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558 results for “Austria”
Deep-soil carbon changes at 62 European beech stands in the Vienna Woods, Austria, 1984-2022
This dataset comprises repeated soil, vegetation, and site measurements from long-term forest monitoring in the Vienna Woods (Wienerwald), Austria, part of the UNESCO Biosphere Reserve “Wienerwald” (48.1°–48.3° N, 15.8°–16.3° E). The study focuses on pure, naturally regenerated European beech (Fagus sylvatica) stands, initially sampled in 1984 and resampled in 2012 and 2022 . Elevations range from ~180 to 800 m a.s.l., with mean annual temperatures of 8–9 °C and precipitation of 600–900 mm. Soil samples were collected from three mineral soil depths (0–5 cm, 30–40 cm, and 80–90 cm) following consistent protocols across sampling years. Variables include total, organic, and inorganic carbon, total nitrogen and sulfur, exchangeable base cations (Ca, Mg, K), pH, total Fe and Mn, fine soil mass, bulk density, rock content, soil texture, and root biomass. Stocks were calculated. Leaf nutrient concentrations (C, N, S, P, Ca, Mg, K) were determined in all sampling years. Dendrochronological measurements were conducted to determine growth trends since stand establishment, and stand-level characteristics (tree density, DBH, aboveground biomass, crown vitality, slope, aspect) were recorded. Site-level climate data (mean annual temperature, annual precipitation) from 1961 to present and atmospheric deposition data for N and S (1990, 2012, 2022) were integrated from national and European gridded datasets. The dataset supports long-term assessments of soil carbon and nutrient dynamics, forest productivity, and environmental change impacts in old-growth beech forests. Data collection is complete for the 1984, 2012, and 2022 campaigns; no ongoing sampling is planned.
Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - Austria
<p>This dataset contains TSE surveillance results in cattle, sheep, goats, cervids and other species, and genotyping in sheep, pursuant to Regulation (EC) 999/2001.</p> <p><strong>Reporting authorities contributing to each data collection</strong>:</p> <ul> <li>TSE_2023_AT: Austrian Agency for Health and Food Safety (AGES)</li> <li>TSE_2022_AT: Austrian Agency for Health and Food Safety (AGES)</li> <li>TSE_2021_AT: Austrian Agency for Health and Food Safety (AGES)</li> <li>TSE_2020_AT: Austrian Agency for Health and Food Safety (AGES)</li> <li>TSE_2019_AT: Austrian Agency for Health and Food Safety (AGES)</li> </ul>
Dataset for "Machine learning predictions on an extensive geotechnical dataset of laboratory tests in Austria"
<p>This dataset comprises over 20 years of geotechnical laboratory testing data collected primarily from Vienna, Lower Austria, and Burgenland. It includes 24 features documenting critical soil properties derived from particle size distributions, Atterberg limits, Proctor tests, permeability tests, and direct shear tests. Locations for a subset of samples are provided, enabling spatial analysis.</p> <p>The dataset is a valuable resource for geotechnical research and education, allowing users to explore correlations among soil parameters and develop predictive models. Examples of such correlations include liquidity index with undrained shear strength, particle size distribution with friction angle, and liquid limit and plasticity index with residual friction angle.</p> <p>Python-based exploratory data analysis and machine learning applications have demonstrated the dataset's potential for predictive modeling, achieving moderate accuracy for parameters such as cohesion and friction angle. Its temporal and spatial breadth, combined with repeated testing, enhances its reliability and applicability for benchmarking and validating analytical and computational geotechnical methods.</p> <p>This dataset is intended for researchers, educators, and practitioners in geotechnical engineering. Potential use cases include refining empirical correlations, training machine learning models, and advancing soil mechanics understanding. Users should note that preprocessing steps, such as imputation for missing values and outlier detection, may be necessary for specific applications.</p> <p><strong>Key Features</strong>:</p> <ul> <li><strong>Temporal Coverage</strong>: Over 20 years of data.</li> <li><strong>Geographical Coverage</strong>: Vienna, Lower Austria, and Burgenland.