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5,805 results for “Data model”
Data, software, and Figures used in a manuscript submitted to Geoscientific Model Development
<p>Data, software, and Figures used in 'A General Comprehensive Evaluation Method for Cross-Scale Precipitation Forecast' submitted to Geoscientific Model Development.</p>
Data from: How far can I extrapolate my species distribution model? Exploring Shape, a novel method
<p>Species distribution and ecological niche models (hereafter SDMs) are popular tools with broad applications in ecology, biodiversity conservation, and environmental science. Many SDM applications require projecting models in environmental conditions non-analog to those used for model training (extrapolation), giving predictions that may be statistically unsupported and biologically meaningless. We introduce a novel method, Shape, a model-agnostic approach that calculates the extrapolation degree for a given projection data point by its multivariate distance to the nearest training data point. Such distances are relativized by a factor that reflects the dispersion of the training data in environmental space. Distinct from other approaches, Shape incorporates an adjustable threshold to control the binary discrimination between acceptable and unacceptable extrapolation degrees. We compared Shape's performance to five extrapolation metrics based on their ability to detect analog environmental conditions in environmental space and improve SDMs suitability predictions. To do so, we used 760 virtual species to define different modeling conditions determined by species niche tolerance, distribution equilibrium condition, sample size, and algorithm. All algorithms had trouble predicting species niches. However, we found a substantial improvement in model predictions when model projections were truncated independently of extrapolation metrics. Shape's performance was dependent on extrapolation threshold used to truncate models. Because of this versatility, our approach showed similar or better performance than the previous approaches and could better deal with all modeling conditions and algorithms. Our extrapolation metric is simple to interpret, captures the complex shapes of the data in environmental space, and can use any extrapolation threshold to define whether model predictions are retained based on the extrapolation degrees. These properties make this approach more broadly applicable than existing methods for creating and applying SDMs. We hope this method and accompanying tools support modelers to explore, detect, and reduce extrapolation errors to achieve more reliable models.</p>
Replication data for estimating the below-ground leak rate of a natural gas pipeline using above-ground downwind measurements: THE ESCAPE−1 MODEL
<p>Gas leak detectors are currently used to survey for below-ground leaks. However, measurements from gas detectors are not precise in quantifying the below-ground leak rate—data in this submission aimed to quantify the below-ground leak rate of a Natural Gas pipeline.</p>
Main output data used in "Coupling the regional climate MAR model with the ice sheet model PISM mitigates the melt-elevation positive feedback" (Delhasse et al., 2024)
<p>Outputs used in:</p> <p><em>Delhasse, A., Beckmann, J., Kittel, C., and Fettweis, X.: Coupling MAR (Modèle Atmosphérique Régional) with PISM (Parallel Ice Sheet Model) mitigates the positive melt–elevation feedback, The Cryosphere, 18, 633–651, https://doi.org/10.5194/tc-18-633-2024, 2024.</em></p> <p>MAR-PISM coupling experiments outputs over 1991-2200. The main experiments are:</p> <ul> <li>MAPI-2w: 2-way coupling, consideration <em>online</em> of the melt-elevation feedback (evolving topography in MAR).</li> <li>MAPI-1w: 1-way coupling, consideration of the melt-elevation feedback only with the <em>offline</em> correction (Franco <em>et al.</em>, 2012) of the MAR outputs (fixed topography in MAR).</li> <li>MAPI-0w: 0-way coupling, no consideration of the melt-elevation feedback (fixed topography in MAR and no correction during interpolation).</li> </ul> <p>MAR files contain yearly SMB (surface mass balance) and ST (surface temperature) interpolated (with correction) on the PISM-4.5km grid. Gradients used for the correction of the melt-elevation feedback are also given for both variables. SMB and ST are the two required MAR fields to couple MAR with PISM. </p> <p>PISM files contain yearly ice thickness (THK) and ice mask (MASK) as simulated by PISM for each of the three experiments. </p> <p>The MAR code used in this dataset is tagged as v3.11.3 on https://gitlab.com/Mar-Group/MARv3# (last access: 23 January 2024) (MARTeam, 2024). The PISM code used is tagged as PISMv1.2.2 on <a href="https://github.com/pism/pism/releases/tag/v1.2.2" target="_blank" rel="noopener noreferrer">https://github.com/pism/pism/releases/tag/v1.2.2</a> (last access: 23 January 2024). Other coupling scripts are also available upon request by email (<a href="mailto:alison.delhasse@uliege.be" target="_blank" rel="noopener noreferrer">alison.delhasse@uliege.be</a>).</p> <p>If you need other variables from MAR or PISM, send us an email (alison.delhasse@uliege.be, johanna.beckmann@monash.edu) and we will be glad to help you. We will also be happy to share the scripts we have developed to analyse the outputs and make the figures in this paper if needed. Please cite the paper if you use these MAR-PISM outputs.