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.
5,805
datasets available to search
ShareScore release 0.9.0
Dataset results
5,805 results for “Data model”
Avoiding Ergodicity Problems in Lattice Discretizations of the Hubbard Model - Data
<p>These are the datasets used to make the figures in "Avoiding Ergodicity Problems in Lattice Discretizations of the Hubbard Model". The data was generated using Isle v0.1. Install Isle in order to run the scripts proved with the data files.</p> <p> </p>
Figure Data for the paper "Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning"
<p><strong>Data Release for Article: <em>Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning</em></strong></p> <p>This package releases a Python notebook reproducing the quantitative<br> figures featured in the research article "Mastering the Game of Stratego with <br> Model-Free Multiagent Reinforcement Learning".</p> <p><strong>Usage</strong></p> <p>The notebook can be uploaded to and executed using the<br> [Colab](https://colab.research.google.com) runtime service. <br> The Python notebook is tested against Python `3.7`.</p> <p><strong>License and disclaimer</strong></p> <p>Copyright 2022 DeepMind Technologies Limited</p> <p>All software is licensed under the Apache License, Version 2.0 (Apache 2.0); you<br> may not use this file except in compliance with the Apache 2.0 license. You may<br> obtain a copy of the Apache 2.0 license at:<br> https://www.apache.org/licenses/LICENSE-2.0</p> <p>All other materials are licensed under the Creative Commons Attribution 4.0<br> International License (CC-BY). You may obtain a copy of the CC-BY license at:<br> https://creativecommons.org/licenses/by/4.0/legalcode</p> <p>Unless required by applicable law or agreed to in writing, all software and<br> materials distributed here under the Apache 2.0 or CC-BY licenses are<br> distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,<br> either express or implied. See the licenses for the specific language governing<br> permissions and limitations under those licenses.</p> <p>This is not an official Google product.</p>
The effect of past saturation changes on noble gas reconstructions of mean ocean temperature: Model Output Data
<p>This dataset contains model output for the simulations presented in "<em>The effect of past saturation changes on noble gas reconstructions of mean ocean temperature</em>".</p> <p>The NetCDF4 files contain the following variables:</p> <ul> <li>Potential Temperature</li> <li>Salinity</li> <li>Sea-ice area</li> <li>Ar, Kr, Xe, and N<sub>2</sub> concentrations</li> <li>AMOC strength and Northern-Sourced Water fraction (only transient runs)</li> </ul>
APARENT2 Training Data and Models
<p>Processed training data for the APARENT2 model (measurements from the random MPRA and designed oligo pool originally published by Bogard et al., 2019; see https://doi.org/10.1016/j.cell.2019.04.046 for reference). This repository also contains the APARENT2 model file. For more information on the training procedure, see the <em>Genome Biology</em> article "Deciphering the impact of genetic variation on human polyadenylation using APARENT2" (https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02799-4). Two versions of the model are available:</p> <p>(a) aparent_all_libs_resnet_no_clinvar_wt_ep_5.h5: The originally trained APARENT2 model.<br> (b) aparent_all_libs_resnet_no_clinvar_wt_ep_5_var_batch_size_inference_mode_no_drop.h5: Identical weights and predictions as model (a), but the normalization layers have been set to inference mode and the dropout layers have been removed (thus making it compatible with the scrambler pipeline).</p>
Supplementary data on car company reduction target analysis and GHG emission model
<p>This dataset contains supplementary information for the Master's thesis "<em>Greenhouse gas emission coverage and reduction potentials of scope 3 emissions in car manufacturing"</em> published on 16 November 2022 at Utrecht University. The data consists of two separately files, including the car company reduction target analysis and the GHG emission calculation model that were covered in the thesis report. The supplementary data can be used in combination with the final thesis report.</p>
Datasets related to paper "Crustal structure and fault geometries of the Garhwal Himalaya, India: Insight from new high-resolution gravity data modeling and PSO inversion"
<p>The files include Satellite gravity data, topography and earthquake data used in the paper "Crustal structure and fault geometries of the Garhwal Himalaya, India: Insight from new high-resolution gravity data modeling and PSO inversion" by Chamoli A., Rana S., Dwivedi D., Pandey A.K.. This has been submitted to Journal of Geophysical Research: Solid Earth. Restrictions applied to the availability of the land gravity data set.</p>
