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”
Dataset for "The magnetized (2+1)-dimensional Gross-Neveu model at finite density"
<p>We perform a lattice study of the (2+1)-dimensional Gross-Neveu model in a background magnetic field <em>B</em> and at non-zero chemical potential <em>μ</em>. The complex-action problem arising in our simulations using overlap fermions is under control. For <em>B</em>=0 we observe a first-order phase transition in <em>μ</em> even at non-vanishing temperatures. Our main finding, however, is that the rich phase structure found in the limit of infinite flavor number <em>N</em>f is washed out by the fluctuations present at <em>N</em>f=1. We find no evidence for inverse magnetic catalysis, i.e., the decrease of the order parameter of chiral symmetry breaking with <em>B</em> for <em>μ</em> close to the chiral phase transition. Instead, the magnetic field tends to enhance the breakdown of chiral symmetry for all values of <em>μ</em> below the transition. Moreover, we find no trace of spatial inhomogeneities in the order parameter. We briefly comment on the potential relevance of our results for QCD.</p> <p>If you use this data, please cite the corresponding paper:<br> https://doi.org/10.48550/arXiv.2304.14812 (or better the not-yet-existing published version)</p>
3D density models of the Los Humeros and Acoculco geothermal fields, Mexico.
<p>The GEMex project addresses different challenges in the development of Enhanced Geothermal Systems (EGS) and Superhot Geothermal Systems (SHGS) in the Trans-Mexican Volcanic Belt. Although they are located in similar tectonic settings, the geothermal conditions in Acoculco and Los Humeros differ and they can be categorized as an EGS and a SHGS system, respectively. The Los Humeros field is currently under conventional exploitation. North of the current production area, temperatures higher than 380°C are expected. The Acoculco site presents temperatures >300°C at a depth of 2 km, but a reservoir has not been identified. The main goal of this work is to visualize and characterize the reservoir conditions using gravity data. To accomplish this, we processed data from a total of 344 gravity stations at Los Humeros and 84 stations at Acoculco. The datasets contain the 3D density model of the Los Humeros and Acoculco geothermal fields as density contrasts values in g/cm³. The background density is 2.67 g/cm³.</p>
Database from: Developing a lateral topographic density model for Brazil.
<p>This dataset is part of the article entitled "DEVELOPING A LATERAL TOPOGRAPHIC DENSITY MODEL FOR BRAZIL".</p> <p>This dataset includes the topographic Lateral Topographic Density model for Brazil (LTDBrasil) and standard deviations (sdLTDBrasil), in Kg/m³, with 30 arc-seconds grid spacing.</p> <p>The files are in *tif and *.tfw format.</p> <p>Reference: Medeiros D.F., Marotta G.S., Yokoyama E., Franz I.B., Fuck R.A. 2021. Developing a lateral topographic density model for Brazil. Journal of South American Earth Sciences, v. 110, p. 103425. https://doi.org/10.1016/j.jsames.2021.103425</p>
DWCox: A Density-Weighted Cox Model for Outlier-Robust Prediction of Prostate Cancer Survival
<p>This package, <strong>DWCox</strong>, implements a <strong>d</strong>ensity-<strong>w</strong>eighted <strong>Cox</strong> regression model that is more robust against outliers in the training data. DWCox gives more accurate predictions than the standard Cox regression on prostate cancer survival, especially in cases where the training data are expected to contain a lot of outliers. More details can be found in our paper (coming soon) and the README file inside this package.</p>
Modelled hydrodynamic profiles and salmon louse larval densities at Norwegian salmon farms
