award Number DE-SC0022311. This research used resources of the Oak Ridge Leadership Computing Facility, a DOE Office of Science User Facility supported under Contract No. DE-AC05-00OR22725.</p>
PyGEDM pre-computed maps of Galactic electron density models
<p>Pre-computed HDF5 data cubes for <a href="https://github.com/FRBs/pygedm">PyGEDM</a> web app.</p> <p>Datasets in HDF5 file:</p> <p>Dimension scales distance (pc), DM (pc/cm3), galactic latitude and galactic longitude (deg).</p> <pre>[('dist', <HDF5 dataset "dist": shape (10,), type "<i8">), ('dm', <HDF5 dataset "dm": shape (12,), type "<i8">), ('gb', <HDF5 dataset "gb": shape (361,), type "<f8">), ('gl', <HDF5 dataset "gl": shape (721,), type "<f8">), gl = np.linspace(-180, 180, 360*2+1) gb = np.linspace(-90, 90, 180*2+1) dist = np.array((0.1, 0.2, 0.5, 1, 2, 5, 8.5, 10, 20, 50)) dm = np.array((1, 2, 5, 10, 20, 50, 100, 200, 500, 1000, 2000, 5000)) Galactocentric coordinates XYZ (pc): ('x', <HDF5 dataset "x": shape (100,), type "<i8">), ('y', <HDF5 dataset "y": shape (100,), type "<i8">), ('z', <HDF5 dataset "z": shape (100,), type "<i8">)]</pre> <p>NE2001 model precomputed datasets:</p> <pre> ('ne2001', <HDF5 group "/ne2001" (5 members)>),</pre> <pre>[('ddm', <HDF5 dataset "ddm": shape (12, 361, 721), type "<f8">), ('ddm_tau', <HDF5 dataset "ddm_tau": shape (12, 361, 721), type "<f8">), ('dmd', <HDF5 dataset "dmd": shape (10, 361, 721), type "<f8">), ('dmd_tau', <HDF5 dataset "dmd_tau": shape (10, 361, 721), type "<f8">), ('xyz', <HDF5 dataset "xyz": shape (100, 100, 100), type "<f8">)] YMW16 precomputed datasets: ('ymw16', <HDF5 group "/ymw16" (5 members)>),</pre> <pre>[('ddm', <HDF5 dataset "ddm": shape (12, 361, 721), type "<f8">), ('ddm_tau', <HDF5 dataset "ddm_tau": shape (12, 361, 721), type "<f8">), ('dmd', <HDF5 dataset "dmd": shape (10, 361, 721), type "<f8">), ('dmd_tau', <HDF5 dataset "dmd_tau": shape (10, 361, 721), type "<f8">), ('xyz', <HDF5 dataset "xyz": shape (100, 100, 100), type "<f8">)]</pre>
Protected by dragons: density surface modeling confirms large population of the critically endangered Yellow-crested Cockatoo on Komodo island
<p>Intense trapping of the critically endangered Yellow-crested Cockatoo <em>Cacatua sulphurea</em> for the International pet trade has devastated its populations across Indonesia such that populations >100 individuals remain at only a handful of sites. We combined distance sampling with density surface modeling (DSM) to predict local densities and estimate total population size for one of these areas, Komodo Island, part of Komodo National Park (KNP) in Indonesia. We modeled local density based on topography (topographic wetness index) and habitat types (percentage of palm savanna and deciduous monsoon forest). Our population estimate of 1,113 (95% CI: 587–2,109) individuals on Komodo Island was considerably larger than previous conservative estimates. Our density surface maps showed cockatoos to be absent over much of the island, but present at high densities in wooded valleys. Coincidence between our DSM and a set of independent cockatoo observations was high (93%).<br> Standardized annual counts by KNP staff in selected areas of the island showed increases in cockatoo records from <400 in 2011 to ~650 in 2017. Taken together, our results indicate that KNP, alongside and indeed because of preserving its iconic Komodo Dragons <em>Varanus komodoensis</em>, is succeeding in protecting a significant population of Indonesia's rarest cockatoo species. To our knowledge this is the first time DSM has been applied to a Critically Endangered species. Our findings highlight the potential of DSM for locating abundance hotspots, identifying habitat associations, and estimating global population size in a range of threatened taxa, especially if independent datasets can be used to validate model predictions.</p>
Supporting Materials for Modeling Multicomponent Gas Adsorption in Nanoporous Materials with Two Versions of Nonlocal Classical Density Functional Theory
<p>This web page archives the simulation input and output used in RASPA related to the publication. Please visit the GitHub repository (https://github.com/MusenZhou/GPU-accelerated-cDFT) and contact Jianzhong Wu (jwu@engr.ucr.edu) and Musen Zhou (mzhou035@ucr.edu) if interested in cDFT code.</p>
Detection of the Fire Drill anti-pattern: 15 real-world projects with ground truth, issue-tracking data, source code density, models and code
