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2,348 results for “germany”
Dataset of Floods in Germany 1950-2010
<p>This is a data set comprising 29,248 flood events which were sampled from time-series of daily streamflow of 374 catchments across Germany over the period 1950-2010. The flood events were sampled by applying a Peak-Over-Threshold}approach on each catchment, separately. For each of the events, a set of preconditions was collected: First, preconditions in catchment averages of daily precipitation sum [mm/d], daily mean soil moisture [\%] and daily mean temperature of each flood event were sampled: Going back in time from the day of the flood event, the means of predictor values over multiple time periods, "delta t" = [0, 1, 3, 5, 7], were extracted. Note that P is the cumulative sum of rainfall and snowmelt. Both P and SM are model output from the "Mesoscale Hydrological Model" (see references below). Second, data on 10 catchment attributes of various types, i.e. static predictors, are included for each of the catchments. These include data on the average climatic conditions (aridity index, mean annual precipitation), topography (slope, elevation), geomorphology (channel slope, drainage density).</p>
quetzal_germany_input_static
<p>This datapackage contains various input data for generation of transport models within the region of Germany. The open source transport model quetzal_germany requires these files for its modelling steps and validation. The whole model can be found on github: <a href="https://github.com/marlinarnz/quetzal_germany">https://github.com/marlinarnz/quetzal_germany</a></p>
Production Temperatures Upper Jurassic Reservoir Germany Bavaria
<p>The dataset provides estimated production temperature prognosis for the Upper Jurassic reservoir in the Bavarian Molasse Basin assessed by a correlation of production temperatures and temperatures at the top of reservoir taken from the GeotIS -platform (www.geotis.de).Further expllanation in Zosseder, K., Pfrang, D., Schölderle, F., Bohnsack, D., Konrad, F. (2022): Characterisation of the upper Jurassic geothermal reservoir in the South German Molasse Basin as basis for a potential assessment to foster the geothermal installation development – Results from the joint research project Geothermal Alliance Bavaria. Geomechanics and Tunnelling 15, No. 1, pp. 17–24. https://doi.org/10.1002/geot.202100087.</p>
Meteor observations and wind estimates from the northern Germany SIMONe radar network on November 5, 2018
<p>This dataset includes meteor observations and wind estimates taken as part of the SIMONe 2018 campaign in northern Germany on November 5, 2018. The files are in netCDF-4 format and follow CF conventions (https://cfconventions.org/). We recommend loading the data using the xarray Python package.</p> <p>The SIMONe 2018 campaign ran from November 2, 2018 through November 9, 2018 in northern Germany. The radar network consisted of two pulsed transmitters in Juliusruh and Collm and a five-element interferometric MIMO-CW transmitter located in Kühlungsborn. Monostatic receiver stations co-located with the pulsed transmitters and six additional receiver stations located in Mechelsdorf, Breege, Neustrelitz, Guderup, Salzwedel, and Bornim were used to form a total of two monostatic and ten bistatic links. The data from these individual links were then processed to detect specular meteor echoes and estimate their parameters, including Doppler shift. The Doppler shifts, imposed by movement of the meteor trail due to the neutral winds, were then used to estimate the 4-D wind field. More details about the campaign can be found in Vierinen et al. (2019). Details for the wind field estimation can be found in Volz et al. (submitted).</p>
Hyperspectral (RGB + Thermal) drone images of Karlsruhe, Germany - Raw images for the Thermal Bridges on Building Rooftops (TBBR) dataset
