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5,805 results for “Data model”
Patient records from the simulation model and raw data
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Preprocessed data and model for scCaT
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Model input data, simulation output data and processed data presented in "Modelling the three-dimensional, diagnostic anisotropy field of an ice rise"
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Fig. 1 A in Phylogeography and potential glacial refugia of terrestrial gastropod Faustina faustina (Rossmässler, 1835) (Gastropoda: Eupulmonata: Helicidae) inferred from molecular data and species distribution models
Fig. 1 A phylogenetic tree of mitochondrial COI sequences of F. faustina associated with the map of the haplotype distribution in the studied area. Only topology of Bayesian tree is shown. Numbers at nodes of each clade
Fig. 6 in Phylogeography and potential glacial refugia of terrestrial gastropod Faustina faustina (Rossmässler, 1835) (Gastropoda: Eupulmonata: Helicidae) inferred from molecular data and species distribution models
Fig. 6 Maps of predicted climatic suitability for F. faustina under present (a) and paleo periods (b–g). Warmer colors indicate higher climatic suitability. Black dots indicate present records used in species distribution
Supplementary Data: Code, Input Data and Model data: PyPSA-Eur: An Open Optimisation Model of the European Transmission System
<p>Supplementary Data (preliminary version)</p> <p>PyPSA-Eur: An Open Optimisation Model of the European Transmission System</p> <p>Authors: J. Hörsch, F. Hofmann, D. Schlachtberger, T. Brown</p> <p>and</p> <p>The role of spatial scale in joint optimisations of generation and transmission for European highly renewable scenarios</p> <p>Authors: J. Hörsch, T. Brown</p> <p>The files in this record contain the scripts to build a <a href="http://pypsa.org/">PyPSA</a> model of the European Electricity System including renewable feed-in from wind, solar and hydro installations derived from reanalysis weather data satellite irradiation. The model PyPSA-Eur is described in the above publication.</p> <p><strong>Scripts</strong></p> <p>To use the scripts, you need the following free software Python libraries:</p> <ul> <li><a href="https://github.com/PyPSA/PyPSA">PyPSA</a> for the modelling framework</li> <li><a href="https://github.com/FRESNA/vresutils">vresutils</a> for various helper functions to build the model instance</li> <li><a href="https://github.com/FRESNA/atlite">atlite</a> to process weather data into power system data</li> <li><a href="https://snakemake.readthedocs.io/en/latest/">snakemake</a> to organise the execution of the software</li> </ul> <p>and other standard libraries from the <a href="https://pypi.python.org/pypi">Python Package Index</a> (PyPI), such as pandas, pyomo, countrycode, etc.</p> <p>snakemake requires that all code runs with Python version 3. The code setup is known to work with the following versions: PyPSA 0.12.0, pandas 0.21.1, numpy 0.14.0, scipy 0.19.1, pyomo 5.2. You may need to downgrade your libraries to these versions for the scripts to work.</p> <p>The Python scripts in this repository (in the directory scripts/) are released under the <a href="https://www.gnu.org/licenses/gpl-3.0.en.html">GNU General Public Licence Version 3.0</a> (GPL 3.0).</p> <p>The scripts build_*.py process all raw input data into a form where it can be used in the model.</p> <p>base_network.py creates the initial PyPSA network topology.</p> <p>add_electricity.py adds generators and storage units to the models, it generates the detailed resolved model described in the PyPSA-Eur paper.</p> <p>simplify_network.py removes stub ac-buses from network topology and simplifies long dc lines.</p> <p>cluster_network.py creates clustered representations of the electricity network for a given number of buses following the topology described in the "spatial scale" paper.