<p>Data compiled for use by the PreventLice web app, a decision support tool intended to help Norwegian salmon farmers avoid salmon louse infestations: <a href="https://havforskningsinstituttet.shinyapps.io/preventlice">https://havforskningsinstituttet.shinyapps.io/preventlice</a></p> <p>Each file contains the relevant data for a registered salmonid farm in Norway, identified by its locality number according to the Norwegian <a href="https://sikker.fiskeridir.no/akvakulturregisteret/web/sites">Aquaculture Registry</a>. A total of 1023 localities are included in version 1.0.0.</p> <p>The data are in long rectangular format, with each row corresponding to a single depth interval on a single date. Each row provides variables for locality number ("loc"), date ("date"), depth (m, "depth"), daily mean temperature (°C, "meanTemp"), daily mean salinity (ppt, "meanSal"), daily mean current speed (ms<sup>-1</sup>, "meanCurrSpd"), daily 95th percentile current speed (ms<sup>-1</sup>, "95PercCurrSpd"), daily salmon louse infestation pressure (copepodids m<sup>-3</sup>, "meanCopDensity"), and daily mean significant wave height (m, "SignWaveHeight").</p> <p>Temperature, salinity and current speeds are taken from the NorFjords-160 model (<a href="https://doi.org/10.1016/j.ecss.2020.107028">Dalsøren et al. 2020</a>), a finer-scale update of the NorKyst-800 model (<a href="https://doi.org/10.1007/s10236-020-01378-0">Asplin et al. 2020</a>). Wave height data are taken from the MyWaveWAM800m Norwegian coastal wave forecasting system (<a href="https://thredds.met.no/thredds/fou-hi/mywavewam800.html">Norwegian Meteorological Institute</a>). Salmon louse copepodid densities are estimated by coupling louse biology and behaviour parameters with hydrodynamic predictions from NorKyst-800 (<a href="https://doi.org/10.1371/journal.pone.0201338">Myksvoll et al. 2018</a>).</p>
Unit cells and resummed interactions for the calculations in "Systematic Analysis of Crystalline Phases in Bosonic Lattice Models with Algebraically Decaying Density-Density Interactions"
<p>This directory contains all the unit cells with the respective resummed interactions used for the optimisation procedure to obtain the results in the work "Systematic Analysis of Crystalline Phases in Bosonic Lattice Models with Algebraically Decaying Density-Density Interactions[1]".</p> <p>To get an overview of the organization of the directory and a description of the data we recommend the README file.</p> <p>[1]: J. A. Koziol et al., Systematic Analysis of Crystalline Phases in Bosonic Lattice Models with Algebraically Decaying Density-Density Interactions, <a href="https://10.21468/SciPostPhys.14.5.136">10.21468/SciPostPhys.14.5.136</a>, 2023</p>
Lake Sunapee Gloeotrichia echinulata density near-term hindcasts from 2015-2016 and meteorological model driver data, including shortwave radiation and precipitation from 2009-2016
Hindcasts were generated for density of Gloeotrichia echinulata, a toxin-producing cyanobacterium, at a nearshore site (South Herrick Cove) in Lake Sunapee, NH, USA, from May-October in 2015 and 2016 using several different Bayesian state-space models as part of a Global Lake Ecological Observatory Network working group project (Lofton et al. 20XX). Hindcasts were produced for one-week to four-week forecast horizons. Models ranged in complexity from a random walk to dynamic linear models with up to two environmental covariates. A subset of the model meteorological driver data for calibration and hindcasting was downloaded from the North American Land Data Assimilation System (NLDAS-2; https://ldas.gsfc.nasa.gov/nldas/) and the Parameter-elevation Regressions on Independent Slopes Model (PRISM; http://www.prism.oregonstate.edu/) for Lake Sunapee, New Hampshire, USA. The model driver data derived from NLDAS-2 data are daily summaries of solar radiation on G. echinulata sampling days from 2009-2016. The model driver data derived from PRISM data are daily sums of precipitation on G. echinulata sampling days from 2009-2016. All other model driver data are also published on the Environmental Data Initiative repository and are specified in the Notes and Comments of this data publication. All code to import data, calibrate models, and generate and analyze hindcasts are available on Github at https://github.com/GLEON/Bayes_forecast_WG/tree/eco_apps_release.
