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2,348 results for “germany”
FIGURE 8 in Two exceptional Balaenomorpha (Cetacea: Mysticeti) from the Biemenhorst Subformation (middle/late Miocene) of Bocholt (W Münsterland, Germany) with a critical appraisal on the anatomy of the periotic bone
FIGURE 8. FIAM in Eomysticetus whitmorei, ChM PV4253, from deep inside (A) to 'surface' (C). For innervation, see remarks of Figure 7.
РИС. 1. Раковины ювенильных крымских клауЗиллид (A–C). A. Mentissa gracilicosta, г. КоШка, СимеиЗ, coll. В. Н. Попов, det. С. В. Леонов. B. Mentissa canalifera, окрестности с. Краснолесье, 23.08.2002, сoll.&det. С. В. Леонов. C. Cochlodina laminata, Артек «Дубрава», 27.05.1999, coll.?, det. С.В. Леонов. Раковины половоЗрелых Balea perversa (D–G). D. Между Виклебю и Ресмо, остров Эланд, ШвециЯ, 07.1958 (фото F. Welter-Shultes). Е. Девичий Замок, Палава, ЮжнаЯ МоравиЯ, ЧехиЯ, 26.09.1998 (фото M. Horsák). F. РаЗвалины Замка РейнграфенШтайн блиЗ Бад-Мюнстер-ам-Штайн, Рейнланд-Пфальц, ГерманиЯ (фото А. В. Сысоев). G. Яйла Южной Демерджи, АлуШта, Республика Крым, РоссиЯ, 14.11.2002, сoll. Н. М. Ковблюк, det. С. В. Леонов. Все иЗображениЯ в одном масШтабе. FIG. 1. Shells of juvenile Crimean clausillids (A–C). A. Mentissa gracilicosta, Koshka mountain, Simeiz, coll. V. N. Popov, det. S. V. Leonov. B. Mentissa canalifera, near Krasnolesye village, 23.08.2002, coll.&det. S. V. Leonov. C. Cochlodina laminata, Artek "Dubrava", 27.05.1999, coll.?, det. S.V. Leonov. Adult Balea perversa shells (D–G). D. Sweden, Öland, between Vickleby and Resmo, 07.1958 (photo F. Welter-Shultes). E. Dívčí hrad, South Moravia, Czech Republic, 26.09.1998 (photo by M. Horsák). F. Ruins of the Rheingrafenstein Castle near Bad Munster am Stein, Rheineland-Pfalz, Germany (photo by A.V. Sysoev). G. Yayla Yuzhnaya Demerdzhi, Alushta, Republic of Crimea, Russia, 14.11.2002, coll. N. M. Kovblyuk, det. S. V. Leonov. All images are at the same scale. in О достоверности находок Balea perversa (Gastropoda: Pulmonata: Clausiliidae) в Крыму
РИС. 1. Раковины ювенильных крымских клауЗиллид (A–C). A. Mentissa gracilicosta, г. КоШка, СимеиЗ, coll. В. Н. Попов, det. С. В. Леонов. B. Mentissa canalifera, окрестности с. Краснолесье, 23.08.2002, сoll.&det. С. В. Леонов. C. Cochlodina laminata, Артек «Дубрава», 27.05.1999, coll.?, det. С.В. Леонов. Раковины половоЗрелых Balea perversa (D–G). D. Между Виклебю и Ресмо, остров Эланд, ШвециЯ, 07.1958 (фото F. Welter-Shultes). Е. Девичий Замок, Палава, ЮжнаЯ МоравиЯ, ЧехиЯ, 26.09.1998 (фото M. Horsák). F. РаЗвалины Замка РейнграфенШтайн блиЗ Бад-Мюнстер-ам-Штайн, Рейнланд-Пфальц, ГерманиЯ (фото А. В. Сысоев). G. Яйла Южной Демерджи, АлуШта, Республика Крым, РоссиЯ, 14.11.2002, сoll. Н. М. Ковблюк, det. С. В. Леонов. Все иЗображениЯ в одном масШтабе. FIG. 1. Shells of juvenile Crimean clausillids (A–C). A. Mentissa gracilicosta, Koshka mountain, Simeiz, coll. V. N. Popov, det. S. V. Leonov. B. Mentissa canalifera, near Krasnolesye village, 23.08.2002, coll.&det. S. V. Leonov. C. Cochlodina laminata, Artek "Dubrava", 27.05.1999, coll.?, det. S.V. Leonov. Adult Balea perversa shells (D–G). D. Sweden, Öland, between Vickleby and Resmo, 07.1958 (photo F. Welter-Shultes). E. Dívčí hrad, South Moravia, Czech Republic, 26.09.1998 (photo by M. Horsák). F. Ruins of the Rheingrafenstein Castle near Bad Munster am Stein, Rheineland-Pfalz, Germany (photo by A.V. Sysoev). G. Yayla Yuzhnaya Demerdzhi, Alushta, Republic of Crimea, Russia, 14.11.2002, coll. N. M. Kovblyuk, det. S. V. Leonov. All images are at the same scale.
