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2,348 results for “germany”
Supplementary material 2 from: Grunewald K, Schweppe-Kraft B, Syrbe R-U, Meier S, Krüger T, Schorcht M, Walz U (2020) Hierarchical classification system of Germany's ecosystems as basis for an ecosystem accounting – methods and first results. One Ecosystem 5: e50648. https://doi.org/10.3897/oneeco.5.e50648
Supplementation of ecosystem types (ETs) by more differentiated spatially and non-spatially explicit data (system of assignment of biotope and habitat types relevant for nature conservation to ETs
Data from: Research culture: a survey of travel behaviour among scientists in Germany and the potential for change
Awareness of the environmental impact of conferences is growing within the scientific community. Here we report the results of a survey in which scientists in Germany were asked about their attendance at conferences, their reasons for attending, and their willingness to explore new approaches that would reduce the impact of conferences on the environment. A majority of respondents were keen to reduce their own carbon footprint and were willing to explore alternatives to the traditional conference.
Load profile data of 50 industrial plants in Germany for one year
<p>This dataset holds the electric load profiles of 50 small and mid-size enterprises in Germany. The load profiles are in 15-minute time resolution for one year. The load is shown in kW as an average over 15 minutes.</p> <p>The dataset is divided into two:</p> <ul> <li>LoadProfile_20IPs_2016 shows load profiles of 20 industrial plants (IP) for the year 2016.</li> <li>LoadProfile_30IPs_2017 shows load profiles of 30 industrial plants (IP) for the year 2017.</li> </ul> <p>The IPs from the dataset for 2016 do not reappear in the dataset for 2017.</p> <p> </p> <p>The dataset LoadProfile_20IPs_2016 is evaluated in the following publication:</p> <ul> <li>Covic, N., Braeuer, F., McKenna, R., Pandzic, H., Optimizing Industrial Facilities’ Active Participation<br> in Electricity Markets under Uncertainty, 2020.</li> </ul> <p>Both datasets together are evaluated in multiple publications:</p> <ul> <li>Braeuer, F., Finck, R., McKenna, R., Comparing empirical and model-based approaches for calculating dynamic grid emission factors: An application to CO2-minimizing storage dispatch in Germany, Journal of Cleaner Production, Volume 266, 2020, 121588, ISSN 0959-6526, https://doi.org/10.1016/j.jclepro.2020.121588.</li> <li>Braeuer, F., Rominger, J., McKenna, R.,Fichtner, W., Battery storage systems: An economic model-based analysis of parallel revenue streams and general implications for industry, Applied Energy, Volume 239, 2019, Pages 1424-1440, ISSN 0306-2619, https://doi.org/10.1016/j.apenergy.2019.01.050.</li> </ul> <p>Enjoy.</p>
List of tissue specimens of Hipposideros spp. used for cytochrome b sequencing and phylogenetic inference, with geographical data. Voucher refers to the location and/or accession number of the voucher, and tissue collection refers to the collection where the tissue is kept, and accession or collector numbers. Acronyms are as follows: Estación Biológica de DoZana, Sevilla, Spain (EBD), South Australia Museum, Adelaide, Australia (SAM), Senckenberg Museum, Frankfurt am Main, Germany (SMF), Instituto de Ecología, Xalapa, México (IEX), Louisiana State University, Baton Rouge, Louisiana, USA (LSU), Charles M. Francis, Canadian Wildlife Service, Ottawa, Ontario, Canada (CMF), Lao Department of Forestry, Vientiane, Lao PDR, no catalogued (LAO). Next column indicates GenBank accession numbers. More information about the specimens is available in the GenBank records in A new species of bat of the Hipposideros bicolor group (Chiroptera: Hipposideridae) from Central Laos, with evidence of convergent evolution with Sundaic taxa
List of tissue specimens of Hipposideros spp. used for cytochrome b sequencing and phylogenetic inference, with geographical data. Voucher refers to the location and/or accession number of the voucher, and tissue collection refers to the collection where the tissue is kept, and accession or collector numbers. Acronyms are as follows: Estación Biológica de DoZana, Sevilla, Spain (EBD), South Australia Museum, Adelaide, Australia (SAM), Senckenberg Museum, Frankfurt am Main, Germany (SMF), Instituto de Ecología, Xalapa, México (IEX), Louisiana State University, Baton Rouge, Louisiana, USA (LSU), Charles M. Francis, Canadian Wildlife Service, Ottawa, Ontario, Canada (CMF), Lao Department of Forestry, Vientiane, Lao PDR, no catalogued (LAO). Next column indicates GenBank accession numbers. More information about the specimens is available in the GenBank records
FIGURE 3 in On the distribution and ecology of Culiseta (Culicella) ochroptera (Peus) (Diptera: Culicidae) in Germany
FIGURE 3. Larval habitats in the 'Dubringer Moor'. A, Small puddle; B, large puddle; C, ditch; D, edges of pond.