<p><strong>Overview:</strong></p> <p>This repository contains the <strong>raw images</strong> for the dataset of <a href="https://doi.org/10.5281/zenodo.4767771"><strong>Thermal Bridges on Building Rooftops (TBBR) dataset</strong></a>.</p> <p>This dataset contains <strong>5696 drone images</strong> (2848 RGB and 2848 thermal) of building rooftops, recorded with a normal (RGB) and a FLIR-XT2 (thermal) camera on a DJI M600 drone. They show six large building blocks of around 20 buildings per block recorded in the city centre of the German city Karlsruhe east of the market square. Because of a high overlap rate of the images, the same buildings are on average recorded from different angles in different images about 20 times.</p> <p>All images were recorded during a drone flight on March 19, 2019 from 7 a.m. to 8 a.m. At this time, temperatures were between 3.78 ° C and 4.97 ° C, humidity between 80% and 98%. There was no rain on the day of the flight, but there was 2.3mm/m² 48 hours beforehand. For recording the thermographic images an emissivity of 1.0 was set. The global radiation during this period was between 38.59 W / m² and 120.86 W / m². No direct sunlight can be seen visually on any of the recordings.</p> <p><strong>Usage:</strong></p> <p>Each zip archive file represents one of the six drone flight paths. The archives contain JPG files of size 4000x3000 pixels (RGB) and 640x512 (Thermal), separated into individual directories for RGB and Thermal:</p> <pre><code>├── Flug_100/ │ ├── RGB/ │ │ ├── DJI_0004.jpg │ │ ├── DJI_0006.jpg │ │ └── ... │ └── Thermal/ │ ├── DJI_0003_R.JPG │ ├── DJI_0005_R.JPG │ └── ... ├── Flug_101/ │ ├── RGB/ │ │ ├── DJI_0001.jpg │ │ ├── DJI_0003.jpg │ │ └── ... │ └── Thermal/ │ ├── DJI_0000_R.JPG │ ├── DJI_0002_R.JPG │ └── ... └── ...</code></pre> <p><strong>File Numbering/Naming Scheme:</strong></p> <p>The pairs of RGB + Thermal images follow the simple numbering scheme of: <strong>RGB = Thermal + 1</strong>.<br> For example, DJI_0003_R.jpg and DJI_0004.JPG are the matching Thermal and RGB images, respectively, that can be merged to form a single hyperspectral drone image.</p> <p>To perform the merging, we recommend using the <strong>merge_image_layers.py</strong> script provided by the associated <strong><a href="https://github.com/Helmholtz-AI-Energy/TBBRDet">TBBRDet software</a></strong> (see the scripts/alignment/ directory).</p> <p>For convenience, we have provided a CSV listing all annotated images in the <a href="https://doi.org/10.5281/zenodo.4767771"><strong>Thermal Bridges on Building Rooftops (TBBR) dataset</strong></a>. The CSV format is as follows:</p> <pre><code>Flight,RGB,Thermal Flug_100,DJI_0048.jpg,DJI_0047_R.JPG Flug_100,DJI_0050.jpg,DJI_0049_R.JPG ...</code></pre> <p> </p>
MEDIATOR Driving Simulator Study Germany: Questionnaire Data User Evaluation HMI
<p>The dataset provided resulted from a driving simulator study conducted by Chemnitz University of Technology (TUC) within work package 3 of the MEDIATOR project. The study focused on the user evaluation of the Mediator system and its functionalities, including the innovative Human Machine interface (HMI). The user evaluation centred on acceptance, trust, usability, comfort and the experience of Transitions of Control (TOCs). The core idea of the Mediator system is to mediate between the human driver and the automated system. Thereby, the Mediator system aims at establishing both as a team that is aware of each other’s strengths, limitations as well as current states in order to achieve safe TOCs, which are actively proposed by the HMI. The main focus of the driving simulator study was on comfort TOCs from manual to automated driving, simulated automation degradation and related TOCs by the human driver, comfort critical situations (i.e., close approach to the rear-end of a traffic jam) as well as the influence of driver characteristics. The provided dataset contains the questionnaire data of 74 German-speaking participants.</p> <p> </p> <p>This document contains information about the study methodology and coding of the variables. For a detailed description, please consult Mediator deliverable D3.3 ‘Results of the MEDIATOR driving simulator evaluation studies’ (Part II – Driving simulator study Germany). Please note that selected passages of this deliverable were adopted (partly in a slightly modified manner) in this document.</p> <p> </p> <p>This dataset is licensed under a <a href="https://spdx.org/licenses/CC-BY-4.0.html">Creative Commons Attribution 4.0 International</a> License.</p> <p> </p> <p>The research leading to this dataset received funding from the European Commission Horizon 2020 programme under the project MEDIATOR (<a href="https://mediatorproject.eu/">https://mediatorproject.eu/</a>), grant agreement number 814735.</p> <p> </p> <p>If you use the dataset, please cite it as: MEDIATOR (2023). MEDIATOR Driving Simulator Study Germany: Questionnaire Data User Evaluation HMI. <a href="https://doi.org/10.5281/zenodo.7638299">https://doi.org/10.5281/zenodo.7638299</a></p> <p> </p> <p>For further information, please contact: <a href="mailto:cornelia.hollander@psychologie.tu-chemnitz.de">cornelia.hollander@psychologie.tu-chemnitz.de</a>.</p>
