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7,949 results for “ALS”
ECOBREED WP3 entomopathogenic fungi-wireworm data related to Razinger et al. (2020)
<p>Raw data related to Figures 1 to 5 and Table 1 plus suplementary raw data of the publication Razinger et al. (2020) Frontiers in Plant Science 11:535005; doi: 10.3389/fpls.2020.535005.</p>
BAM reference data: XPS raw data of Al-coated titania nanoparticles (JRCNM62001a and JRCNM62002a)
<p>The raw data are given as VAMAS-File. The measurement condtions are given in the file. The C1s-fits are provided as ascii-files.</p> <p>For further information please look at Radnik, J. Kersting, R., Hagenhoff, B., Bennet, F., Ciornii, D.; Nymark, P., Grafström R. and Hodoroaba, V.-D. <em>Nanomaterials </em><strong>2021</strong>, <em>11</em>, 639. https://doi.org/10.3390/nano11030639.</p> <p>The transmission function is obtained as ascii-file trm.dat. The energy scale is kinetic energy.</p> <p> </p> <p>Measurement conditions:</p> <p>XPS measurements were performed at an Axis Ultra DLD (KRATOS, Manchester, UK) with monochromatic Al K radiation (E = 1486.6 eV). The electron emission angle was 0° and the source-to-analyzer angle was 60°. The binding energy scale of the instrument was calibrated following a Kratos analytical procedure, which uses ISO 15472 binding energy data. The setting of the instrument was the hybrid lens mode and the slot mode with an analysis area of approximately 300x700 m². Furthermore, charge neutralization with a flood gun was used. All spectra were recorded in the fixed analyzer transmission (FAT) mode. The samples were measured as powders prepared on a special stainless-steel sample holder.</p> <p> </p> <p> </p> <p> </p>
S60 | SWISSPEST19 | Swiss Pesticides and Metabolites from Kiefer et al 2019
<p>This is the collection associated with list S60 SWISSPEST19 on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>Swiss pesticides (plant protection products) and metabolites from Kiefer et al 2019 (Eawag), Tables SI-B 1 and 2, DOI: <a href="https://doi.org/10.1016/j.watres.2019.114972">10.1016/j.watres.2019.114972</a></p> <p>Update 25 April 2020: fixed many naming issues in xlsx and csv file. No structural information changed. 25 Mar 2023: fixed date CAS. 25 May 2023: fixed non-live CIDs to <a href="https://gitlab.lcsb.uni.lu/eci/pubchem/-/commit/56abd8e2bacbbb6969c961101df792189d605bfd">live CIDs</a>. 6 Jul 2023: added <a href="https://gitlab.lcsb.uni.lu/eci/pubchem/-/commit/3ff26d704ad9ac831a5d276256c1c89020fb2e02">NOA 413161</a> structures and transformations file. 8 April 2025: fixed three CIDs that went non-live to match CIDs from NORMAN deposition. Mismatch of InChIKeys due to difference in treatment of the C=N-O groups between PubChem and Open Babel; files now match PubChem's output. 2 Jun 2025: updated TFA synonym. 31 Aug 2025: fixed triazole alanine structure for one entry (removing incorrect CID <span><a href="https://pubchem.ncbi.nlm.nih.gov/compound/139597001"><span>139597001</span></a></span>).</p>
Model simulation data used in "The global impact of the transport sectors on atmospheric aerosol in 2030 – Part 2: Aviation" (Righi et al., Atmos. Chem. Phys., 2016)
<p>This dataset contains the output of the EMAC global model simulations analysed and discussed in Righi et al. (<i>Atmos. Chem. Phys.</i>, 2016). For details see the README.md file.</p>
Model simulation data used in "The global impact of the transport sectors on atmospheric aerosol in 2030 – Part 1: Land transport and shipping" (Righi et al., Atmos. Chem. Phys., 2015)
<p>This dataset contains the output of the EMAC global model simulations analysed and discussed in Righi et al. (<i>Atmos. Chem. Phys.</i>, 2015). For details see the README.md file.</p>
Data files for figures in "Sensitivity of extreme precipitation to climate change inferred using artificial intelligence shows high spatial variability" by Bird et al.