Database of the ODYM-RECC v2.4 model, used for the Germany case study on material efficiency and climate change mitigation
<p>Database of the ODYM-RECC v2.4 model, used for the Germany case study on material efficiency and climate change mitigation. For the model code, see https://github.com/YaleCIE/RECC-ODYM. The model results archived here were calculated by running the ODYM-RECC scripts of commit no. cb3a388 with the data in this archive.</p>
Clim2Power Austria-Germany Case Study
<p>This archive contains neural-network-based forecasts of electricity generation and electricity market variables for a Germany-Austria case study conducted within the project Clim2Power. Depending on the selected variable (some time series are shorter), the folder named “C2P Hindcasts” contains forecast time series from 2010 to 2017 for 30 (February, May, August forecasts) respectively 10 (November forecasts) climate model ensemble members. The folder named “C2P Forecasts” contains forecast time series starting from February 2018 to May 2020 for 50 (February, May, August forecasts) respectively 10 (November forecasts) climate model ensemble members.</p> <p>Both folders are structured as follows:</p> <p>Subfolder names contain the respective variable name (Wind Electricity Generation, PV Electricity Generation, Electricity Load and Electricity Price/Climate Impact on Electricity Prices) and the short code “AT” for Austria, “DE” for Germany or “ATDE” for both. Within the subfolders, forecast time series are available in CSV, Feather and NetCDF file format. The digits in the single file names within the folder named “C2P Forecasts” denote the forecast start month and year.</p>
GeoVectors-Germany-location (v0.1)
<p><strong>Description</strong></p> <p>The GeoVectors corpus is a comprehensive large-scale linked open corpus of OpenStreetMap (https://www.openstreetmap.org/) entity embeddings that provides latent representations of over 980 million entities. The GeoVectors capture the semantic and geographic similarities of OpenStreetMap entities and make them directly accessible to machine learning applications. The "-tags" datasets provide embeddings that capture the semantic similarities of OpenStreetMap entities. The "-location" datasets provide the geographic similarities.</p> <p><strong>Contents</strong></p> <p>This dataset was derived from an OpenStreetMap snapshot that was taken on November 10, 2020 (© OpenStreetMap contributors).</p> <p>We provide the GeoVectors in region-specific subsets. This subset contains location-embeddings for the region "Germany" including the following countries:</p> <ul> <li>Germany</li> </ul> <p><strong>File format</strong></p> <p>The embeddings are provided in the tab-separated values (tsv) format. Each row contains the embedding of a single OpenStreetMap entity. The first column contains the OpenStreetMap type and the second column contains the OpenStreetMap id of the respective entity. The type can either be node (n), way (w), or relation (r). The remaining columns represent the dimensions of the embedding space. (See also header.tsv)</p> <p><strong>Further information:</strong></p> <p>For further information, please visit <a href="http://geovectors.l3s.uni-hannover.de">http://geovectors.l3s.uni-hannover.de</a></p> <p><strong>Funding</strong>:</p> <p>This work was partially funded by DFG, German Research Foundation (“WorldKG", DE 2299/2-1), the Federal Ministry of Education and Research (BMBF), Germany (“Simple-ML", 01IS18054), the Federal Ministry for Economic Affairs and Energy (BMWi), Germany (“d-E-mand", 01ME19009B), and the European Commission (EU H2020, “smashHit", grant-ID 871477).</p>
GeoVectors-Germany-Ways-tags (v1.0)
<p><strong>Description</strong></p> <p>The GeoVectors corpus is a comprehensive large-scale linked open corpus of OpenStreetMap (https://www.openstreetmap.org/) entity embeddings that provides latent representations of over 980 million entities. The GeoVectors capture the semantic and geographic similarities of OpenStreetMap entities and make them directly accessible to machine learning applications. The "-tags" datasets provide embeddings that capture the semantic similarities of OpenStreetMap entities. The "-location" datasets provide the geographic similarities.</p> <p><strong>Contents</strong></p> <p>This dataset was derived from an OpenStreetMap snapshot that was taken on November 10, 2020 (© OpenStreetMap contributors).