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5,805 results for “Data model”
Figure 1 from: Cardoso A, Tsiamis K, Gervasini E, Schade S, Taucer F, Adriaens T, Copas K, Flevaris S, Galiay P, Jennings E, Josefsson M, López B, Magan J, Marchante E, Montani E, Roy H, von Schomberg R, See L, Quintas M (2017) Citizen Science and Open Data: a model for Invasive Alien Species in Europe. Research Ideas and Outcomes 3: e14811. https://doi.org/10.3897/rio.3.e14811
Figure 1 - Participants of the workshop on "Citizen Science and Open Data: a model for Invasive Alien Species in Europe". Image: COST Association.
Figure 4 from: Cardoso A, Tsiamis K, Gervasini E, Schade S, Taucer F, Adriaens T, Copas K, Flevaris S, Galiay P, Jennings E, Josefsson M, López B, Magan J, Marchante E, Montani E, Roy H, von Schomberg R, See L, Quintas M (2017) Citizen Science and Open Data: a model for Invasive Alien Species in Europe. Research Ideas and Outcomes 3: e14811. https://doi.org/10.3897/rio.3.e14811
Figure 4 - Flipchart with participants notes addressing the topic "Main characteristics of a model for a citizen participation replicable across different policies" during Session 1. Image: COST Association.
Figure 3 from: Cardoso A, Tsiamis K, Gervasini E, Schade S, Taucer F, Adriaens T, Copas K, Flevaris S, Galiay P, Jennings E, Josefsson M, López B, Magan J, Marchante E, Montani E, Roy H, von Schomberg R, See L, Quintas M (2017) Citizen Science and Open Data: a model for Invasive Alien Species in Europe. Research Ideas and Outcomes 3: e14811. https://doi.org/10.3897/rio.3.e14811
Figure 3 - Discussion of a round table through the "world café" method, addressing the topic "List of methods for mainstreaming inputs from CS in policy making including quality assurance and validations and other parameters" during Session 1. Image: COST Association.
Figure 2 from: Cardoso A, Tsiamis K, Gervasini E, Schade S, Taucer F, Adriaens T, Copas K, Flevaris S, Galiay P, Jennings E, Josefsson M, López B, Magan J, Marchante E, Montani E, Roy H, von Schomberg R, See L, Quintas M (2017) Citizen Science and Open Data: a model for Invasive Alien Species in Europe. Research Ideas and Outcomes 3: e14811. https://doi.org/10.3897/rio.3.e14811
Figure 2 - Discussion of a round table through the "world café" method, addressing the topic "List of successful case-studies and examples of good practices in environment and IAS" during Session 1. Image: COST Association.
Figure 2 from: Guillaumot C, Martin A, Fabri-Ruiz S, Eléaume M, Saucède T (2016) Echinoids of the Kerguelen Plateau – occurrence data and environmental setting for past, present, and future species distribution modelling. ZooKeys 630: 1-17. https://doi.org/10.3897/zookeys.630.9856
Figure 2 - Distribution of the 12 echinoid species based on the specimens collected since 1872 on the Kerguelen Plateau.
Figure 1 from: Guillaumot C, Martin A, Fabri-Ruiz S, Eléaume M, Saucède T (2016) Echinoids of the Kerguelen Plateau – occurrence data and environmental setting for past, present, and future species distribution modelling. ZooKeys 630: 1-17. https://doi.org/10.3897/zookeys.630.9856
Figure 1 - Sampling effort. Red dots depict echinoid occurrences. Black squares correspond to visited sites at which no echinoid was sampled.
Data and model files of neutron imaging experiments
Open the record for dataset details and reuse information.