<br><br>Data usage notice:</p> <p>If you use any of these results, please acknowledge the work of the people involved in producing them. Acknowledgments should be similar to the one below that contains information related to MAR and PISM. To document MAR scientific impact and enable ongoing support of the model, users are likely encouraged to contact me to add their works to the list of MAR-related publications. </p> <p>"We thank A. Delhasse and J. Beckmann, as well as the MAR and PISM teams which make available the model outputs. We also thank agencies (F.R.S - FNRS, CÉCI, and the Walloon Region) that provided computational resources for MAR-PISM simulations. "</p> <p>You should also refer to and cite the following paper in its latest version:</p> <p><em>Delhasse, A., Beckmann, J., Kittel, C., and Fettweis, X.: Coupling MAR (Modèle Atmosphérique Régional) with PISM (Parallel Ice Sheet Model) mitigates the positive melt–elevation feedback, The Cryosphere, 18, 633–651, https://doi.org/10.5194/tc-18-633-2024, 2024.</em></p> <p>Reference</p> <p><em>Franco, B., Fettweis, X., Lang, C., and Erpicum, M.: Impact of spatial resolution on the modelling of the Greenland ice sheet surface mass balance between 1990–2010, using the regional climate model MAR, The Cryosphere, 6, 695–711, https://doi.org/10.5194/tc-6-695-2012, 2012.</em></p> <p><em>MARTeam: MARv3.11, GitLab [data set], <a href="https://gitlab.com/Mar-Group/MARv3" target="_blank" rel="noopener">https://gitlab.com/Mar-Group/MARv3#</a> (last access: 28 May 2022), 2021.</em></p>
Model simulation data used in "How non-equilibrium aerosol chemistry impacts particle acidity: the GMXe AERosol CHEMistry (GMXe-AERCHEM, v1.0) sub-submodel of MESSy"
<p>This dataset contains the output of the ECHAM/MESSy Atmospheric Chemistry (EMAC) model simulation analyzed in the work "How non-equilibrium aerosol chemistry impacts particle acidity: the GMXe AERosol CHEMistry (GMXe-AERCHEM, v1.0) sub-submodel of MESSy" submitted to Geoscientific Model Development.</p>
Hellisheiði geothermal field: Hydraulic data for pseudo-prospective forecasting models (Dec. 2018 - Jan. 2021)
<p>This dataset comprises the compound volumes processed from injection and production rates in the Hellisheiði field between December 2018 and January 2021. This dataset corresponds to the input data for the ETAS-f and Seismogenic Index models of Ritz et al., 2023 (doi:<a href="https://doi.org/10.22541/essoar.168500354.49240043/v1">10.22541/essoar.168500354.49240043/v1</a>)</p><p>The hydraulic data was acquired and processed by Reykjavik Energy/ON power, the operator of the Hellisheiði geothermal field.</p>
Surface Ozone, NO2, and PM2.5 Concentrations Estimated by the Deep Learning model (Air Transformer) based on Satellite data.
<p>Surface ozone, NO2, and PM2.5 concentrations Estimated by the deep learning model (Air Transformer) based on massive ground-level monitoring, satellite observations, meteorological conditions, dynamic industrial emissions, and other ancillary data from May 2018 to June 2021.</p>
Data, code and supplementary plots for "A hierarchical spline model for correcting and hindcasting temperature data"
<p>Data, code and supplementary plots for the paper "A hierarchical spline model for correcting and hindcasting temperature data". Please see README.txt for detailed description of the files and relevant instructions.</p>
AlphaFold models and supporting data for the annotation of Vairimorpha necatrix
<p>V_necatrix_alphafold.zip - Contains AlphaFold models and associated files for all V. necatrix proteins.</p><p>chimerax_annotater_plugin.zip - Contains the ChimeraX plugin we developed and used to annotate the V. necatrix proteome.</p><p>v_necatrix_annotation_data.zip - Contains all data used in the ChimeraX plugin to annotate the V. necatrix proteome.</p><p>V_necatrix_proteome.fasta - Protein fasta file containing all protein sequences</p><p>V_necatrix_haplotype[1-4].fasta - Nucleotide fasta file for each haplotype</p>
Exploring Localized Geomagnetic Disturbances in Global MHD: Physics and Numerics (Model Data)
<p>Model Data to reproduce plots from article "Exploring Localized Geomagnetic Disturbances in Global MHD: Physics and Numerics". README contains information on where to access model and visualization tools.</p>
Simulation data for paper "Evaluation of Fendiline Treatment in VP40 System with Nucleation-Elongation Process: A Computational Model of Ebola Virus Matrix Protein Assembly"
<p>This is the original simulation data sets for paper "Evaluation of Fendiline Treatment in VP40 System with Nucleation-Elongation Process: A Computational Model of Ebola Virus Matrix Protein Assembly".</p>
Mapping and modelling global mobility infrastructure stocks, material flows and their embodied greenhouse gas emissions - Data
<p>Dynamics of societal material stocks such as buildings and infrastructures and their spatial patterns drive surging resource use and emissions. Building up and maintaining stocks requires large amounts of resources; currently stock-building materials amount to almost 60% of all materials used by humanity. Buildings, infrastructures and machinery shape social practices of production and consumption, thereby creating path dependencies for future resource use. They constitute the physical basis of the spatial organization of most socio-economic activities, for example as mobility networks, urbanization and settlement patterns and various other infrastructures. </p><p>The data in this repository show the material stocks contained in global mobility infrastructure networks at the country-level and mapped at 5arcmins, as well as country-level estimates of material flows for maintenance, replacement and expansion of those infrastructures, and the associated GHG emissions from materials production. This repository contains all data as shown in figures of the article, including the GeoTIFF files for figure 3, and the supplementary data file containing full country-level results.