Chemical analysis dataset for contaminants of emerging concern and bioanalytical data for samples from a low mountain stream in Central Germany
<p>In 2022, river-water samples were collected at six sampling sites along the Holtemme River in Central Germany using large-volume solid phase extraction. The extracts were analysed by target chemical analysis for contaminants of emerging concern. In addition, the extracts were analysed in a bioanalytical test battery using effect-based tools. The battery included assays for cytotoxicity (neutral red retention assay), oxidative stress (Nrf2-CALUX®), endocrine disruption (ER-, AR-, anti-ER-, anti-AR-, GR- and PR-CALUX®) and the fish embryotoxicity test with zebrafish (<em>Danio rerio</em>). The data obtained are included in the .csv files in this repository.</p>
Dataset: The blue halite in Morsleben, Germany: a natural lyoluminescence dosimeter?
<p>This repository provides the complete dataset and supplementary files used in our luminescence study, submitted to the <em>Journal of Luminescence</em>. Included materials support reproducibility and allow for further exploration of lyoluminescence properties of salts from the <span>Morsleben repository managed by the Bundesgesellschaft für Endlagerung (BGE)</span>.</p> <p><span>The work done at the IFJ PAN was supported by the National Science Centre, Poland (grant No 2021/43/D/ST5/03042).</span></p>
DBFZ Resource Database: DE-Biomass Monitor. Biomass Potentials and Utilization of Biogenic Wastes and Residues in Germany 2010-2020
<p>Excerpt from the<a href="https://datalab.dbfz.de/resdb?lang=en"> DBFZ Resource Database</a> (DE Biomass Monitor) as of 02.12.2024 (v6.3.2).</p>
High-Resolution Canopy Fuel Maps Based on GEDI: A Foundation for Wildfire Modeling in Germany
<p>Open access publication under review.</p> <p>Visit <a href="https://ee-forestfuels-ger.projects.earthengine.app/view/gedi-fuels"><strong>this Earth Engine app</strong></a> to explore the data interactively.</p> <p> </p> <p>Abstract:</p> <p>Forest fuels are essential for wildfire behavior modeling and risk assessments but difficult to quantify accurately. An increase in fire frequency in recent years, particularly in regions traditionally not prone to fire, such as central Europe, has increased demands for large-scale remote sensing fuel information. This study develops a methodology for mapping canopy fuels over large areas (Germany) at high spatial resolution, exclusively relying on open remote sensing data.</p> <p><br>We propose a two-step approach where we first use measurements from NASA’s GEDI instrument to estimate canopy fuel variables at the footprint level, before predicting high-resolution raster maps. Instead of using field measurements, we generate (GEDI-) footprint-level estimates for Canopy (Base) Height (CH, CBH),<br>Cover (CC), Bulk Density (CBD), and Fuel Load (CFL) by segmenting airborne LiDAR point clouds and processing tree-level metrics with allometric crown biomass<br>models. To predict footprint-level canopy fuels we fit and tune Random Forest models, which are cross-validated using k-fold Nearest Neighbor Distance Matching.<br>Predictions at >1.6 M GEDI footprints and biophysical raster covariates are combined with a Universal Kriging method to produce countrywide maps at 20-meter resolution.</p> <p><br>Agreement (RMSE/R²) with validation data (from the same population) was strong for footprint-level predictions and moderate for map predictions. A validation<br>with estimates based on National Forest Inventory data revealed low to modest agreement. Better accuracy was achieved for variables related to height (CH, CBH)<br>rather than to cover or biomass (CBD, CFL). Error analysis pointed towards a mixture of biases in model predictions and validation data, as well as underestimation of<br>model prediction standard errors. Contributing factors may be simplification through allometric equations and spatial and temporal mismatch of data inputs.<br>The proposed workflow has the potential to support regions where wildfire is an emerging issue, and fuel and field information is scarce or unavailable.</p> <p> </p> <p>Data:</p> <p>This repository contains modeling data, model objects (R), and predicted maps. The TIFF-files each have six bands, which includes (1) the final Universal Kriging result, (2) the linear model prediction (3) the prediction of residual Kriging, (4) the Kriging variance, (5) the linear model prediction standard error, and (6) Universal Kriging standard error.</p> <p> </p> <p>Disclaimer:<br>Maps in this repository are predicted using canopy fuel estimates from GEDI measurements. These are limited the region between 51.6° North and South. Map predictions exceeding this range should be considered an extrapolation of the model to an unknown biophysical domain. Error maps (6) can aid in utilizing our canopy fuel maps.</p>
