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27,923 results for “model”
Input data and results of the RECC-ODYM model for Greater Oslo study (v1.0)
<p>This repository contains the input data and results of the modified RECC-ODYM model for Greater Oslo study (v1.0) used in "Reducing material use and their greenhouse gas emissions in Greater Oslo" by Lola Rousseau, Jan Sandstad Næss, Fabio Carrer, Sara Amini, Helge Brattebø, and Edgar Hertwich.</p> <p>The publication and its supplementary information are available at: <a href="https://doi.org/10.1111/jiec.13611">https://doi.org/10.1111/jiec.13611</a></p> <p>The following data are included in this repository:</p> <ul> <li>A description (<strong>How_to_use_RECCODYM_Greater_Oslo.pdf</strong>) how to run the code with the database to generate the results </li> <li>The database (<strong>CURRENT_VN1_0.zip</strong>) with the parameters, the master classification file RECC_Classifications_Master_V2.0.xlsx, the model config file RECC_Config.xlsx and the list of scenario configurations RECC_ModelConfig_List.xlsx</li> <li>The results organized (<strong>results_organized.zip</strong>) by folder depending on the model run (results_organized) </li> </ul> <p>The code used with this database and generating these results is archived as v1.0 (<a href="https://github.com/LolaRousseau/RECC-ODYM/releases" target="_blank" rel="noopener">https://github.com/LolaRousseau/RECC-ODYM/releases</a>). The latest version is available on GitHub: <a href="https://github.com/LolaRousseau/RECC-ODYM" target="_blank" rel="noopener">https://github.com/LolaRousseau/RECC-ODYM</a></p> <div> <p>Please note that this is a modified version of RECC-ODYM with changes made for this study specifically. More general information about RECC-ODYM can be found on: <a href="https://www.industrialecology.uni-freiburg.de/odym-recc">https://www.industrialecology.uni-freiburg.de/odym-recc</a> and the original framework is also described here: <a href="https://doi.org/10.1111/jiec.13023">https://doi.org/10.1111/jiec.13023</a></p> </div>
Data needed to reproduce the flood hazard modeling of Pollack et al., 2024
<p>This repository contains some of the data needed (Data_Flood_Modeling) to reproduce the flood hazard modeling of Pollack et al., 2024 "[Funding rules that promote equity in climate adaptation outcomes](https://osf.io/preprints/osf/6ewmu)." which are required to run the codes https://github.com/CoRE-Lab-UCF/Pollack_et_al_2024.git </p> <p>Specifically, this repository contains:</p> <ol> <li>dem_subgrid_1m_nbd.asc (DEM at 1m resolution in ascii format. Source DEM is CoNED, see Supporting material of Pollack et al., 2024)</li> <li>Gloucester_street_light_utm.tif (Basemap in UTM coordinates, UTM18N with EPSGcode=26918)</li> <li>sfincs.inp (Model file of SFINCS)</li> <li>Unique_Land_Classes_CN.xls (Table including the land cover classes and corresponding Manning coefficients used for surface roughness)</li> </ol>
A uniaxial hysteretic superelastic constitutive model applied to additive manufactured lattices - data and postprocessing tools
<p>This data set contains all result data obtained during the implementation of an uniaxial hysteretic superelastic constitutive model and its application to additive manufactured lattices.</p> <p>Furthermore, it contains all ABAQUS .inp files, the implemented subroutine of the hysteretic superelastic constitutive model, diagrams generated from the data, as well as postprocessing tools for generating the diagrams.</p>
Data for "emIAM v1.0: an emulator for Integrated Assessment Models using marginal abatement cost curves"
<p>This dataset contains codes, data, tables, andd figures (high resolution) related to the following publication: Xiong, W., K. Tanaka, P. Ciais, D. J. A. Johansson, M. Lehtveer (2022) emIAM v1.0: an emulator for Integrated Assessment Models using marginal abatement cost curves. Submitted to arXiv on 23 December 2022.</p>
