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5,803 results for “data model”
Mixed DG-FEM for the Darcy-Brinkman-Stokes model: supplementary simulation data
<p>This dataset contains simulation results used in the publication<em> "Stable across regimes: A mixed DG method for Darcy-Brinkman-Stokes type flows"</em>. Detailed descriptions of the individual cases can be found in the paper.</p> <p>The simulation outputs are enriched with the respective inputs used to set up the finite element simulations. Setups include definition of the mesh (sizes), material and numerical parameters. Setups are given as Python for scripted inputs (e.g. function definitions) and human-readable <em>.yaml</em> files for simple parameters. <br><br>Simulation outputs are written in paraview .vtk and .vtu files, which are contained in the <em>outputs/MODEL_NAME/paraview</em> folder of the respective simulation. <em>MODEL_NAME</em> corresponds to the model. See also the <em>readme.md.</em></p> <p>The additional folder <em>figure_collection</em> contains the raw result plots from the publication, along with the respective simulation inputs used to obtain the figure.</p>
FluxDataKit v3.4.2: A comprehensive data set of ecosystem fluxes for land surface modelling
<p>The Flux data kit is an effort to expand upon the existing work by Ukkola et a. (2022) to synthesize various sources of ecosystem flux data (i.e. the PLUMBER2 data set, gathered from all major networks). We further expand upon the original data set by integrating data which was either expanded upon (temporally) or where sites were added (e.g. the integration of ICOS data).</p> <p>The effort uses the FluxnetLSM package by the above mentioned authors, as well as their general workflow. In contrast to the PLUMBER2 data set we do not apply stringent quality control, and all quality control on the availability of variables and/or their duration <em>should be done by the user</em>. Furthermore, we include both leaf area index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) in the netcdf output, where PLUMBER2 only provided LAI. On all other parts the formatting and naming conventions as well as quality control specifications remain the same as in PLUMBER2. We therefore refer to Ukkola et al. (2022) for details.</p> <p><strong>Data included</strong></p> <p>The data included consists of the following files, containing different versions of the same data and site meta information.</p> <ul> <li><code>FLUXDATAKIT_LSM.tar.gz</code> file contains compressed NetCDF files compatible with the ALMA scheme for land surface modelling. </li> <li><code>FLUXDATAKIT_FLUXNET.tar.gz</code> file contains data in a CSV format according to the FLUXNET specifications.</li> <li><code>rsofun_driver_data_v3.3.rds</code> file is a compressed serialized R file containing data formatted for use with the {rsofun} R package.</li> <li><code><a href="../api/records/11370417/draft/files/fdk_site_info.csv/content" target="_blank" rel="noopener noreferrer">fdk_site_info.csv</a></code> contains site meta information in tabular form</li> <li><a href="../api/records/11370417/draft/files/fdk_site_fullyearsequence.csv/content" target="_blank" rel="noopener noreferrer"><code>fdk_site_fullyearsequence.csv</code></a> contains information about complete sequences of good-quality data by site (see also <a href="https://geco-bern.github.io/FluxDataKit/articles/04_data_use.html">here</a>).</li> </ul> <p><strong>Data generation</strong></p> <p>Data is generated using the FluxDataKit project. Although this project is not meant for continuous releases, and no support is provided in using this code with data provided AS IS, it might still be useful to some:</p> <p><a href="https://github.com/geco-bern/FluxDataKit">https://github.com/geco-bern/FluxDataKit</a></p> <p>The data can be further complimented using the FluxnetEO dataset, which is accessible through the package with the same name as found here:</p> <p><a href="https://github.com/geco-bern/FluxnetEO">https://github.com/geco-bern/FluxnetEO</a></p> <p><strong>Acknowledgements</strong></p> <p>The flux data kit is part of the LEMONTREE project and funded by Schmidt Futures and under the umbrella of the Virtual Earth System Research Institute (VESRI).</p> <p><strong>References:</strong></p> <ul> <li>Ukkola, Anna M., Gab Abramowitz, and Martin G. De Kauwe. "A flux tower dataset tailored for land model evaluation." Earth System Science Data 14.2 (2022): 449-461.</li> </ul>
Data for paper publication "Modelling emission and transport of key components of primary marine organic aerosol using the global aerosol-climate model ECHAM6.3–HAM2.3"