Caravan-DE: Caravan extension Germany - German dataset for large-sample hydrology
<p><em>Caravan</em> is an open community dataset of meteorological forcing data, catchment attributes, and discharge data for catchments around the world:<a href="../record/6578598" target="_blank" rel="noopener"> https://zenodo.org/record/6578598</a>. <br>We have employed the published code to derive meteorological forcing data and catchment attributes from global data sources to extend Caravan with data for <strong>1887 catchments</strong> in Germany. The time series data are in <strong>daily resolution and span up to 70 years, from January 1951 to December 2020</strong>.</p> <p>Most of the catchments in Caravan-DE are also part of the CAMELS-DE dataset (<a href="https://doi.org/10.5281/zenodo.13837553" target="_blank" rel="noopener">10.5281/zenodo.13837553</a>, 1582 catchments). As CAMELS-DE relies on meteorological forcing data that is only available within the borders of Germany, catchments going beyond the German national borders had to be discarded. Caravan uses global data products for the meteorological forcing data and catchment attributes, which is why Caravan-DE includes these catchments that are partly located outside of Germany. As catchments in Caravan-DE and CAMELS-DE are identified by the same ID, the datasets can be used together.<br>Please refer to the CAMELS-DE paper (<a href="https://doi.org/10.5194/essd-2024-318">https://doi.org/10.5194/essd-2024-318</a>) for information about discharge data and the catchment geometries used for both Caravan-DE and CAMELS-DE.</p> <p>For the processing of the data the following guide was followed step by step: <a href="https://github.com/kratzert/Caravan/wiki/Extending-Caravan-with-new-basins">https://github.com/kratzert/Caravan/wiki/Extending-Caravan-with-new-basins</a></p> <h3> </h3> <h3>Disclaimer for discharge and water level data provided by the German federal state agencies:</h3> <p>english:<em><br>The state agencies do not guarantee the accuracy or completeness of the discharge or water level data provided. In addition, all hydrological data may be subject to future revisions, including adjustments to the rating curves or corrections of errors. Therefore, it is necessary to obtain the most recent discharge time series directly from the federal state authorities for projects that require water law permits. Additionally, the regulations of the respective federal state apply and specific enquiries should be made as needed. It is also important to note that the state agencies explicitly disclaim any warranty as to the accuracy or completeness of the data and therefore any liability claims against any of the federal states are also excluded.</em></p> <p>german:<em><br>Die Ländesämter gewährleisten nicht die Genauigkeit oder Vollständigkeit der bereitgestellten Abfluss oder Wasserstandsdaten. Zudem können alle hydrologischen Daten zukünftigen Überarbeitungen unterliegen, einschließlich Anpassungen der Wasserstands-Abflussbeziehung oder der Korrektur von Fehlern. Daher ist es notwendig, die aktuellsten Abflusszeitreihen direkt bei den Landesbehörden zu beziehen, falls Wasserrechtsgenehmigungen erforderlich sind. Zusätzlich gelten die Vorschriften des jeweiligen Bundeslandes, und spezifische Anfragen sollten bei Bedarf gestellt werden. Es ist ebenfalls wichtig zu beachten, dass die staatlichen Behörden ausdrücklich jegliche Gewährleistung hinsichtlich der Genauigkeit oder Vollständigkeit der Daten ausschließen und somit auch jegliche Haftungsansprüche gegenüber einem der Bundesländer ausgeschlossen sind.</em></p> <p> </p> <h3>Changelog</h3> <ul> <li>v1.0.1 <ul> <li>Small changes to be consistent with other Caravan extensions (see <a href="https://github.com/kratzert/Caravan/issues/35" target="_blank" rel="noopener">https://github.com/kratzert/Caravan/issues/35</a>): <ul> <li>camelsde_basin_shapes.shp: removed column gauge_name</li> <li> <div>attributes_other_camelsde.csv: added river name to gauge_name column</div> </li> </ul> </li> </ul> </li> <li>v1.1.0 <ul> <li>Added FAO Penman-Monteith PET (potential_evaporation_sum_FAO_PENMAN_MONTEITH) and renamed the ERA5-LAND potential_evaporation band to potential_evaporation_sum_ERA5_LAND. Also added all PET-related climate indices derived with the Penman-Monteith PET band (suffix "_FAO_PM") and renamed the old PET-related indices accordingly (suffix "_ERA5_LAND"). <br>This was conducted to be consistent with <a href="https://doi.org/10.5281/zenodo.14673536" target="_blank" rel="noopener">Caravan version 1.5</a>.</li> </ul> </li> <li>v1.1.1 <ul> <li>By mistake, the new variables for v1.1.0 were missing in the climatic indices, these have now been added.</li> </ul> </li> </ul>
