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Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - Germany
<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>REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION: </p> <ul> <li>TSE_2023_DE: Federal Ministry of Food and Agriculture (BMEL)</li> <li>TSE_2022_DE: Federal Ministry of Food and Agriculture (BMEL)</li> <li>TSE_2021_DE: Federal Ministry of Food and Agriculture (BMEL)</li> <li>TSE_2020_DE: Federal Ministry of Food and Agriculture (BMEL)</li> <li>TSE_2019_DE: Federal Ministry of Food and Agriculture (BMEL)</li> </ul>
UVA_VPTS - Vertical profiles of biological targets derived from weather radars in Belgium, Germany and the Netherlands
<p><em>UVA_VPTS - Vertical profiles of biological targets derived from weather radars in Belgium, Germany and the Netherlands</em> is a vertical profile time series dataset published by the <a href="https://www.inbo.be/en">Research Institute for Nature and Forest (INBO)</a>. It contains animal movement data derived from 24 weather radars in Belgium, Germany and the Netherlands, with varying coverage from 2008 to 2023. These data were created by processing weather radar data - provided by the Royal Meteorological Institute of Belgium (<a href="https://www.meteo.be/">RMI</a>), German Meteorological Service (<a href="https://www.dwd.de/">DWD</a>) and Royal Netherlands Meteorological Institute (<a href="https://www.knmi.nl/">KMNI</a>) - with methods optimized for extracting bird targets. The resulting data are vertical profile time series (VPTS), containing the density, speed and direction of biological targets within a weather radar (<code>radar</code>) volume, grouped into altitude bins (<code>height</code>) and measured over time (<code>datetime</code>). The data are also available in the <a href="https://aloftdata.eu/browse/?prefix=uva/">Aloft bucket</a>.</p> <p>See Desmet et al. (2025, <a href="https://doi.org/10.1038/s41597-025-04641-5">https://doi.org/10.1038/s41597-025-04641-5</a>) for a more detailed description of this dataset.</p> <h2>Files</h2> <p>VPTS data in this deposit are organized per country (.tgz file), radar (directory), year (directory) and month (.csv.gz file). Fields in the data follow the <a href="https://aloftdata.eu/vpts-csv/">VPTS CSV</a> format and are described in <code>vpts-csv-table-schema.json</code>. An overview of what data are available is provided in <code>coverage.csv</code>. Radar metadata can be found at <a href="https://aloftdata.eu/radars/">https://aloftdata.eu/radars/</a>.</p> <ul> <li><strong>coverage.csv</strong>: coverage of the VPTS data, representing the number of unique hours, heights, source files and records for each radar and date combination.</li> <li><strong>vpts-csv-table-schema.json</strong>: technical description of the fields in the VPTS data.</li> <li><strong>be.tgz</strong>: VPTS data from 3 radars in Belgium.</li> <li><strong>de.gz</strong>: VPTS data from 18 radars in Germany.</li> <li><strong>nl.gz</strong>: VPTS data from 3 radars in the Netherlands.</li> </ul> <h2>Acknowledgements</h2> <p>This dataset was processed using infrastructure provided by the University of Amsterdam, SURF Cooperative, Ghent University and the Research Institute for Nature and Forest (INBO). It was mainly supported by the <a href="https://globam.science/">GloBAM project</a>, funded through the 2017-18 Belmont Forum and BiodivERsA joint call for research proposals under the BiodivScen ERA-Net COFUND programme.</p>
Temperature and wind field data for Fendt, Germany
<p>This dataset includes high-resolution temperature and wind vector data from a network of sensors within a 20x20x9m [LxWxH] domain. The observations included air temperature by Distributed Temperature Sensing (DTS), surface temperature by thermal infrared imaging (TIR; surface brightness temperature), horizontal and vertical profiles of air temperature and wind vectors (EC; two-axial and three-axial sonic anemometers; temperature derived from speed of sound) and a vertical profile of air temperature (TT; aspirated temperature in a shielded enclosure).</p> <p>The data were collected at the DE-Fen observatory, Fendt-Peissenberg, Germany, during the ScaleX 2016 Campaign, Jun-Aug 2016 (<a href="https://scalex.imk-ifu.kit.edu">https://scalex.imk-ifu.kit.edu</a>).</p> <p> </p>