</li> <li><strong>Tests Included</strong>: <ul> <li>Particle Size Distribution</li> <li>Atterberg Limits</li> <li>Proctor Tests</li> <li>Permeability Tests</li> <li>Direct Shear Tests</li> </ul> </li> <li><strong>Number of Variables</strong>: 24</li> <li><strong>Potential Applications</strong>: Correlation analysis, predictive modeling, and geotechnical design.</li> </ul> <p><strong>Technical Details</strong>:</p> <ul> <li>Missing values have been addressed using K-Nearest Neighbors (KNN) imputation, and anomalies identified using Local Outlier Factor (LOF) methods in previous studies.</li> <li>Data normalization and standardization steps are recommended for specific analyses.</li> </ul> <p><strong>Acknowledgments</strong>:<br>The dataset was compiled with support from the European Union's MSCA Staff Exchanges project 101182689 Geotechnical Resilience through Intelligent Design (GRID).</p>
Hourly air pollution data for Graz, Austria
<p>The dataset spans from January 1, 2014, to March 15, 2020, with measurements recorded on an hourly basis.</p> <p> </p> <ul> <li> <p>The environmental and pollutant data was provided by the Austrian government under the following license: CC-BY-4.0: Land Steiermark - <a href="http://data.steiermark.gv.at/">data.steiermark.gv.at</a></p> <ul> <li> <p>Air quality by means of NO2, NO, NOx, PM10 and O3 was measured at five sites in Graz, Austria (Süd (eng. South) - S, Nord (eng. North) - N, West (eng. West) - W, Don Bosco – D, Ost (eng. East) – O). </p> </li> <li> <p>Temperature, precipitation, relative humidity, pressure, and wind speed are among the weather conditions considered. To represent wind direction, the wind speed was multiplied by the sine and cosine of the wind direction.</p> </li> <li> <p>Lags were generated using weather data, considering the last 12 data points. The mean of these 12 values was then calculated to represent an hourly metric.</p> </li> </ul> </li> <li> <p>The ERA5-Land data is subject to the Copernicus licence from following source <a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fcds.climate.copernicus.eu%2Fcdsapp%23!%2Fdataset%2F10.24381%2Fcds.e2161bac%3Ftab%3Doverview&data=05%7C01%7Cmlovric%40know-center.at%7C2ba06457329349623a5608da631632c9%7C0d3c92e977ae4f49bd126ff29e8f1c37%7C0%7C0%7C637931244242754711%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=lt5NcIfbIRGse01Naha8bolxEkdtLmyp2VNcrz38Rk8%3D&reserved=0">https://cds.climate.copernicus.eu/cdsapp#!/dataset/10.24381/cds.e2161bac?tab=overview</a> </p> <ul> <li> <p>it includes following variables :</p> <ul> <li> <p>Snowfall - sf</p> </li> <li> <p>Surface latent heat flux - slhf</p> </li> <li> <p>Snowmelt - smlt</p> </li> <li> <p>Snow cover - snowc</p> </li> <li> <p>Windspeed - speed</p> </li> <li> <p>Surface latent heat flux sshf</p> </li> <li> <p>Soil temperature level 4 - stl4</p> </li> <li> <p>Skin temperature - str</p> </li> <li> <p>Surface thermal radiation downwards - strd</p> </li> <li> <p>Total precipitation - tp</p> </li> <li> <p>Temperature of snow layer - tsn</p> </li> <li> <p>10m u-component of wind - u10</p> </li> <li> <p>10m v-component of wind - v10</p> </li> <li> <p>Surface net radiation - rsn</p> </li> <li> <p>Snow depth - sd</p> </li> <li> <p>Snow depth water equivalent - sde</p> </li> <li> <p>2m dewpoint temperature - d2m</p> </li> <li> <p>Forecast albedo - fal</p> </li> </ul> </li> </ul> </li> <li> <p>Temporal values are also incorporated into this dataset, values such as holidays, weekdays, seasons, and months.</p> </li> <li> <p>The dataset includes Prophet values for all pollutants, which were determined by considering various metrics such as trend, seasonality (weekly, yearly, and daily), as well as yhat lower and upper bounds.</p> </li> </ul>
Research Data of the 2014 Census of Open Access Repositories in Germany, Austria and Switzerland