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>
Modelled gridded population estimates for the Kasaï-Oriental Province in the Democratic Republic of Congo (2024) version 4.2
<h2><strong>Content</strong></h2> <p>This repository contains the input data and scripts used to create the modeled gridded population estimates for Kasaï-Oriental Province in the Democratic Republic of Congo. It also includes the grid-cell posterior distributions and scripts to aggregate them within user-defined geographic boundaries.</p> <p> In particular, this repository contains two compressed files (.zip):</p> <p><strong>1. <code>population_estimates.zip</code></strong></p> <ul> <li>Includes raster files (<code>.tif</code>) with summaries of population count posterior predictions at the grid-cell level, specifically the mean, median, lower credible interval, and upper credible interval.</li> <li>Includes spatial files (<code>.gpkg</code>) with summaries of population count posterior predictions at the health-area and health-zone levels, specifically the mean, median, lower credible interval, and upper credible interval.</li> </ul> <p><strong>2. <code>population_model.zip</code></strong></p> <p>This directory comprises five subdirectories with scripts, input data, and output data necessary to replicate the population model:</p> <ul> <li><code><strong>01_model_stan</strong></code>: Contains the Stan model, input data, and an R script (<code>01_model_stan.R</code>) with a function to run the model.</li> <li><code><strong>02_model_run</strong></code>: Includes an R script (<code>02_model_run.R</code>) for running the model, along with output data.</li> <li><code><strong>03_model_evaluate</strong></code>: Features a Quarto report template (<code>03_model_evaluate.qmd</code>) and model evaluation summary files(.pdf).</li> <li><code><strong>04_predict_posterior</strong></code>: Provides R scripts (<code>04_predict_posterior.R</code> and <code>04_predict_run.R</code>) for generating predictions, along with input and output data, namely the posterior predictions files (.rds).</li> <li><code><strong>05_aggregate_posterior</strong></code>: Contains R scripts (<code>05_aggregate_posterior.R</code> and <code>05_aggregate_run.R</code>) and associated input and output data, namely the population count posterior summaries as presented in the file <code>population_estimates.zip</code> .</li> </ul> <p>The work was carried out in <code>R</code> (version 4.4.0), with the packages <code>tidyverse</code> (version 2.0.0), <code>terra</code> (version 1.7-78), <code>sf</code> (version 1.0-16), <code>furrr</code> (version 0.3.1), <code>doParallel</code> (version 1.0.17), <code>foreach</code> (version 1.5.2), <code>rstudioapi</code> (version 0.16.0), and <code>rstan</code> (version 2.32.6), on macOS Sequoia (version 15.1.1). While the scripts are designed to be portable, minor adjustments may be required for compatibility with other operating systems.</p> <h2><strong>Important</strong></h2> <p>This version includes changes in the STAN model <code>10h_survey_survey_covariate_building_random_effect_hierarchy_building_covariate_density_fixed_effect_hierarchy_density.stan</code>. Consequentely, all the files generated in the previous versions are now changed.</p> <p> </p> <p>For inquiries regarding the model and the data, please contact Gianluca Boo at gianluca.boo@soton.ac.uk.</p>
LigPCDS: Labeled Dataset of X-ray Protein Ligand Images in 3D Point Cloud and Validated Deep Learning Models