<p>The dataset presented here is related to the article by Leon-Marcos et al. 2025: "Modelling emission and transport of key components of primary marine organic aerosol using the global aerosol-climate model ECHAM6.3–HAM2.3" accepted for publication in GMD. It comprises global fields of the FESOM2.1-REcoM3 biogeochemistry model tracers employed to calculate the ocean biomolecule concentration that serve as input data for the aerosol model. Additionally, the ECHAM6.3–HAM2.3 code of the marine aerosol implementation and the required scripts to run the model experiments are provided here. The aerosol-climate model simulation results of the marine aerosol emission, as well as the evaluation of the model results compared to observations, are also included. For further information, please refer to the attached data description. </p> <p> </p> <h2> </h2>
Data from 'Local Regions Associated With Interdecadal Global Temperature Variability in the Last Millennium Reanalysis and CMIP5 Models'
<p><strong>Abstract from '<em>Local Regions Associated With Interdecadal Global Temperature Variability in the Last Millennium Reanalysis and CMIP5 Models</em>':</strong></p> <p>Despite the importance of interdecadal climate variability, we have a limited understanding of which geographic regions are associated with global temperature variability at these timescales. The instrumental record tends to be too short to develop sample statistics to study interdecadal climate variability, and Coupled Model Intercomparison Project, Phase 5 (CMIP5) climate models tend to disagree about which locations most strongly influence global mean interdecadal temperature variability. Here we use a new paleoclimate data assimilation product, the Last Millennium Reanalysis (LMR), to examine where local variability is associated with global mean temperature variability at interdecadal timescales. The LMR framework uses an ensemble Kalman filter data assimilation approach to combine the latest paleoclimate data and state-of-the-art model data to generate annually resolved field reconstructions of surface temperature, which allow us to explore the timing and dynamics of preinstrumental climate variability in new ways. The LMR consistently shows that the middle- to high-latitude north Pacific and the high-latitude North Atlantic tend to lead global temperature variability on interdecadal timescales. These findings have important implications for understanding the dynamics of low-frequency climate variability in the preindustrial era.</p>
Model simulation data used in "Exploring the uncertainties in the aviation soot-cirrus effect" (Righi et al., Atmos. Chem. Phys., 2021)
<p>This dataset contains the output of the EMAC global model simulations analysed and discussed in Righi et al. (<i>Atmos. Chem. Phys.</i>, 2021). For details see the README.md file and Table 1 in the paper.</p>
Graph Data: Hydrological impact of widespread afforestation in Great Britain using a large ensemble of modelled scenarios
<p>Data used for creating the figures in the paper: Hydrological impact of widespread afforestation in Great Britain using a large ensemble of modelled scenarios.</p> <p>It contains the flow exceedances (as mm day<sup>-1</sup>), flow duration slope, median elasticity and runoff ratio for the different afforestation scenarios. Also included is the information on the changes of broadleaf afforestation. </p> <p>If you have any questions, please email marcus.buechel@ouce.ox.ac.uk.</p>
Modified WRF/Chem source code, output data, and post-processing scripts for the GMD manuscript "Evaluation of WRF/Chem model (v3.9.1.1) real-time air quality forecasts over the Eastern Mediterranean"
<p>Here you will find the modified WRF/Chem code used in the simulations, the scripts used for post-processing and the model output data used in the manuscript. </p> <p>Two modifications have been made in module_aerosols_soa_vbs.F:</p> <ol> <li>ch_dust is set to1.0D-9*0.36</li> <li>The model is set not to initialize during restarts</li> </ol> <p>The model data directory includes:</p> <ol> <li>Two csv files (Winter and Summer) with the hourly concentrations of atmospheric pollutants at the locations of the ground stations. These data were used to produce Figures 4-8 in the manuscript as well as all the metrics.</li> <li>Two netcdf files (Winter and Summer) with the average ground concentrations of atmospheric pollutants over Cyprus. These data were use to produce Figure 3 in the manuscript. </li> </ol>
Data from PISM-LakeCC: Implementing an adaptive proglacial lake boundary in an ice sheet model