<p>The "2014 Census of Open Access Repositories in Germany, Austria and Switzerland” (2014 Census) is a study on the green open access landscape conducted in the course of a project seminar at the Berlin School of Library and Information Science (BSLIS) at Humboldt-Universität zu Berlin. The 2014 Census not only succeeds the "2012 Census of Open Access Repositories in Germany"[1] but enhances it by adding an online survey to the qualitative analysis of the open access repository websites and the automatic validation of its metadata. Like in 2012 the 2014 Census gives insights into the development of open access repositories and current trends in repository design being of substantial use to open access repository operators.</p> <p>This 2014 Census data set represents the data collected in three different ways:</p> <ul> <li>qualitative analysis of the open access repository websites</li> <li>automatic validation of the metadata via OAI-PMH using the DINI-Validator [2] </li> <li>online survey of repository operators</li> </ul> <p>As in 2012 [3] the data set is provided in XLSX as well as in CSV format. The columns represent the criteria and the rows represent the analyzed open access repositories. In the XLSX file the header row gives the definition of each criterion in English and German. In the CSV "content" file the header row is in English short terms. The respective English and German definition can be found in the CSV "readme" file.</p> <p> </p> <p>[1] Vierkant, P. (2013). 2012 Census of Open Access Repositories in Germany: Turning Perceived Knowledge Into Sound Understanding. <em>D-Lib Magazine</em>, 19. http://dx.doi.org/10.1045/november2013-vierkant </p> <p>[2] http://oanet.cms.hu-berlin.de/validator/pages/validation_dini.xhtml</p> <p>[3] Vierkant, Paul; Voigt, Michaela; Dupski, Jens; David, Sammy; Lösch, Mathias (2013): 2012 Census of Open Access Repositories in Germany. fig<strong>share</strong>. <br /> http://dx.doi.org/10.6084/m9.figshare.677099</p>
Dataset with the results of the e-infrastructures Austria National Survey about Research Data
<p>This is the dataset accompanying the report with the results of our national survey regarding the management of research data</p>
Physioclimatic clusters of Austria
<p><strong>physioclimatic_features_grid_AT_average_1992-2021.nc</strong></p> <p>A netcdf dataset on a 1 km grid covering Austria and it's associated catchment areas. The spatial dimensions are 329 by 584 gridpoints in the projection ETRS89 / Austria Lambert (<a href="https://epsg.io/3416">EPSG:3416</a>). The data consist of 176 different climatological and geomorphometric indices, which are calculated for each gridpoint and then averaged across the climatological normal 01-01-1992 to 31-12-2021. The basis variables from which indices are calculated are elevation, temperature, precipitation, reference evapotranspiration, sunshine duration, snow height and snow water equivalent.</p> <p> </p> <p><strong>physioclimatic_clusters_raster_AT.tif</strong></p> <p>A GeoTiff file of derived physioclimatic subregions comprising 7 characteristic clusters and one noise class, which are the main regionalisation results of the paper below.</p> <p> </p> <p><strong>physioclimatic_clusters_vector_AT.gpkg</strong></p> <p>A GeoPackage consisting of vectorized features of type <em>multipolygon </em>comprising spatially filtered physioclimatic regions based on the gridded output clusters. Note that the small valley clusters have been filtered out and the remaining larger clusters have been spatially joined in order to derive simple multipolygons. This derived, spatially filtered set of clusters can be used for less granular applications compared to the above fine-grained cluster output.</p> <p> </p> <p><strong>base_variables.nc</strong></p> <p>Base Variables mean Temperature, Precipitation and slope, used to evaluate the clustering output.</p> <p> </p> <p><strong>principal_components_dim20.nc</strong></p> <p>First 20 principal components of the full feature space, used as direct input for UMAP/HDBSCAN and k-means and to evaluate the clustering output.</p> <p> </p> <p>Please refer to the <a href="https://github.com/Geosphere-Austria/subregion-derivation">GitHub</a> repository for further details. The peer-reviewed, open access paper can be found here: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.envsoft.2025.106324" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.envsoft.2025.106324</a>.</p> <p> </p> <p><strong>Version History</strong></p> <p>v1.0.1: Fixed a small hole between two polygons in the southern part of the domain in the physioclimatic_clusters_vector_AT.gpkg file.</p> <p>v1.0.0: Initial upload.</p>
Eleven years of training data for south foehn for three regions of Western Austria