</p> <p>We provide the GeoVectors in region-specific subsets. This subset contains tag-embeddings of the ways for the region "Germany" including the following countries:</p> <ul> <li>Germany</li> </ul> <p><strong>File format</strong></p> <p>The embeddings are provided in the tab-separated values (tsv) format. Each row contains the embedding of a single OpenStreetMap entity. The first column contains the OpenStreetMap type and the second column contains the OpenStreetMap id of the respective entity. The type can either be node (n), way (w), or relation (r). The remaining columns represent the dimensions of the embedding space. (See also header.tsv)</p> <p><strong>Further information:</strong></p> <p>For further information, please visit <a href="http://geovectors.l3s.uni-hannover.de">http://geovectors.l3s.uni-hannover.de</a></p> <p><strong>Funding</strong>:</p> <p>This work was partially funded by DFG, German Research Foundation (“WorldKG", DE 2299/2-1), the Federal Ministry of Education and Research (BMBF), Germany (“Simple-ML", 01IS18054), the Federal Ministry for Economic Affairs and Energy (BMWi), Germany (“d-E-mand", 01ME19009B), and the European Commission (EU H2020, “smashHit", grant-ID 871477).</p>
GeoVectors-Germany-Nodes-Relations-tags (v1.0)
<p><strong>Description</strong></p> <p>The GeoVectors corpus is a comprehensive large-scale linked open corpus of OpenStreetMap (https://www.openstreetmap.org/) entity embeddings that provides latent representations of over 980 million entities. The GeoVectors capture the semantic and geographic similarities of OpenStreetMap entities and make them directly accessible to machine learning applications. The "-tags" datasets provide embeddings that capture the semantic similarities of OpenStreetMap entities. The "-location" datasets provide the geographic similarities.</p> <p><strong>Contents</strong></p> <p>This dataset was derived from an OpenStreetMap snapshot that was taken on November 10, 2020 (© OpenStreetMap contributors).</p> <p>We provide the GeoVectors in region-specific subsets. This subset contains tag-embeddings for the nodes and relations of the region "Germany" including the following countries:</p> <ul> <li>Germany</li> </ul> <p><strong>File format</strong></p> <p>The embeddings are provided in the tab-separated values (tsv) format. Each row contains the embedding of a single OpenStreetMap entity. The first column contains the OpenStreetMap type and the second column contains the OpenStreetMap id of the respective entity. The type can either be node (n), way (w), or relation (r). The remaining columns represent the dimensions of the embedding space. (See also header.tsv)</p> <p><strong>Further information:</strong></p> <p>For further information, please visit <a href="http://geovectors.l3s.uni-hannover.de">http://geovectors.l3s.uni-hannover.de</a></p> <p><strong>Funding</strong>:</p> <p>This work was partially funded by DFG, German Research Foundation (“WorldKG", DE 2299/2-1), the Federal Ministry of Education and Research (BMBF), Germany (“Simple-ML", 01IS18054), the Federal Ministry for Economic Affairs and Energy (BMWi), Germany (“d-E-mand", 01ME19009B), and the European Commission (EU H2020, “smashHit", grant-ID 871477).</p>
FIGURES 1–7. Rhyacophila albardana McLachlan 1879, 5 in The larva of Rhyacophila albardana McLachlan 1879, including a discriminatory matrix to the Rhyacophila larvae with comb-shaped gills of Austria, Germany and Switzerland (Rhyacophilidae, Trichoptera)
FIGURES 1–7. Rhyacophila albardana McLachlan 1879, 5th instar larva. 1, head, dorsal (ac = anteclypeus; fa = frontal apotome; lb = labrum; p = parietale; small numbers refer to setal positions); 2, head, ventral (ps = prosternite; va = ventral apotome; small numbers refer to setal positions); 3, head and prothorax, right lateral (es = episternum; ps = pleural suture; tr = trochantin; small numbers refer to setal positions); 4, mandibles, ventro-apical (a = apical tooth; b = basal tooth; c = cutting edge; l = labial palp; lm = left mandible; m = maxillary palp; rm = right mandible; s = submentum; va = ventral apotome;); 5, prothorax, dorsal (small numbers refer to setal positions; 6, prosternite, surface view; 7, prosternite pitched anteriorly upward. Scale bars: 0.5 mm.
