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5,805 results for “Data model”
Abeshu-etal_2023_GMD: Model Input and Output data
<p>1. Input data required to conduct the experiment</p> <p>2. Model output data for reproducing the manuscript figures</p>
Modeling water flow and solute transport in unsaturated soils using physics-informed neural networks trained with geoelectrical data
<p>Numerical codes and results for the article: Modeling water flow and solute transport in unsaturated soils using physics-informed neural networks trained with geoelectrical data</p>
Species-aware DNA language modeling - data
<p>Data accompanying the publication Species-aware DNA language modeling.</p> <p>For code, see: https://github.com/DennisGankin/species-aware-DNA-LM (for the latest version) or the code.zip file.</p> <p>The data directory contains model checkpoints, baselines models, evaluation results and datasets used for training, testing and downstream tasks. It has the following structure:</p> <p>data/ Datasets and subdirectories</p> <p> - data/results/ Results from different test runs</p> <p> - data/models/ Model checkpoints</p> <p> - data/baselines/ Baseline models</p>
Detection of the Fire Drill anti-pattern: 15 real-world projects with ground truth, issue-tracking data, source code density, models and code
<p>This package contains artifacts for <strong>15</strong> real-world software projects. The data is supposed to aid the detection of the presence of the Fire Drill anti-pattern. We include original data, ground truth, code (experimental setups and models), and notebooks. The data supports two distinct methods of detecting the AP: a) through issue-tracking data, and b) through the underlying source code. This version of the dataset corresponds to <strong>v8</strong> of the <a href="https://arxiv.org/abs/2104.15090v8">technical report</a> and the <a href="https://github.com/MrShoenel/anti-pattern-models/releases/tag/arxiv-v8">GitHub repository</a>. The package includes the following:</p> <p>Original data:</p> <ul> <li>For each project, its <strong>original</strong> artifacts (e.g., wikis, meeting minutes, mentor's notes, etc.)</li> <li>Evaluation of raters' notes by the assessor</li> </ul> <p>Fire Drill in issue-tracking data:</p> <ul> <li><strong>Ground truth</strong> for whether and how strong each project exhibits the Fire Drill AP, on a scale from [0,10]. This was determined by two individual raters, who also reached a consensus.</li> <li>Coefficients for indicators for the first method, per project.</li> <li>Detailed issue-tracing data for each project: what occurred and when.</li> <li>Time logs for each project.</li> </ul> <p>Fire Drill in source-code data:</p> <ul> <li><strong>Four</strong> technical reports that document the developed method of how to translate a description into a detectable pattern, and to use the pattern to detect the presence and to score it (similar to the rating). Also includes a report for how activities were assigned to individual commits.</li> <li>Source code density data (metrics) for each commit in each of the nine projects as a separate dataset.</li> <li>Code: a snapshot of the repository that holds all code, models, notebooks, and pre-computed results, for utmost reproducibility (the code is written in R).</li> </ul>
ROM model data for Eastern Mediterranean Dense Water Formation
<p>ROM model data for Eastern Mediterranean Dense Water Formation</p>
Dataset for paper Pavel Perezhogin, Laure Zanna, Carlos Fernandez-Granda "Generative data-driven approaches for stochastic subgrid parameterizations in an idealized ocean model" submitted to JAMES.
<p>The dataset consists of the directory tree of .zarr archives. See <a href="https://github.com/m2lines/pyqg_generative/blob/master/Google-Colab/dataset.ipynb">Github repository</a> for the description of the dataset.</p> <p>The directory tree is:</p> <pre><code>├── eddy │ ├── 48 │ │ ├── gauss │ │ ├── hires-gauss │ │ ├── hires-sharp │ │ ├── lores │ │ └── sharp │ ├── 64 │ │ ├── gauss │ │ ├── hires-gauss │ │ ├── hires-sharp │ │ ├── lores │ │ └── sharp │ ├── 96 │ │ ├── gauss │ │ ├── hires-gauss │ │ ├── hires-sharp │ │ ├── lores │ │ └── sharp │ └── hires ├── jet │ ├── 48 │ │ ├── gauss │ │ ├── hires-gauss │ │ ├── hires-sharp │ │ ├── lores │ │ └── sharp │ ├── 64 │ │ ├── gauss │ │ ├── hires-gauss │ │ ├── hires-sharp │ │ ├── lores │ │ └── sharp │ ├── 96 │ │ ├── gauss │ │ ├── hires-gauss │ │ ├── hires-sharp │ │ ├── lores │ │ └── sharp │ └── hires</code></pre> <ul> <li>Every individual dataset is a <code>.zarr</code> <a href="https://zarr.readthedocs.io/en/stable/">archive</a></li> <li><code>eddy/jet</code> - configuration of the pyqg; eddy is default; See <a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2022MS003258">Ross2022</a> for description</li> <li><code>hires.zarr</code> - high-resolution simulation at 256x256 grid</li> <li><code>48/64/96</code> - resolution of the coarse models</li> <li><code>lores.zarr</code> - low-resolution simulation</li> <li><code>gauss.zarr</code>, <code>sharp.zarr</code> - training datasets for prediction of subgrid forcing obtained with Gaussian or Sharp filters</li> <li><code>hires-gauss.zarr</code>, <code>hires-sharp.zarr</code> - high-resolution simulation projected onto coarse grid with Gaussian or Sharp filters</li> </ul> <p>The directory tree is split into small tar.gz files each representing a separate .zarr archive. Download any required parts of the dataset and unpack with:</p> <p><strong>tar -xf *.tar.gz </strong></p> <p><strong>The directory tree will be restored automatically!</strong></p>
Data from: The phylogeographic history of Megistostegium (Malvaceae) in the dry, spiny thickets of southwestern Madagascar using RAD-seq data and ecological niche modeling.