<p>The data files for figures in <i>Sensitivity of extreme precipitation to climate change inferred using artificial intelligence shows high spatial variability</i> by Bird, Bodeker and Clem. The files required to create each figure in the paper and in the supplementary material are described in a readme.txt file which is also provided below:</p><p><strong>Figure 1</strong></p><p>The background image was obtained from the 'NaturalEarthFeature' function of the python Cartopy library (Figure1_background.png). The data required to generate the plots shown in Figure 1 are provided in the Figure1.nc file:</p><ul><li>The latitudes and longitudes for the 10,000 training sites are provided in Training_location_latitudes and Training_location_longitudes variables. </li><li>The latitudes and longitudes for the 8 sites used to demonstrate the ability of the CNN to generalise spatially are provided in the Validation_location_latitudes and Validation_location_longitudes variables. </li><li>The block maxima at each of the 8 sites are provided in the Location_1year_block_maxima variable.</li><li>The GEV fits at 0°C are provided in the GEV_fit_at_0.0C variable.</li><li>The GEV fits at 1.5°C are provided in the GEV_fit_at_1.5C variable.</li></ul><p><strong>Figure 2</strong></p><ul><li>The 1-in-100 year precipitation fields for values of T'Global from 0.0°C to 2.0°C in 0.1°C increments are provided in netCDF files named Figure2_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li><li>The ratios of the 1-in-100 year precipitation depths with respect to the long-term (20 years or more) average of the annual maximum 1-day precipitation are provided in text files named Figure2_Precipitation_mean_block_max_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li></ul><p><strong>Figure 3</strong></p><ul><li>The cumulative distribution functions (CDFs) shown in the lower four panels are provided as text files listing the ARI in years and the daily total precipitation depth in mm. These files are named CDF_<lat>_<long>.dat where <lat> is the latitude and <long> is the longitude. Files for each region are zipped into .7z files named Figure3_<region>.7z where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li><li>The latitudes and longitudes for the upper panels can be inferred from the file names for each region.</li></ul><p><strong>Figure 4</strong></p><p>The data for each panel are provided in a netCDF file named Figure4_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</p><p><strong>Figure 5</strong></p><p>The data are provided as text files named Figure5_<region> where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. Each file lists the sensitivities at ARIs of 10, 20, 50, 100, and 200 years.</p><p><strong>Figure 6</strong></p><p>The data are provided as text files named Figure6_<region> where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. Each file lists the precipitation depths and the sensitivities at ARIs of 10, 20, 50, 100, and 200 years. </p><p><strong>Figure 7</strong></p><p>The data for each panel are provided in a netCDF file named Figure7_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</p><p><strong>Figure 8</strong></p><p>The data are provided as text files named Figure8_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. Each file lists the global surface temperature anomaly (°C) and the average negative log likelihood.</p><p><strong>Figure S1 and Figure S3</strong></p><p>The data are provided as text files named Figure_S1_and_S3_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. A header at the top of each file describes its contents.</p><p><strong>Figure S2</strong></p><ul><li>The 1-in-20 year precipitation fields for values of T'Global from 0.0°C to 2.0°C in 0.1°C increments are provided in netCDF files named FigureS2_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li><li>The ratios of the 1-in-20 year precipitation depths with respect to the long-term (20 years or more) average of the annual maximum 1-day precipitation are provided in text files named FigureS2_Precipitation_mean_block_max_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li></ul><p><strong>Figure S4</strong></p><ul><li>The block maxima for each site are provided in text files named FigureS4_blockmaxima_siteA.txt and FigureS4_blockmaxima_siteB.txt. A header at the top of each column described the column contents. </li><li>The GEV-derived curves for each site are provided in text files named FigureS4_gevcurves_siteA.txt and FigureS4_gevcurves_siteB.txt. A header at the top of each column described the column contents. </li></ul><p><strong>Figure S5</strong></p><p>There are no data associated with this figure. This figure was made using Microsoft Powerpoint.</p><p><strong>Figure S6</strong></p><p>The data are provided as text files named FigureS6_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. A header at the top of each file describes the files contents.</p>
Datasets for "Pulsational pair-instability supernovae in gravitational-wave and electromagnetic transients" from Hendriks et al 2023.
