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2,348 results for “germany”
Bathymetry from multibeam data collected on the RV Heincke, cruises HE-324 and HE-349, North Sea, Helgoland (Germany)
<p>Mosaicked multibeam-derived bathymetry from Simrad EM710 on board RV Heincke collected during Spring 2010 (HE- 324) and 2011 (HE-349). Water depths range from 20 to 50 meters. The range of opening angles of the EM710 multibeam varies between 50 and 65 degrees. EM710 data were used in conjunction with an OCTANS motion sensor, ship’s DGPS and Sound Velocity Profiles (SVP). CRS information: WGS 84 / UTM zone 32N.</p>
Long-term moss monitoring network for atmospheric deposition in Germany, link to research data and scientific software
<p>Research data and scientific software related to a study that aims to restructure a long-term monitoring network using moss as biomonitor for atmospheric deposition in Germany. Data from the European Moss Survey 2005 and a statistically based methodology including a decision support system were used to design the spatial network for the 2005 survey.</p>
Estimating heavy metal deposition in Germany using model calculations and biomonitoring data, link to research data and scientific software
<p>Research data and scientific software related to an investigation dealing with modelled data on Cd and Pb deposition (LOTOS-EUROS, EMEP/MSC-East) and monitoring data from the International Cooperative Programme on Effects of Air Pollution on Natural Vegetation and Crops (ICP Vegetation Moss Survey) and the German Environmental Specimen Bank (ESB) providing corresponding parameters on HM concentration in various biota. The study aimed at examining, whether an integrated use of model calculations and monitoring data can extend established methods for estimating and evaluating spatial patterns of atmospheric Pb and Cd deposition across Germany.</p>
Fuzzy modelling and mapping soil moisture in Germany, link to research data and scientific software
<p>Research data and scientific software related to spatio-temporal estimations of ecological soil moisture with available data covering the whole territory of Germany and the Kellerwald National Park (Hesse). Temporal trends of modelled soil moisture for the time period 1961–2070 were statistically analyzed. Soil moisture changes (drying-out) at both national and regional levels were mapped.</p>
Supplementary material to the manuscript: Regionalised Heat Demand and Power-To-Heat Capacities in Germany - An Open Data Set for Assessing Renewable Energy Integration
<p>This is the supplementary material for the manuscript:</p> <p>"Regionalised Heat Demand and Power-To-Heat Capacities in Germany - an Open Data Set for Assessing Renewable Energy Integration"</p> <p>Article DOI: <a href="https://doi.org/10.1016/j.apenergy.2019.114161">https://doi.org/10.1016/j.apenergy.2019.114161</a></p> <p>Open access preprint: <a href="https://arxiv.org/abs/1912.03763">https://arxiv.org/abs/1912.03763</a></p> <p> </p> <p><strong>DESCRIPTION OF THE DATASET AND LICENSES:</strong></p> <p>The subdirectory "04_results" contains the regionalised heat demand an power-to-heat capacity data on administrative district level (NUTS-3) for Germany. The subdirectories "01_census_special_evaluation_data" and "02_other_input_data" contain the utilised input data. The subdirectory "03_code" contains the developed and applied source code.</p> <p>The data in this repository are provided under open source licenses. For license information and other general information on the supplementary material, refer to the LICENSE files and README files in the respective subdirectories.</p> <p>For a detailed description of the approach developed by the author, the input data used and the generated results, refer to the manuscript "Regionalised Heat Demand and Power-To-Heat Capacities in Germany - an Open Data Set for Assessing Renewable Energy Integration".</p> <p><strong>METADATA:</strong></p> <p>Sector: Residential Buildings – Space Heating and Domestic Hot Water</p> <p>Geographical scope: Germany</p> <p>Geographical resolution: Administrative districts (NUTS-3)</p> <p>Temporal scope: 2011, three scenarios for 2030</p> <p>Temporal resolution: 15min</p> <p> </p> <p><strong>UNITS:</strong></p> <p>In the final results folders (04_results/01_installed_heating_p2h_capacity; 04_results/02_daily_time_series; 04_results/03_yearly_time_series) the units of the data are indicated in the file names or the column names, e.g. by "in_MW". In case of unit indication in the file name, the unit refers to all columns in the file.</p> <p>In the intermediate results folder (04_results/00_sql_tables_exported_to_csv) all units referring to power are "kW" and all units referring to energy are "kWh".</p> <p><strong>NEWS AND CONTACT:</strong></p> <p>This dataset will be used as part of the <a href="https://wiki.openmod-initiative.org/wiki/Region4FLEX">region4FLEX model</a>. We are currently enhancing the data by temporally and spatially resolved COP time series and determining load shifting potentials. If you wish to receive news or have general questions please contact: wilko.heitkoetter@dlr.de. </p>