<p class="MsoCommentText">The spiny thicket of southwestern Madagascar represents an extreme and ancient landscape with extraordinary levels of biodiversity and endemism. Few hypotheses exist for explaining speciation in the region and few plant studies have explored hypotheses for species diversification. Here we investigate three species in the endemic genus <i>Megistostegium </i>(Malvaceae) to evaluate phylogeographic structure and explore the roles of climate, soil and paleoclimate oscillations on population divergence and speciation throughout the region. We combine phylogenetic and phylogeographic inference of RADseq data with ecological niche modeling across space and time. Population structure is concurrent with major rivers in the region and we identify a new, potentially important biogeographic break coincident with several landscape features. Our data further suggests that niches occupied by species and populations differ substantially across their distribution. Paleodistribution modelling provide evidence that past climatic change could be responsible for the current distribution, population structure and maintenance of species in <i>Megistostegium.</i></p>
Data-specific substitution models improve protein-based phylogenetics - data
<p>Amino-acid sequence data sets, estimated data-specific amino-acid substitution models, and optimal ML trees.</p> <p>Data are divided in five folders, each one with a readme.txt file describing it.</p> <p>├── 1_simulated_data_sets<br> │ ├── 1500-site_alignments<br> │ ├── 400-site_alignments<br> │ └── 8000-site_alignments<br> ├── 2_simulated_data_specific_models<br> │ ├── Codeml_models<br> │ ├── FastMG_models<br> │ ├── IQTREE_models<br> │ ├── P4_BI_models<br> │ └── P4_ML_models<br> ├── 3_optimal_ML_trees_simulated_data<br> │ ├── commonly-used_empirical_models<br> │ │ ├── cpREV_model_analyses<br> │ │ └── WAG_model_analyses<br> │ ├── data_specfic_model_analyses<br> │ │ ├── Codeml-estimated_model_analyses<br> │ │ ├── FastMG-estimated_model_analyses<br> │ │ ├── IQTREE-estimated_model_analyses<br> │ │ ├── P4BI-estimated_model_analyses<br> │ │ └── P4ML-estimated_model_analyses<br> │ └── simulation_model_analyses<br> ├── 4_data_specific_models_empirical_data<br> │ └── Toussaint18_data_specific_models_27partitions<br> └── 5_optimal_ML_trees_empirical_data</p>
Simulation data for: Semi-continuum modelling of unsaturated porous media flow to explain the Bauters' paradox
<p>This dataset includes the simulation data needed to create the plots for a manuscript: Semi-continuum modelling of unsaturated porous media flow to explain the Bauters' paradox. All data can be reproduced using the code of the semi-continuum model available in https://doi.org/10.5281/zenodo.6837742</p> <p>The readMe file was not included in Version 3 (all simulation data are the same as in version 3).</p>
Data for "Models of orientational disorder in hybrid organic-inorganic piezoelectric materials"
<p>Data for "Models of orientational disorder in hybrid organic-inorganic piezoelectric materials"</p>
Ocean Model Data
<p>Experiments 4, 6, 8, and 10 rpm.</p>
Numerical results data of 'Impact of Injection Pressure and Polyaxial Stress on Hydraulic Fracture Propagation and Permeability Evolution in Greywacke: Insights from Discrete Element Models of a Laboratory Test'
<p>Numerical results data of '<strong>Impact of Injection Pressure and Polyaxial Stress on Hydraulic Fracture Propagation and Permeability Evolution in Greywacke: Insights from Discrete Element Models of a Laboratory Test</strong>'</p>
Forward modelling of Dα camera view in ST40 informed by experimental data (dataset)
<p>Database for reproducing the calculations presented in the publication "Forward modelling of Dα camera view in ST40 informed by experimental data", submitted to <em>Fusion Engineering and Design</em>.</p>
Modeling polar cap airglow patch intensities using three-dimensional ionospheres produced by data assimilations
<p>Qaanaaq imager data supporting the paper entitled Modeling polar cap airglow patch intensities using three-dimensional ionospheres produced by data assimilations.</p>
Toward a Better Understanding of the Global Ocean Copper Distribution and Speciation through a Data-constrained Model
<p>MATLAB codes for a global marine copper model under the AWESOME OCIM framework. </p> <p>Prepared for manuscript submission.</p>
Data and GrADS scripts for "Changes in March mean snow water equivalent since the mid-twentieth century and the contributing factors in reanalyses and CMIP6 climate models", submitted to The Cryosphere
