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”
Testing the trade balance model_ raw data_NP
Open the record for dataset details and reuse information.
Model data for normal modes of poloidal field line oscillations
Open the record for dataset details and reuse information.
Idealized estuary model data
<p>Idealized estuaries with different channel dimensions and forcing conditions</p>
Data for "Richardson model with complex level structure and spin-orbit coupling for hybrid superconducting islands: Stepwise suppression of pairing and magnetic pinning"
Open the record for dataset details and reuse information.
Data and Code associated to the paper "Quantum simulation of the tricritical Ising model in tunable Josephson junction ladders"
<p>Modern hybrid superconductor-semiconductor Josephson junction arrays are a promising platform for analog quantum simulations. Their controllable and non-sinusoidal energy/phase relation opens the path to implement nontrivial interactions and study the emergence of exotic quantum phase transitions. Here, we propose the analysis of an array of hybrid Josephson junctions defining a 2-leg ladder geometry for the quantum simulation of the tricritical Ising phase transition. This transition provides the paradigmatic example of minimal conformal models beyond Ising criticality and its excitations are intimately related with Fibonacci non-Abelian anyons and topological order in two dimensions. We study this superconducting system and its thermodynamic phases based on bosonization and matrix-product-states techniques. Its effective continuous description in terms of a three-frequency sine-Gordon quantum field theory suggests the presence of the targeted tricritical point and the numerical simulations confirm this picture. Our results indicate which experimental observables can be adopted in realistic devices to probe the physics and the phase transitions of the model. Additionally, our proposal provides a useful one-dimensional building block to design exotic topological order in two-dimensional scalable Josephson junction arrays.</p>
DeepGO-SE protein function prediction model data
<p>Data for training and running DeepGO-SE protein function prediction model</p>
Fig. 4 in Data from: Dealing with uncertainty in landscape genetic resistance models: a case of three co-occurring marsupials
Fig. 4 View of the tupe localitu of Neusticomys vossi sp. nov
Data from 'Modeling and Solving Framework for Tactical Maintenance Planning Problems with Health Index considerations'
<p>Datasets used in the manuscript 'Modeling and Solving Framework for Tactical Maintenance Planning Problems with Health Index considerations'</p> <p>Format: CPLEX .dat data files</p>
Data Set for Predicting the Performance of ATL Model Transformations Based on Generated Models
<p>Predicting the execution time of model transformations can help to understand how a transformation reacts to a given input model without creating and transforming the respective model.</p> <p>In our previous data set (https://doi.org/10.5281/zenodo.8385957), we have documented our experiments in which we predict the performance of ATL transformations using predictive models obtained from training linear regression, random forest and support vector regression. As input for the prediction, our approach uses a characterization of the input model. In these experiments, we only used data from real models.</p> <p>However, a common problem is that transformation developers do not have enough models available to use such a prediction approach. Therefore, in a new variant of our experiments, we investigated whether the three considered machine learning approaches can predict the performance of transformations if we use data from generated models for training. We also investigated whether it is possible to achieve good predictions with smaller training data. The dataset provided here offers the corresponding raw data, scripts, and results.</p> <p>A detailed documentation is available in documentaion.pdf.</p>
FESOM model data used in the study on the buffer zone for great salinity anomaly
<p>Model data used in the study on the buffer zone for great salinity anomaly</p>
RAW DATA for "Kalium channelrhodopsins effectively inhibit neurons in the small model animals"
Open the record for dataset details and reuse information.
The code and data for paper "Reassessing Java Code Readability Models with a Human-Centered Approach"
<p>The code and data for paper "Reassessing Java Code Readability Models with a Human-Centered Approach".<br> Please refer to README.md for detailed instructions.</p>
The simulation data of paper "Modeling the inner part of the jet in M87: confronting jet morphology with theory"
Open the record for dataset details and reuse information.
Data from: Assessing Electrogenetic Activation via a Network Model of Biological Signal Propagation
<p>Simulation data from Assessing Electrogenetic Activation via a Network Model of Biological Signal Propagation, doi: 10.3389/fsysb.2024.1291293</p> <p>There are 10 csv files per network type, each dataset contains the Timestep, Node, Strain, Inducer Concentration/Duration, and Node weights for the timecourse of the simulation. </p>
FESOM-REcoM model data: 21st-century environmental change decreases habitat overlap of Antarctic toothfish (Dissostichus mawsoni) and its prey