</p> <p>prepare_network.py adds parameters like the CO2 limit and the transmission expansion volume relevant for the optimization to the model.</p> <p>All scripts are managed with the <a href="http://snakemake.readthedocs.io/en/latest/">snakemake</a> workflow management tool.</p> <p>To run the scripts, adjust the parameters in config.yaml and cluster.yaml to your local configuration. Then simply execute</p> <pre><code>snakemake</code></pre> <p>for the rule you want to run.</p> <p><strong>Data</strong></p> <p>The input data include:</p> <ul> <li>Electricity sector data</li> <li>Topology derived from the analysis of an extract of the <a href="https://www.entsoe.eu/data/map/">ENTSO-E online map</a> using <a href="https://github.com/bdw/GridKit">GridKit</a> .</li> <li>A cost database with literature sources.</li> </ul> <p> </p>
Code and data for: A computational model for driver's cognitive state, visual perception and intermittent attention in a distracted car following task
<p>A source code and data dump for analyses of the article "A computational model for driver’s cognitive state, visual perception and intermittent attention in a distracted car following task"</p> <p>Code is under GNU AGPL-v3. Data under CC-BY-4.0</p> <p>Versioned code is available at https://gitlab.com/mulsimco/follow17 and https://gitlab.com/mulsimco/cfmodels</p> <p>See README.md in follow17 for usage.</p>
model data
<p>Model results that are used to draw the Figs. 1-5 in our paper.</p>
Uncertainty Quantification in Multivariate Mixed Models for Mass Cytometry Data (Processed Data)
<p>Processed data computed using R packages <a href="https://christofseiler.github.io/CytoGLMM">CytoGLMM</a> and <a href="https://christofseiler.github.io/cytoeffect">cytoeffect</a>. Raw data available <a href="http://flowrepository.org/id/FR-FCM-ZY3Q">here</a>.</p>
The "lastfm" data set used in the article "A comparative study of social network models: Network evolution models and nodal attribute models"
<p>This is the "lastfm" network used in the article:</p> <p>Toivonen, R., Kovanen, L., Kivelä, M., Onnela, J. P., Saramäki, J., & Kaski, K. (2009). A comparative study of social network models: Network evolution models and nodal attribute models. Social networks, 31(4), 240-254.</p> <p>doi:10.1016/j.socnet.2009.06.004</p> <p>The data set is described in the article. Please cite the original article when using this data set.</p> <p>Format of the data set is an edge list, where row in the file is an edge connecting the two nodes indicated by the two numbers separated by a whitespace. Each node number corresponds to a single account in the website.</p> <p>The original data in which this network is based on was licensed under the "Creative Commons Attribution-NonCommercial-ShareAlike 2.0 UK: England & Wales" licese, and accordinly this data set uses the same license. License available at https://creativecommons.org/licenses/by-nc/2.0/uk/</p>
Kelp Farm N/C Content Data and Model
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Data for Global Convection-Permitting Model Improves Subseasonal Forecast of Plum Rain around Japan
<p>Data and plot scripts for this manuscript <strong>"<span>Global Convection-Permitting Model Improves Subseasonal Forecast of</span><span><span> </span></span><span><span>Plum Rain around Japan".</span></span></strong></p> <blockquote> <p><span><span>The md5 value is 7bf40160a0f97e94f048ff68048dee02</span></span></p> </blockquote>
Pretrained-Guided Conditional Diffusion Models for Microbiome Data Denoising
<p>All datasets and corresponding metadata used in mbVDiT.</p>
Data for Figures in "Dynamics of K$_2$Ni$_2$(SO$_4$)$_3$ governed by proximity to a 3D spin liquid model"
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Simulation data and code for "Modeling radiation belt dynamics using a positivity-preserving finite volume method on general meshes"
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Data for: Free-Breathing Liver Fat, R2* and B0 Field Mapping Using Multi-Echo Radial FLASH and Regularized Model-based Reconstruction (Part 4/5)