<p>Data and GrADS (Grid Analysis and Display System) scripts for reproducing the figures and numerical results included in the manuscript "Changes in March mean snow water equivalent since the mid-twentieth century and the contributing factors in reanalyses and CMIP6 climate models". Revised for The Cryosphere in March 2023.</p> <p>In addition to the README file, there are two zipped archives:</p> <p>swe_trends.zip (2.3 GB) includes both the data (mostly as GrADS binaries), the GrADS data descriptor files and the scripts.</p> <p>swe_trends_no_data.zip (74 kB) includes just the scripts and the data descriptor files.</p> <p>Please see the README file for further details on the content and use of the archives.</p>
Data to "Functional relationships reveal differences in the water cycle representation of global water models"
<p>This repository contains 30-year (climatological) averages (1975-2004) from 8 global water models, following the ISIMIP 2b protocol: CLM4.5, CWatM, H08, JULES-W1, LPJmL, MATSIRO, PCR-GLOBWB, and WaterGAP2.</p> <p>Variables included are precipitation (<em>pr</em>), temperature (<em>tas</em>), minimum temperature (<em>tasmin</em>), maximum temperature (<em>tasmax</em>), downwelling shortwave radiation (<em>rsds</em>), downwelling longwave radiation (<em>rlds</em>), net radiation (<em>netrad</em>), potential evapotranspiration (<em>potevap</em>), actual evapotranspiration (<em>evap</em>), groundwater recharge (<em>qr</em>), and total runoff (<em>qtot</em>). For details see <a href="https://www.isimip.org/">https://www.isimip.org/</a>.</p> <p><em>domains.csv </em>contains different definitions of the domains used in the paper (wet warm, wet cold, dry cold, dry warm).</p> <p><em>days_below_x.csv </em>contains the average fraction of days where the temperature is below <em>x</em> °C.</p>
data for hackathon geoscience 7 facies model npy format
<p>This dataset is an extraction of zenodo deposit: DOI: 10.5281/zenodo.7244958</p> <p>Dataset will be used for an Hackathon </p>
Preliminary DOI/Repository of ALPINE3D and SNOWPACK data of the submitted paper "Towards a fully physical representation of snow on Arctic sea ice using a 3D snow-atmosphere model"
<p>There are 2 zip folders in this repository.</p> <p>"a3d_jgr.zip" contains a folder structure that must be kept as it is in order to run the simulation in the current configuration.<br> The setup contains both input and output data as well as the model configuration as used in the submitted manuscript <br> "Towards a fully physical representation of snow on Arctic sea ice using a 3D snow-atmosphere model".</p> <p>The zip file contains 3 main folders:</p> <ul> <li>base_setup_files</li> <li>a3d_jgr_alpha1</li> <li> a3d_jgr_alpha3</li> </ul> <p>The "base_setup_files" contains all input files that are necessary to run the reference (R) simulation ("a3d_jgr_alpha1" folder) and the comparison "C" scenario ("a3d_jgr_alpha3") folder. In the a3d_jgr_alpha1 and a3d_jgr_alpha3 folders you find the corresponding outputs as used in the paper, as well as the settings used - which only differ by the changed "SCHMIDT_DRIFT_FUDGE" value that is found in each a3d_jgr_alphax/setup/io.ini file. The input data is already linked accordingly in each io.ini file.</p> <p>a3d_jgr_alpha1 also contains the detailed snow profiles for each point along the transects.</p> <p>To reproduce the results, download and compile the source code for the adjusted ALPINE3D model first, which can be obtained from https://gitlabext.wsl.ch/snow-models/alpine3d.git under the "alpine3d_mosaic" branch. After installing, you can run the provided model setup uploaded here.</p> <p>_________________________________________________________________________________________________________<br> <br> "SNOWPACK_JGR.zip" contains both input and output data for SNOWPACK as well as the model configuration as used in the submitted manuscript "Towards a fully physical representation of snow on Arctic sea ice using a 3D snow-atmosphere model".</p> <p>The zip file contains 2 main folders: </p> <ul> <li>SNOWPACK_JGR_ALPHA1</li> <li>SNOWPACK_JGR_ALPHA3</li> </ul> <p>In the SNOWPACK_JGR_ALPHA1 (reference "SP_R" simulation) and SNOWPACK_JGR_ALPHA3 (comparison "SP_C" scenario) folders you find the corresponding inputs, outputs and configuration as used in the paper, as well as the settings used - which only differ by the changed "SCHMIDT_DRIFT_FUDGE" value that is found in each SNOWPACK_JGR_ALPHA/setup/io.ini file. The input data is already linked accordingly in each io.ini file.</p> <p>To reproduce the results, download and compile the source code for the adjusted SNOWPACK model first, which can be obtained from https://gitlabext.wsl.ch/snow-models/snowpack.git under the "snowpack_mosaic" branch. After installing, you can run the provided model setup uploaded here.</p>
Artificial neural network model and metabolomics data of selected microbial strains
<p>Metabolomics data, metadata, sample R code, and a pre-trained artificial neural network model to predict group memberships of the bacterial strains in the dataset.</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.