<p>This south foehn training data is suited for machine learning purposes. </p> <p>It was created by applying objective foehn classification (OFC, Vergeiner 2004) on hourly data of various stations in Western Austria. Three regions (Vorarlberg, Tiroler Unterland, Tiroler Oberland) and two intensities are available, where</p> <ul> <li>0.0 means no foehn on that day,</li> <li>0.5 means localised foehn on that day (up to half the stations in the region responded to OFC),</li> <li>1.0 means widespread foehn on that day (more than half the stations in the region responded to OFC),</li> </ul> <p>provided for each region individually.</p> <p>A paper, where the process of creation is described, is in preperation and will be linked as soon as it is reviewed. </p> <p> </p>
Horizon Raster Austria
<p>Horizon dataset for Austria on a rasterbasis, stored in a netcdf to combine all viewing directions in 10 degree steps (35 steps in total). The data was calculated from DHM raster data (10mx10m) available here: https://www.data.gv.at/katalog/dataset/b5de6975-417b-4320-afdb-eb2a9e2a1dbf (despite the description it is DHM not DGM).</p>
Forest stages in OAL-Austria (2007)
<p>Forest stages in the upslope contributing area of OAL-Austria, derived from airborne laserscanning data</p> <p>Further details can be found in D4.5 of the OPERANDUM project.</p>
Multi-temporal digital terrain models of the NBS experiment in OAL-Austria
<p>Multi-temporal digital terrain models of the NBS experiment in OAL-Austria with a spatial resolution of 10cm, derived from 3D point clouds acquired with a terrestrial laser scanner (Riegl-VZ2000i); Projection: EPSG 31254</p> <p>The TLS-monitoring is intended for assessing the stability of the embankment at the NBS field demonstrator in OAL-Austria. The digital terrain models were acquired after applying the ground classification filter proposed by Axelsson (2000)</p>
Stacked remote sensing indices covering OAL-Austria
<p>Stacked indices derived from Sentinel-2A/B imagery (processing level 2A) covering the period from 2017/04/24 to 2022/01/16</p> <p># Normalized Difference Vegetation Index(Rouse etal. 1974) NDVI = (NIR ‒ R)/(NIR + R)<br> # Visible Difference Vegetation Index (Wang et al. 2015) VDVI = ((2*G) - R - B)/((2 * G) + R + B)<br> # Enhanced vegetation index (Schwieder et al. 2022) EVI=G*(nir-red)/(nir+C1*red-C2*blue+X)<br> # Excess green index ExGI=2*g-(r+b)<br> # Green chromatic coordinate GCC=g/(r+g+b)<br> # Normalized difference moisture index (Lastovicka et al. 2020) NDMI = (NIR − SWIR) / (NIR + SWIR)</p>
First Early Cretaceous ichthyosaurs of Austria and the problem of Jurassic–Cretaceous ichthyosaurian faunal turnover
<p>The uploaded images are the basic data for mirco-CT reconstructions on an ichthyosur skull with internal teeth.</p> <p>The specimen is located in the collections of the Natural History Museum Vienna, Geological Palaontologival Department.</p> <p>Repository Number NHMW 2022/0001/0001.</p> <p>Publishe with the title: <strong>Alexander Lukeneder, Nikolay Zverkov, Christina Kaurin, Valentin Blüml. 2022. First Early Cretaceous ichthyosaurs of Austria and the problem of Jurassic–Cretaceous ichthyosaurian faunal turnover. Cretaeous Research, current stage after review.</strong></p>
Air pollution, atmospheric and local meteorological data for Graz, Austria from 2014 to end of 2021
<p>The data covers a timeframe from January 2014 to November 2021 in a daily frequency, and covers two sources:</p> <ul> <li>The environmental and pollutant data was provided by the Austrian government under the following license: CC-BY-4.0: Land Steiermark - <a href="http://data.steiermark.gv.at">data.steiermark.gv.at</a> <ul> <li>Air quality (<em>Lovric_et_al_air_pollutants.csv</em>) by means of NO<sub>2</sub>, NO, NO<sub>x</sub>, PM<sub>10</sub> and O<sub>3</sub> was measured at five sites in Graz, Austria (Süd (<em>eng. South</em>) - S, Nord (<em>eng. North</em>) - N, West (<em>eng. West</em>) - W, Don Bosco – D, Ost (<em>eng. East</em>) – O). In addition weather conditions like temperature, percipitation, relative humidity, pressure, wind speed and direction are added (<em>Lovric_et_al_local_meteorology.csv</em>)</li> </ul> </li> <li>The ERA5-Land data (<em>Lovric_et_al_era5_recalculated.csv</em>) is subject to the Copernicus licence from following source <a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fcds.climate.copernicus.eu%2Fcdsapp%23!%2Fdataset%2F10.24381%2Fcds.e2161bac%3Ftab%3Doverview&data=05%7C01%7Cmlovric%40know-center.at%7C2ba06457329349623a5608da631632c9%7C0d3c92e977ae4f49bd126ff29e8f1c37%7C0%7C0%7C637931244242754711%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=lt5NcIfbIRGse01Naha8bolxEkdtLmyp2VNcrz38Rk8%3D&reserved=0">https://cds.climate.copernicus.eu/cdsapp#!/dataset/10.24381/cds.e2161bac?tab=overview</a> <ul> <li>it includes following variables : <ul> <li>Cloud_Cover_Mean</li> <li>Temperature_Air_2m_Max_Day_Time</li> <li>Temperature_Air_2m_Min_Night_Time</li> <li>Wind_Speed_10m_Mean</li> </ul> </li> </ul> </li> </ul>
