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5,805 results for “Data model”
Support Data Assets for Figures and Tables in gmd-12-3449-2019 (Geosci. Model Dev.)
<p>Support datasets for "VISIR-1.b: ocean surface gravity waves and currents for energy-efficient navigation", by G. Mannarini and L. Carelli (Geosci. Model Dev. paper, https://doi.org/10.5194/gmd-12-3449-2019):</p><ul><li><strong> graphs_gmd-2018-292.tar.gz</strong></li></ul><p>Graphs for running VISIR computations </p><ul><li><strong>figTab_DATA_v3.tar.gz:</strong></li></ul><p>Source data for Figures and Tables</p>
Models and Data for Simple Applications of BERT for Ad Hoc Document Retrieval
<p>This submission includes all pretrained models, test data and prediction files for the arXiv paper "<a href="https://arxiv.org/abs/1903.10972">Simple Applications of BERT for Ad Hoc Document Retrieval</a>". Please follow the instructions at the <a href="https://github.com/castorini/birch">Birch repo</a> to reproduce the results.</p>
Data for the publication "The global aerosol-climate model ECHAM6.3-HAM2.3 – Part 2: Cloud evaluation, aerosol radiative forcing and climate sensitivity"
<p>This repository contains the data for the paper:</p> <p>"Neubauer, D., Ferrachat, S., Siegenthaler-Le Drian, C., Stier, P. Partridge, D. G., Tegen, I., Bey, I., Stanelle, T., Kokkola, H., and Lohmann, U.: The global aerosol-climate model ECHAM6.3-HAM2.3 – Part 2: Cloud evaluation, aerosol radiative forcing and climate sensitivity, Geosci. Mod. Dev., https://doi.org/10.5194/gmd-2018-307, 2019."</p> <p>Each tar-file contains the data (or instructions how to obtain the data) to reproduce a figure or table in our paper.</p> <p>Note that the scripts to plot this data are to be found in the accompanying package (http://dx.doi.org/10.5281/zenodo.2553891)</p> <p> </p>
Analysis of correlation-based biomolecular networks from different omics data by fitting stochastic block models
<p><strong>Baum_et_al_2019_Supplementary_Figures.pdf: </strong>Supplementary Figures S1-S4. Legends are included under each figure.</p> <p><strong>sbm-for-correlation-based-networks-master.zip: </strong>Archived source code of R and Python functions for the analyses and example workflow description at time of publication. Files are maintained at https://gitlab.com/biomodlih/sbm-for-correlation-based-networks and https://gitlab.com/kabaum/sbm-for-correlation-based-networks.</p>
Data for the publication "Incorporation of inline warm rain diagnostics into the COSP2 satellite simulator for process-oriented model evaluation"
<p>Michibata et al. (2019), currently under peer-review for publication in <em>Geoscientific Model Development</em>, incorporated a diagnostic tool for warm rain microphysics into the CFMIP Observation Simulator Package (COSP; Bodas-Salcedo et al. 2011; Swales et al., 2018), designed to evaluate model representations of aerosol–cloud–precipitation interactions at a fundamental process-level. The tool automatically generates two diagnostics related to warm rain microphysics during COSP execution in a host model. One is the contoured frequency by optical depth diagram (CFODD), which visualizes a cloud-to-rain microphysical vertical structure (Suzuki et al., 2015). The other diagnostic is a global map of warm rain fraction classified as non-precipitating clouds (< –15 dBZ<sub>e</sub>), drizzling clouds (–15 < dBZ<sub>e</sub>< 0), and precipitating clouds (0 < dBZ<sub>e</sub>).</p> <p>This repository contains the MIROC6/COSP2 input data and A-Train satellite statistics used in Michibata et al. (2019). A sample of the post-processing scripts for visualization using the GrADS software is also included in this repository.</p>
Data Sets and Prediction Models Created using MLP, RNN and LSTM
<p>Binary Data Sets</p> <p>- 2018tbi219_shuffled3.csv and 2018tbi219_shuffled5.csv</p> <p>- these are stratified data sets that were able to produce prediction models with high prediction rates.</p> <p>Models.zip</p> <p>- this zip file contains the prediction models created using Keras Deep Learning Algorithms: MLP, RNN and LSTM</p> <p>Model Creation - Python Code Snippets</p> <p>- Code snippets for creating the prediction models</p>
Visual and inertial data for validation of gliding models of ornithopters
<p>This dataset contains data from different gliding flights with an ornithopter in low wind conditions. For each experiment, the inertial information is provided.</p> <p>Additionally, the flights have been recorded from three different points of view to track and triangulate its position. The videos are provided and the position of the camera has been determined using a Leica Total Station system with submillimeter accuracy. A sample of the 2D track of the ornithopter is provided for each video and experiment. The tracking along the three cameras are synchronized.</p> <p> </p> <p>------Camera Pose structure------</p> <p> </p> <p>Three cameras with four points: three to measure orientation and the last one the lens position. The last two points are the measured fall.<br> Camera 1 -> top left, bottom left and top right.<br> Camera 2 -> top left, top rigth and bottom right.<br> Camera 3 -> top left, bottom left and top right.<br> Then there are 14 rows. The pattern is: Point1, Point2, Point3 and Lens Position.</p> <p>------IMU structure------</p> <p>time, quaternion w, quaternion x, quaternion y, quaternion z, accelerometer x, accelerometer y, accelerometer z, Gyroscope x, Gyroscope y, Gyroscope z, magnetometer x ,magnetometer y ,magnetometer z<br> units: time->ms, accelerometer->g, gyroscope->ยบ/s</p> <p> </p>
QSPR models for bioconcentration factor (BCF): Are they able to predict data of industrial interest?