<p>This package contains artifacts for <strong>15</strong> real-world software projects. The data is supposed to aid the detection of the presence of the Fire Drill anti-pattern. We include original data, ground truth, code (experimental setups and models), and notebooks. The data supports two distinct methods of detecting the AP: a) through issue-tracking data, and b) through the underlying source code. This version of the dataset corresponds to <strong>v8</strong> of the <a href="https://arxiv.org/abs/2104.15090v8">technical report</a> and the <a href="https://github.com/MrShoenel/anti-pattern-models/releases/tag/arxiv-v8">GitHub repository</a>. The package includes the following:</p> <p>Original data:</p> <ul> <li>For each project, its <strong>original</strong> artifacts (e.g., wikis, meeting minutes, mentor's notes, etc.)</li> <li>Evaluation of raters' notes by the assessor</li> </ul> <p>Fire Drill in issue-tracking data:</p> <ul> <li><strong>Ground truth</strong> for whether and how strong each project exhibits the Fire Drill AP, on a scale from [0,10]. This was determined by two individual raters, who also reached a consensus.</li> <li>Coefficients for indicators for the first method, per project.</li> <li>Detailed issue-tracing data for each project: what occurred and when.</li> <li>Time logs for each project.</li> </ul> <p>Fire Drill in source-code data:</p> <ul> <li><strong>Four</strong> technical reports that document the developed method of how to translate a description into a detectable pattern, and to use the pattern to detect the presence and to score it (similar to the rating). Also includes a report for how activities were assigned to individual commits.</li> <li>Source code density data (metrics) for each commit in each of the nine projects as a separate dataset.</li> <li>Code: a snapshot of the repository that holds all code, models, notebooks, and pre-computed results, for utmost reproducibility (the code is written in R).</li> </ul>
Estimating red fox density using non-invasive genetic sampling and spatial capture–recapture modelling
<p>Data and scripts for our paper:</p> <p>Lindsø, L.K., Dupont, P., Rød-Eriksen, L. <em>et al.</em> Estimating red fox density using non-invasive genetic sampling and spatial capture–recapture modelling. <em>Oecologia</em> <strong>198</strong>, 139–151 (2022). https://doi.org/10.1007/s00442-021-05087-3</p>
Velocity and density profiles from thermodynamic modeling
<p>Seismic velocity and density profiles for various mantle temperature and compositions using the stx08 and stx11 data bases. See README in each directory for more detailed descriptions.</p>
Data files for Peizhi Mai et al., "Robust charge-density wave correlations in the electron-doped single-band Hubbard model" (2023)
<p>Data files for "Robust charge-density wave correlations in the electron-doped single-band Hubbard model" by P. Mai, N. S. Nichols, S. Karakuzu, F. Bao, A Del Maestro, T. A. Maier, and Steven Johnston</p> <p>Preprint: https://arxiv.org/abs/2210.14930</p>
A new lithospheric density and magnetic susceptibility model of Iran, starting from high resolution seismic tomography
<p>The Iranian collisional belt formed through different geological events, some of which are still in progress, such as the convergence between Eurasian and Arabian plates, that led to the formation of a complex structure throughout the entire area. To better investigate these structures, we realize a 3D model of the lithosphere in Iran showing the density and the magnetic susceptibility distribution, obtained from a Bayesian joint gravity and magnetic field inversion, starting from a high-resolution seismic tomography (Kaviani et al., 2020). With these models we also calculate the rigidity distribution of the area. The Data Cube uploaded contains the density, magnetic susceptibility and shear modulus volumes, the Bouguer gravity field and the magnetic field, and the Moho, Curie depth and sediment base depth surfaces.</p>
Active Brownian particles in external force fields: field-theoretical models, generalized barometric law, and programmable density patterns
<p>Supplementary data for the following manuscript: Jens Bickmann, Stephan Bröker, Michael te Vrugt, Raphael Wittkowski, "Active Brownian particles in external force fields: field-theoretical models, generalized barometric law, and programmable density patterns".</p>
Clinical, Electrophysiological and E-field Modelling Evidence of High Density Transcranial Direct Current Stimulation in Motor Stroke
ClinicalTrials.gov study NCT05329818. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.