<p>The difference electron density from X-ray protein crystallography was used to create the first dataset of labeled ligand images in 3D point clouds, named <strong>LigPCDS</strong>. The dataset contain 244,226 entries of free organic ligands containing 3D representations labeled with two major labeling approaches: SP-based and AtomSymbol-based.</p> <p> </p> <p>The data from free organic molecules (non-covalent ligands) was retrieved from the Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) in december 2019 with resolutions ranging from 1.5 to 2.2 Å. The ligand images (blobs) were interpolated from their calculated difference electron density map in a 3D grid-like bounding box, around their atomic positions, and stored in point clouds. These ligand grid representations were further processed to retrive the final ligands representation in 3D point clouds using a mask of the shape of the ligand. A grid spacing of 0.5 Å gave the best results. The density value of the grid points was used as feature. The labeling approach used the structure of the ligands to propose vocabularies of chemical classes based on the chemical atoms themselves and their cyclic substructures. These structure annotations were applied pointwise to the ligand 3D representations using an atomic sphere model. Four proposed vocabularies were validated by successfully training good performance deep learning models for the semantic segmentation of a stratified dataset from LigPCDS, using 78902 entries.</p> <p>The four validated deep learning models are: (i) the LigandRegion, composed by generic atoms of any type; (ii) the AtomCycle, composed by generic atoms outside cycles and generic cycles; (iii) the AtomC347CA56, composed by generic atoms outside cycles, not aromatic cycles of size 3 to 7 and aromatic cycles of size 5 and 6; and (iv) the AtomSymbolGroups, composed by the atoms symbols with groupings. The mean accuracy of these models in their cross-validation was between 49.7% <span lang="EN-GB">[-19.4,20.</span><span lang="EN-GB">2]</span> and 77.4% <span lang="EN-GB">[-11.7,12.1]</span> in terms of Intersection over Union (mIoU) metric and between 62.4% <span lang="EN-GB">[-18.8,19.</span><span lang="EN-GB">7]</span> and 87.0% <span lang="EN-GB">[-8.4,8.8]</span> in F1-score (mF1), confidence interval between squared brackets. The models i, ii and iii and the used labeled representations in 3D point cloud are contained in the SP-based record; and model iv and its used labeled representations are contained in the AtomSymbol-based record.</p> <p>The dataset and validated models may be used to tackle problems regarding known and unknown ligand building to drug discovery and fragment screening pipelines. </p> <p>The code used to create and validated the LigPCDS is available at the following repository: https://github.com/danielatrivella/np3_ligand</p> <p>This repository also contains the NP³ Blob Label application for ligand building using the validated deep learning models from LigPCDS.</p>
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>
Database from: Developing a lateral topographic density model for Brazil.
<p>This dataset is part of the article entitled "DEVELOPING A LATERAL TOPOGRAPHIC DENSITY MODEL FOR BRAZIL".</p> <p>This dataset includes the topographic Lateral Topographic Density model for Brazil (LTDBrasil) and standard deviations (sdLTDBrasil), in Kg/m³, with 30 arc-seconds grid spacing.</p> <p>The files are in *tif and *.tfw format.</p> <p>Reference: Medeiros D.F., Marotta G.S., Yokoyama E., Franz I.B., Fuck R.A. 2021. Developing a lateral topographic density model for Brazil. Journal of South American Earth Sciences, v. 110, p. 103425. https://doi.org/10.1016/j.jsames.2021.103425</p>
3D model of a cairn grave near the Bear Trap in Northwest Greenland