Doppler lidar datasets of UDINE measurement campaign at TROPOS Leipzig, Germany
<p>The Doppler lidar dataset of the Up- and Downdraft in Drop and Ice Nucleation Experiment (UDINE, 2010-2013) is published. This dataset is part of the publication Bühl et al., "Impact of vertical air motions on ice formation rate in mixed-phase cloud layers", NPJ Climate and Atmospheric Science, 2019.</p> <p>Time-height resolved measurements of mean vertical Doppler velocity of aerosol and cloud particles over the measurement site. Most data is recorded in vertical stare with 2s measurement time. Files are in NetCDF-format and contain the following variables:</p> <p>amp(time,height): The signal strength (SNR) recorded by the data acquisition.<br> data(time,height): The first moment of the main peak in the Doppler spectrum<br> heightresolution: The resolution of the data acquisition in nanoseconds<br> measurement_time(time,datetime): Time of recording in human readable format [YYYYMMDD,hhmmss]<br> scanposition(time,scanposition): Two element array with position of the scanner in [azimuth,off-zenith-angle]<br> shots(time): The number of shots used for averaging. Spectra are averaged at a rate of 750 shots/s so this variables indicates if the system has functioned nominally.</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>
Magnetic Field Measurements above a Phonolite Diatreme near Rockeskyll, West Eifel, Germany
Open the record for dataset details and reuse information.
National Checklists 2017: Germany 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 Germany collected using effechecka and geonames polygons
National Checklists 2019: Germany 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 Germany collected using effechecka and geonames polygons
Terrestrial laser scan data of a experimental plot in Forstamt Billenhagen, 340 a31 (Mecklenburg-Vorpommern, Germany 2023) v2
<p>The area was surveyed using terrestrial laser scanning, and the subsequent derivation of individual tree yield data (BHD, tree height, volume, etc.) was carried out as part of a study to assess the ecosystem services of different forest stands (recorded in March 2023). In this version of the data, transmission errors and unit errors were corrected.</p>
Perceptions of green facades among residents of buildings with and without a greened envelope – Data from a household survey in Leipzig, Germany
<p>The data set stems from a survey of residents in two neighborhoods of Leipzig, Germany, and was implemented in April and May of 2022. The primary aim of the study was to better understand resident perceptions of green facades, including their (perceived) benefits as well as concerns. Additionally, residents were asked for a number of other perceptions, including heat stress, noise and air pollution. The sample includes both residents of buildings with and without an existing green facade.</p> <p>All variables included in this data publication are described in the codebook. The original German language wording of the survey questions can be found in the questionnaire enclosed with the data set. We include responses to all questions from the survey that were close-ended or had a numerical response. Open-ended questions were excluded from this publication for data privacy reasons. </p>
Daily weather data averages for Germany aggregated over official weather stations
<p>Daily data averaged across Germany for the period of 2016-01-01 till 2021-06-27:</p> <p><strong>temperature_mean: </strong>mean daily temperature in degree Celsius averaged across all weather stations in Germany.</p> <p><strong>temperature_max:</strong> maximum daily temperature in degree Celsius averaged across all weather stations in Germany.</p> <p><strong>precipitation:</strong> daily precipitation sum in millimeter (equals liter per square meter) averaged across all weather stations in Germany.</p> <p><strong>sunshine: </strong>sunshine duration per day averaged across all weather stations in Germany.</p> <p> </p> <p> </p> <p>gemittelte Werte basierende auf Daten des Deutschen Wetterdiensts, Vermessungsverwaltungen der Länder und BKG (https://gdz.bkg.bund.de/)</p>