<p>In our study, we describe the implementation of an adaptive proglacial lake boundary in the Parallel Ice Sheet Model (PISM). The model was tested by applying it to the glacial retreat of the North American ice sheets after the LGM.</p> <p>This dataset contains selected timeslices and variables of the model output for our three main experiments (LAKE, CTRL and DEF). More details about the experiments can be found in our study:</p> <blockquote> <p>Hinck, S., Gowan, E. J., Zhang, X., and Lohmann, G.: PISM-LakeCC: Implementing an adaptive proglacial lake boundary in an ice sheet model, The Cryosphere, 16, 941–965, https://doi.org/10.5194/tc-16-941-2022, 2022.</p> </blockquote>
Data for "Modelling soil carbon stocks following reduced tillage intensity: a framework to estimate decomposition rate constant modifiers for RothC-26.3, demonstrated in north-west Europe"
<p>Dataset of paired observations of conventional tillage (CT) with no tillage (NT) and reduced tillage (RT) from studies in temperate oceanic regions of Western Europe, extracted from a recent systematic review (Jordon et al. preprint, see DOI below).</p> <p>R code of modelling framework to estimate tillage rate modifiers (TRM) for simulating adoption of RT and NT using RothC-26.3, and meta-estimates of TRM across studies.</p>
Figure data and model used in Stranded fossil-fuel assets translate to major losses for investors in advanced economies
<p>The package contains i) the figure code and underlying data to create all figures in the main paper and supplementary information of the journal article and ii) the network and imputation model used to calculate the shock calculation.</p>
Urban form data for climate modelling: Sydney at 300 m resolution derived from building-resolving and 2 m land cover datasets
<p><strong>Sydney morphology and land surface dataset</strong></p> <p>This dataset for Sydney, Australia, represents land cover, building morphology, vegetation morphology and other parameters appropriate for input into local or mesoscale urban climate models.</p> <p>The dataset is provided in netCDF4 and GeoTiff formats.</p> <p>Associated manuscript:</p> <blockquote> <p><a href="https://doi.org/10.3389/fenvs.2022.866398">A transformation in city-descriptive input data for urban climate models</a></p> </blockquote> <p>Citation for the open dataset:<br> - Lipson, M., Nazarian, N., Hart, M. A., Nice, K. A., and Conroy, B.: Urban form data for climate modelling: Sydney at 300 m resolution derived from building-resolving and 2 m land cover datasets (v1.01), <a href="https://doi.org/10.5281/zenodo.6579061">https://doi.org/10.5281/zenodo.6579061</a>, 2022.</p> <p>Citation for the associated manuscript:<br> - Lipson, M. J., Nazarian, N., Hart, M. A., Nice, K. A., and Conroy, B.: A Transformation in City-Descriptive Input Data for Urban Climate Models, Frontiers in Environmental Science, 10, <a href="https://doi.org/10.3389/fenvs.2022.866398">https://doi.org/10.3389/fenvs.2022.866398</a>, 2022.</p> <p>Location of associated processing code:<br> - <a href="https://github.com/matlipson/geoscape_processing_public.git">https://github.com/matlipson/geoscape_processing_public.git</a></p> <p><strong>Acknowledgments</strong></p> <p>We gratefully acknowledge the Australian Urban Research Infrastructure Network (AURIN) and Geoscape Australia for <br> providing the datasets necessary for this study, drawing on Geoscape Buildings, Surface Cover and Trees datasets, <br> © Geoscape Australia, 2020: https://geoscape.com.au/legal/data-copyright-and-disclaimer/. <br> This research was supported by the Australian Research Council (ARC) Centre of Excellence for Climate System Science <br> (grant CE110001028), the ARC Centre of Excellence for Climate Extremes (grant CE170100023). </p> <p> </p>
Additional evidence for a pulsar wind nebula in SN 1987A from multi-epoch X-ray data and MHD modelling
<p>This is a basic reproduction package for the paper "Additional evidence for a pulsar wind nebula in the hearth of sN 1987A from multi-epoch X-ray data and MHD modeling" by Greco et al. 2022. It aims to provide the most important data products to check and reproduce the main results of the paper.</p>
Southern African Power Pool GridPath Model Output Data - Chowdhury et al 2022 Joule
<p>This data repository holds GridPath model output data for the paper Chowdhury, A.K., Deshmukh, R., Wu, G., Uppal, A., Mileva, A., Curry, T., Armstrong, L., Galelli, S., and Kudakwashe, N. (2022) “Enabling a low-carbon electricity system for Southern Africa”, Joule. See Readme for more details. </p>