<p>Data related to the paper "Pulsational pair-instability supernovae in gravitational-wave and electromagnetic transients" by Hendriks et al 2023 <a href="https://doi.org/10.1093/mnras/stad2857">https://doi.org/10.1093/mnras/stad2857</a>.</p><ul><li>`EVENTS_V2.2.2_SEMI_HIGH_RES*.tar.gz: main PPISN prescription variation simulation results for the GW mergers. These contain configurations for the populations and the convolved merger results which in turn contain merger rates, merger properties and events that preceded the mergers (RLOF episodes, SNe). These results are used in figures 2, 3, 6, and 7. Figure 8 uses the SFR used in one of these simulations.</li><li>`EVENTS_V2.2.2_MID_RES*.tar.gz`: PPISNe prescription variation results for the transient rate evolution. These contain configurations for the populations and the convolved merger results which in turn contain merger rates, merger properties and events that preceded the mergers (RLOF episodes, SNe). These results are used in figure 4.</li><li>`grid_single_mass_metallicity_data.tar.gz`: data containing single-star remnant-mass data as a function of initial mass vs. final mass for our fiducial model and three variations: Farmer 2019 PPISN prescription, M_extra_ppisn_ML=10 Msun (i.e. where 10 solarmass of additional mass loss occur for each PPISN), M_co_shift_ppisn=-5 Msun (i.e. the CO core mass range that undergoes PPISN is shifted to lower masses by 5 solarmass). This data is used in figure 5.</li><li>`schematic_overview_data.tar.gz`: data containing single-star remnant-mass data as a function of pre-SN core mass for our fiducial models and several variations: Farmer 2019 PPISN prescription, M_extra_ppisn_ML = 5 Msun, M_co_shift_ppisn=-5 Msun, M_co_shift_ppisn=+5 Msun. This data is used in figure 1.</li><li>`paper_ppisne_scripts-main.tar.gz`: git-repository that contains the routines to generate the figures. The readme in this script should contain enough information, but relevant to the data here: the user needs to store the files contained in this zenodo repository in a directory that they point to at with an environment variable called `paper_PPISNe_Hendriks2023_data_dir`. These scripts are also hosted on <a href="https://gitlab.com/dhendriks/paper_ppisne_scripts">https://gitlab.com/dhendriks/paper_ppisne_scripts</a></li></ul>
Saharan HPEs identified and described in Armon et al. (in rev.)
<p>This dataset represents heavy precipitation events (HPEs) identified and described in Armon et al. (in rev., Weather and Climate Extremes). </p><p>It contains two files, both similar to Table 1 in the manuscript: </p><p>(a) 'Sahara_HPEs.csv' contains details of 41,978 HPEs throughout the Sahara</p><p>(b) 'Sahara_Core_HPEs.csv' contains details of 650 HPEs identified over the core of the Sahara.</p><p>Please note, coordinates represent the centre-of-mass of precipitation for every HPE.</p>
Data from Nölle et al. 2023 (published in MNRAS)
<p>Supplementary data for Nölle et al. 2023 (MNRAS) including an event table of the Cassini's Cosmic Dust Analyzer instrument as well as measurements at the LILIBID-MS laboratory of Freie Universität Berlin, used to analyze potential effects of space weathering processes on the radial composition on the microscopic icy dust grains of Saturn's large, diffuse E ring.</p>
Selected properties of galaxy and SMBH populations (Spinoso et al. 2023)
<p>This record presents the catalogs of galaxy and Black Holes properties associated to the two runs of the modified version of the L-Galaxies Semi-Analytic Model (SAM) presented in Spinoso et al. 2023. These catalogs are aimed at providing the basic properties to study the population of Black Holes (BHs) and their host galaxies across cosmic times, obtained by running the L-Galaxies SAM over the whole Millennium-II box (see Boylan-Kolchin et al. 2009). The L-Galaxies SAM outputs summarized in these catalogs were obtained at several redshifts/snapshots, for two different runs which differ for the initial occupation fraction of BHs at the time of their formation. This initial occupation fraction is parametrized by the Gp parameter(see Spinoso et al. 2023 for details), with the two runs being characterized by Gp=1 and Gp=0.01. The catalogs are organized in two group of files, one group for each run. Each of these groups is composed by 18 