RE-Lab-Projects/TRY_DE_2015_2045: Test Reference Years (TRY) for 15 typical regions in germany with special regards on realisitc radiation data on a 1min timescale
<p>Test Reference Years (TRY) for 15 typical regions in germany with special regards on realisitc radiation data on a 1min timescale</p> <p><strong>Summary:</strong></p> <p>The data set contains the updated test reference years (TRY) of the German Weather Service (DWD). By subdividing into 15 TRY regions, each postcode area can be assigned a representative weather data set. It should be emphasized that in addition to a mean, current test reference year for a region, there is also a year with extreme summer and extreme winter weather. To take climate change into account, there is then a time series for the year 2045 for each test reference year based on the IPCC climate models. This means that a total of 90 weather data sets are available with a one-hour time resolution.</p> <p>In order to use the data in simulations with a temporal resolution of 1min or 15min, the data set was extended by linear interpolation. While this approach is justifiable for air pressure and temperature, for example, it does not depict high fluctuations in solar radiation. Therefore, based on the one-minute open data measurement data set of the Baseline Surface Radiation Network, with an algorithm by Hofmann et. al. the time series of global radiation are newly generated for all test reference years. Another algorithm by Hofmann et. al. was used to calculate the corresponding diffuse radiation times series.</p> <p><strong>Sources:</strong></p> <ul> <li>Raw data from DWD: <a href="https://kunden.dwd.de/obt/">https://kunden.dwd.de/obt/</a> -> <code>1_raw-data</code></li> <li>Synthetic 1min radiation data: <a href="http://pvmodelling.org/">http://pvmodelling.org/</a> -> <code>2_synthetic-radiation</code></li> </ul> <p><strong>How to use or recreate the final dataset:</strong></p> <ol> <li>clone/download this repository</li> <li>unzip the files from the data.zip file <ol> <li><a href="https://github.com/RE-Lab-Projects/TRY_DE_2015_2045/releases/download/v1.4.0/data.zip">https://github.com/RE-Lab-Projects/TRY_DE_2015_2045/releases/download/v1.4.0/data.zip</a></li> </ol> </li> <li>Use or recreate the final dataset <ol> <li>use: Final datasets are then located in -> <code>3_processed-data</code></li> <li>recreate: run the <code>process-data.py</code></li> </ol> </li> </ol> <p><strong>Test reference stations / regions</strong></p> <p>No. | lon | lat | station | region<br> 1 | 53.5591 | 8.5872 | Bremerhaven | Nordseeküste<br> 2 | 54.0878 | 12.1088 | Rostock | Ostseeküste<br> 3 | 53.5299 | 10.0078 | Hamburg | Nordwestdeutsches Tiefland<br> 4 | 52.3938 | 13.0651 | Potsdam | Nordostdeutsches Tiefland<br> 5 | 51.4562 | 7.0568 | Essen | Niederrheinisch-westfälische Bucht und Emsland<br> 6 | 550.6461 | 7.9426 | Bad Marienburg | Nördliche und westliche Mittelgebirge, Randgebiete<br> 7 | 51.3334 | 9.4725 | Kassel | Nördliche und westliche Mittelgebirge, zentrale Bereiche<br> 8 | 51.7239 | 10.6069 | Braunlage | Oberharz und Schwarzwald (mittlere Lagen)<br> 9 | 50.8233 | 12.9181 | Chemnitz | Thüringer Becken und Sächsisches Hügelland<br> 10 | 50.3226 | 11.9124 | Hof | Südöstliche Mittelgebirge bis 1000 m<br> 11 | 50.4312 | 12.9522 | Fichtelberg | Erzgebirge, Böhmer- und Schwarzwald oberhalb 1000 m<br> 12 | 49.4902 | 8.4637 | Mannheim | Oberrheingraben und unteres Neckartal<br> 13 | 48.2432 | 12.5286 | Mühldorf | Schwäbisch-fränkisches Stufenland und Alpenvorland<br> 14 | 48.6536 | 9.8666 | Stötten | Schwäbische Alb und Baar<br> 15 | 47.4945 | 11.1046 | Garmisch Partenkirchen | Alpenrand und -täler</p> <p><strong>Content</strong></p> <ul> <li><strong>files</strong>: 90 test reference years (TRY) <pre><code>15 test reference regions x 3 reference conditions (average year, extreme summer, extreme winter) x 2 reference projections (year 2015 and year 2045) </code></pre> </li> <li><strong>columns per file</strong>: <pre><code>datetime [yyyy-MM-dd hh:mm:ss+01:00/02:00] temperature [degC] pressure [hPa] wind direction [deg] wind speed [m/s] cloud coverage [1/8] humidity [%] direct irradiance [W/m^2] diffuse irradiance [W/m^2] synthetic global irradiance [W/m^2] synthetic diffuse irradiance [W/m^2] clear sky irradiance [W/m^2] </code></pre> </li> <li><strong>length</strong>: 1 year</li> <li><strong>time increment</strong>: 60s / 900s / 3600s</li> </ul> <p><strong>Important hints</strong>:</p> <ul> <li>all files in <code>3_processed-data</code> were calculated with the skript <code>process-data.py</code></li> <li><em>A value with, for example, a timestamp 12:00:00 represents the mean value from this timestamp until the following timestamp.</em></li> <li><em>datetime column is in CET / CEST</em></li> </ul>