Supplementary Material to the Publication Genotyping of Francisella tularensis subsp. holarctica from Hares in Germany
<p>Supplementary Material in Open Data Format to Publication Genotyping of Francisella tularensis subsp. holarctica from Hares in Germany</p>
Supplementary dataset to publication: "Genomic epidemiology of Campylobacter fetus subsp. venerealis from Germany"
<p>Supplementary dataset to publication: "Genomic epidemiology of Campylobacter fetus subsp. venerealis from Germany" by Abdel-Glil Mostafa Y., Hotzel Helmut, Tomaso Herbert, Didelot Xavier, Brandt Christian, Seyboldt Christian, Linde Jörg, Schwarz Stefan, Neubauer Heinrich, El-Adawy Hosny. Genomic epidemiology of Campylobacter fetus subsp. venerealis from Germany. Frontiers in Veterinary Science. volume 9 (https://www.frontiersin.org/articles/10.3389/fvets.2022.1069062) <br> </p>
Soil water content before and after rain events, May-July 2014-2016, Hainich, Germany, project AquaDiva
<p>This dataset contains soil water content data used for the analysis published in Fischer-Bedtke et al., (2023). It gives spatially distributed soil water contents evaluated at a rain event scale covering the following periods:</p> <p>May 5 - July 26 2014</p> <p>May 13 - July 28 2015</p> <p>May 25 - July 24 2016</p> <p>The enclosed files cover two different soil depth: topsoil (soil depth 7 cm) and subsoil (soil depth 27 cm).</p> <p>Fischer et al. (2023) give details about the included data and should be cited along the with the dataset when using the data.</p> <p><strong>Related datasets</strong></p> <p>Design information on the soil water content measurement points, including position to the next tree, hydraulic soil properties can be found in the follwowing associated dataset</p> <p>Metzger, Johanna Clara, Hildebrandt, Anke, & Filipzik, Janett. (2023). Soil moisture sensor network, design, location attributes and soil properties, Hainich, Germany, project AquaDiva (1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8065170</p> <p> </p> <p><strong>References</strong></p> <p>Fischer, C., Metzger, J. C., Demir, G., Wutzler, T., and Hildebrandt, A.: Throughfall spatial patterns translate into spatial patterns of soil moisture dynamics – empirical evidence, Hydrology and Earth System Sciences, https://doi.org/10.5194/hess-2022-418, 2023.</p>
Pandemic severity indicator for COVID-19 in Germany dataset
<p>The datasets included in this repository represent a pandemic severity indicator for the COVID-19 pandemic in Germany based on a composite indicator for the years 2020 and 2021. The pandemic severity index consists of three indicators: the incidence of patients tested positive for COVID-19, the incidence of patients with COVID-19 in intensive care, and the incidence of registered deaths due to COVID-19. The datasets have been developed within the CODIFF project (Socio-Spatial Diffusion of COVID-19 in Germany) at Leibniz Insitute for Research on Society and Space. The project received funding by Deutsche Forschungsgemeinschaft (DFG, project number 492338717). The datasets have been used in the following publications, in which further methodological details on the indicator can be found:</p> <ul> <li><a href="https://doi.org/10.1101/2023.02.17.23286084">Stabler, M., & Kuebart, A. (2023). Tempo-spatial dynamics of COVID-19 in Germany: A phase model based on a pandemic severity indicator. <em>medRxiv</em>, 2023-02</a>.</li> <li><a href="https://doi.org/10.1016/j.sste.2023.100605">Kuebart, A., & Stabler, M. (2023). Waves in time, but not in space – An analysis of pandemic severity of COVID-19 in Germany. <em>Spatial and Spatio-temporal Epidemiology</em>, 2023.</a></li> </ul> <p>This repository consists of two files:</p> <p><strong>pandemic_severity_germany </strong></p> <p>This table contains the composite indicator for daily pandemic severity for Germany on the national scale as well as the three sub-indicators for each day between 2020-03-01 and 2021-12-31. The sub-indicators were sourced from the <a href="https://github.com/robert-koch-institut">Robert Koch Institute</a>, the German government agency responsible for disease control and prevention.</p> <p><strong>pandemic_severity_counties</strong></p> <p>This table contains the composite indicator for daily pandemic severity for Germany on the level of the 400 individual counties, as well as the three sub-indicators for each day between 2020-03-01 and 2021-12-31. The sub-indicators were sourced from the <a href="https://github.com/robert-koch-institut">Robert Koch Institute</a>, the German government agency responsible for disease control and prevention. The counties can be identified by name (kreis) or by county identification number (ags5)</p>