Data, scripts, and R Notebook for Carneiro et al 2023. Flight performance and wing morphology in the bat Carollia perspicillata: biophysical models and energetics. Integrative Zoology DOI:10.1111/1749-4877.12707
<p>Files provided as supporting information for the paper by Carneiro et al. 2023. Flight performance and wing morphology in the bat <em>Carollia perspicillata</em>: biophysical models and energetics. Integrative Zoology. DOI:10.1111/1749-4877.12707</p> <p>File descriptions</p> <p>ArmTA.txt - Temperature and surface areas for arms of <em>C. perspicillata</em> after flight experiment<br> BodyTA.txt - Temperature and surface areas for body of <em>C. perspicillata</em> after flight experiment<br> HeadTA.txt - Temperature and surface areas for head of <em>C. perspicillata</em> after flight experiment<br> WingTA.txt - Temperature and surface areas for wings (patagium) of <em>C. perspicillata</em> after flight experiment<br> WingMorph.txt - Morphological variables measured in the body and wings of <em>C. perspicillata</em><br> HeatLoss.R - Function to estimate heat loss (Qt)<br> PowFlight.R - Function to estimate minimum power required to fly<br> Script-HeatLoss-FlightPerformance.R - R script with set of analyses performed<br> SupportingInformationFile.docx - R notebook with set of analyses performed, word format<br> SupportingInformationFile.nb.html - R notebook with set of analyses performed, html format<br> SupportingInformationFile.Rmd - R notebook with set of analyses performed (R markdown)</p> <p>For the R scripts (Script-HeatLoss-FlightPerformance.R) and notebook (<br> SupportingInformationFile.Rmd) to work and be compiled, all files need to be copied to the same folder.</p>
Using machine learning to integrate genetic and environmental data to model genotype-by-environment interactions
<p>Files generated from the study described in <a href="https://doi.org/10.1101/2024.02.08.579534">Fernandes et. al (2024)</a> .</p> <p>The file "cvs_h2s.csv" comprises the coefficient of variation and the Cullis heritability for each environment.</p> <p>The file "all_predictions.csv" contains the predictions from all the models evaluated, in different cross-validation (CV) scenarios.</p> <p>The file "coincidence_index.csv" has the Coincidence Index (CI) for each CV and models evaluated in our study.</p> <p>Our study used the multi-environment maize yield trials data from the Genomes to Fields 2022 initiative (<a href="https://doi.org/10.1186/s13104-023-06421-z">Lima et. al 2024</a>).</p>
Data Repository: Land surface modelling activities at Weierbach catchment.
<p>The data in this repository comes from the modelling activities with the Community Land Model version 5.0 (CLM5) carried out at the Weierbach catchment, Luxembourg. The repository contains:</p> <ol> <li>A list of matric potentials of <em>Fagus sylvatica </em>at which it experiences a specific loss of conductivity (i.e., 12%, 50%, 88%) obtained from published data [File: additional_PHT_Fagus_sylvatica_Europe.csv].</li> <li>The hourly atmospheric forcing used during the simulations with CLM 5.0 in a NetCDF format [File: atmospheric_forcing.zip].</li> <li>All model results per experiment [model_results.zip].</li> <li>The R scripts for processing the model results for obtaining the information required for each figure [Files: manuscript_figure_#.R].</li> <li>A daily summary of the tree water deficit calculated per PFT, individual tree species, and the whole ecosystem [File: twd.csv].</li> <li>A daily summary of tree transpiration scaled at the catchment level per PFT, individual tree species, and the whole ecosystem [File: et_mm_wei.csv]. This daily summary is based on the hourly data available on: Klaus, J., Fabiani, G., Schoppach, R., Chun, K. P., Iffly, J. F., Penna, D., & Juilleret, J. (2024). Detailed sap flow monitoring data at Weierbach catchment, Luxembourg (Version v01) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.11381618" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.11381618</a></li> </ol>
Modelling of ready biodegradability based on combined public and industrial data sources