<p>This dataset consists of a 3D dense point cloud and a textured mesh model (see the README file) of a cairn grave that is positioned near ‘The Bear Trap’. The Bear Trap is a Norse ruin at the western end of the Nuussuaq Peninsula. The 3D model was created from 639 digital photographs that were processed using Agisoft Metashape Pro v1.7; Linux Ubuntu). A 24 megapixel Sony a6000 APS-C mirrorless camera fitted with a 17 mm lens was used to acquire ground-level imagery of the structure.</p> <p>The image survey was conducted as part of the Vaigat Iceberg-Microbial Oil Degradation and Archaeological Heritage Investigation (VIMOA) project, which was funded by the Danish Centre for Marine Research and supported by the Arctic Research Centre at Aarhus University, the National Museum of Denmark, the Greenland Institute of Natural Resources, and The Greenland National Museum and Archives in Nuuk. Permits for the survey were obtained in advance from the Greenland National Museum and Archives in Nuuk. Walsh et al. (2020) provides an overview of the archaeological surveys conducted during the VIMOA project and Walsh et al. (in prep) provides further details specific to The Bear Trap and surrounding archaeological contexts. </p> <p>Walsh et al. (2020) The VIMOA project and archaeological heritage in the Nuussuaq Peninsula of north-west Greenland. <em>Antiquity</em> 94:e6 doi:10.15184/aqy.2019.230</p> <p>Walsh, Matthew J., Daniel F. Carlson, Pelle Tejsner, and Steffen Thomsen. The Bear Trap: Reinvestigating a unique stone structure on the northwest tip of the Nuussuaq Peninsula, Greenland. Submitted to <em>Arctic Anthropology</em>.</p>
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>
SmartBlades 2.0 Rotor Blade Nastran Models
<p>The <a href="https://www.iwes.fraunhofer.de/en/research-projects/finished-projects-2020/smart-blades-2.html">SmartBlades 2.0 project</a> was funded by the Federal Ministry for Economic Affairs and Energy (BMWi) under the Funding number: 0324032.</p> <p>The reference finite element model in which this dataset is based upon consists of a validated wind turbine blade model for this 20-meters blade:</p> <blockquote> <p>Christian Willberg. (2020). Smartblades 2 finite element reference wind turbine blade model (1.1) [Data set]. Zenodo. <a href="http://doi.org/10.5281/zenodo.4604693">https://doi.org/10.5281/zenodo.4604693</a></p> </blockquote> <p>This dataset includes four finite element models of the SmartBlades 2.0 Rotor Blade, described below. All input files "*.bdf" were used in MSC Nastran version 2018.2 to generate the output files "*.h5".</p> <p>1) SmartBlades2 Rotor Blade Model V02 Topology Update</p> <p><a href="http://doi.org/10.5281/zenodo.4604693">Willberg, C. model</a> updated with topology features in the trailing edge, and spar-web joint regions. Including additional strucutral and test sensor masses, and refined mesh of 50mm element size.</p> <p>1A) Model incorporating a clamped root boundary condition and test sensor masses:</p> <ul> <li>Input: <a href="https://zenodo.org/record/5729717/files/SmartBlades2_Rotor_Blade_Nastan_Model_V02-Topology_Update-Root_Clamped.bdf">SmartBlades2_Rotor_Blade_Nastan_Model_V02-Topology_Update-Root_Clamped.bdf</a></li> <li>Output: <a href="http://zenodo.org/record/5729717/files/SmartBlades2_Rotor_Blade_Nastan_Model_V02-Topology_Update-Root_Clamped.h5">SmartBlades2_Rotor_Blade_Nastan_Model_V02-Topology_Update-Root_Clamped.h5</a></li> </ul> <p>1B) Model incorporating a free-free boundary condition:</p> <ul> <li>Input: <a href="http://zenodo.org/record/5729717/files/SmartBlades2_Rotor_Blade_Nastan_Model_V02-Topology_Update-Free_Free.bdf">SmartBlades2_Rotor_Blade_Nastan_Model_V02-Topology_Update-Free_Free.bdf</a></li> <li>Output: <a href="http://zenodo.org/record/5729717/files/SmartBlades2_Rotor_Blade_Nastan_Model_V02-Topology_Update-Free_Free.h5">SmartBlades2_Rotor_Blade_Nastan_Model_V02-Topology_Update-Free_Free.h5</a></li> </ul> <p><br> 2) SmartBlades2 Rotor Blade Model V02b Sparweb Free Joint Variant</p> <p>The V02 Topology Update model including RBE2 connections along the spar-web joints with decoupled rotation in the spanwise axis.