Results from the monitoring of veterinary medicinal product residues and other substances in live animals and animal products - Germany
<p>This dataset contains the monitoring results of veterinary medicinal product residues and other substances measured in live animals and animal products analysed by the national competent authority of Germany. The presence of unauthorised substances, residues of veterinary medicinal products in food may pose a risk factor for public health.</p> <p>For this reason and in order to ensure a high level of consumer protection, a comprehensive legislative framework has been established in the European Union (EU) which defines maximum limits permitted in food and monitoring programmes for the control of the presence of these substances in the food chain. Regulation (EU) No 37/2010 establishes maximum limits for residues of veterinary medicinal products in food-producing animals and animal products. Maximum residue levels for pesticides in or on food and feed of plant and animal origin are laid down in Regulation (EC) No 396/2005. Commission Implementing Regulation (EU) 2022/1646 lays down practical arrangements for and specific content of official controls of the use of veterinary medicinal products in live animals and products of animal origin through three different official national control plans: a national risk-based control plan for production in the Member States, a national randomised surveillance plan for production in the Member States and a national risk-based control plan for third-country imports. Additionally, Commission Delegated Regulation (EU) 2022/1644 lays down the range of samples and stage of production, processing and distribution at which the samples are to be taken.</p> <p>Since 2018 until 2022, the data on the national residue monitoring plan were reported to EFSA in accordance with Council Directive 96/23/EC.</p> <p>The dataset contains the results of laboratory tests from samples taken from bovines, pigs, sheep, goats, horses, poultry, rabbits, farmed game, wild game aquaculture, milk, eggs and honey, and from 2023 also samples from casings, insects and reptiles.</p> <p>Targeted samples are taken with the aim of detecting illegal treatment or controlling compliance with the maximum levels laid down in the relevant legislation. This means that, in their national plans Member States target the groups of animals (species, gender, age) where the probability of finding residues is the highest. Conversely, the objective of random sampling is to collect significant data to evaluate, for example, consumer exposure to a specific substance.</p> <p>Suspect samples are taken as a consequence of i) non-compliant results on samples taken in accordance with the control plans, ii) possession or presence of prohibited substances at any point during manufacture, storage, distribution or sale through the food and feed production chain, or iii) suspicion or evidence of illegal treatment or non-compliance with the withdrawal period for an authorised medicinal veterinary product.</p> <p>Residues of pharmacologically active substances mean active substances, excipients or degradation products and their metabolites, which remain in food.</p> <p>Unauthorised substances mean substances that are not authorised as veterinary medicinal products or as a feed additive under European Union legislation.</p> <p>Prohibited substances mean substances which are prohibited for use in food producing animals according to the European Union legislation.</p> <p>Non-compliant sample is a sample that has been analysed for the presence of one or more substances and failed to comply with the legal provisions for at least one substance. Thus, a sample can be non-compliant for one or more substances.</p> <p><strong>REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION:</strong></p> <p>VMPR_2023 – Federal Office of Consumer Protection and Food Safety</p> <p>VMPR_2022 – Federal Office of Consumer Protection and Food Safety</p> <p>VMPR_2021 – Federal Office of Consumer Protection and Food Safety</p> <p>VMPR_2020 – Federal Office of Consumer Protection and Food Safety</p> <p>VMPR_2019 – Federal Office of Consumer Protection and Food Safety</p> <p>VMPR_2018 – Federal Office of Consumer Protection and Food Safety</p> <p>VMPR_2017 – Federal Office of Consumer Protection and Food Safety</p>
ABSOLUT input data for an example application on the districts of Germany