Data set supporting journal article: Markwitz, C., Knohl, A. and Siebicke, L.: "Evapotranspiration over agroforestry sites in Germany", Biogeosciences, 2020
<p>This data set contains all necessary data needed to replicate figures and analysis presented in the research article: Markwitz, C., Knohl, A. and Siebicke, L.: "Evapotranspiration over agroforestry sites in Germany", Biogeosciences, 2020.</p> <p>In detail, this data set contains 1) meteorological data and half-hourly evapotranspiration rates obtained by a conventional eddy covariance set-up, a low-cost eddy covariance set-up and an energy balance eddy covariance set-up for measurement campaigns of approximately four weeks duration (*_Fluxes_Campaigns_*); 2) raw data to recalculate flux footprints for the campaigns of approximately four weeks duration (*_Campaign_Footprints_*) and for the whole year (*_Annual_Footprints_*); 3) half-hourly evapotranspiration rates obtained by a low-cost eddy covariance set-up and an energy balance eddy covariance set-up gap-filled and corrected for energy balance closure (*_Fluxes_Annual_*). The data were collected at five agroforestry systems and five monoculture agriculture systems without trees across Northern Germany. </p>
Building height map of Germany
<p>Urban areas have a manifold and far-reaching impact on our environment, and the three-dimensional structure is a key aspect for characterizing the urban environment. </p> <p>This dataset features a map of building height predictions for entire Germany on a 10m grid based on Sentinel-1A/B and Sentinel-2A/B time series. We utilized machine learning regression to extrapolate building height reference information to the entire country. The reference data were obtained from several freely and openly available 3D Building Models originating from official data sources (building footprint: cadaster, building height: airborne laser scanning), and represent the average building height within a radius of 50m relative to each pixel. Building height was only estimated for built-up areas (European Settlement Mask), and building height predictions <2m were set to 0m.</p> <p><strong>Temporal extent</strong><br> The acquisition dates of the different data sources vary to some degree:<br> - Independent variables: Sentinel-2 data are from 2018; Sentinel-1 data are from 2017.<br> - Dependent variables: the 3D building models are from 2012-2020 depending on data provider.<br> - Settlement mask: the ESM is based on a mosaic of imagery from 2014-2016.<br> Considering that net change of building stock is positive in Germany, the building height map is representative for ca. 2015. </p> <p><strong>Data format</strong><br> The data come in tiles of 30x30km (see shapefile). The projection is EPSG:3035. The images are compressed GeoTiff files (*.tif). Metadata are located within the Tiff, partly in the FORCE domain. There is a mosaic in GDAL Virtual format (*.vrt), which can readily be opened in most Geographic Information Systems. Building height values are in meters, scaled by 10, i.e. a pixel value of 69 = 6.9m.</p> <p><strong>Further information</strong><br> For further information, please see the publication or contact David Frantz (david.frantz@geo.hu-berlin.de).<br> A web-visualization of this dataset is available <a href="https://ows.geo.hu-berlin.de/webviewer/building-height/">here</a>.