Emissions from building materials - concentration of micropollutants and heavy metals in stormwater runoff of two new development areas in Berlin (Germany)
<p>This dataset includes concentrations of micropollutants (27) and heavy metals (7) for stormwater runoff from different sampling points at two test sites (A and B) in Berlin, Germany. Both sites are new development areas of similar size that were both constructed in 2017 (1 – 1.5 years prior to the start of the monitoring campaign). Composite samples of individual rain events were taken at three sampling points of each test site: façade runoff, roof runoff and corresponding stormwater runoff from the catchment area. Samples were taken as part of the research project BaSaR (<a href="http://www.kompetenz-wasser.de/en/forschung/projekte/basar/">www.kompetenz-wasser.de/en/forschung/projekte/basar/</a>) of Kompetenzzentrum Wasser Berlin, Ostschweizer Fachhochschule and Berliner Wasserbetriebe. More information including sampling and analytical methods are detailed in the corresponding journal paper "Emissions from building materials – a thread for the environment?", submitted to the MDPI-journal <em>Water</em>.</p> <p><strong>Description of fields:</strong></p> <ul> <li><strong>SiteID</strong>: site identifier <ul> <li>A: new development site with typical architecture for multi-storey apartment buildings with plastered and painted facades in northern part of Berlin (124 apartments)</li> <li>B: new development site with typical architecture for multi-storey apartment buildings with plastered and painted facades in southeastern part of Berlin (122 appartments)</li> </ul> </li> <li><strong>SamplingPoint</strong> <ul> <li>facade runoff: runoff from plastered facade collected with gutters during individual rain events</li> <li>roof runoff: roof runoff collected from one downpipe during individual rain events</li> <li>storm sewer: stormwater runoff sampled during individual rain events in a manhole receiving runoff from the entire catchment (A or B)</li> </ul> </li> <li><strong>LocalDateTime_StartRain</strong>: start time of sampled rain event (CET / CEST)</li> <li><strong>LocalDateTime_EndRain</strong>: end time of sampled rain event (CET / CEST)</li> <li><strong>CardinalDirection</strong>: only relevant for facade runoff <ul> <li>N: runoff from facade oriented to the north</li> <li>W: runoff from facade oriented to the west</li> </ul> </li> <li><strong>VariableName</strong>: name of analysed substance/parameter</li> <li><strong>CensorCode</strong>: either "lt" (less than) for concentration below detection limit (value is detection limit) or "nc" (not censored) for concentration above detection limit</li> <li><strong>UnitsAbbreviation</strong>: either "ug/L" (microgram per litre) or "mg/L" (milligram per litre)</li> <li><strong>DataValue</strong>: measured value (if censor code is lt, value indicates detection limit)</li> </ul> <p>One data file is provided in comma separated format:<br> "BaSaR_data.csv" contains concentrations of all samples.</p>
Geografisches hexagonales Gitter mit 1 Quadratkilometer Zellengröße für Deutschland - Geographical hexagonal grid with one square kilometer cell size for Germany
<p>Ein räumliches Gitter stellt eine abstrakte Definition von einheitlich großen Bezugszellen für statistische Auswertungen dar. Dieses macht Auswertungen und Vergleiche gegenüber räumlichen Abgrenzungen auf der Basis von administrativen Grenzen wie Kreisen oder Gemeinden unterschiedlicher Größen einfacher. Geografische Gitterdefinitionen werden eingesetzt, um eine vordefinierte Raumbezugsstruktur zu haben mit einer einheitlicheren Verteilung von Zellen in der Fläche.</p> <p><br> Ein Gitter mit sechseckigen, eben hexagonalen Zellen bietet eine Alternative zu rechteckigen statistischen Gittern wie das GeoGitter vom Bundesamt für Kartographie und Geodäsie (BKG, 2020). Ein Hexagon-Gitter bietet neben der Eigenschaft homogene Einheiten für statistische Analysen zu bilden (Schindler et al. 2008) mit sechs unmittelbaren Nachbarzellen vorteilhaftere Voraussetzungen für Nachbarschaftsanalysen im Vergleich zu quadratischen Zellen mit vier Kantennachbarn (White et al. 1992). Als Beispiel werden im Bild 1 mittels Hexagonen-Gitter Volumenunterschiede der aufragenden Vegetation pro Zelle dargestellt.</p>
High-resolution maps of material stock and population in Germany from 1985 to 2018