<p>These are data for running the ABSOLUT R programs published separately on Zenodo: <a href="https://doi.org/10.5281/zenodo.4468608">10.5281/zenodo.4468608</a>.</p> <p>The data published here consist of:</p> <p><strong>absolutcontrol.dat</strong> – text file (UTF-8) with case-specific settings for program execution, may be edited by the user</p> <p><strong>crop-areas.csv</strong> – CSV table of crop areas in hectares for different crops in German administrative areas. Modified from data originally provided by the statistical offices of Germany (© Statistische Ämter des Bundes und der Länder, Deutschland, 2020) and re-distributed here in this modified form also under the terms of the Data licence Germany – attribution – version 2.0, see https://www.govdata.de/dl-de/by-2-0</p> <p><strong>districtweather.zip</strong> – zipped directories DistrictWeather and DistrictFeatures. DistrictWeather contains 401 ASCII DAT files with monthly weather variables, one per district. These have been generated using rasterized weather data from the German meteorological service (Deutscher Wetterdienst, DWD), an official digital map of administrative boundaries provided by the German Federal Agency for Cartography and Geodesy, and CLC-2012 land use data provided by Copernicus. DistrictFeatures is an empty directory to be used by the corresponding program.</p> <p><strong>climatescenarios.zip</strong> - zipped directories ClimateScenarios and ClimateScenarioFeatures. ClimateScenarios contains three subdirectories with example climate scenario realisations in the same format as DistrictWeather. ClimateScenarioFeatures is an empty directory to be used by the corresponding program.</p> <p><strong>yield-indat.csv </strong>– CSV table of crop yields in dt/ha for different crops in German administrative areas, annual values for the years 1999–2020. Modified from data originally provided by the statistical offices of Germany (© Statistische Ämter des Bundes und der Länder, Deutschland, 2021) and re-distributed here in this modified form also under the terms of the Data licence Germany – attribution – version 2.0, see https://www.govdata.de/dl-de/by-2-0</p>
Estimating historic N- and S-deposition with publicly available data – An example from Central Germany
<p>The deposition of reactive nitrogen and sulphur has profound effects on ecosystem functioning. In the last decades, monitoring networks providing high resolution spatio-temporal deposition estimates have been set up, but equivalent information on historic deposition is mostly missing. However, understanding vegetation change and mitigate future loss of biodiversity and ecosystem functioning is only possible evaluating the effects of its strongest drivers, which includes deposition in many ecosystems. Here, we combine different data sources to provide estimates of historic deposition in forested ecosystems on a high spatio-temporal scale for a federal state in Central Germany from 1880 to present.</p> <p>We make use of data from field measurement stations together with elevation and precipitation data from the last three decades to build a simple deposition model, validate this model with a model publicly available covering the time range from 2000 to present, and extrapolate deposition from this joint model to the past using European deposition trends from the last 150 years.</p> <p>Our approach can easily be adapted to other data and spatial areas shows how to use raw deposition data together with publicly available data on elevation and precipitation to construct simple deposition models covering recent and historic times in areas and for times for which no data are available.</p>
#energy_graph on renewable shares in electricity and CO2 emission factors in Australia and Germany
<p>This is the little graph I used for my #energy_graph tweet, including the underlying data, in an Excel file. Here is the tweet: https://twitter.com/WPSchill/status/1464368711817740298?s=20. And here is last year's tweet: https://twitter.com/WPSchill/status/1336633040676720640?s=20</p> <p>I occasionally tweet stuff like this. Follow me, if you like ;) https://twitter.com/WPSchill</p>
Figure 7 in On R. Kinzelbach's euscorpiid specimens from Turkey deposited in the Naturhistorisches Museum Mainz, Germany (Scorpiones: Euscorpiidae)
Figure 7. Distribution of Alpiscorpius phrygius (triangles), Euscorpius avcii (circles) and E. koci (squares). Black icons indicate type localities; blue icons, records of Kinzelbach (1982); red triangles, all published records according to Tropea et al. (2015); red circles, records of Tropea et al. (2012); green circles, records of Parmakelis et al. (2013); red squares, records of Tropea & Yağmur (2015), green squares, records of Şenl & Karataş (2021).