</p> <p>2A) Model incorporating a clamped root boundary condition and test sensor masses:</p> <ul> <li>Input: <a href="http://zenodo.org/record/5729717/files/SmartBlades2_Rotor_Blade_Nastan_Model_V02b-Sparweb_Free_Joint-Root_Clamped.bdf">SmartBlades2_Rotor_Blade_Nastan_Model_V02b-Sparweb_Free_Joint-Root_Clamped.bdf</a></li> <li>Output: <a href="http://zenodo.org/record/5729717/files/SmartBlades2_Ro tor_Blade_Nastan_Model_V02b-Sparweb_Free_Joint-Root_Clamped.h5">SmartBlades2_Ro tor_Blade_Nastan_Model_V02b-Sparweb_Free_Joint-Root_Clamped.h5</a></li> </ul> <p>2B) Model incorporating a free-free boundary condition:</p> <ul> <li>Input: <a href="http://zenodo.org/record/5729717/files/SmartBlades2_Rotor_Blade_Nastan_Model_V02b-Sparweb_Free_Joint-Free_Free.bdf">SmartBlades2_Rotor_Blade_Nastan_Model_V02b-Sparweb_Free_Joint-Free_Free.bdf</a></li> <li>Output: <a href="http://zenodo.org/record/5729717/files/SmartBlades2_Rotor_Blade_Nastan_Model_V02b-Sparweb_Free_Joint-Free_Free.h5">SmartBlades2_Rotor_Blade_Nastan_Model_V02b-Sparweb_Free_Joint-Free_Free.h5</a><br> </li> </ul> <table> <caption>General data of the blade (from <a href="https://elib.dlr.de/131955/1/AG_FVW_in_der_Windenergie_191024_Stueve.pdf">https://elib.dlr.de/131955/1/AG_FVW_in_der_Windenergie_191024_Stueve.pdf</a>)</caption> <tbody> <tr> <td>Diameter of the rotor</td> <td>46.61m</td> </tr> <tr> <td>Nominal rotational speed</td> <td>37.1 rpm</td> </tr> <tr> <td>Length of rotor blade</td> <td>19.99 m</td> </tr> <tr> <td>Maximum chord length</td> <td>2.38 m</td> </tr> <tr> <td>Max. pre-bend</td> <td>1 m</td> </tr> <tr> <td>Surface of main shell</td> <td>69.8 m²</td> </tr> <tr> <td>Blade nominal mass</td> <td> <p>Fiber mass (dry) 889.5 kg</p> <p>Infusion Resin 579.3 kg</p> <p>Bonding Resin 44 kg</p> <p>Other materials (e.g. Foam) 80.5 kg</p> <p>Extra masses (e.g. Sensors) 123.4 kg</p> <p>Total mass of the blade 17168 kg</p> </td> </tr> </tbody> </table> <p> </p>
Datasets for "A unified framework to estimate the origins of atmospheric moisture and heat using Lagrangian models"
<p>This repository contains the post-processed model outputs from HAMSTER v1.2.0 as used in the following paper: </p> <p>Keune, J., Schumacher, D. L., and Miralles, D. G.: A unified framework to estimate the origins of atmospheric moisture and heat using Lagrangian models, Geosci. Model Dev., 15, 1875–1898, https://doi.org/10.5194/gmd-15-1875-2022, 2022.<br> <br> The data set contains (1) global validation statistics for the three fluxes (evaporation, precipitation, sensible heat), and (2) the climatological source regions of precipitation and heat for Denver, Beijing and Windhoek. The former are found in the directory 'validation/global', and the latter are found in the directories '1001' (Denver), '3001' (Beijing) and '5002' (Windhoek). Multiple experiments were performed to assess the uncertainty of the source regions. Thus, multiple files exist, that show the same variables but for multiple experiments (indicated by the names "ALLPBL", "RH-10-20", "SOD08-SCH19", "SCH20", "FAS19" in the file name). For the moisture source regions, the uncertainty of the attribution methodology was assessed; these are indicated by the different folders, i.e. 'linear_upscaled' and 'random2_upscaled'. For each city and each experiment, the climatologically averaged source regions ('_mean.nc') and the climatologically averaged individual backward day contributions ('_bwmean.nc') are provided. Data sets are in the netCDF format and contain metadata following the CF convention.</p>
Sogenannter Hexenturm von Schloss Ulmerfeld, NÖ – Datengrundlage des 3D-Modells des Innenraums
<p>Photos for the 2015's 3d model of the interior of the so-called witch tower (’Hexenturm’) at the south easteren corner of the outer fortifications of Ulmerfeld Castle. The model was made using 3d photogrammetry (image based modeling) and mast aerial photography.</p>
Atmospheric Halocarbon Observations at Beromünster, Switzerland, and Bayesian Inverse Modeling to assess Emissions