<p>This dataset is described and studied in the article </p> <p>"QSPR models for bioconcentration factor (BCF): Are they able to predict data of industrial interest?"</p> <p>published in <em>SAR and QSAR Environmental Research</em> (Taylor&Francis).</p> <p>Files description:</p> <p>SI_BCFtrainset.xlsx: a collection of 1129 chemical structures and CAS identifiers with their logBCF values extracted from various literature sources.</p> <p>SI_BCFtestset.xlsx: a collection of 204 chemical structures for which the logBCF is considered of lower reliability and used as an external test set.</p> <p>SI_FullDataset_rawdata.csv: the raw data composed of 15372 entries with the following columns: CASRN, Tissue, Duration [d], Test organism, Exposure type, Steady state, RESPONSE, RESPONSE UNIT, Media type, TakenFrom, TITLE, AUTHOR, YEAR, SOURCE, SMILES</p> <p>SI_ExcludedOutliers34.csv: 34 chemical structures that have been identified as suspicious during analysis.</p> <p> </p>
Data from "Predicting Global Ground Geoelectric Field With Coupled Geospace and Three‐Dimensional Geomagnetic Induction Models"
<p>Data presented in http://dx.doi.org/10.1029/2018SW001859 excluding the first and last hours which were determined to contain partially unphysical results and probably should not be used.</p> <p>Each file contains the external ground magnetic field components calculated on 5x5 degree geographic grid and the results of induction modeling using 1d and 3d ground conductivity models: total ground magnetic field components, horizontal ground electric field components. Times are given in UTC.</p> <p>To reproduce Figure 8 in above reference use:</p> <p> </p> <p>import numpy<br> import matplotlib.pyplot<br> data = numpy.load('2006-12-14T22:58:00.npz') # or 2006-12-14T22_58_00.npz<br> matplotlib.pyplot.colorbar(<br> matplotlib.pyplot.imshow(<br> data['B_3D_north'],<br> cmap = matplotlib.pyplot.get_cmap('bwr'),<br> vmin = -800, vmax = 800,<br> extent = (-180, 180, -90, 90)<br> ),<br> format = '%.0f', fraction = 0.02, pad = 0.03<br> )<br> matplotlib.pyplot.show()</p> <p> </p> <p>and substitute B_3D_east, E_3D_north, etc. for the different panels.</p>
Research data from the survey on Smart Cities professional profiles for the Article "Modelling and analyzing the availability of technical professional profiles for the success of Smart Cities projects in Europe"
<p>The file includes the complete version of data collected through the surrvey on recommended profile for two professional roles in the context of Smart Cities (SC) projects: SC engineer and SC technician. It complements the previous version focused on IoT implementation stired at <a href="../doi/10.5281/zenodo.7492254">https://zenodo.org/doi/10.5281/zenodo.7492254</a></p>
EOL v3 data model Ontologies: stylesheet (.css)
Note: Some XML files need the stylesheet (.xsl and .css).<p></p>For questions or use cases calling for large, multi-use aggregate data files, please visit the EOL Services forum at <p></p>http://discuss.eol.org/c/eol-services
EOL v3 data model Ontologies: stylesheet (.xsl)
Note: Some XML files need the stylesheet (.xsl and .css).<p></p>For questions or use cases calling for large, multi-use aggregate data files, please visit the EOL Services forum at <p></p>http://discuss.eol.org/c/eol-services
EOL v3 data model Ontologies: occurrence_extension.xml
Note: Some XML files need the stylesheet (.xsl and .css).<p></p>For questions or use cases calling for large, multi-use aggregate data files, please visit the EOL Services forum at <p></p>http://discuss.eol.org/c/eol-services
EOL v3 data model Ontologies: measurement_extension.xml