Screen captures illustrating and depicting EM densities of the ACE2 (PDB ID 6CS2) model
<p>Depicting of cryo-EM density maps using the provided python script option of the first example of our paper (see links). The lack of sufficient density for a few of the outer loops is quite obvious from these images.</p>
Plasma density turbulence obtained from a Hasegawa-Wakatani drift-wave turbulence model within the BOUT++ framework
<p>A Hasegawa-Wakatani drift-wave turbulence model [1] within the BOUT++ framework [2] was used to generate a set of turbulent plasma density profiles. Drift-wave turbulence is thought to be the dominant mechanism responsible for the anomalous transport observed in the edge of tokamak plasmas.</p> <p>The motivation to generate this rather large set of density profiles was to properly study the influence of plasma density fluctuations on traversing microwaves which requires ensemble-averaging to get statistically relevant results.</p> <p>The density data is given in normalized units (1.0 would correspond to 100 % fluctuation degree) on a mean-free background and the spatial coordinates are given in units of the ion Larmor radius.</p> <p>The form of equations used was the un-modified Hasegawa-Wakatani model [3]. Input parameters were kappa = 1.0 (normalised background density gradient), alpha = 0.5 (adiabaticity parameter), and diffusion constant D=1e-2.</p> <ol> <li>Wakatani, M., Hasegawa, A. (1984). <em>A collisional drift wave description of plasma edge turbulence.</em> Phys. Fluids <strong>27</strong>(3), 611. doi:10.1063/1.864660</li> <li>Dudson, B. <em>et al.</em> (2009). <em>BOUT++: A framework for parallel plasma fluid simulations</em>. Comp. Phys. Comm. <strong>180</strong>(9), 1467. doi:10.1016/j.cpc.2009.03.008</li> <li>Numata, R., Ball, R., & Dewar, R. L. (2007). <em>Bifurcation in electrostatic resistive drift wave turbulence</em>. Phys. Plasmas <strong>14</strong>(10), 102312. doi:10.1063/1.2796106</li> </ol>
Interim model output of estimated bare peat/erosion density in Aberdeenshire and Angus at 1m resolution.
<p>This dataset contains the estimated unit density of peat erosion accross Aberdeenshire and Angus at 1m resolution. These outputs are derived from high resolution aerial imagery using the PeatNet pipeline for predicting the location and extent of peatland degradation features. This output was created to estimate the particulate organic carbon (POC) emissions from eroding peat based on multiple scenarios.</p>
Electronic Supplement / Data Archive for "Comparison of a Neutral Density Model With the SET HASDM Density Database"
<p>These files provide supplemental data to accompany the paper "Comparison of a Neutral Density Model With the SET HASDM Density Database,” submitted to <em>Space Weather, </em>with manuscript number 2021SW002888. Details are provided in the file DataArchiveDocumentation.pdf.</p>
Glaciological data (point mass balance, SWE, snow depth, bulk snow density, modelled runoff) from Werenskioldbreen (Svabard) 2009-2020
<p>This repository contains supporting data associated to the manuscript to <em>Earth System Science Data: </em></p> <p><strong>Ignatiuk D., Błaszczyk M., Budzik T., Grabiec M., Jania J., Kondracka M., Laska M., Małarzewski Ł., Stachnik Ł. A decade of glaciological and meteorological observations in the High Arctic (Werenskioldbreen, Svalbard)</strong></p> <p>In 2009-2020, 9 ablation stakes were installed on the Werenskioldbreen.<strong> </strong>Based on the data collected, the following glaciological variables are available for Werenskioldbreen: annual and seasonal point ablation and accumulation, snow cover depth, bulk snow density and SWE (snow water equivalent) at the measuring points and modelled total runoff from the surface ablation. </p>
Fortran code used in 'A fractal model for effective excess charge density in variably saturated fractured rocks'
<p>This code is uploaded to support the research study 'A fractal model for effective excess charge density in variably saturated fractured rocks' by L. Guarracino and D. Jougnot (submitted to JGR: Solid Earth, 2021).</p> <p>Files:<br> a) Fortran source code (qvfrac.f) for estimating the effective excess charge density in fractured rocks. The calculation is based on model equations described in the research study.<br> b) Input data (network1.dat) to calculate the effective excess charge density for fracture network 1 described in Section 3 (Figure 5a).</p>
On the estimation of landslide intensity, hazard and density via data-driven models.