<p>Global societal material stocks such as buildings and infrastructure accumulated rapidly within recent decades, along with population growth. Material stocks constitute the physical basis of most socio-economic activities and services, such as mobility, housing, health, or education. The dynamics of stock growth, and its relation to the population that demands those services, is an essential indicator for long-term societal resource use and patterns of emissions. The creation of societal material stock creates path dependencies for future resource use, with an important impact on how the transformation towards sustainable societies can succeed.</p> <p>This dataset features detailed maps of material stock and population for Germany on a 30m grid. The data is based on recent maps of material stock and building volume (compare to Haberl et al. 2021, doi: 10.1021/acs.est.0c05642), recent and historic census data, and a time series of Landsat TM, ETM+, and OLI Earth Observation data.</p> <p><strong>Temporal extent</strong></p> <p>The data contains annual maps from 1985 to 2018.</p> <p><strong>Data format and units</strong></p> <p>Per German federal state, the data come in tiles of 30x30km. The projection is EPSG:3035. The images are compressed GeoTiff files (*.tif). There is a mosaic in GDAL Virtual format (*.vrt), which can readily be opened in most Geographic Information Systems. Please consider the generation of image pyramids before using *.vrt files.</p> <p>All image data has 34 bands, where band 1 is data for 1985, and band 34 is data for 2018.</p> <p>The dataset features</p> <ul> <li>population (Scaled by 100 to reduce data storage size. Divide by 100 to get people per cell)</li> <li>mass (in tons) of … <ul> <li>total material stock <ul> <li>… material stock in buildings <ul> <li>… in commercial and industrial buildings</li> <li>… in multi-family residential buildings</li> <li>… in single-family residential buildings</li> <li>… in high-rise buildings</li> <li>… in lightweight buildings</li> </ul> </li> <li>… material stock in road infrastructure</li> <li>… material stock in rail infrastructure</li> <li>… material stock in other infrastructure</li> </ul> </li> </ul> </li> </ul> <p>Material stock in high-rise and lightweight buildings is not featured in the corresponding publication due to its overall negligible amount. It is, however, included here for completeness.</p> <p><strong>Further information</strong></p> <p>For further information, please see the publication or contact Franz Schug (fschug@wisc.edu). Visit our website to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.</p> <p><strong>Corresponding publication</strong></p> <p>Schug, F., Frantz, D., Wiedenhofer, D., Virág, D., Haberl, H., van der Linden, S., Hostert, P. (in rev.): High-resolution mapping of 33 years of material stock and population growth in Germany. Journal of Industrial Ecology</p> <p><strong>Funding</strong></p> <p>This research was funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950).</p> <p> </p>
Geospatial data on indicators for parks in the city of Berlin, Germany
<p>The data contains features and indicators for 224 parks (at least 2 ha in size) in the city of Berlin and overall scores (indices) for natural elements, built elements (infrastructure) and spatial context (e.g. distance to public transport). All data is supplement to linked online web map.</p> <p><strong>List of data and content</strong></p> <ul> <li>Park_Berlin_Indicators: vector files (*.shp, *.geojson)</li> <li>Park_Berlin_Indicators: excel files (*.xlsx)</li> </ul> <p><strong>Spatial reference</strong><br> All data is projected in ETRS 1989 UTM Zone 33N (<a href="https://spatialreference.org/ref/epsg/25833/">EPSG:25833</a>)</p> <p><strong>Web-GIS</strong><br> View data and explore interactively using the <a href="https://arcg.is/5a9me">online application.</a></p> <p><strong>Data sources and processing</strong><br> For details on underlying data sources (e.g. availabilty, spatial resolution, time reference) and on data processing please refer to the linked publication, incl. Appendix 1</p> <p><strong>Acknowledgments</strong><br> We thank the City of Berlin for providing data. We greatly acknowledge OpenStreetMap (OSM) and contributers for providing important parts of the used data. This work was supported by the research project “Environmental‐Health Interactions in Cities (GreenEquityHEALTH) ‐ Challenges for Human Well‐Being under Global Changes” (project duration 2017–2022), funded by the German Federal Ministry of Education and Research (BMBF; no.01LN1705A).</p> <p><strong>Based on related original publication</strong><br> Kraemer, R., & Kabisch, N. (2021). Parks in context: Advancing citywide spatial quality assessments of urban green spaces using fine-scaled indicators. Ecology and Society, 26(2). <a href="https://doi.org/10.5751/ES-12485-260245">https://doi.org/10.5751/ES-12485-260245 </a></p>