Data for: Temperature and hygrometry of amphibian agar models in behavioral simulation and operational temperature of two forested areas
<p>We investigated how thermoregulatory behaviors affect hydro-thermoregulation in anurans, using agar models as a sampling unit, simulating four behaviors related to behavioral fever and sickness behavior. We collected data in two forest environments (Wet forest and transitional forest) in the Parque Estadual Intervales (PEI), an Integral Conservation Unit of the Atlantic Forest (24°12' - 24°25' S; 48°03 - 48°30' W). The Wet forest is a mature Atlantic Forest, and the Transitional forest is a young secondary forest adjacent to open areas. We measured operational temperatures (temperatures of inanimate objects comparable to real frog species in size and shape) of agar models across 8 replicates in the two forest environments. We also measured agar models' temperature and water loss in different behavior simulations. In each transect, we used eight sampling unit (called tetrad) that was composed of two sets of four sensor-fit agar models. One of the sets was used to collect the operational temperature of the forest areas. These agar models were fitted with a 170 cm HOBO® Data Logger (U12-008) sensor programmed to record the temperature every 15 min. The second set of tetrads was used to collect the agar models' body temperature and water loss in different behavior simulations. The behavioral simulations were defined as: (a) strong behavioral fever (SBF); (b) apathy behavior (AB); (c) single thermoregulatory event (STE); and (d) control model (CO).</p> <p>After the temperature data were collected at 0600 h, three of the agar models (SBF, AB, and STE) were moved immediately, each one according to their corresponding protocol (SBF: Warmest Neighboring Site, AB: closest shelter, mainly small burrows, or accumulations of leaf litter; STE: Alternative Warmest Neighboring site). The selection of the places was made by using a FLIR TG165 Thermal Imaging Thermometer. One hour later, at about 0700 h, and hereafter hourly, a similar procedure was repeated, but only the SBF model required movement. We placed each model within 5 cm of another in this set to ensure similar initial thermal conditions. These tetrads were left undisturbed overnight, and no manipulation occurred after 2000 h. On the next day, at 0600 h, we measured the surface temperatures of the models and immediately applied the corresponding behavioral rule. This procedure was performed hourly until 2000 h. To analyze water loss, we recorded the mass of each model at 0600 h just after measuring temperature and repeated this every two hours. We used a portable balance (A&D Newton EJ-123, 0.01g accuracy) and calculated water loss rates from the difference between the initial model mass and mass measured at each subsequent 2-hour period. We express water loss as a percentage of maximum hydration.</p>
Optimization used in in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>OptimizationResults contain the expanded input model for each data set, in the C field are the indices of the core reactions in the expanded input model used to build the multi-cell population for the 121 different parameters, A contains the indices of reaction in the multi-cell population model for the 121 runs, thresh the parameter setting for the cover and REI. The REI is given in %. So 5 REI means 0.05. While A might change because of alternative optimals when run on a different computer, C and the expanded Input model will remain unchanged.</p> <p> </p> <p>Multi_cell_population_CRC and Multi_cell_population_NM are the models obtained by scFASTCORMICS with the optimal setting for Dataset1 and Dataset2, respectively. </p> <p>Please, check the publication (scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data) for more details.</p>
Data supplement for: Validating the Nernst--Planck transport model under reaction-driven flow conditions using RetroPy v1.0
<p>This is the repository for the publication's supplementary data and plotting scripts: Validating the Nernst–Planck transport model under reaction-driven flow conditions using RetroPy v1.0.</p> <p>The dependency of the scripts can be installed using conda and pip:</p> <pre><code>conda create -n plot numpy matplotlib==3.6.1 h5py python=3.9 conda activate plot pip install palettable</code></pre> <p>To reproduce the figures, execute the files using python:</p> <pre><code>python figure03.py</code></pre> <p> </p>
Intermediate data belonging to "Process-based climate change assessment for European winds using EURO-CORDEX and global models"