</p><p><strong>Data</strong><br>This dataset includes the following data:</p><ul><li>Global maps of material stocks in mobility infrastructure networks at 5 arcmins, separate for all roads, all rail-based infrastructure, as well as in total and per capita</li><li>Global country-level material stock estimates for mobility infrastructures</li><li>Global country-level estimates of material flows and associated GHG emissions for materials production</li><li>Material intensity in mass per area of road (kg/m²) per road type</li><li>Material intensity in mass per area of railway track (kg/m²) per railway type</li><li>Material intensity in mass per area (kg/m²) per bridges and tunnels</li></ul><p>Material intensity factors are available for iron and steel, concrete, asphalt, aggregate (sand & gravel), timber, and other.</p><p><strong>Further information</strong><br>This dataset complements the following scientific article:</p><p>Wiedenhofer, Dominik, André Baumgart, Sarah Matej, Doris Virág, Gerald Kalt, Maud Lanau, Danielle Densley Tingley, u. a. "Mapping and Modelling Global Mobility Infrastructure Stocks, Material Flows and Their Embodied Greenhouse Gas Emissions". <i>Journal of Cleaner Production</i>, November 2023, 139742. <a href="https://doi.org/10.1016/j.jclepro.2023.139742">https://doi.org/10.1016/j.jclepro.2023.139742</a>.</p><p>For further information please see the publication. You can also contact Dominik Wiedenhofer <a href="mailto:dominik.wiedenhofer@boku.ac.at">dominik.wiedenhofer(a)boku.ac.at</a> and visit our <a href="https://boku.ac.at/understanding-the-role-of-material-stock-patterns-for-the-transformation-to-a-sustainable-society-mat-stocks">website</a> to learn more about our project: <i>MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.</i></p><p><strong>Funding</strong><br>This research was funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950). </p>
Model simulations utilizing the latest urban underlying surface and anthropogenic heat data
<p>Based on numerical simulations utilizing the latest urban underlying surface and anthropogenic heat data over the Yangtze River Delta urban agglomeration, we find that LU change and AH emission can result in opposite effects on summer precipitation. The related model simulations are included in this dataset.</p>
Data for the publication "Developing a climatological simplification of aerosols to enter the cloud microphysics of a global climate model" - part 2
<p>The data is split into two datasets, for each to be smaller than 50 GB.</p>
Particle tracking data in idealized and realistic estuary models
<p>Particle tracking data in the realistic North River estuary model, Delaware estuary model, and idealized estuary models with different channel dimensions.</p>
Model version, input data, results, and processing scripts for the Speizer et al. zero emissions transport paper
<p>Includes the files needed to run the GCAM scenarios, analyze the outputs, and produce the figures for the Speizer et al. zero emissions transport paper.</p>
Forecasts, score summary files, target observational data and meteorological driver files to accompany the manuscript "Skill of process-based forecasts relative to multiple null models varies across time and depth for water temperature and dissolved oxygen"
<p>This data publication includes raw ensemble forecast output (forecasts.zip), as well as summary score files (scores.zip) for process-based forecasts produced with the Forecasting Lake and Reservoir Ecosystems (FLARE) framework. In addition, it includes scores for climatology (climatology_scores.csv) and random walk (RW_scores.csv) null forecasts, formatted observational data of target variables (sunp-targets-insitu.csv), and meteorological driver files required for analysis to accompany the manuscript "Skill of process-based forecasts relative to multiple null models varies across time and depth for water temperature and dissolved oxygen". Forecasts were made of water temperature and dissolved oxygen at Lake Sunapee, NH in 2021 and 2022.</p>
Telemetry data from: Realized thermal niche approach eliminates temperature bias in 3 bioenergetic model estimates
<h4>Raw data for Ivanova et al paper in Ecology and Evolution</h4><p>Datafile is an .rds file.</p>
Data supporting "Lessons learned from multi-objective automatic optimizations of classical three-site rigid water models using microscopic and macroscopic target experimental observables"
<p>This repository contains the set of data and the code to reproduce the results shown in "Lessons learned from multi-objective automatic optimizations of classical three-site rigid water models using microscopic and macroscopic target experimental observables" published on Journal of Chemical Engineering and Data (DOI: 10.1021/acs.jced.3c00538).</p>
Research data supporting ""Lessons learned from multi-objective automatic optimizations of classical three-site rigid water models using microscopic and macroscopic target experimental observables""
<p>This repository contains the set of data and the code to reproduce the results shown in "Lessons learned from multi-objective automatic optimizations of classical three-site rigid water models using microscopic and macroscopic target experimental observables" published on Journal of Chemical Engineering and Data (DOI: 10.1021/acs.jced.3c00538).</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.