different files, one per each availablle redshift, roughly corresponding to: z = 0, 0.5, 1, 1.5, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15. The two group of files can be easily distinguished by their names: indeed, the strings "Gp1" and "Gp001" are referred to the runs corresponding to the Gp=1 and Gp=0.01 values, respectively. In addition, every file includes a string of the form: "z[x.yz]" which specifies its redshift. </p> <p>The content of these catalogs is as follows: each redshift-file contains the same collection of arrays, each array being a galaxy or BH property. At each redshift, L-Galaxies outputs properties for the number 'NGAL' of galaxies identified in the Millennium-II box, at that specific redshift/snapshot. Therefore, most of the arrays have length equal to 'NGAL' (i.e. one value per each galaxy). Few of the arrays have a length of N * NGAL (i.e. N values per each galaxy). The content, units and data type of these arrays are as follows: </p> <ul> <li>"StellarMass" - Total stellar mass of each galaxy - [10^10 Msun / h] - array[NGAL]</li> <li>"Sfr" - Star formation rate of each galaxy - [Msun / yr] - array[NGAL]</li> <li>"SeedMass" - BH-seed mass. 7 values per galaxy; one value for each of the 7 possible BH-seed "flavors" modeled - [10^10 Msun / h] - array[NGAL, 7]</li> <li>"Rvir" - Virial radius of the DM halo hosting each galaxy - [Mpc / h] - array[NGAL]</li> <li>"Pos" - X, Y and Z position of each galaxy - [Mpc / h] - array[NGAL, 3]</li> <li>"Mvir" - Virial mass of the DM halo hosting each galaxy - [10^10 Msun / h] - array[NGAL]</li> <li>"Lbol" - Bolometric luminosity associated to the central AGN (==0 if the BH is not active) - [10^40 erg / s] - array[NGAL]</li> <li>"HotGas" - Mass of the hot-phase of each galaxy's gas component - [10^10 Msun / h] - array[NGAL]</li> <li>"fEDD" - Eddington ration (defined as Lbol/L_Edd, with L_Edd being the Eddington luminosity) for each AGN (==0 if the BH is not active) - [adim] - array[NGAL]</li> <li>"ColdGas" - Mass of the cold-phase of each galaxy's gas component - [10^10 Msun / h] - array[NGAL]</li> <li>"BlackHoleMass" - Mass of the central massive BH hosted by each galaxy (==0 if the galaxy does not host a central BH) - [10^10 Msun / h] - array[NGAL]</li> <li>"SeedType" - Identifier of the type of BH-seed which originated each BH (see below for details) - array[NGAL]</li> <li>"Redshift" - Redshift of each galaxy (within a single file, this is an array of identical values) - array[NGAL]</li> </ul> <p>NOTE:<br>The model presented in Spinoso et al. 2023 follows 7 different types of BH-seeds. The "SeedMass" array contains 7 mass values (one for each of these types of BH-seeds) for each galaxy in the Millennium-II box.This is the reason why the data type of "SeedMass" is [NGAL, 7]. Each of these 7 values is the sum, across the whole evolution of each galaxy, of the contributions to the total BH mass coming from each BH-seed who merged to form the final BH. In the vast majority of cases, BHs are associated to only one type of BH-seed. In those cases, 6 out of the 7 "SeedMass" values would be zero. Each element of "SeedMass" corresponds to one type of BH seed according the following scheme:<br>SeedMass[0] : total seed mass of light-seeds inherited from the GQd model (see Spinoso et al. 2023 for details)<br>SeedMass[1] : total seed mass of heavy-seeds inherited from the GQd model (see Spinoso et al. 2023 for details)<br>SeedMass[2] : un-resolved mass-growth driven by gas-accretion before the halo hosting the BH was resolved<br>SeedMass[3] : total seed mass formed as light-seeds in L-Galaxies<br>SeedMass[4] : total seed mass formed as Direct-Collapse BHs (DCBHs)<br>SeedMass[5] : total seed mass formed as intermediate-mass BH originated via Runaway Stellar Mergers (RSM)<br>SeedMass[6] : total seed mass formed as Merger-Induced Direct-Collapse BH (miDCBH)</p> <p>NOTE:<br>Similarly to "SeedMass", also the "Pos" array has more than one element per galaxy. These are the three cartesian positions of each galaxy.</p> <p>NOTE:<br>The possible values of the "SeedType" array are as follows (see Spinoso et al. 2023 for details):<br>-1 - No BH seed (the galaxy never hosted a BH)<br>1 - light seed (PopIII remnant)<br>6 - Direct-Collapse BH (DCBH)<br>7 - intermediate-mass BH originated via Runaway Stellar Mergers (RSM)<br>8 - Merger-Induced Direct-Collapse BH (miDCBH)<br>9 - mixed type: light+DCBH (the BH is the result of hierarchical mergers between light and DCBH seeds)<br>10 - mixed type: light+RSM (the BH is the result of hierarchical mergers between light and RSM seeds)</p>