<p>Atmospheric halocarbon (CFCs, halons, HCFCs, HFCs, PFCs, SF<sub>6</sub>, NF<sub>3</sub>, HFOs) and carbon monoxide (CO) observations (mole fractions) from the tall tower site at Beromünster, Switzerland (47.2 °N, 8.2 °E, 797 m a.s.l., 212 m a.g.l.), covering the period September 2019 to September 2020. The halocarbon measurements were conducted using a Medusa pre-concentration unit, coupled to gas chromatography (Agilent 6890N) and mass spectrometry (Agilent 5975, GC-MS).</p> <p>For further details see: Miller, B. R., Weiss, R. F., Salameh, P. K., Tanhua, T., Greally, B. R., Mühle, J., and Simmonds, P. G.: Medusa: A Sample Preconcentration and GC/MS Detector System for in Situ Measurements of Atmospheric Trace Halocarbons, Hydrocarbons, and Sulfur Compounds, Anal. Chem., 80, 1536–1545, https://doi.org/10.1021/ac702084k, 2008).</p> <p>The data format follows that used within the AGAGE network (see AGAGE data archive: <a href="http://agage.mit.edu/data/agage-data">http://agage.mit.edu/data/agage-data</a>).</p> <p>Data results for the Bayesian inversion conducted based on the measurement data from Beromünster to assess Swiss halocarbon emissions. Files are provided in netCDF format for the 28 individual substances discussed in (Rust, D. et al., 2022, <em>Swiss halocarbon emissions for 2019 to 2020 assessed from regional atmospheric observations</em>, Atmospheric Chemistry and Physics). Each file contains the a priori and a posteriori emissions as used or calculated in the Bayesian inversion. Data are provided on the grid used in the inversion (irregular longitude/latitude). Metadata are included as netCDF attributes. The netCDF files follow the CF conventions and are readable with any netcdf interface/tool.</p>
3D magnetotelluric modeling using high-order tetrahedral Nédélec elementson massively parallel computing platforms
<p>Accompanying data to journal article</p> <blockquote> <p>Castillo-Reyes, O., Modesto, D., Queralt, P., Marcuello, A., Ledo, J., Amor-Martin, A., de la Puente, J., García-Castillo, L.E. (2021) 3D magnetotelluric modeling using high-order tetrahedral Nédélec elements on massively parallel computing platforms. Computers & Geosciences, vol.(160): 105030 DOI: 10.1016/j.cageo.2021.105030. ISSN 0098-3004, Elsevier.</p> </blockquote>
3D Models of 6dof Motion
<p>This repository contains a 3d model (head_6dof.blend) visualizing motions with 6 degrees of freedom. For the head we use a 3D model created with <a href="http://www.makehumancommunity.org">make human</a> under the <a href="https://creativecommons.org/licenses/by-sa/3.0/">CC BY-SA 3.0</a> license. All body parts but the head were removed from the model used. The main model contains the head, all planes, axes, and labels. Examples of rendered images and an animation are included.The model was created with Blender.</p> <p><strong>Nomenclature</strong><br> Names of the axes and planes according to <a href="https://link.springer.com/content/pdf/10.1007/s00405-015-3835-y.pdf">Bremova et al., 2016</a>:</p> <table align="center"> <thead> <tr> <th scope="col">Abreviation</th> <th scope="col">Axis</th> </tr> </thead> <tbody> <tr> <td>IA</td> <td>inter-aural</td> </tr> <tr> <td>HV</td> <td>head-vertical</td> </tr> <tr> <td>NO</td> <td>naso-occipital</td> </tr> </tbody> </table> <p>Colors<br> Colors are taken from the <a href="https://www.nature.com/articles/nmeth.1618.pdf">Bang Wong color palette</a> which is designed to be accessible to people who are colorblind.</p> <table align="center"> <thead> <tr> <th scope="col">Axis</th> <th scope="col">Color</th> <th scope="col">RGB</th> </tr> </thead> <tbody> <tr> <td>IA</td> <td>bluish green</td> <td>000,158,115</td> </tr> <tr> <td>HV</td> <td>blue</td> <td>000,114,178</td> </tr> <tr> <td>NO</td> <td>vermillion</td> <td>213,094,000</td> </tr> <tr> <td>roll</td> <td>reddish purple</td> <td>204,121,167</td> </tr> <tr> <td>pitch</td> <td>orange</td> <td>230,159,000</td> </tr> <tr> <td>yaw</td> <td>sky blue</td> <td>086,180,233</td> </tr> </tbody> </table> <p> </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.