Supplementary Material to "Partial melting of amphibole–clinozoisite eclogite at the pressure maximum (eclogite type locality, Eastern Alps, Austria)"
<p><span>Here we briefly describe the supplementary materials for the publication “Partial melting of amphibole–clinozoisite eclogite at the pressure maximum (eclogite type locality, Eastern Alps, Austria)” in the European Journal of Mineralogy, 35(5), 715-735 Schorn, S., Rogowitz, A., & Hauzenberger, C. A. (2023).</span></p>
(PRE) Socio-economic and cultural dataset in relation to Persuasive Strategies to boost Energy Efficiency and in the UK, Spain, Greece and Austria
<p>The dataset has been created from obtaining answers from 303 participants of four different countries in the EU (the questionnaire can be studied in <strong>GreenSoul_Questionnaire.pdf</strong>). It is composed by several factors which are explained in different TXT files. All these factors are contained in a "<strong>all_code_final_zenodo.xlsx</strong>" along with their answers by participants. In the following a short descrition of each TXT which explian the dataset is provided.:</p> <p> * <strong>socio-economic_description_not_dependent_of_work</strong><br> - Contains all the information from participants which is irrespective of their current workplace. This file contains typical socio-demographic and cultural attributes from respondents.</p> <p> * <strong>socio-economic_description_dependent_of_work</strong><br> - Contains socio-economic and cultural information from participants which is relevant to the workplace in relation to energy efficient practices in such environment.</p> <p> * <strong>actions-at-work</strong><br> - Are a set of attributes which describe certain practices of employees in relation to energy efficiency.</p> <p> * <strong>persuasive_strategies</strong><br> - Explain the ratings from 1 to 5 that participants attributed to a set of persuasive strategies. These strategies are framed within Phychological Persuasive principles which are also explained in the file.</p> <p> * <strong>all_attributes_together</strong><br> - All the variables together without distinction of the environment where they are applicable.</p> <p>Finally, plots from every construct or attribute are provided in a zip file (<strong>plots_descriptive_analysis_per_city.zip</strong>) which contains the plots uploaded in "PNG" extension</p>
A new Geo-Lithological Map (Geo-LiM) for Central Europe (Germany, France, Switzerland, Austria, Slovenia, and Northern Italy)
<p><strong>We introduce a new geo-lithological map of Central Europe (Geo-LiM) elaborated adopting a lithological classification compliant to the methods more used in the litterature for estimating the consumption of atmospheric CO2 due by chemical weathering. <br> Geo-LiM represents a novelty if compared with published global geo-lithological maps. The first novelty is due by the attention paid in discriminating metamorphic rocks that were classified according to the chemistry of protoliths. The second novelty is that the procedure used for the definition of the map is made available on the web to allow the replicability and reproducibility of the product.</strong></p>
National Checklists 2017: Austria Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details<p></p>A list of species from Austria collected using effechecka and geonames polygons
National Checklists 2019: Austria Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details.<p></p>A list of species from Austria collected using effechecka and geonames polygons
Supplementary material (part 2): "Evaluation of AROME Model Valley Wind Simulations in the Inn Valley, Austria"