Mapping of the dataset of the German National Regsitry for Rare Diseases (NARSE) to Observational Medical Outcomes Partnership Common Data Model (OMOP CDM)
<p>Mapping between the data set of the German National Registry for Rare Diseases ("Nationales Register für Seltene Erkrankungen"; <a href="https://www.narse.de/">NARSE</a>) to Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) using international standards.</p>
Data and scripts (1) for Storkey et al, "Resolution dependence of interlinked Southern Ocean biases in global coupled HadGEM3 models", GMD (2024)
<p><br> ================================================================<br> Data and scripts for producing plots from Storkey et al (2024):<br> "Resolution dependence of interlinked Southern Ocean biases in<br> global coupled HadGEM3 models"<br> ================================================================</p> <p>The plots in the paper consist of 10-year mean fields from the third <br>decade of the spin up and timeseries of scalar quantities for the first<br>150 years of the spin up. The data to produce these plots are stored<br>in the MEANS_YEARS_21-30 and TIMESERIES_DATA directories respectively.</p> <p>Note that due to the size limit on records on Zenodo, the 10-year mean <br>output from the N216-ORCA12 integration has been stored as a separate<br>record.</p> <p>Scripts to produce the plots are in SCRIPT, with section definitions<br>in SECTIONS. Bespoke plotting scripts are included in SCRIPT. They use<br>python 3 including the Matplotlib, Iris and Cartopy packages. The <br>plotting of the timeseries data used the Marine_Val VALSO-VALTRANS <br>package which is available here:</p> <p> https://github.com/JMMP-Group/MARINE_VAL/tree/main/VALSO-VALTRANS </p> <p>Much of the processing of the model output data was performed with the<br>CDFTools package, which is available here:</p> <p> https://github.com/meom-group/CDFTOOLS</p> <p>and the NCO package:</p> <p> https://web.mit.edu/course/13/13.715/nco-2.8.1/doc/</p>
Data from: Modeling 2020 regulatory changes in international shipping emissions helps explain 2023 anomalous warming
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Simulation data for "Modeling radiation belt dynamics using a positivity-preserving finite volume method on general meshes"
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Morphological/WB/ELISA/cell counting data of the paper 'Serotonergic and dopaminergic neurons in the dorsal raphe are differentially altered in a mouse model for parkinsonism'
<p>The files contain the data included in Figure 2B, Figure 2C, Figure 4B, Figure 4C, Figure 6B, Figure 6C, Suppl.Fig.4, Suppl. Figure 5, Suppl. Figure 6I, Suppl. Figure 6J.</p>
Data of Building information modelling (BIM), Historic BIM (HBIM), Digital Twins and IoT
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Data for: Origin and fate of the pseudogap in the doped Hubbard model
<p>Data for publication "Origin and fate of the pseudogap in the doped Hubbard model"</p>
Data used to estimate models
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ChEMBL Data for 'Achieving Well-Informed Decision-Making in Drug Discovery: A Comprehensive Calibration Study using Neural Network-Based Structure-Activity Models'
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Modeling data
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Automated electrosynthesis reaction mining with multimodal large language models - raw data and prompts
<p>Compilation of the prompts used, together with the raw response files from the tested MLMM models. </p>
Data from: miRglmm: a generalized linear mixed model of isomiR-level counts improves estimation of miRNA-level differential expression and uncovers variable differential expression between isomiRs
<p>These datasets can be used to reproduce all analyses from the publication "miRglmm: a generalized linear mixed model of isomiR-level counts improves estimation of miRNA-level differential expression and uncovers variable differential expression between isomiRs" in conjunction with codes found at https://github.com/mccall-group/miRglmm_paper. </p> <p>"Monocyte_data_subset.rda", "monocyte_exact_subset_filtered2.rda" and "sims_N100_m2_s1_rtruncnorm.rda" can be used to reproduce the simulation analysis. </p> <p>"panel_B_SE.rda" and "ERCC_filtered.rda" can be used to reproduce the ERCC synthetic data analysis with known ground truth.</p> <p>"study89_data_subset.rda" and "study89_data_subset_filtered2.rda" can be used to reproduce the immune cell-type analysis. </p> <p>"bladder_testes_data_subset.rda" and "bladder_testes_data_subset_filtered2.rda" can be used to reproduce the bladder vs testes tissue analysis.</p>
Regularized Benders Decomposition for High Performance Capacity Expansion Models: Data and Code
<p>This dataset contains all code, input and results data relevant to the working paper <a href="https://arxiv.org/abs/2403.02559">‘Regularized Benders Decomposition for High Performance Capacity Expansion Models’</a>, in IEEE Transactions on Power Systems DOI 10.1109/TPWRS.2025.3526413.</p> <p>The dataset is comprised of three folders:<br><br><code><em>GenX-Benders.zip</em></code> Contains the source code used in this study, which extends the open-source electricity capacity expansion model GenX (<a href="https://github.com/GenXProject/GenX.jl/releases/tag/v0.3.5">@v0.3.5</a>), implementing different regularized Benders decomposition algorithms as solving routines.</p> <p><em><code>Test_Systems.zip</code> </em>Includes the input files for generating the GenX model for the five considered test systems. Note that GenX-Brazil has been derived from the <a href="https://gitlab.com/dlr-ve/esy/open-brazil-energy-data">Open Brazil Energy Data</a> as detailed in the manuscript.<br><br><em><code>Results.zip</code> </em>Includes all result files for the computaitonal experiments reported in the manuscript.</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.