Figures 1–6 in On R. Kinzelbach's euscorpiid specimens from Turkey deposited in the Naturhistorisches Museum Mainz, Germany (Scorpiones: Euscorpiidae)
Figures 1–6: Specimens deposited in NMM including labels. Figures 1–2. Female Alpiscorpius phrygius. Figures 3–4. Juvenile Euscorpius avcii. Figures 5–6. Two males Euscorpius koci.
FIG. 3. — A-N in Two poorly known species of Foraminifera: Polystomella minuta Reuss, 1865 from the Oligocene of Germany (Bavaria) and P. falunica Allix, 1913 from the Miocene of western France (Touraine). Designation of a neotype for P. falunica
FIG. 3. — A-N, Elphidiella falunica (Allix, 1913); A, B, Saucats; C, D, Sallespisse; E, F, Saint-Selve; G, H, Étang des Charmes; I-L, Carry-le-Rouet, lateral and oral views; M, sutural canal; N, section; O, P, Elphidiella hannai (Cushman & Grant, 1927), lateral and oral views. Scale bars: A-L, N-P, 200 mm; M, 50 mm.
FIG. 2. — A in Two poorly known species of Foraminifera: Polystomella minuta Reuss, 1865 from the Oligocene of Germany (Bavaria) and P. falunica Allix, 1913 from the Miocene of western France (Touraine). Designation of a neotype for P. falunica
FIG. 2. — A, Polystomella falunica Allix, 1913; B-P, Elphidiella falunica (Allix, 1913), Thenay (les Gandes); B, C, lateral and oral views; D, sutural pores; F-H, Thenay (le Piziou); A, B, lateral and oral views; C, enlargement of the oral part; I, J, Thenay (la Rangère), lateral and oral views; K, L, Chemillé, lateral and oral views; M, N, Mirebeau, lateral and oral views; O, P, Noyant, lateral and oral views. Scale bars: A-C, I-P, 200 mm; F, G, 100 mm; H, 50 mm; D, 20 mm; E, 5 mm.
FIG. 1 in Two poorly known species of Foraminifera: Polystomella minuta Reuss, 1865 from the Oligocene of Germany (Bavaria) and P. falunica Allix, 1913 from the Miocene of western France (Touraine). Designation of a neotype for P. falunica
FIG. 1. — Geographical areas: A, Touraine: Pontlevoy, le Piziou, le Mincé, les Gandes, la Rangère, Château Gabillon, Savigné-sur-Lathan – Anjou (Chemillé) – Poitou: Mirebeau; B, Aquitaine: Saucats, Saint-Selve, Étang des Charmes at southern Saint-Selve, Saubrigues, Sallepisse, Baudignan, Lafaurie; C, Provence: Carry-le-Rouet; D, France, general map with the localization of the geographical zones A-C.