<p>This dataset contains the intermediate results of Wohland (2022) that are needed to redo the analysis und produce the figures. It allows to bypass those steps that rely on access to the supercomputers at the German Climate Computing Centre (DKRZ). When using this data in academic work, please reference</p> <blockquote> <p>Jan Wohland, Process-based climate change assessment for European winds using EURO-CORDEX and global models, Environmental Research Letters (provisionally accepted on 28/11/2022), 2022</p> </blockquote> <p><strong>Using this data to reproduce results</strong></p> <p>The data can be used together with the code provided in https://github.com/jwohland/kliwist_modelchain</p> <p>In the above mentioned github repository, there is a `run_all.py` script that repeats the analysis presented in Wohland (2022). After downloading and extracting this data, you can ignore the steps under "calculations", and begin with "plots".</p> <p><strong>Underlying data</strong></p> <p>The dataset draws on output from the CMIP5, CMIP6 and EURO-CORDEX initiatives. I thank the climate modeling groups for making their data openly available. In particular, I acknowledge the World Climate Research Programme’s Working Group on Regional Climate, and the Working Group on Coupled Modelling, former coordinating body of CORDEX and responsible panel for CMIP5. I also acknowledge the Earth System Grid Federation infrastructure an international effort led by the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison, the European Network for Earth System Modelling and other partners in the Global Organisation for Earth System Science Portals (GO-ESSP). I also acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP5 and CMIP6.</p> <p><strong>Funding</strong></p> <p>This work is part of the project "The influence of climate change on wind energy site assessments – KliWiSt" funded by the German Federal Ministry for Economic Affairs and Climate Action (BMWK).</p> <p><strong>References to raw data journal articles</strong></p> <blockquote> <p>Jacob, D. <em>et al.</em> EURO-CORDEX: new high-resolution climate change projections for European impact research. <em>Reg Environ Change</em> <strong>14</strong>, 563–578 (2014).</p> </blockquote> <blockquote> <p>Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An Overview of CMIP5 and the Experiment Design. <em>Bull. Amer. Meteor. Soc.</em> <strong>93</strong>, 485–498 (2012).</p> </blockquote> <blockquote> <p>Hurtt, G. C. <em>et al.</em> Harmonization of land-use scenarios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands. <em>Climatic Change</em> <strong>109</strong>, 117–161 (2011).</p> </blockquote>
Data set for: "Model atmospheric aerosols convert to vesicles upon entry into aqueous solution"
<p>This document compiles raw data used in the aerosol to vesicle transformation study carried out by <strong>Serge Nader <em>et al.</em></strong><br> For detailed information and context, refer to the main article and its supplementary material published in ACS Earth and Space Chemistry.</p> <p>The Excel file contains data relevant to each figure in the main article and supporting information. The additional compressed file contains raw Transmission Electron Microscopy (TEM) photographs.</p>
Derived data for: 'Modelling the Time-Dependent Magnetic Fields that BepiColombo will use to Probe Down into Mercury's Mantle'
<p>Derived data for the manuscript entitled 'Modelling the Time-Dependent Magnetic Fields that BepiColombo will use to Probe Down into Mercury’s Mantle'.</p>
Considerations on premises of recent (< 120 years) sedimentation rate models with unsupported Pb210: a study case of sediment cores from mud shelf depocenters :: Supplementary data
<p>Supplementary data (Table S1) for the manuscript "Considerations on premises of recent (< 120 years) sedimentation rate models with unsupported <sup>210</sup>Pb: a study case of sediment cores from mud shelf depocenters".</p>
Processed data used for spatial analysis of DMD mouse models
<p>This repository contains seurat objects and .H5AD files that were used in the analysis described in the paper titled <strong>"Spatial transcriptomics reveal markers of histopathological changes in Duchenne muscular dystrophy mouse models"</strong> Authors: L.G.M. Heezen, T. Abdelaal, M. van Putten, A. Aartsma-Rus, A. Mahfouz and P. Spitali</p> <p>It contains datafiles obtained from spatial transcriptomics (Visium, 10x Genomics) experiments on skeletal muscle samples from two wildtypes: C57BL10 and DBA/2J and two DMD mouse models: mdx and D2-mdx. All ten weeks old male mice, 10micron thick sections of the quadriceps.</p>
GG2 - Cluster-DEPP Models and Auxiliary data
<p>This is the auxiliary file for placing 16S onto the tree built in the GG2 project. </p>
Data for: Can language representation models think in bets?