</p> <p><strong>Publication</strong><br> Frantz, D., Schug, F., Okujeni, A., Navacchi, C., Wagner, W., van der Linden, S., & Hostert, P. (2021). National-scale mapping of building height using Sentinel-1 and Sentinel-2 time series. Remote Sensing of Environment, 252, 112128. DOI: <a href="https://doi.org/10.1016/j.rse.2020.112128">https://doi.org/10.1016/j.rse.2020.112128</a></p> <p><strong>Acknowledgements</strong><br> The dataset was generated by FORCE v. 3.1 (<a href="https://doi.org/10.3390/rs11091124">paper</a>, <a href="https://github.com/davidfrantz/force">code</a>), which is freely available software under the terms of the GNU General Public License v. >= 3. Sentinel imagery were obtained from the <a href="https://scihub.copernicus.eu/">European Space Agency and the European Commission</a>. The European Settlement Mask was obtained from the <a href="https://data.jrc.ec.europa.eu/dataset/8bd2b792-cc33-4c11-afd1-b8dd60b44f3b">European Commission</a>. 3D building models were obtained from <a href="https://www.businesslocationcenter.de/en/economic-atlas/download-portal/">Berlin Partner für Wirtschaft und Technologie GmbH</a>, <a href="http://suche.transparenz.hamburg.de/dataset/3d-stadtmodell-lod2-de-hamburg4?forceWeb=true">Freie und Hansestadt Hamburg / Landesbetrieb Geoinformation und Vermessung</a>, <a href="https://opendata.potsdam.de/explore/dataset/3d-gebaudemodell-lod2-citygml/information">Landeshauptstadt Potsdam</a>, <a href="https://www.bezreg-koeln.nrw.de/brk_internet/geobasis/3d_gebaeudemodelle/index.html">Bezirksregierung Köln / Geobasis NRW</a>, and <a href="https://www.geoportal-th.de/de-de/Downloadbereiche/Download-Offene-Geodaten-Th%C3%BCringen/Download-3D-Geb%C3%A4ude">Kompetenzzentrum Geodateninfrastruktur Thüringen</a>. This dataset was partly produced on <a href="https://eodc.eu">EODC</a> - we thank Clement Atzberger for supporting the generation of this dataset by sharing disc space on EODC.</p> <p><strong>Funding</strong><br> This dataset was produced with funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (<a href="https://boku.ac.at/understanding-the-role-of-material-stock-patterns-for-the-transformation-to-a-sustainable-society-mat-stocks">MAT_STOCKS</a>, grant agreement No 741950).</p>
Management and plant physiology data for grassland sites in Germany
<p>Management and vegetation data for sites Fendt (DE-Fen), Rottenbuch (DE-RbW) and Graswang (DE-Gwg) in Southern Germany, observed between 2012 and 2017. Weekly resolution vegetation traits are included for 2015.</p> <p>The sites are part of TERENO, a network of observatories in Germany. The time period includes the ScaleX intensive observation campaigns that took place in 2015 and 2016. The data format is NetCDF4. A Jupyter notebook is available (see Related identifiers, GitLab) with technical notes and examples. </p>
Meteorology, environment and surface flux data for grassland sites in Germany
<p>Observation and model data for locations Fendt (DE-Fen), Rottenbuch (DE-RbW) and Graswang (DE-Gwg), in conjunction with selected journal publications. These data have primarily been used for investigation of surface carbon fluxes (Net Ecosystem Exchange, Gross Primary Productivity), seasonal climatic trends and land management. </p> <p>The sites are part of TERENO, a network of observatories in Germany. The TERENO Data Portal should provide other and more up-to-date information. The time period includes the ScaleX intensive observation campaigns that took place in 2015 and 2016. The data format is NetCDF4. A Jupyter notebook is available (see Related identifiers, GitLab) with technical notes and examples. </p>
Wind field and cloud layer data for Stuttgart, Germany
<p>This dataset contains computed wind field, cloud base and mixing layer height time series for locations in the City Centre of Stuttgart, Germany. The observations were made using a network of Doppler lidar and ceilometer instrumentation. The measurements were conducted within the framework of the Urban Climate under Change [UC]2 program. </p> <p>Up-to-date information about the program can be found at: <a href="http://uc2-program.org">http://uc2-program.org</a></p> <p>Versions:</p> <ul> <li>1.x.y: Data are in compliance with the [UC]2 Data Standard (<a href="http://uc2-program.org/uc2_data_standard.pdf">http://uc2-program.org/uc2_data_standard.pdf</a>). Included are computed variables for days during the two Intensive Observation Periods (IOPs) in winter and summer of 2017.</li> <li>2.x.y: Data include computed variables for most of the available observations during 2017. The data stores conform to an approach developed for ScaleX.</li> </ul>
Grain-size data from the loess profiles Ostrau and Gleina in Saxony (Germany)