Potential forest conservation value rasters for Denmark from Assmann et al. "LiDAR data fusion and machine learning identify temperate forests of high conservation value"
<p>Potential forest conservation value (high / low) rasters for Denmark based on a remote sensing data fusion approach. Please see manuscript (below) for a detailed description of the methods and data products. </p> <p><br>Jakob J. Assmann, Pil B. M. Pedersen, Jesper E. Moeslund, Cornelius Senf, Urs A. Treier, Derek Corcoran, Zsófia Koma, Thomas Nord-Larsen, Signe Normand. In prep. LiDAR data fusion and machine learning identify temperate forests of high conservation value.</p> <p><br>When using the data, please cite the above manuscript. </p> <p><br>Files description:</p> <ul> <li>Compressed and cloud optimised rasters of potential forest conservation value projections for Denmark (10 m res.) in EPSG:3857 <ul> <li>forest_quality_ranger_biowide_10m_cog_epsg3857.tif RandomForest model projections based on BIOWIDE stratification (!! best performing model !!)</li> <li>forest_quality_ranger_sustainscapes_10m_cog_epsg3857.tif RandomForest model projections based on SustainScapes stratification</li> <li>forest_quality_gbm_biowide_10m_cog_epsg3857.tif GBM model projections based on BIOWIDE stratification</li> <li>forest_quality_gbm_sustainscapes_10m_cog_epsg3857.tif GBM model projections based on SustainScapes stratification</li> </ul> </li> </ul> <p> </p> <ul> <li>Aggregated rasters of potential forest conservation value projections for Denmark (100 m res.) in EPSG:25832 <ul> <li>forest_quality_ranger_biowide_100m.tif RandomForest model projections based on BIOWIDE stratification (!! best performing model !!)</li> <li>forest_quality_ranger_sustainscapes_100m.tif RandomForest model projections based on SustainScapes stratification</li> <li>forest_quality_gbm_biowide_100m.tif GBM model projections based on BIOWIDE stratification</li> <li>forest_quality_gbm_sustainscapes_100m.tif GBM model projections based on SustainScapes stratification </li> </ul> </li> </ul> <p> </p> <ul> <li>Uncompressed and tiled rasters of potential forest conservation value projections for Denmark (10 m res.) in EPSG:25832<br>Please note: the archives contain approx. 42k tiles, each 10 x 10 km, as well as a VRT file for covenient loading. <ul> <li>forest_quality_ranger_biowide_10m.zip RandomForest model projections based on BIOWIDE stratification (!! best performing model !!)</li> <li>forest_quality_ranger_sustainscapes_10m.zip RandomForest model projections based on SustainScapes stratification</li> <li>forest_quality_gbm_biowide_10m.zip GBM model projections based on BIOWIDE stratification</li> <li>forest_quality_gbm_sustainscapes_10m.zip GBM model projections based on SustainScapes stratification</li> </ul> </li> </ul>
Data and Code for Lowman et al. 2024, Macroscale controls determine the recovery of river ecosystem productivity following flood disturbances
<p>Data and code for analyses in Lowman et al. 2024, Macroscale controls determine the recovery of river ecosystem productivity following flood disturbances.</p> <p>See publication and ReadMe file for analysis description and further details. </p>
Divergent evolution of sleep functions - Joyce et al 2024 Dataset - Part 1 of 4
<p>This is the full experimental dataset associated to "Divergent evolution of sleep functions" by Joyce et al 2024, Nature Communications. See https://lab.gilest.ro/papers/divergent-evolution-of-sleep-functions/ for more information.</p> <p>The dataset contains 86 zip files for a total of about 170GB. Once uncompressed, they will explode 622 ethoscope db files for a total of 330Gb, covering behavioural analysis of more than 11.000 animals. These are the RAW data as collected from the ethoscopes. A separate zip archive with all the R/Python scripts and the metadata is also provided. This also contains confocal images for BRP analysis.</p> <ul> <li>Part 1 of 4: <a href="https://doi.org/10.5281/zenodo.10554851" target="_blank" rel="noopener">10.5281/zenodo.10554851 (this page) </a></li> <li>Part 2 of 4: <a href="https://doi.org/10.5281/zenodo.10557238" target="_blank" rel="noopener">10.5281/zenodo.10557238</a></li> <li>Part 3 of 4: <a href="https://doi.org/10.5281/zenodo.10557310" target="_blank" rel="noopener">10.5281/zenodo.10557310</a></li> <li>Part 4 of 4: <a href="https://doi.org/10.5281/zenodo.10966461" target="_blank" rel="noopener">10.5281/zenodo.10966461 </a></li> </ul>