Data set on the Higher-Education Pact in Germany, 2007 - 2020
\begin{tabular}{@{\extracolsep{5pt}} ll} \\[-1.8ex]\hline \hline \\[-1.8ex] Variable & Description \\ \hline \\[-1.8ex] HASC\_1 & State name \\ Nb & State number \\ ccluster & 1 = Western Territorial State, 2 = Eastern State, \\ & 3 = City State \\ Ltype & 1 = Western Territorial State, 2 = Eastern State. \\ ZstA.0717 & Additional entrants (sum 2007 – 2017) to 2005-value \\ zStA.U0717 & Additional entrants a traditional universities \\ zStA.F0717 & Additional entrants a UAS \\ zStA.Fratio0717 & Share of entrants at UAS to total entrants. \\ Ant.StA05 & Enrollment rate 2005 \\ Ant.StA18 & Enrollment rate 2018 \\ RelStVZA05 & Adivsing relationship 2005 \\ RelStVZA18 & Adivsing relationship 2018 \\ GW05 & Share of graduates in the humanities 2005 \\ GW18 & Share of graduates in the humanities 2018 \\ NaWi05 & share of graduates in the natural sciences 2005 \\ NaWi18 & share of graduates in the natural sciences 2018 \\ SRW05 & share of graduates in the social sciences 2005 \\ SRW18 & share of graduates in the social sciences 2018 \\ WandS05 & migration balance 2005 \\ WandS18 & migration balance 2017 \\ \hline \\[-1.8ex] BUMI0717 & federal funds received by the HSP-programme \\ & (sum 2007 – 2018) \\ BUMIzStA0717 & BUMI0717 per additional entrant \\ Einw18 & population size 2018 \\ BUMI.GWP16 & federal funds covering all federal grants-in-aid \\ & in the higher-education field \\ STUD18 & number of students in 2018 \\ zstaeinw & additional entrants per capita \\ deltaantsta & relative change in the enrollment rate \\ deltabetrrel & relative change in the adising relationship \\ deltagw & relative change in the humanities \\ deltanw & relative change in the natural sciences \\ deltasrw & relative change in the social sciences \\ bumieinw & Federal funds (HSP) per capita \\ bumigwp16einw & Overall federal funds per capita \\ bumistud & Federal funds (HSP) per student \\ Prof05 & Number of professors 2005 \\ Prof17 & Number of professors 2017 \\ \hline \\[-1.8ex] \end{tabular}
mDRONES4rivers-project: UAV-imagery of the project area Nonnenwerth at the Rhine River, Germany
<p>Spatially and temporally high-resolution data was acquired with the aid of multispectral sensors mounted on UAV and a gyrocopter platform for the purpose of classification. The work was part of the research and development project „Modern sensors and airborne remote sensing for the mapping of vegetation and hydromorphology along Federal waterways in Germany“ (mDRONES4rivers) in cooperation of the German Federal Institute of Hydrology (BfG), Geocoptix GmbH, Hochschule Koblenz und JB Hyperspectral Devices. <br> Within the project period (2019-2022) data was collected at different sites situated in Germany along the Rivers Rhine and Oder. All published data produced within the project can be found by searching for the keyword ‘mDRONES4rivers‘. <br> In this dataset, the following UAS data and metadata of the project site ‘Nonnenwerth’ (center coordinates [WGS84]: 50.637541°N, 7.208834°E; area: 8 ha) at the Rhine River in Germany is available for download:<br> • Multispectral orthophotos (GeoTiff; 6 bands: B, G, R, Red-Edge, NIR, Flag; camera: Micasense; resolution: 25 cm; abbreviation: MS_RAW)<br> • RGB-orthophotos (GeoTiff; 3 bands: R, G, B; camera: Phantom; resolution: 25 cm; abbreviation: PH_ORTHO)<br> • Digital Surface Models (GeoTiff; 1 band; camera: Phantom; resolution: ca. 5 cm; abbreviation: PH_DEM)<br> • associated Technical Reports (PDF; technical metadata concerning data acquisition, and processing using Agisoft Metashape, 1x for multispectral orthophotos, 1x for RGB-orthophotos + digital surface model)<br> The above-mentioned files are provided for download as dataset stored in one directory per season depending on the date of data acquisition (e.g. mDRONES4rivers_NW_UAV_2019_01_Winter.zip = projectname_projectsite_platform_year_no.season_name.season). To provide an overview of all files and general background information plus data preview the following files are stored in the info.zip folder: <br> • Overview table and metadata of the above-mentioned data (xlsx)<br> • Summary (PDF, Detailed description of sensors and data acquisition procedure, 1x for multispectral orthophotos, 1x for RGB-orthophotos + digital surface models)<br> Note: the data was processed with focus on spectral information and not for geodetic purposes. Georeferencing accuracy has not been checked in detail.</p>
mDRONES4rivers-project: UAV-imagery of the project area Emmericher Ward at the Rhine River, Germany