<p><strong>Grain-size data from the loess profiles Ostrau and Gleina in Saxony (Germany)</strong></p> <p>The samples were taken between 2009 and 2010 in the framework of the DFG project <a href="https://gepris.dfg.de/gepris/projekt/46526743"><em>"Rekonstruktion der Umweltbedingungen des Spätpleistozäns in Mittelsachsen anhand von Löss-Paläobodensequenze</em>n" (DFG FU 417/7-1 and FA 239/13-1</a>) from the loess records Ostrau and Gleina. Both located in the Saxonian-Loess-Region in Germany. For further details on the project profiles (with further references therein), we refer to Meszner et al. (2011,2013), Kreutzer et al. (2012), Meszner (2015) and Zech et al. (2017). </p> <p>Samples for the data reported here were selected in 2015. 212 samples were taken from the loess profile Ostrau and 269 samples from the loess profile Gleina. Full details on sampling and sample preparation can be found in the Grassl (2016) (unpublished master thesis in Germany, available upon request). The most relevant details are extracted below. </p> <p><strong>Preparation and measurements</strong></p> <p>Sample preparation and measurements were carried out at the GFZ in Potsdam (Germany). Thirty-nine samples from the profile Ostrau were separated into eight equal parts to obtain representative samples. The samples were labeled with "G" for Gleina and "O" for Ostrau. All other samples were sampled without applying this separation method. <br> For samples from the profile Ostrau, the suffix "mT" (with separation) and "oT" (without separation) indicates whether this <br> separation method was used. </p> <p>The samples were treated with HCl (10 %, 12 h to 20 h) and rinsed in the demineralized water. To suspend the samples, NO<sub>3</sub>P0<sub>4</sub> was used on twelve pars of H<sub>2</sub>O<sub>2</sub>. </p> <p>A <em>Retch Laser Scattering Particle Size Distribution Analyzer (HORIBA LA- 950)</em> was used for the grain-size measurements. Details <br> on the settings are reported separately in each file.</p> <p><br> <strong>The data in the repository </strong></p> <p>Grainsize_data.zip This folder contains 4,853 ASCII TXT-files with the raw granulometric data. Filenames are unique timestamps (measurement date and time in the format <em>YYYYMMDDHHMMSS</em> CET). Each file comes with a header with relevant metadata and the measurement data. The metadata also contains the sample name, e.g., <em>O_55_oT</em> reads "O" for Ostrau, "55" sampling depth in cm, and "oT" for "ohne Teiler" (without separator, while "mT", "mit Teiler" would stand for with separator). For files for the profile Gleina, a "G" is used followed by the sampling depth range (two numbers, e.g., <em>G_380_382</em>) in cm. </p> <p>The files <em>Gleina_depth.txt</em>, <em>Ostrau02_depth.txt</em>, and <em>Ostrau03_depth.txt</em> allow a correlation with the profiles graphs published in Meszner (2015). </p> <p><br> <strong>References</strong></p> <p>Grassl, W., 2016. End-Member-Modellierungsanalyse an hochauflösenden Korngrößen der Lössprofile Ostrau und Gleina, Lommatzscher Pflege, Sachsen. unpublished Master thesis, TU Dresden.</p> <p>Kreutzer, S., Fuchs, M., Meszner, S., Faust, D., 2012. OSL chronostratigraphy of a loess-palaeosol sequence in Saxony/Germany using quartz of different grain sizes. Quaternary Geochronology 10, 102–109. doi:10.1016/j.quageo.2012.01.004</p> <p>Meszner, S., Fuchs, M., Faust, D., 2011. Loess-Paleosol-Sequences from the loess area of Saxony (Germany). E & G, Quaternary Science Journal 60, 47–65.</p> <p>Meszner, S., 2015. Loess from Saxony. A reconstruction of the Late Pleistocene landscape evolution and palaeoenvironment based on loess-palaeosol sequences from Saxony (Germany). Dresden. PhD thesis. TU Dresden. </p> <p>Meszner, S., Kreutzer, S., Fuchs, M., Faust, D., 2013. Late Pleistocene landscape dynamics in Saxony, Germany: Paleoenvironmental reconstruction using loess-paleosol sequences. Quaternary International 296, 95–107. doi:10.1016/j.quaint.2012.12.040</p> <p>Zech, M., Kreutzer, S., Zech, R., Goslar, T., Meszner, S., McIntyre, C., Häggi, C., Eglinton, T., Faust, D., Fuchs, M., 2017. Comparative 14C and OSL dating of loess-paleosol sequences to evaluate post-depositional contamination of n-alkane biomarkers. Quaternary Research 87, 180–189. doi:10.1017/qua.2016.7</p>
Data for "Crop Diversification in Viticulture with Aromatic Plants: Effects of Intercropping on Grapevine Productivity in a Steep-Slope Vineyard in the Mosel Area, Germany"