SeasoNet: A Seasonal Scene Classification, Segmentation and Retrieval Dataset for Satellite Imagery over Germany
<p>This dataset consists of 1,759,830 multi-spectral image patches from the Sentinel-2 mission, annotated with image- and pixel-level land cover and land usage labels from the German land cover model LBM-DE2018 with land cover classes based on the CORINE Land Cover database (CLC) 2018. It includes pixel synchronous examples from each of the four seasons, plus an additional snowy set, spanning the time from April 2018 to February 2019. The patches were taken from 519,547 unique locations, covering the whole surface area of Germany, with each patch covering an area of 1.2km x 1.2km. The set is split into two overlapping grids, consisting of roughly 880,000 samples each, which are shifted by half the patch size in both dimensions. The images in each of the both grids themselves do not overlap.</p> <p><strong>Contents</strong></p> <p>Each sample includes:</p> <ul> <li>3 10m resolution bands (RGB), 120px x 120px</li> <li>1 10m resolution band (infrared), 120px x 120px</li> <li>6 20m resolution bands, 60px x 60px</li> <li>2 60m resolution bands, 20xp x 20px</li> <li>1 pixel-level label map</li> <li>2 binary masks for cloud and snow coverage</li> <li>2 binary masks for easy and medium segmentation difficulties, marks areas <300px and <100px respectively</li> <li>1 JSON-file containing additional meta-information</li> </ul> <p>The meta.csv contains the following information about each sample:</p> <ul> <li>Which season it belongs to</li> <li>Which of the two grids it belongs to</li> <li>Coordinates of the patch center</li> <li>Whether it was acquired from Sentinel-2 Satellite A or B</li> <li>Date and time of image acquisition</li> <li>Snow and cloud coverage percentages</li> <li>Image-level multi-class labels</li> <li>Three additional image-level urbanization labels, based on the center pixel (details below)</li> <li>The path to the sample</li> </ul> <p><strong>Classes</strong></p> <table> <thead> <tr> <th scope="col">ID</th> <th scope="col">Class</th> </tr> </thead> <tbody> <tr> <td>1</td> <td>Continuous urban fabric</td> </tr> <tr> <td>2</td> <td>Discontinuous urban fabric</td> </tr> <tr> <td>3</td> <td>Industrial or commercial units</td> </tr> <tr> <td>4</td> <td>Road and rail networks and associated land</td> </tr> <tr> <td>5</td> <td>Port areas</td> </tr> <tr> <td>6</td> <td>Airports</td> </tr> <tr> <td>7</td> <td>Mineral extraction sites</td> </tr> <tr> <td>8</td> <td>Dump sites</td> </tr> <tr> <td>9</td> <td>Construction sites</td> </tr> <tr> <td>10</td> <td>Green urban areas</td> </tr> <tr> <td>11</td> <td>Sport and leisure facilities</td> </tr> <tr> <td>12</td> <td>Non-irrigated arable land</td> </tr> <tr> <td>13</td> <td>Vineyards</td> </tr> <tr> <td>14</td> <td>Fruit trees and berry plantations</td> </tr> <tr> <td>15</td> <td>Pastures</td> </tr> <tr> <td>16</td> <td>Broad-leaved forest</td> </tr> <tr> <td>17</td> <td>Coniferous forest</td> </tr> <tr> <td>18</td> <td>Mixed forest</td> </tr> <tr> <td>19</td> <td>Natural grasslands</td> </tr> <tr> <td>20</td> <td>Moors and heathland</td> </tr> <tr> <td>21</td> <td>Transitional woodland/shrub</td> </tr> <tr> <td>22</td> <td>Beaches, dunes, sands</td> </tr> <tr> <td>23</td> <td>Bare rock</td> </tr> <tr> <td>24</td> <td>Sparsely vegetated areas</td> </tr> <tr> <td>25</td> <td>Inland marshes</td> </tr> <tr> <td>26</td> <td>Peat bogs</td> </tr> <tr> <td>27</td> <td>Salt marshes</td> </tr> <tr> <td>28</td> <td>Intertidal flats</td> </tr> <tr> <td>29</td> <td>Water courses</td> </tr> <tr> <td>30</td> <td>Water bodies</td> </tr> <tr> <td>31</td> <td>Coastal lagoons</td> </tr> <tr> <td>32</td> <td>Estuaries</td> </tr> <tr> <td>33</td> <td>Sea and ocean</td> </tr> </tbody> </table> <p><strong>Urbanization classes</strong></p> <ul> <li><strong>SLRAUM</strong> <ul> <li>0: None</li> <li>1: Ländlicher Raum (~ rural area)</li> <li>2: Städtischer Raum (~ urban area)</li> </ul> </li> <li><strong>RTYP3</strong> <ul> <li>0: None</li> <li>1: Ländliche Regionen (~ rural areas)</li> <li>2: Regionen mit Verstädterungsansätzen (~ urbanizing areas)</li> <li>3: Städtische Regionen (~ urban areas)</li> </ul> </li> <li><strong>KTYP4</strong> <ul> <li>0: None</li> <li>1: Dünn besiedelte ländliche Kreise</li> <li>2: Kreisfreie Großstädte</li> <li>3: Ländliche Kreise mit Verdichtungsansätzen</li> <li>4: Städtische Kreise</li> </ul> </li> </ul> <p>Further information on the urbanization classes can be found here:</p> <p><strong>SLRAUM</strong></p> <p><a href="https://www.bbsr.bund.de/BBSR/DE/forschung/raumbeobachtung/Raumabgrenzungen/deutschland/kreise/staedtischer-laendlicher-raum/kreistypen.html">https://www.bbsr.bund.de/BBSR/DE/forschung/raumbeobachtung/Raumabgrenzungen/deutschland/kreise/staedtischer-laendlicher-raum/kreistypen.html</a></p> <p><strong>RTYP3</strong></p> <p><a href="https://www.bbsr.bund.de/BBSR/DE/forschung/raumbeobachtung/Raumabgrenzungen/deutschland/regionen/siedlungsstrukturelle-regionstypen/regionstypen.html">https://www.bbsr.bund.de/BBSR/DE/forschung/raumbeobachtung/Raumabgrenzungen/deutschland/regionen/siedlungsstrukturelle-regionstypen/regionstypen.html</a></p> <p><strong>KTYP4</strong></p> <p><a href="https://www.bbsr.bund.de/BBSR/DE/forschung/raumbeobachtung/Raumabgrenzungen/deutschland/kreise/siedlungsstrukturelle-kreistypen/kreistypen.html">https://www.bbsr.bund.de/BBSR/DE/forschung/raumbeobachtung/Raumabgrenzungen/deutschland/kreise/siedlungsstrukturelle-kreistypen/kreistypen.html</a></p> <p><strong>License of landcover model</strong></p> <p>Bundesamt für Kartographie und Geodäsie</p> <p>dl-de/by-2-0 from <a href="https://www.govdata.de/dl-de/by-2-0">https://www.govdata.de/dl-de/by-2-0</a></p> <p>© GeoBasis-DE / <strong>BKG</strong> 2022</p> <p><strong>Source of landcover model</strong></p> <p><a href="https://gdz.bkg.bund.de/index.php/default/catalog/product/view/id/1071/s/corine-land-cover-5-ha-stand-2018-clc5-2018/">https://gdz.bkg.bund.de/index.php/default/catalog/product/view/id/1071/s/corine-land-cover-5-ha-stand-2018-clc5-2018/</a></p>
Flower strip data Germany 2021
<p>The data concerns 23 flower strips, sampled in 2019 in Germany. In all files, "Location" is the ID of the flower strips. </p> <p>The file "FlowerStripInformation" contains data regarding the sampled flower strips, including soil properties (sand, silt, clay weight%), pH, age of the flower strip (age; years since establishment), aboveground C input (AGC; estimated from measured aboveground biomass, Mg C/ha) belowround C input (BGC; estimated from measured belowground biomass, Mg C/ha), calculated root exudates (Exudates; 0.33*BGC, Mg C/ha), and the total belowground C input (BGC_tot, Mg C/ha), and the total C input from the flower strips (TotC, Mg C/ha). Coordinates for each flower strip is also available in UTM32 and lat/lon degrees. </p> <p>The file "FlowerStripClimate" contains monthly information about the mean temperature (temp, deg. C), total precipitation (precip, mm) and sunshine duration from the climate station closest to each of the sampled flower strips. </p> <p>The file "FlowerStripSOCstock" contains the calculated SOC stock of each flower strip and adjacent cropland for 0-10 cm, 10-30 cm and 0-30 cm. The equal soil mass-corrected mass of each layer (Mass; Mg/ha), SOC stock (SOC; Mg C/ha), total N (TN; Mg N/ha) and corresponding C:N ratio (CN, unitless) is available. </p> <p>The file "FlowerStripSpeciesCount" contains the count and ground cover of the observed species in the 23 flower strips. Five plots of 0.5 x 0.5 m (0.25 m2 area) were sampled at each flower strip location. The species name is given in German (Species_DE). The mean count of each species across the five plots in given in Count_mean, and the ground cover (5) is given in Cover_percent. The a sum of Cover_percent can be greater than 100 due to rounding. If the sum is less than 100, the remaining area was bare soil. </p>
AI results complementing the 2021 Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Germany