<p>Spatially and temporally high-resolution data was acquired with the aid of multispectral sensors mounted on UAV and a gyrocopter platform for the purpose of classification. The work was part of the research and development project „Modern sensors and airborne remote sensing for the mapping of vegetation and hydromorphology along Federal waterways in Germany“ (mDRONES4rivers) in cooperation of the German Federal Institute of Hydrology (BfG), Geocoptix GmbH, Hochschule Koblenz und JB Hyperspectral Devices. <br> Within the project period (2019-2022) data was collected at different sites situated in Germany along the Rivers Rhine and Oder. All published data produced within the project can be found by searching for the keyword ‘mDRONES4rivers‘. <br> In this dataset, the following UAS data and metadata of the project site ‘Emmericher Ward’ (center coordinates [WGS84]: 50.385264°N, 6.198692°E; area: 53ha) at the Rhine River in Germany is available for download:<br> • Multispectral orthophotos (GeoTiff; 6 bands: B, G, R, Red-Edge, NIR, Flag; camera: Micasense; resolution: 25 cm; abbreviation: MS_RAW)<br> • RGB-orthophotos (GeoTiff; 3 bands: R, G, B; camera: Phantom; resolution: 25 cm; abbreviation: PH_ORTHO)<br> • Digital Surface Models (GeoTiff; 1 band; camera: Phantom; resolution: ca. 5 cm; abbreviation: PH_DEM)<br> • associated Technical Reports (PDF; technical metadata concerning data acquisition, and processing using Agisoft Metashape, 1x for multispectral orthophotos, 1x for RGB-orthophotos + digital surface model)<br> The above-mentioned files are provided for download as dataset stored in one directory per season depending on the date of data acquisition (e.g. mDRONES4rivers_EW_UAV_2019_01_Winter.zip = projectname_projectsite_platform_year_no.season_name.season). To provide an overview of all files and general background information plus data preview the following files are stored in the info.zip folder: <br> • Overview table and metadata of the above-mentioned data (xlsx)<br> • Summary (PDF, Detailed description of sensors and data acquisition procedure, 1x for multispectral orthophotos, 1x for RGB-orthophotos + digital surface models)</p> <p><br> Note: the data was processed with focus on spectral information and not for geodetic purposes. Georeferencing accuracy has not been checked in detail. </p>
mDRONES4rivers-project: UAV-imagery of the project area Reitwein at the River Oder, Germany
<p>Spatially and temporally high-resolution data was acquired with the aid of multispectral sensors mounted on UAV and a gyrocopter platform for the purpose of classification. The work was part of the research and development project „Modern sensors and airborne remote sensing for the mapping of vegetation and hydromorphology along Federal waterways in Germany“ (mDRONES4rivers) in cooperation of the German Federal Institute of Hydrology (BfG), Geocoptix GmbH, Hochschule Koblenz und JB Hyperspectral Devices. <br> Within the project period (2019-2022) data was collected at different sites situated in Germany along the Rivers Rhine and Oder. All published data produced within the project can be found by searching for the keyword ‘mDRONES4rivers‘. <br> In this dataset, the following UAS data and metadata of the project site ‘Reitwein’ (center coordinates [WGS84]: 52.500961°N, 14.629186 °E; area: 60 ha) at the River Oder in Germany is available for download:<br> • Multispectral orthophotos (GeoTiff; 6 bands: B, G, R, Red-Edge, NIR, Flag; camera: Micasense; resolution: 25 cm; abbreviation: MS_RAW)<br> • RGB-orthophotos (GeoTiff; 3 bands: R, G, B; camera: Phantom; resolution: 25 cm; abbreviation: PH_ORTHO)<br> • Digital Surface Models (GeoTiff; 1 band; camera: Phantom; resolution: ca. 5 cm; abbreviation: PH_DEM)<br> • associated Technical Reports (PDF; technical metadata concerning data acquisition, and processing using Agisoft Metashape, 1x for multispectral orthophotos, 1x for RGB-orthophotos + digital surface model)<br> The above-mentioned files are provided for download as dataset stored in one directory per season depending on the date of data acquisition and flight altitude, as the data was collected here from two different flight altitudes (e.g. mDRONES4rivers_RW_UAV_2020_03_summer_70m.zip = projectname_projectsite_platform_year_no.season_name.season_flightaltitude.zip). To provide an overview of all files and general background information plus data preview the following files are stored in the info.zip folder: <br> • Overview table and metadata of the above-mentioned data (xlsx)<br> • Summary (PDF, Detailed description of sensors and data acquisition procedure, 1x for multispectral orthophotos, 1x for RGB-orthophotos + digital surface models)</p> <p>Note: the data was processed with focus on spectral information and not for geodetic purposes. Georeferencing accuracy has not been checked in detail.</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.