FIGURES 15–19. Figure 15. Rhyacophila albardana McLachlan 1879, 5 in The larva of Rhyacophila albardana McLachlan 1879, including a discriminatory matrix to the Rhyacophila larvae with comb-shaped gills of Austria, Germany and Switzerland (Rhyacophilidae, Trichoptera)
FIGURES 15–19. Figure 15. Rhyacophila albardana McLachlan 1879, 5th instar larva, tip of abdomen and anal proleg, right lateral (ac = anal claw; bd = distal 'b' sclerite; bv = ventral 'b' sclerite; c = 'c' sclerite; h = basoventral hook of sclerite 'b'; pb = proximal 'b' sclerite; sp = sword process; small numbers refer to setal positions; arrows = teeth on distal section of anal claw). Figures 16–19. Rhyacophila spp., 5th instar larvae. 16, R. torrentium Pictet 1834, head, dorsal; 17, R. torrentium, head, ventral; 18, R. torrentium, prosternite; 19, R. evoluta McLachlan 1879, prosternite. Scale bars: 0.5 mm (except Figs 18, 19: 0.2 mm).
FIGURES 20–21. Rhyacophila evoluta McLachlan 1879, 5 in The larva of Rhyacophila albardana McLachlan 1879, including a discriminatory matrix to the Rhyacophila larvae with comb-shaped gills of Austria, Germany and Switzerland (Rhyacophilidae, Trichoptera)
FIGURES 20–21. Rhyacophila evoluta McLachlan 1879, 5th instar larva. 20, head, dorsal; 21, head, ventral. Scale bars: 0.5 mm.
Microsatellites for northern pike (Esox lucius) populations in Germany
<ol> <li><span><i>Esox lucius</i> L. (northern pike) inhabits river, lake and brackish water ecosystems of the temperate region in North America and Eurasia. In many areas of its native range, the species is confronted with human-induced disturbances that can affect local and regional genetic diversity. Conservation of genetic diversity constitutes an important policy objective. Therefore, we investigated how the genetic population structure and patterns suggestive of hybridization among subpopulations from different catchments vary with the type of ecosystem (river vs. lake) and the integrity of the ecosystem assessed using ecological status assessments of the European Water Framework Directive (WFD) and indices of the wetland quality (in rivers) and trophic state (in lakes). </span></li> <li><span>1,384 pike samples from the North, Baltic and Black Sea drainages in Germany were analysed. We differentiated between metapopulations from each hydrogeographic region using genotypes from 15 microsatellites and mitochondrial <i>cyt</i> <i>b</i> sequences. </span></li> <li><span>Individual populations showed signs of genetic admixture ranging from almost zero to complete replacement by foreign genotypes originating from different catchments. Hierarchical general linear modeling revealed a significantly positive association of the degree of genetic hybridization with decreasing ecological status. An eroded population substructure indicative of hybridization was also significantly elevated in rivers relative to lakes and in heavily modified water bodies. Results were consistent when using ecological status assessments by the WFD and an alternative environmental quality index based on the health of wetlands in rivers and trophic state in lakes. </span></li> <li><span>We speculate that macrophyte loss through loss of wetlands or in response to eutrophication fostered genetic mixing among different subpopulations, which were brought into secondary contact either due to stocking or due to natural dispersal from foreign catchments. Our findings suggest that anthropogenic alteration of freshwater ecosystems may influence the genetic structures present in a freshwater top piscivore at both local and regional scales.</span></li> </ol>