<p>This dataset contains the results of the EU co-funded surveillance activities conducted in 2021, which consisted of:</p> <ul> <li>Serological surveys to monitor the circulation of AIV subtypes H5 and H7 in poultry (active surveillance). These surveys should preferentially target poultry species or production systems with increased risk for introduction of avian influenza (AI).</li> <li>Passive surveillance aiming at the virological detection of AI in wild birds found dead or moribund.</li> </ul>
Spatially Aggregated Rain Radar Forecast for the Koeln Weiden, Germany
<p>radar_forecast.csv contains time series data generated by spatial aggregation of rain radar forecasts constructed using robust local optical flow extrapolation.</p>
Wastewater Treatment Plant Inflow Rate, Network Water Levels and Rain Rate for Koeln Weiden, Germany
<p>wtp_network_rain.csv contains time series data of measured wastewater treatment plant inflow rates, water levels from multiple locations within the wastewater sewer network and rain rate measured from a rain recorder located at the wastewater treatment plant.</p>
Marteloscope data of the experimental plot, Naundorf 710 b1 (Saxony, Germany 2020)
<p>Yield data (diameter breast height and tree height) of a mixed stand in the Tharandt Forest, Germany. The data was collected for the establishment of a marteloscope site within the I+ network of the European Forest Institute.</p> <p> </p>
Supplementary material for: "Typology of Dementia-Specific Care Units: A Nationwide Survey Study in Germany"
<p>This is a dataset and R code of statistical software R version 4.2.1 (2022-06-23) to develop a typology focusing on dementia-specific care units in German nursing homes.</p> <p>The dataset is based on a national survey. For this purpose, 2020, a stratified, randomized sample of 134 care units was included. A telephone interview with facility managers and a standardized questionnaire were used to collect 28 organization-specific variables, which were analyzed as constituent variables for a typology. In addition, the dataset contains seven variables on nonpharmacological interventions (Drugs, Pain, Behavior, Training, Expert, DCM, Music), a variable on "Provider of the nursing home" (Provider) and the variable (sub-question) about "Admission criteria for residents contractually regulated with cost bearers" (Criteria) that were queried for the care units to analyze associations related to typology.</p>
dataset: Responses of the structure and function of the understory plant communities to precipitation reduction across forest ecosystems in Germany
<p><strong>Context</strong>: Understory plant communities play a central role in forest biogeochemistry and the recruitment of trees making up the future forest. It is so far poorly understood how climate change will affect understory structure and functions in forest of different management intensity.</p> <p> </p><p><strong>Aims</strong>: We monitored understory functional traits including transpiration and carbon isotope discrimination, community structure and diversity during two growing seasons as affected by drought in forests subjected to different management intensities. We hypothesized that drought would affect ecophysiological traits such as transpiration but not species richness and diversity. Moreover, we assumed that stand-specific characteristics and forest management intensity modify the drought-resistance of the understory community.</p> <p></p> <p><strong>Methods</strong>: We set up roofs in beech and conifer stands with different management intensity in three different regions across Germany and a drought event close to the 2003 drought was imposed in two consecutive years.</p> <p><strong>Results</strong>: Precipitation reduction decreased soil water content by 2 to 8%, depending on stand and region, in comparison to the control subplots. In the first year, leaf level transpiration was reduced for different functional groups, which scaled to community transpiration modified by additional effects of drought on functional group specific leaf area. Acclimation effects in most functional groups were observed in the second year. We did not observe a significant reduction of plant diversity or a consistent management effect upon drought.</p> <p><strong>Conclusion</strong>: Our results indicate high plasticity and acclimation responses of the forest understory vegetation to changing climate conditions and recurrent drought events.