Note: Some XML files need the stylesheet (.xsl and .css).<p></p>For questions or use cases calling for large, multi-use aggregate data files, please visit the EOL Services forum at <p></p>http://discuss.eol.org/c/eol-services
EOL v3 data model Ontologies: media_extension.xml
Note: Some XML files need the stylesheet (.xsl and .css).<p></p>For questions or use cases calling for large, multi-use aggregate data files, please visit the EOL Services forum at <p></p>http://discuss.eol.org/c/eol-services
EOL v3 data model Ontologies: reference_extension.xml
Note: Some XML files need the stylesheet (.xsl and .css).<p></p>For questions or use cases calling for large, multi-use aggregate data files, please visit the EOL Services forum at <p></p>http://discuss.eol.org/c/eol-services
EOL v3 data model Ontologies: agent_extension.xml
Note: Some XML files need the stylesheet (.xsl and .css).<p></p>For questions or use cases calling for large, multi-use aggregate data files, please visit the EOL Services forum at <p></p>http://discuss.eol.org/c/eol-services
Integration of data sets from different sources for modeling gender violence and perception of insecurity
<p>The dataset is composed of three distinct files which aggregate processed data derived from open datasets of three cities: Dublin, San Francisco, and Valencia. The data has been mapped to a grid of 25m² for Valencia and 50m² for Dublin and San Francisco. The respective files are named DATA_ES_VLC.csv, DATA_IE_DUB.csv, and DATA_US_SFO.csv. Additionally, there is a dataset for tweets named DATA_TWT.csv, which contains tweets collected through web scraping and analysed using natural language processing (NLP) algorithms and neural networks. The aim is to identify and classify tweets that discuss gender-based violence in the city of Valencia. Another file, MAP_ES_VLC.csv, includes points collected during various mapathons conducted by the Polytechnic University of Valencia campus for a science project aimed at identifying potentially insecure locations.</p>
Model data for "Melt sensitivity of irreversible retreat of Pine Island Glacier"
<p>Model inputs and outputs for the experiments in Reed et al., 2024 "Melt sensitivity of irreversible retreat of Pine Island Glacier".</p>
Data and Code for: 'Stellar Models are Reliable at Low Metallicity: An Asteroseismic Age for the Ancient Very Metal-Poor Star KIC 8144907', Huber et al. 2024.
<p>Data and code to reproduce plots for the paper '<em>Stellar Models are Reliable at Low Metallicity: An Asteroseismic Age for the Ancient Very Metal-Poor Star KIC 8144907'</em>, Huber et al. 2024.</p> <p>Descriptions of the enclosed data files are as follows:</p> <div> <ul> <li>Freqs_best_fit.dat: Best-fitting GARSTEC model frequencies (Figure 3, right)</li> <li>KIC10006158_spec.txt: Reduced and normalized HDS spectrum of KIC10006158 (Figure 1)</li> <li>KIC8144907_ps.txt: Power spectrum of the Kepler light curve of KIC8144907 (Figure 3, left)</li> <li>KIC8144907_spec.txt: Reduced and normalized HDS spectrum of KIC8144907 (Figure 1)</li> <li>apokasc2.tsv: APOKASC sample from Pinsonneault+ 2014 (Figure 2)</li> <li>dwarfs.csv: Asteroseismic sample from Serenelli+ 2017 (Figure 2) </li> <li>freqs.csv: Data in Table 1</li> <li>hosts.tsv: Asteroseismic ages from Silva Aguirre+ 2015 (Figure 4) </li> <li>legacy-t1.tsv: Asteroseismic ages from Silva Aguirre+ 2017 (Figure 4) </li> <li>legacy-t2.tsv: Asteroseismic ages from Silva Aguirre+ 2017 (Figure 4) </li> <li>li-2020.csv: Asteroseismic ages from Li+ 2020 (Figure 4) </li> <li>matsuno.txt: Asteroseismic sample from Matsuno+ 2021 (Figure 2) </li> </ul> </div>
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.