<p>The geographic prediction of landslide occurrence is undertaken by assessing whether a slope may be stable or unstable. In other words, current practices treat slopes where a single landslide occurred in the same way as slopes where many landslides occurred. At the slope scale, this procedure inevitably underestimates the effect of multiple landslides.<br> Here we model the number of landslides per slope instead. Then, thanks to the close relation that the number of failures shows with respect to landslide size, we convert the estimated number of landslides into estimated landslide areas. Ultimately, we also estimate the expected proportion of a slope affected by landslides. This framework is more informative than the stable/unstable paradigm and may help landslide risk mitigation strategies.</p>
Fig. 2 in Some Factors Behind Density Dynamics Of Bat Flies (Diptera, Nycteribiidae) - Ectoparasites Of The Boreal Chiropterans: Omitted Predictors And Hurdle Model Identification
Fig. 2. Observed (bars) and expected (PMF) host infestation by Nycteribiidae bat flies: before/after (top/bottom) host mating; host females/males (left/right). No zero truncation and the used categorisation (pooled both host species and bat flies species) are the reasons of relatively bad fit to Poisson distribution.
Fig. 3 in Some Factors Behind Density Dynamics Of Bat Flies (Diptera, Nycteribiidae) - Ectoparasites Of The Boreal Chiropterans: Omitted Predictors And Hurdle Model Identification
Fig. 3. Observed (all kinds of dots) and expected (lines: y = exp (m+Acos (2pi (x–c)/36 — f) or y = b0exp (–b1x)) seasonal density dynamics of Nycteribiidae bat flies. Filled circles and thin lines — normally infested males; open circles and solid lines — normally infested females; double crosses and dashed lines — super-infested males; crosses and dashed lines — super-infested females. N o t e: Infested host only.
Dataset to "Hydrogen in tungsten trioxide by membrane photoemission and density functional theory modeling"
<p>Dataset to "Hydrogen in tungsten trioxide by membrane photoemission and density functional theory modeling" as published in Physical Review B, 103 (2021), 205304</p>
Dataset for: Using the quasi-chemical model beyond the quadruplet approximation: Density and Viscosity Models for Molten Salt Fuel Systems
<p>Contains data plotted in the figures of the manuscript with the same title (submitted, 2021). </p>
MESA histories for "Characterizing Observed Extra Mixing Trends in Red Giants using the Reduced Density Ratio from Thermohaline Models"
<p>This repository provides MESA history files for each of the stellar models in the publication "Characterizing Observed Extra Mixing Trends in Red Giants using the Reduced Density Ratio from Thermohaline Models". The MESA version used was stable release version 21.12.21. Runs are organized into tarballs according to the thermohaline mixing prescription used:</p> <ul> <li>BGS13 = Brown, Garaud, Stellmach 2013</li> <li>Kipp1e-1 = Kippenhahn with alpha_th = 0.1</li> <li>Kipp2 = Kippenhahn with alpha_th = 2</li> <li>Kipp7e2 = Kippenhahn with alpha_th = 700</li> </ul> <p>and are additionally grouped according to the stellar mass (M = 0.9, 1.1, 1.3, 1.5, 1.7 in units of Msol). Within each tarball is a number of run directories which contain a LOGS/history.data file from the MESA run. The subdirectories in the tarball contain runs at various metallicities; conversion between Z (MESA input) and [Fe/H] (paper reported value) are found in Table 2 of the manuscript.<br> <br> Inlists and information for recreating these MESA simulations can be found online at the paper's github repository: <a href="https://github.com/afraser3/Empirical-Magnetic-Thermohaline">https://github.com/afraser3/Empirical-Magnetic-Thermohaline</a> (a copy of the code from this Github repository is located in this Zenodo repository in: Empirical-Magnetic-Thermohaline-main.zip)</p>
Counting animals in aerial images with a density map estimation model
<p>Animal abundance estimation is increasingly based on drone or aerial survey photography. Manual post-processing has been used extensively, however, volumes of such data are increasing, necessitating some level of automation, either for complete counting or as a labour-saving tool. Any automated processing can be challenging when using such tools on species that nest in close formation such as <em>Pygoscelis</em> penguins. We present here a customized CNN-based density map estimation method for counting of penguins from low-resolution aerial photography. Our model, an indirect regression algorithm, performed significantly better in terms of counting accuracy than standard detection algorithm (Faster RCNN) when counting small objects from low-resolution images and gave an error rate of only 0.8 percent. Density map estimation methods as demonstrated here can vastly improve our ability to count animals in tight aggregations, and demonstrably improve monitoring efforts from aerial imagery. </p>
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.