<p>This data set belongs to "<strong>21st-century environmental change decreases habitat overlap of Antarctic toothfish (Dissostichus mawsoni) and its prey</strong>" by Cara Nissen, Jilda Alicia Caccavo and Anne L. Moree (accepted for publication in "Global Change Biology")</p> <p>Contact: cara.nissen@colorado.edu</p> <p>The data provided here are post-processed from the raw FESOM1.4-REcoM2 model output which can be obtained <br>at the World Data Center for Climate (WDCC): <a href="https://www.wdc-climate.de/ui/project?acronym=HighRes_highLat_SO">https://www.wdc-climate.de/ui/project?acronym=HighRes_highLat_SO</a></p> <p>simA (historical simulation): <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_hist_vA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_hist_vA_vC</a><br>simA-ssp126: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s126_vA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s126_vA_vC</a><br>simA-ssp245: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s245_vA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s245_vA_vC</a><br>simA-ssp370: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s370_vA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s370_vA_vC</a><br>simA-ssp585: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s585_vA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s585_vA_vC</a><br>simB (control simulation): <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_B_1921_cA_cC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_B_1921_cA_cC</a></p> <p>The raw model output was first post-processed with <strong>MASTER_toothfish_postprocessing_AGI_save_netcdf_files_monthly_with_drift_correction.ipynb</strong> to get fields of in-situ temperature (t_insitu) and partial pressure of oxygen (pO2) on the regular grid used in this study (see <strong>Mesh_ancillary_information_v20220919.nc</strong>).</p> <p>Subsequently, these data are post-processed with <strong>reduce_depth_levels_drift_corr_files.sh</strong>. </p> <p>2-dimensional distribution data of Antarctic Toothfish prey used in this study can be accessed via<br><a href="https://doi.org/10.5281/zenodo.10598488">https://doi.org/10.5281/zenodo.10598488</a></p> <p><strong>The following files are provided here: </strong></p> <p><strong>Monthly climatological pO2 and t_insitu 1995-2014</strong> (used to compute preferred temperature, pO2 threshold and critical AGI of each species):<br>- pO2_fesom_simA_monthly_clim_1995_2014_v2.nc<br>- t_insitu_fesom_simA_monthly_clim_1995_2014_v2.nc</p> <p><strong>Annual pO2 and t_insitu for the historical period 1995-2014</strong>:<br>- pO2_fesom_historical_1995_2014_annual_mean_AGImesh.tar.gz<br>- t_insitu_historical_1995_2014_annual_mean_AGImesh.tar.gz</p> <p><strong>Drift-corrected annual pO2 and t_insitu 2091-2100 for four emission scenarios</strong>: <br>- pO2_fesom_ssp126_2091_2100_drift_corrected_annual_mean_AGImesh.tar.gz<br>- pO2_fesom_ssp245_2091_2100_drift_corrected_annual_mean_AGImesh.tar.gz<br>- pO2_fesom_ssp370_2091_2100_drift_corrected_annual_mean_AGImesh.tar.gz<br>- pO2_fesom_ssp585_2091_2100_drift_corrected_annual_mean_AGImesh.tar.gz<br>- t_insitu_fesom_ssp126_2091_2100_drift_corrected_annual_mean_AGImesh.tar.gz<br>- t_insitu_fesom_ssp245_2091_2100_drift_corrected_annual_mean_AGImesh.tar.gz<br>- t_insitu_fesom_ssp370_2091_2100_drift_corrected_annual_mean_AGImesh.tar.gz<br>- t_insitu_fesom_ssp585_2091_2100_drift_corrected_annual_mean_AGImesh.tar.gz</p> <p><strong>Sensitivity to chosen future time period / Drift-corrected annual pO2 and t_insitu 2081-2100 and 2098-2100 for the highest-emission scenario SSP5-8.5</strong>: <br>- pO2_fesom_ssp585_2081_2100_drift_corrected_annual_mean_AGImesh.tar.gz<br>- pO2_fesom_ssp585_2098_2100_drift_corrected_annual_mean_AGImesh.tar.gz<br>- t_insitu_fesom_ssp585_2081_2100_drift_corrected_annual_mean_AGImesh.tar.gz<br>- t_insitu_fesom_ssp585_2098_2100_drift_corrected_annual_mean_AGImesh.tar.gz</p> <p><strong>Attributing change to warming and deoxygenation / Drift-corrected annual pO2 2091-2100 at clim. t_insitu for the highest-emission scenario SSP5-8.5</strong>:<br>- pO2_fesom_ssp585_2091_2100_drift_corrected_at_clim_t_insitu_annual_mean_AGImesh.tar.gz</p> <p><strong>Information on model drift</strong> (used to correct the above files): <br>- oxygen_fesom_simB_1995_2014_2091_2100_monthly.tar.gz<br>- pO2_fesom_simB_1995_2014_2091_2100_monthly.tar.gz<br>- salinity_fesom_simB_1995_2014_2091_2100_monthly.tar.gz<br>- t_insitu_fesom_simB_1995_2014_2091_2100_monthly.tar.gz</p>
Raw data for the article: Two Sides of The Same Coin: Normal and Tumoral Stem Cells, The Relevance of In Vitro Models and Therapeutic Approaches: The Experience with Zika Virus in Nervous System Development and Glioblastoma Treatment
<p>Neural stem cells (NSCs) were described for the first time more than two decades ago for their ability to differentiate into all neural cell lineages. The isolation of NSCs from adults and embryos was carried out by various laboratories and in different species, from mice to humans. Similarly, no more than two decades ago, cancer stem cells were described. Cancer stem cells, previously identified in hematological malignancies, have now been isolated from several solid tumors (breast, brain, and gastrointestinal compartment). Though the origin of these cells is still unknown, there is a wide consensus about their role in tumor onset, propagation and, in particular, resistance to treatments. Normal and neoplastic neural stem cells share common characteristics, and can thus be considered as two sides of the same coin. This is particularly true in the case of the Zika virus (ZIKV), which has been described as an inhibitor of neural development by specifically targeting NSCs. This understanding prompted us and other groups to evaluate ZIKV action in glioblastoma stem cells (GSCs). The results indicate an oncolytic activity of this virus vs. GSCs, opening potentially new possibilities in glioblastoma treatment.</p>
Model data for "Factors Modulating Variability of Eddy Kinetic Energy in the Southern Ocean from Idealized Simulations" "
<p>This dataset contains the all the idealized simulations with different topographic features.</p>
MT data file and output resistivity model file
Open the record for dataset details and reuse information.
Lab experiment data and ILS model matlab script
<p>The uploaded files include dataset of lab experiments for thermal property estimation in thawed and frozen quartz sand, ice, and dry quartz sand. </p> <p>The temperature breakthrough curve fitting is completed by the model described in the Matlab script. </p>
Data Supporting High-Level to Low-Level Requirements Coverage Reviewing with Large Language Models
Open the record for dataset details and reuse information.
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.