Integrating stable isotopes, parasite and ring-reencounter data to quantify migratory connectivity - a case study with Barn Swallows breeding in Switzerland, Germany, Sweden and Finland
<p>Ecosystems around the world are connected by seasonal migration. The migrant animals themselves are influenced by migratory connectivity through effects on the individual and the population level. Measuring migratory connectivity is notoriously difficult due to the simple requirement of data conveying information about the non-breeding distribution of many individuals from several breeding populations. Explicit integration of data derived from different methods increases the precision and the reliability of parameter estimates.</p> <p>We combine ring-reencounter, stable isotope and blood parasite data of Barn Swallows <i>Hirundo rustica</i> in a single integrated model to estimate migratory connectivity for three large scale breeding populations across a latitudinal gradient from Central Europe and Scandinavia. To this end, we integrated a non-Markovian multi-state mark-recovery model for the ring-reencounter data with normal and binomial mixture models for the stable isotope and parasite data.</p> <p>The integration of different data sources within a mark-recapture modelling framework enables the most precise quantification of migratory connectivity on the given broad spatial scale. The results show that northern breeding populations and Southern Africa as well as southern breeding populations and Western-Central Africa are more strongly connected through Barn Swallow migration than central European breeding populations with any of the African wintering areas. The non-breeding distribution of Barn Swallows from central European breeding populations seems to be a mixture of those populations breeding further North and South, indicating a migratory divide.</p>
FIG. 186 in A Revision of fossil Bibionidae (Insecta: Diptera) from the Oligocene of Germany
FIG. 186. Male, wing. GDKE PE 1995 6435. FIG. 187. Male, head. GDKE PE 2010 5377. FIG. 188. Male, hypopygium, dorsal. GDKE PE 2014 5307. FIG. 189. Male, hypopygium, ventral. GDKE PE 2014 5307.
FIG. 150. Female, wing. LACMIP 2533.296. FIG. 151. Male, habitus. LACMIP 2533.261. FIG. 152. Male, wing. LACMIP 2533.261 in A Revision of fossil Bibionidae (Insecta: Diptera) from the Oligocene of Germany
FIG. 150. Female, wing. LACMIP 2533.296. FIG. 151. Male, habitus. LACMIP 2533.261. FIG. 152. Male, wing. LACMIP 2533.261.
FIG. 153 in A Revision of fossil Bibionidae (Insecta: Diptera) from the Oligocene of Germany
FIG. 153. Male, head and thorax. LACMIP 2533.261. FIG. 154. Male, terminalia. LACMIP 2533.261. FIG. 155. Male, abdomen. LACMIP 2533.262.
FIG. 134. Male, habitus. LACMIP 2533.260. FIG. 135. Male, wing. LACMIP 2533.260. FIG. 136 in A Revision of fossil Bibionidae (Insecta: Diptera) from the Oligocene of Germany
FIG. 134. Male, habitus. LACMIP 2533.260. FIG. 135. Male, wing. LACMIP 2533.260. FIG. 136. Male, head and thorax. LACMIP 2533.260. FIG. 137. Male, terminalia. LACMIP 2533.260.
FIG. 119 in A Revision of fossil Bibionidae (Insecta: Diptera) from the Oligocene of Germany
FIG. 119. Plecia gracilenta, male. NHMUK In 58805. FIGS. 120–121 Plecia grossa: FIG. 120. Female, habitus. NHMUK In 58791. FIG. 121. Female, wing. NHMUK In 58791.
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.