Supplementary Data for Wueller et al. (2024): Geologic History of the Amundsen Crater Region Near the Lunar South Pole: Basis for Future Exploration
<p>Supplementary Data for Wueller et al. (2024): Geologic History of the Amundsen Crater Region Near the Lunar South Pole: Basis for Future Exploration</p> <p>Data contains the georeferenced map plate of our geologic map that can be used in any geoinformation system (GIS).</p> <p><strong>If you use these data, please cite BOTH the Planetary Science Journal publication and the Zenodo dataset.</strong></p> <p>Wueller, L., Iqbal, W., Frueh, T., van der Bogert, C. H., & Hiesinger, H. (2024). Geologic history of the Amundsen crater region near the Lunar South Pole: Basis for future exploration. <em>The Planetary Science Journal</em>, <em>5</em>(6), 147. <a href="https://iopscience.iop.org/article/10.3847/PSJ/ad2c04">https://iopscience.iop.org/article/10.3847/PSJ/ad2c04</a></p> <p>Wueller, L., Iqbal, W., Frueh, T., van der Bogert, C. H., & Hiesinger, H. (2024). Supplementary Data for Wueller et al. (2024): Geologic history of the Amundsen crater region near the Lunar South Pole: Basis for future exploration. <em>Zenodo Dataset</em>. <a href="https://doi.org/10.5281/zenodo.10693820" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10693820</a></p> <p>-----------------------------------------------------------------------------------------------------------------------------------------</p> <p>Mapping Scale is 1:100,000</p> <p>Print Scale is 1:1,000,000</p> <p>-----------------------------------------------------------------------------------------------------------------------------------------</p> <p>For further questions contact lwueller@uni-muenster.de</p> <p>Lukas Wueller, Institut für Planetologie, Universität Münster, Germany, June 2024</p>
Al morir don Quijote - AmadissigloXX
<p>AmadissigloXX. Base de datos de las reescrituras modernas de los libros de caballerías y del <em>Don Quijote (</em>dir. Elisabetta Sarmati)</p> <p>Ficha de: Andrés Trapiello, "Al morir don Quijote", 2004.</p> <p>Se ofrecen dos ficheros:</p> <ul> <li>formato JSON: contiene los metadatos asociados a la obra (autor, fuente, año de publicación, idioma, sinopsis del argumento, anotaciones, fuentes caballerescas, bibliografía crítica, enlaces externos)</li> <li>formato XML: contiene la base de datos completa con indicación de la versión.</li> </ul>
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>
Nikas_et_al_2021_STOTEN_DATASET
<p>This dataset contains the underlying data for the following publication: Nikas, A., Elia, A., Boitier, B., Koasidis, K., Doukas, H., Cassetti, G., ... & Chiodi, A. (2021). Where is the EU headed given its current climate policy? A stakeholder-driven model inter-comparison. <em>Science of The Total Environment</em>, 148549. <a href="https://doi.org/10.1016/j.scitotenv.2021.148549">https://doi.org/10.1016/j.scitotenv.2021.148549</a></p>
Sognnaes_et_al_2021_NCC_DATASET
<p>This dataset contains the underlying data for the following publication: <strong>I. Sognnaes, A. Gambhir, D.-J. Van de Ven, A. Nikas, A. Anger-Kraavi, H. Bui, L. Campagnolo, E. Delpiazzo, H. Doukas, S. Giarola, N. Grant, A. Hawkes, A. Koberle, A. Kolpakov, S. Mittal, J. Moreno, S. Perdana, J. Rogelj, M. Vielle, & G.P. Peters. (2021). A multi-model analysis of long-term emissions and warming implications of current mitigation efforts. Nature Climate Change.</strong></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>
Lalas_et_al_2021_Energies_DATASET
<p>This dataset contains the underlying data for the following publication: Lalas, D., Gakis, N., Mirasgedis, S., Georgopoulou, E., Sarafidis, Y., & Doukas, H. (2021). Energy and GHG Emissions Aspects of the COVID Impact in Greece. Energies, 14(7), 1955. <a href="https://doi.org/10.3390/en14071955">https://doi.org/10.3390/en14071955</a>.</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.