<p>The European REACH (Registration, Evaluation, Authorization and restriction of Chemicals) Regulation, requires marketed chemicals to be evaluated for Ready Biodegradability (RB). In-silico prediction is a valid alternative to expensive and time-consuming experimental testing. However, currently available models may not be relevant to predict compounds of industrial interest, due to accuracy and applicability domain restriction issues.</p> <p>In this work we present a new and extended RB dataset (2830 compounds), issued by the merging of several public data sources. It was used to train classification models, which were externally validated and benchmarked against already-existing tools on a set of 316 compounds coming from the industrial context. New models showed good performances in terms of predictive power (BA = 0.74 – 0.79) and data coverage (83 – 91 %).</p> <p>The Generative Topographic Mapping approach was employed to compare the chemical space of the various data sources: several chemotypes and structural motifs unique to the industrial dataset were identified, highlighting for which chemical classes currently available models may have less reliable predictions.</p> <p>Finally, public and industrial data were merged into Global dataset containing 3146 compounds and including a significant subset of compounds coming from the industrial context. This is the biggest dataset reported in the literature so far which covers some chemotypes absent in the public data. Thus, predictive model developed on the Global dataset has much larger applicability domain than related models built on publicly available data. The developed model is available for the user on the Laboratory of Chemoinformatics website.</p> <p>This dataset is only the "All-Public" set, since the industrial compounds cannot be disclosed.</p> <p>This update contains additional entries from [J. Chem. Inf. Model. 52 (2012), pp. 655–669] and [J. Chem. Inf. Model. 53 (2013), pp. 867–878]</p>
The data that support the findings of a review paper "From urban data to city-scale models: A review of traffic simulation case studies"
<p>This dataset contains the data that were used in a review paper "From urban data to city-scale models: A review of traffic simulation case studies". It contains the following files:</p> <ul> <li>keywords with counts.txt - list of keywords and their counts in the considered corpus of traffic simulation case studies. The data were used to produce Figure 2 and Figure 3 in the paper.</li> <li>Papers analysis.xlsx - Excel file containing the data on the reviewed studies. The document has the following sheets: <ul> <li> Appendix A - contains a table short reference, location, simulation period, spatial scale, simulated units and marked categories for a paper;</li> <li>Geography - contains data on geographical distribution of simulated areas between world regions and countries, these data were used to produce Figure 4 in the paper;</li> <li>Software tools - contains data on simulation tools used in the studies. </li> <li>Journals and conferences - contains data on where the reviewed papers were published.</li> </ul> </li> </ul>
Expedited Modeling of Burn Events Results (EMBER) Data Files
<p>This dataset includes photochemical air quality modeling files for simulations of fire impacts on ground-level ozone cocnentrations in the U.S. during the summer of 2023. A data dictionary describes what is included in the each of the files. Detailed information on the model simulations and the file contents is included in a journal article documenting the dataset: Simon, H., Beidler, J., Baker, K.R., Henderson, B.H., Fox, L., Misenis, C., Campbell, P., Vukovich, J. Possiel, N., Eyth, E. Expediated Modeling of Burn Events Results (EMBER): A Screening-Level Dataset of 2023 Ozone Fire Impacts in the US, <em>Data in Brief</em>, https://doi.org/10.1016/j.dib.2024.111208</p> <p>A web-based tool for browsing this dataset is also available at: https://www.epa.gov/air-quality-analysis/expedited-modeling-burn-events-results-ember</p>
Underlying data for: "Capturing the mechanosensitivity of cell proliferation in models of epithelium"
<p>For our publication "Capturing the mechanosensitivity of cell proliferation in models of epithelium" (available as a preprint at <a title="BioRXiv Link" href="https://doi.org/10.1101/2023.01.31.526438" target="_blank" rel="noopener">DOI: 10.1101/2023.01.31.526438 )</a> we here provide the raw data for the included plots and the code used to generate the Delayed Fisher Kolmogorov (DFK) data referenced in the main publication</p> <p>The archive '<em>underlying_data.zip</em>' contains raw data underlying the plots in the publication. <br>The archive '<em>puls_proliferation_rate-1.0.zip</em>' contains the code for generating DFK trajectories referenced in the publication and its SI. <br>The archive '<em>ddesolver-1.0.zip</em>' contains the python code for solving delayed differential equations used by the puls_proliferation_rate project. It is included to ensure completeness and reproducibility of the simulations. </p>
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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.