<p>This dataset is corresponding to an open-access article named "Crop Diversification in Viticulture with Aromatic Plants: Effects of Intercropping on Grapevine Productivity in a Steep-Slope Vineyard in the Mosel Area, Germany" published in Agriculture (https://www.mdpi.com/2077-0472/11/2/95; <a href="https://doi.org/10.3390/agriculture11020095">https://doi.org/10.3390/agriculture11020095</a>), funded by the European Commission Horizon 2020 project Diverfarming [grant agreement 728003]. </p>
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>
Representative Counties of Germany and Their Structural Data
<h2>Content</h2> <p>A dataset of counties that are representative for Germany with regard to</p> <ul> <li>the average disposable income,</li> <li>the quota of divorces,</li> <li>the respective quotas of employees working in the services (excluding logistics, security, and cleaning) and the MINT sectors,</li> <li>the proportions of age groups in the total proportion of the respective population, with age groups in five-year strata for the population aged between 30 and 65 and the population in the age range between 65 and 75 each considered separately for the calculation of representativeness.</li> <li>population density, if the benchmark is defined as the national average density weighted by county population. This value is calculated by averaging the population-per-square-kilometer figures of all German counties, weighted by the population size of each county. In contrast to a simple arithmetic mean of county-level densities, which would disproportionately reflect sparsely populated areas, this method reflects the actual density of the environments where people live. The resulting value for the selected counties falls within the 95% confidence interval of the national weighted average.</li> </ul> <p>In addition, data from the four big cities Berlin, München (Munich), Hamburg, and Köln (Cologne) were collected and reflected in the dataset.</p> <p>The dataset is based on the most recent data available at the time of the creation of the dataset, mainly deriving from 2022, as set out in detail in the readme.md file.</p> <h2>Method applied</h2> <p>The selection of the representative counties, as reflected in the dataset, was performed on the basis of official statistics with the aim of obtaining a confidence rate of 95%. The selection was based on a principal component analysis of the statistical data available for Germany and the addition of the regions with the lowest population density and the highest and lowest per capita disposable income. A check of the representativity of the selected counties was performed.</p> <p>In the case of Leipzig, the city and the district had to be treated together, in deviation from the official territorial division, with respect to a specific use case of the data.</p>
Data and Code for Publication "Estimating inter-individual Mahalanobis distances from mixed incomplete high-dimensional data: Application to human skeletal remains from 3rd to 1st millennia BC Southwest Germany"
<p>Data and code for publication: H. Rathmann, S. Lismann, M. Francken, A. Spatzier, Estimating inter-individual Mahalanobis distances from mixed incomplete high-dimensional data: Application to human skeletal remains from 3<sup>rd</sup> to 1<sup>st</sup> millennia BC Southwest Germany. <em>Journal of Archaeological Science</em> 156: 105802. <a href="https://doi.org/10.1016/j.jas.2023.105802">https://doi.org/10.1016/j.jas.2023.105802</a></p> <p>The repository contains:</p> <ul> <li>“R code for FLEXDIST.txt”: R code for executing FLEXDIST, a tool to estimate inter-individual Mahalanobis-type distances, taking correlations among variables into account, applicable to multiple variable scales (nominal, ordinal, continuous, or any mixture thereof), accommodating missing values, and handling high-dimensional data. <strong>Please refer to the latest version of this repository for the most up-to-date R code</strong>.</li> <li>“data.csv”: Pre-processed dataset comprising 85 dental morphological features collected from 64 archaeological human remains from Final Neolithic to Early Iron Age Southwest Germany used for analysis.</li> <li>“complete dataset.xlsx”: Complete dataset comprising 199 dental morphological features collected from 144 archaeological human remains from Final Neolithic to Early Iron Age Southwest Germany.</li> </ul>
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