<p>The dataset contains three files: *Item_Sets.xlsx*, *Value_Questions.xlsx*, and *Bet_Questions.xlsx*. Each of the three files corresponds to each of the three benchmarks in the manuscript currently under submission to Royal Society Open Science and is also available as a preprint: <a href="https://arxiv.org/abs/2210.07519">https://arxiv.org/abs/2210.07519</a>.</p> <p>The items in Item_Sets.xlsx is used to create the other two files. Value_Questions.xlsx is used in RQ1, and Bet_Questions.xlsx is used in both RQ2 and RQ3.</p>
Data for: Free-Breathing Myocardial T1 Mapping using Inversion-Recovery Radial FLASH and Motion-Resolved Model-Based Reconstruction (Part 2/2)
<p>Magnetic Resonance Imaging measurement data used in our paper about "Free-Breathing Myocardial T1 Mapping using Inversion-Recovery Radial FLASH and Motion-Resolved Model-Based Reconstruction". The data is provided in a file format used by the BART toolbox (DOI: <a href="http://doi.org/10.5281/zenodo.592960">10.5281/zenodo.592960</a>)</p>
Data from: Microclimate-based species distribution models in complex terrain indicate widespread cryptic refugia under climate change
<p class="MsoNoSpacing"><i>Aim: </i>Species' climatic niches may be poorly predicted by regional climate estimates used in species distribution models (SDMs) due to microclimatic buffering of local conditions. Here, we compare SDMs generated using a locally validated below-canopy microclimate model to those based on interpolated weather station data at two spatial scales to determine the effects of scale, topography, and forest cover on potential future ground-level warming and species distributions.</p> <p class="MsoNoSpacing"><i>Location:</i> Great Smoky Mountains National Park (2090 km<sup>2</sup>; NC, TN, USA)</p> <p class="MsoNoSpacing"><i>Time period: </i>1970 – 2006</p> <p class="MsoNoSpacing"><i>Major taxa:</i> Vascular plant species of the Southern Appalachians</p> <p class="MsoNoSpacing"><i>Methods:</i> We compared the fit and predictions of SDMs generated using a database of plant occurrences and three climate models: macroclimate (1 km, WorldClim), fine-scale (30 m) interpolation of macroclimate with elevation, and fine-scale below-canopy microclimate from a ground-level sensor network.</p> <p class="MsoNoSpacing"><i>Results: </i>We found that, although SDM fit was similar across models, microclimate-derived SDMs predicted substantially greater species persistence with 4 °C of regional warming, with a difference of 50% of the species pool in some areas. Microclimate SDMs predicted higher stability of mid-elevation species, particularly in thermally buffered areas near streams, and critically, less change in species composition at high elevation. In contrast, predictions of macroclimate and interpolation models were similar despite improved resolution.</p> <p class="MsoNoSpacing"><i>Main conclusions:</i> Our results demonstrate that careful selection of climate drivers, including local near-ground validation rather than interpolation, is critical for projecting distributions. They also suggest that some species at risk from climate change might persist, even with 4 °C of macroclimate warming, in cryptic refugia buffered by microclimate, pointing to the roles of forest cover and topography in explaining slower-than-expected changes in understory communities. However, certain species, such as those currently occurring on low-elevation ridges that are sensitive to atmospheric changes, may be at more risk than macroclimate or interpolated SDMs suggest.</p> <p class="MsoNoSpacing"> </p>
Data supplement for "Nonreciprocity induces resonances in two-field Cahn-Hilliard model".
<p>Mathematica notebooks that support the weakly nonlinear analysis and the codes that create all figures in "Nonreciprocity induces resonances in two-field Cahn-Hilliard model".</p> <p>".</p>
Lidar and model data for manuscript submitted to GRL "First Simultaneous Observation of Secondary and Tertiary Gravity Waves by Lidar and Investigation with HIAMCM simulations"
<p>This dataset contains the lidar and model data used in the manuscript submitted GRL titlled '<strong>First Simultaneous Observation of Secondary and Tertiary Gravity Waves by Lidar and Investigation with HIAMCM simulations'</strong></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.