</p> <p><strong>Abbreviations:</strong></p> <p>sp12 - campaign spring 2012; ls12 - campaign late summer 2012; es13 - campaign early summer 2013; ls13-campaign late summer 2013</p> <p>SEW16 - Schorfheide plot 16; SEW49 - Schorfheide plot 49; SEW48 - Schorfheide plot 48;HEW03 - Hainich plot 03; HEW12 - Hainich plot 12; HEW47- Hainich plot 47; AEW13 - Alb plot 13; AEW29 - Alb plot 29; AEW08 - Alb plot 08<br> explo - exploratory<br> SEW - Schorfheide; HEW - Hainich; AEW - Schwäbische Alb<br> in - conifer intensive managed; ma - beech managed; un - beech unmanaged<br> c- control; r - roof<br> LAIs - community leaf area index m<sup>2</sup>/m<sup>2</sup>; H - Shannon´s diversity index; Ts - community transpiration rate (weighted by LAI) mmol H<sub>2</sub>O m-<sup>2</sup> leaf area s-<sup>1</sup>; Ets - Evapotranspiration (mmol/m2/sec); E - Evaporation (mmol/m2/sec); C - leaf photosynthetic carbon isotope discrimination (∆<sup>13</sup>C) according to Farquhar et al. (1982); Cs - community photosynthetic carbon isotope discrimination (∆<sup>13</sup>C) according to Farquhar et al. (1982) (weighted by LAI)</p> <p> </p>
High-resolution basin-wide correlations with dynamic time-warping: code and data for a case study from the Usseln Limestone (Late Devonian, Rhenish Massif, Germany)
<p>This dataset accompanies the manuscript of Wichern et al. (GRL, 2024), entitled "Decoding Deep-Time Rhythms: Probing the limit of Stratigraphic Correlation in the Usseln Limestone's (Late Devonian) Time-Specific Facies". It contains both datasets and code. </p> <p>The dataset concerns samples collected from the Usseln Limestone, a rock unit that underlies the Late Devonian Kellwasser Crisis deposits in the Rhenish Massif, western Germany. The data consists of high-resolution thin-section composite photos, as well as micro-XRF scanning data (both maps and depth records) for three localities. The code contains the workflow to structure and plot the micro-XRF maps and convert them to depth records, as well as the workflow to analyse these depth records using dynamic time warping. Further analytical details can be found in the supporting material of the associated manuscript.</p>
High-frequency wind (u, w, v, Ts) and gas concentration measurements of CO2 and H2O over an agricultural field in Braunschweig, Germany
<p>This dataset contains high-frequency eddy covariance (EC) measurements over a flat agricultural field at the Thünen Institute in Braunschweig, Germany (52.30° N, 10.45° E).</p> <p>The data collection period spanned <strong>77 days </strong>in the year 2020 split into three files</p> <p> </p> <table> <tbody> <tr> <td>BS2020_06.rds</td> <td>June 11 to July 15</td> </tr> <tr> <td>BS2020_10.rds</td> <td>October 1 to November 10</td> </tr> <tr> <td>BS_2020_07_subset.rds</td> <td>July 11 to July 25</td> </tr> </tbody> </table> <p> </p> <p>Included in the dataset are 3D wind velocity data, recorded using a uSonic-3 Class A sonic anemometer from Metek GmbH. Additionally, the dataset provides gas concentration measurements for carbon dioxide (CO2) and water vapor (H2O), captured using an LI-7500A open-path infra-red gas analyzer from LI-COR Biosciences GmbH, Germany.</p> <p>Variables in the dataset</p> <table> <tbody> <tr> <th>Variable Name</th> <th>Description</th> <th>Units</th> </tr> </tbody> <tbody> <tr> <td>time</td> <td>Unique time stamp (POSIXct format)</td> <td>Seconds since Unix epoch</td> </tr> <tr> <td>CO2</td> <td>Wet molar density of carbon dioxide</td> <td>µmol m⁻³</td> </tr> <tr> <td>H2O</td> <td>Wet molar density of water vapor</td> <td>mmol m⁻³</td> </tr> <tr> <td>Ts</td> <td>Sonic temperature</td> <td>Kelvin</td> </tr> <tr> <td>u, v, w</td> <td>3D wind velocity components</td> <td>m s⁻¹</td> </tr> </tbody> </table> <p> </p> <p> </p> <div> <div> <div> <p>The dataset is in RDS format (version 3), compatible with R version 3.5.0 or higher.</p> <p>RDS is a binary file format native to the R programming environment</p> </div> </div> </div>
Anonymised transcriptions (local and translated versions) of 18 Focus Groups with RWPP voters in Spain, UK, Denmark, Germany, Hungary, Switzerland