Dataset on adolescent preferences toward EQ-5D-Y in Germany
<p>The dataset includes preferences toward EQ-5D-Y health states valued by adolescents themselves in Germany. </p>
Dataset on preferences of rheumatic patients towards EQ-5D-5L health states in Germany
<p>The dataset includes preferences of rheumatic patients towards EQ-5D-5L health states in Germany, adjacent codebooks.</p>
Dataset on preferences of patients with diabetes towards EQ-5D-5L health states in Germany
<p>The dataset includes data on preferences of patients with diabetes toward EQ-5D-5L health states in Germany and adjacent codebooks. </p>
Dataset on adult preferences toward EQ-5D-Y health states in Germany
<p>The dataset include adult preferences towards EQ-5D-Y health states in Germany and adjacent codebook.</p>
GENeSYS-MOD Germany: Technology, demand, and renewable data
<p>This dataset contains renewable potentials, timeseries, technology data, and additional data tables for the current implementation of GENeSYS-MOD Germany.</p>
Annual land cover maps of Germany based on Sentinel-2 MSI Level 3A (WASP) data
<p>Overview:<br> This annual land cover product is available for the years 2016, 2019, 2020, 2021 for the whole of Germany. It was generated based on Sentinel-2 MSI L3A WASP Data provided by DLR (https://geoservice.dlr.de/data-assets/4hcq6dgkj648.html). For a complete description of the classification procedure please refer to<br> Riembauer, G.; Weinmann, A.; Xu, S.; Eichfuss, S.; Eberz, C.; Neteler, M.: Germany-wide Sentinel-2 based land cover classification and change detection for settlement and infrastructure monitoring. In: Proceedings of the 2021 conference on Big Data from Space (doi:10.2760/125905), 2021.</p> <p>Source data:</p> <ul> <li>Satellite data <ul> <li>German Aerospace Center (DLR): Sentinel-2 MSI - Level 3A (MAJA/WASP Tiles) - Germany, DOI: 10.15489/4hcq6dgkj648</li> </ul> </li> <li>Auxiliary data <ul> <li>European Union, Copernicus Land Monitoring Service, European Environment Agency (EEA), <strong>Copernicus High Resolution Layer: Imperviousness Status Map, 2018 </strong>(https://land.copernicus.eu/pan-european/high-resolution-layers/imperviousness/status-maps/imperviousness-density-2018)</li> <li><strong>OpenStreetMap</strong> Planet dump retrieved from https://planet.osm.org, https://www.openstreetmap.org</li> <li><strong>S2GLC Map of Europe</strong> (R. Malinowski, S. Lewiński, M. Rybicki, E. Gromny, M. Jenerowicz, M. Krupiński, A. Nowakowski, C. Wojtkowski, M. Krupiński, E. Krätzschmar, and P. Schauer, "Automated Production of a Land Cover/Use Map of Europe Based on Sentinel-2 Imagery," Remote Sensing, vol. 12, no. 21, p. 3523, 2020.)</li> </ul> </li> </ul> <p>File naming:<br> classification_map_germany_[year].tif example: classification_map_germany_2020.tif</p> <p>Projection + EPSG code:<br> WGS 84 / UTM zone 32N (EPSG: 32632)</p> <p>Spatial extent:<br> north: 55:03:38.646483N<br> south: 47:08:24.738401N<br> west: 5:33:47.816647E<br> east: 15:34:24.108516E</p> <p>Spatial resolution:<br> 10 m</p> <p>Format: COG (Cloud-Optimized GeoTIFF)</p> <p>Pixel values:<br> 10: forest<br> 20: low vegetation<br> 30: water<br> 40: built-up<br> 50: bare soil<br> 60: agriculture</p> <p>Temporal coverage:<br> Years 2016, 2019, 2020, 2021</p> <p>Software used:<br> GRASS 7.8, actinia</p> <p>Original dataset license:<br> The Sentinel-2 level 3A data produced and distributed by DLR are based on Copernicus Sentinel-2 level 1C data, which are subject to the following license: https://theia.cnes.fr/atdistrib/documents/TC_Sentinel_Data_31072014.pdf One of the following citations is mandatory for using the provided MAJA/WASP L3A product: German Aerospace Center (DLR): Sentinel-2 MSI - Level 3A (MAJA/WASP Tiles) - Germany, DOI: 10.15489/4hcq6dgkj648 or Contains modified Copernicus Sentinel data, processed by DLR, licensed under CC-BY 4.0</p> <p>Processed by:<br> mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p>
ScienceDex guides
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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.