<p><em>Part 2</em> of supplementary material for the Master's Thesis: "Evaluation of AROME Model Valley Wind Simulations in the Inn Valley, Austria" (Wibmer 2024, available <a href="https://resolver.obvsg.at/urn:nbn:at:at-ubi:1-151801">here</a>).</p> <p>Due to memory constraints, the supplementary material consists of two parts:</p> <ul> <li><em><strong>Part 1: </strong></em>(available <a href="https://doi.org/10.5281/zenodo.10849397" target="_blank" rel="noopener">here</a>) Includes Python scripts and model setup files, along with the first part of the datasets, including ERA-reanalysis data, observational data, and the preprocessed AROME model output (NetCDF files) of the <em>0.5-km</em> simulation.</li> <li><em><strong>Part 2: </strong></em>Includes the preprocessed AROME model output (NetCDF files) of the <em>1.0-km </em>and <em>2.5-km</em> simulations (see description below).</li> </ul> <p>To reproduce part of the figures, users must download the Python scripts and the preprocessed AROME model datasets (NetCDF files). <br>The Python scripts should be placed in the same parent folder because some of them depend on each other <strong>(!! Important !!).</strong><br>Original AROME model output files (GRIB2 format) are not published due to their large size.</p> <p>The naming convention for the AROME simulations uses OP* (where * represents the grid spacing in meters) to differentiate the model runs based on their horizontal<br>grid spacing:</p> <ul> <li><strong><em>OP2500:</em></strong> for 2.5 km</li> <li><em><strong>OP1000: </strong></em>for 1.0 km</li> <li><em><strong>OP500:</strong></em> for 0.5 km</li> </ul> <h3><strong>Datasets Part 2</strong></h3> <p>Due to memory constraints, the datasets needed for the analyses are split up into two parts. The second part of the supplementary material contains:</p> <ul> <li><strong>datasets_OP1000.tar.xz</strong>: Contains the preprocessed AROME-Aut model output data (NetCDF files) of the <em>1.0-km</em> simulation. </li> <li><strong>datasets_OP2500.tar.xz</strong>: Contains the preprocessed AROME-Aut model output data (NetCDF files) of the <em>2.5-km</em> simulation. </li> </ul> <p>The datasets of the<em> 0.5-km</em> simulation (<strong>datasets_OP500.tar.xz</strong>)<strong> </strong>can be found in Part 1 of the supplementary material (available <a href="https://doi.org/10.5281/zenodo.10849397" target="_blank" rel="noopener">here</a>).<br>The NetCDF datasets of the performed AROME-Aut simulations are packaged and compressed into <code><em><strong>.tar.xz</strong></em></code> files.<br>The Python scripts, available <a href="https://doi.org/10.5281/zenodo.10849397" target="_blank" rel="noopener">here</a>, require these NetCDF datasets for plotting and analyses routines.<br>The provided NetCDF datasets are preprocessed from the <em>GRIB2</em> output of the AROME-Aut simulations. <br>For the scripts to function properly, you need to adjust the path to the datasets within <code><strong>path_handling.py</strong></code><strong>.</strong></p> <p>Each <strong><code>datasets_OP*.tar.xz</code></strong> file contains NetCDF files for different type of levels: <em>surface, hybridPressure </em>(model levels)<em>, isobaricInhPa </em>(pressure levels),<em> meanSea </em>(mean sea level)<em>, heightAboveGround </em>(constant height levels)<em>. </em> The following naming convention for the datasets is used:</p> <ul> <li><strong>ds_OP*_<em>var</em>_hybridPressure_<em>[lon1, lon2, lat1, lat2]</em>.nc</strong>: Contains data on <em>hybrid pressure model levels</em> for a specific variable (<em>var</em>; e.g., <em>u, v, z, pres, q, t</em>) for the geographical extent defined in the brackets. </li> <li><strong>ds_OP*_interp_hybridPressure_<em>(lon,lat)</em>.nc</strong>: Combined dataset on <em>hybrid pressure model levels.</em> The data is bilinearly interpolated to the specified location <em>(lon, lat)</em>.</li> <li><strong>ds_OP*_<em>var</em>_surface_<em>whole</em>.nc</strong>: Contains data on <em>model surface </em>for a specified variable (<em>var</em>; e.g., <em>z, sp, t, tcc</em>) for the <em>whole </em>available domain extent.</li> <li><strong>ds_OP*_heightAboveGround_instant_<em>whole</em>.nc</strong>: Combined dataset on <em>height levels </em>(e.g., <em>2-m and 10-m</em>) for the <em>whole </em>available domain extent.</li> <li><strong>ds_OP*_<em>var</em>_meanSea_<em>whole</em>.nc</strong>: Contains data on <em>mean sea level</em> for specified variable (<em>var;</em> e.g., <em>prmsl</em>) for the <em>whole</em> available domain extent. </li> </ul> <p>The original GRIB2 files are not provided due to their large size. For further information about the GRIB2 files or the NetCDF datasets, please feel free to contact me.</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.