<p><strong>Anonymised transcriptions (local and translated versions) of 18 Focus Groups with RWPP voters in Spain, UK, Denmark, Germany, Hungay, Switzerland</strong></p> <p>In the UNTWIST project, we have carried out a total of 18 focus groups in Denmark, Germany, Hungary, Spain, Switzerland and the United Kingdom. They explore RWPP voters’ subjective perceptions of their needs and demands, their horizon of expectations, and their level of ‘gender fatigue’. Groups’ design followed two minimum criteria: same-sex composition (with a minimum of two same sex -male and female- groups per country) and voting behaviour (current voters of RWPP who have previously voted for mainstream parties or abstained or have doubts about RWPP and mainstream or abstain in case of voting for the first time).</p> <p> The composition of the groups varied between 6 and 10 participants per group in all but one partner’s country. In Denmark, all focus groups experienced dropouts. These unforeseen issues led to conducting the focus groups with fewer participants than was initially designed.</p> <p> In 83% of countries, the empirical composition of focus groups was considered and controlled for participants’ age, social class position and level of education.</p> <p>Finally, groups were same-sex moderated.</p> <p>Two comprised folders are provided. One contains the anonymised transcriptions of 18 Focus Groups carried out for WP2 of the UNTWIST project in their local languages. The other contains the IA-translated (Deepl) version of the same focus groups. Please note that the translations have not been human-supervised. </p> <p>FG_CHE_1 Female <br>Female Group, Switzerland</p> <p>FG_CHE_2 Male<br>Male Group, Switzerland </p> <p>FG_DEN_1 Female <br>Female Groups, Denmakr</p> <p>FG_DEN_2 Male<br>Male Group, Denmark </p> <p>FG_DEN_3 Male <br>Male Group, Denmark</p> <p>FG_DEN_4 Mixed <br>Mix Male and Female Group, Switzerland</p> <p>FG_ESP_1 Male<br>Male Group, Spain</p> <p>FG_ESP_2 Male<br>Male Group, Spain </p> <p>FG_ESP_3 Female <br>Female Group, Spain</p> <p>FG_ESP_4 Female<br>Female Group, Spain</p> <p>FG_GBR_1 Female <br>Female Group, UK</p> <p>FG_GBR_2 Male <br>Male Group, UK</p> <p>FG_GER_1 Female <br>Female Group, Germany</p> <p>FG_GER_2 Male<br>Male Group, Germany</p> <p>FG_HUN_1 Female <br>Female Group, Hungary</p> <p>FG_HUN_2 Female <br>Female Group, Hungary</p> <p>FG_HUN_3 Male<br>Male Group, Hungary </p> <p>FG_HUN_4 Male<br>Male Group, Hungary </p>
Synthetic Dataset of charging processes by electric vehicles at workplace in Germany
<p>The dataset shows eight cluster groups that depict the mobility behavior of electric vehicle users in the employee context. For this purpose, 23.9 million data entries were analyzed, corresponding to 37,238 charging sessions. These data were collected over the year 2023. The 220 charging points were exclusively accessible to employees (private use case). From the data, cluster groups were derived using the Gaussian Mixture Model, and a synthetic dataset was generated through Monte Carlo sampling.</p> <p><span>The dataset consists of 8000 synthetic profiles, offering a robust scientific basis. By retaining the same statistical attributes as the empirical data, the synthetic profiles represent eight different mobility clusters, each containing 1000 entries, including full-time and part-time employees, shift workers, pool vehicle users, and opportunists.</span> Each cluster is represented by the mean parking start hours (arrival time - in decimal hours), mean parking duration (in decimal hours), the average energy recharged, and the average charging duration, each including the cluster-specific standard deviation and median.</p> <p>Further information can be obtained from the upcoming publication: "Synthetic Dataset of charging processes by electric vehicles at workplace in Germany."</p>
Supplementary dataset for the publication "Prevalence of tick-borne bacterial pathogens in Germany – has the situation changed after a decade?"
<p>The dataset supplements the journal article "Prevalence of tick-borne bacterial pathogens in Germany – has the situation changed after a decade?" published by Katja Mertens-Scholz, Bernd Hoffmann, Jörn M. Gethmann, Hanka Brangsch, Mathias W. Pletz and Christine Klaus in the journal mdpi microorganisms (DOI: <a href="https://doi.org/10.3389/fcimb.2024.1429667">https://doi.org/10.3389/fcimb.2024.1429667 </a><a>)</a></p> <p>The file "Tick samples" contains all data regarding time, location and detected pathogens". The files "Rickettsia sequences" and Borrelia sequences" contains all sequence information of positive samples. The file "Rickettsia reference genes" contains information of used reference genes for phylogenetic analysis.</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.