<p>This data set consists of MRI measurement data for liver scans acquired with a 3D stack-of-stars multi-echo FLASH sequence during free breathing as described in:</p> <p>Zhengguo Tan, Christina Unterberg-Buchwald, Moritz Blumenthal, Nick Scholand, Philip Schaten, Christian Holme, Xiaoqing Wang, Dirk Raddatz, Martin Uecker. <a href="https://doi.org/10.1109/TMI.2022.3228075">Free-Breathing Liver Fat, R2* and B0 Field Mapping Using Multi-Echo Radial FLASH and Regularized Model-based Reconstruction.</a> IEEE Transactions on Medical Imaging 42:1374-1387 (2023) </p> <p>The data stored in the format of the BART toolbox (DOI: 10.5281/zenodo.592960).</p>
Data for: Free-Breathing Liver Fat, R2* and B0 Field Mapping Using Multi-Echo Radial FLASH and Regularized Model-based Reconstruction (Part 5/5)
<p>This data set consists of MRI measurement data for liver scans acquired with a 3D stack-of-stars multi-echo FLASH sequence during free breathing as described in:</p> <p>Zhengguo Tan, Christina Unterberg-Buchwald, Moritz Blumenthal, Nick Scholand, Philip Schaten, Christian Holme, Xiaoqing Wang, Dirk Raddatz, Martin Uecker. <a href="https://doi.org/10.1109/TMI.2022.3228075">Free-Breathing Liver Fat, R2* and B0 Field Mapping Using Multi-Echo Radial FLASH and Regularized Model-based Reconstruction.</a> IEEE Transactions on Medical Imaging 42:1374-1387 (2023) </p> <p>The data stored in the format of the BART toolbox (DOI: 10.5281/zenodo.592960).</p>
Data for: Free-Breathing Liver Fat, R2* and B0 Field Mapping Using Multi-Echo Radial FLASH and Regularized Model-based Reconstruction (Part 3/5)
<p>This data set consists of MRI measurement data for liver scans acquired with a 3D stack-of-stars multi-echo FLASH sequence during free breathing as described in:</p> <p>Zhengguo Tan, Christina Unterberg-Buchwald, Moritz Blumenthal, Nick Scholand, Philip Schaten, Christian Holme, Xiaoqing Wang, Dirk Raddatz, Martin Uecker. <a href="https://doi.org/10.1109/TMI.2022.3228075">Free-Breathing Liver Fat, R2* and B0 Field Mapping Using Multi-Echo Radial FLASH and Regularized Model-based Reconstruction.</a> IEEE Transactions on Medical Imaging 42:1374-1387 (2023) </p> <p>The data stored in the format of the BART toolbox (DOI: 10.5281/zenodo.592960).</p>
Data for: Free-Breathing Liver Fat, R2* and B0 Field Mapping Using Multi-Echo Radial FLASH and Regularized Model-based Reconstruction (Part 1/5)
<p>This data set consists of MRI measurement data for liver scans acquired with a 3D stack-of-stars multi-echo FLASH sequence during free breathing as described in:</p> <p>Zhengguo Tan, Christina Unterberg-Buchwald, Moritz Blumenthal, Nick Scholand, Philip Schaten, Christian Holme, Xiaoqing Wang, Dirk Raddatz, Martin Uecker. <a href="https://doi.org/10.1109/TMI.2022.3228075">Free-Breathing Liver Fat, R2* and B0 Field Mapping Using Multi-Echo Radial FLASH and Regularized Model-based Reconstruction.</a> IEEE Transactions on Medical Imaging 42:1374-1387 (2023) </p> <p>The data stored in the format of the BART toolbox (DOI: 10.5281/zenodo.592960).</p>
Data for: Free-Breathing Liver Fat, R2* and B0 Field Mapping Using Multi-Echo Radial FLASH and Regularized Model-based Reconstruction (Part 2/5)
<p>This data set consists of MRI measurement data for liver scans acquired with a 3D stack-of-stars multi-echo FLASH sequence during free breathing as described in:</p> <p>Zhengguo Tan, Christina Unterberg-Buchwald, Moritz Blumenthal, Nick Scholand, Philip Schaten, Christian Holme, Xiaoqing Wang, Dirk Raddatz, Martin Uecker. <a href="https://doi.org/10.1109/TMI.2022.3228075">Free-Breathing Liver Fat, R2* and B0 Field Mapping Using Multi-Echo Radial FLASH and Regularized Model-based Reconstruction.</a> IEEE Transactions on Medical Imaging 42:1374-1387 (2023) </p> <p>The data stored in the format of the BART toolbox (DOI: 10.5281/zenodo.592960).</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.