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Figure 3a from: Steinbeck C, Koepler O, Bach F, Herres-Pawlis S, Jung N, Liermann JC, Neumann S, Razum M, Baldauf C, Biedermann F, Bocklitz TW, Boehm F, Broda F, Czodrowski P, Engel T, Hicks MG, Kast SM, Kettner C, Koch W, Lanza G, Link A, Mata RA, Nagel WE, Porzel A, Schlörer N, Schulze T, Weinig H-G, Wenzel W, Wessjohann LA, Wulle S (2020) NFDI4Chem - Towards a National Research Data Infrastructure for Chemistry in Germany. Research Ideas and Outcomes 6: e55852. https://doi.org/10.3897/rio.6.e55852
Figure 3a Community survey: usefulness of metadata and habits of data sharing. -
Figure 2c from: Steinbeck C, Koepler O, Bach F, Herres-Pawlis S, Jung N, Liermann JC, Neumann S, Razum M, Baldauf C, Biedermann F, Bocklitz TW, Boehm F, Broda F, Czodrowski P, Engel T, Hicks MG, Kast SM, Kettner C, Koch W, Lanza G, Link A, Mata RA, Nagel WE, Porzel A, Schlörer N, Schulze T, Weinig H-G, Wenzel W, Wessjohann LA, Wulle S (2020) NFDI4Chem - Towards a National Research Data Infrastructure for Chemistry in Germany. Research Ideas and Outcomes 6: e55852. https://doi.org/10.3897/rio.6.e55852
Figure 2c Community survey: digitalisation status, solutions to store data and data types. -
Figure 2b from: Steinbeck C, Koepler O, Bach F, Herres-Pawlis S, Jung N, Liermann JC, Neumann S, Razum M, Baldauf C, Biedermann F, Bocklitz TW, Boehm F, Broda F, Czodrowski P, Engel T, Hicks MG, Kast SM, Kettner C, Koch W, Lanza G, Link A, Mata RA, Nagel WE, Porzel A, Schlörer N, Schulze T, Weinig H-G, Wenzel W, Wessjohann LA, Wulle S (2020) NFDI4Chem - Towards a National Research Data Infrastructure for Chemistry in Germany. Research Ideas and Outcomes 6: e55852. https://doi.org/10.3897/rio.6.e55852
Figure 2b Community survey: digitalisation status, solutions to store data and data types. -
Figure 1 from: Steinbeck C, Koepler O, Bach F, Herres-Pawlis S, Jung N, Liermann JC, Neumann S, Razum M, Baldauf C, Biedermann F, Bocklitz TW, Boehm F, Broda F, Czodrowski P, Engel T, Hicks MG, Kast SM, Kettner C, Koch W, Lanza G, Link A, Mata RA, Nagel WE, Porzel A, Schlörer N, Schulze T, Weinig H-G, Wenzel W, Wessjohann LA, Wulle S (2020) NFDI4Chem - Towards a National Research Data Infrastructure for Chemistry in Germany. Research Ideas and Outcomes 6: e55852. https://doi.org/10.3897/rio.6.e55852
Figure 1 Key data from the community survey in 2019 with N=530 responses.
Figure 2a from: Steinbeck C, Koepler O, Bach F, Herres-Pawlis S, Jung N, Liermann JC, Neumann S, Razum M, Baldauf C, Biedermann F, Bocklitz TW, Boehm F, Broda F, Czodrowski P, Engel T, Hicks MG, Kast SM, Kettner C, Koch W, Lanza G, Link A, Mata RA, Nagel WE, Porzel A, Schlörer N, Schulze T, Weinig H-G, Wenzel W, Wessjohann LA, Wulle S (2020) NFDI4Chem - Towards a National Research Data Infrastructure for Chemistry in Germany. Research Ideas and Outcomes 6: e55852. https://doi.org/10.3897/rio.6.e55852
Figure 2a Community survey: digitalisation status, solutions to store data and data types. -
dsmlib - region4FLEX example (Supplementary Material for the manuscript: Assessment of the regionalised demand response potential in Germany using an open source tool and dataset)
<p>This is the supplementary material for the manuscript:<br> Heitkoetter, Wilko, et al. "Assessment of the regionalised demand response potential in Germany using an open source tool and dataset." <em>Advances in Applied Energy</em> (2020): 100001.<br> Article DOI (open access): <a href="https://doi.org/10.1016/j.adapen.2020.100001">https://doi.org/10.1016/j.adapen.2020.100001</a></p> <p><strong>REPOSITORY CONTENT</strong><br> This repository contains the dsmlib python tool for calculating regionalised load shifting potentials and cost-potential curves.<br> Further, the input data and load shifting potential results of the region4FLEX example are provided (<a href="https://wiki.openmod-initiative.org/wiki/Region4FLEX">region4FLEX model description</a>). In the region4FLEX example dsmlib is applied to the 401 German administrative districts (NUTS-3 regions) considering multiple demand sectors and technologies (see Metadata).</p> <p><strong>quick_start_dsmlib_region4FLEX.zip:</strong><br> This file contains the python dsmlib source code and the resulting average and maximum load shifting potential values per administrative district for all technologies (e.g.: see "/results/2030/extreme_values/av_values_p_max.csv" for average load increase potentials in 2030). Unless otherwise stated the units of the results are in MW for power and MWh for energy. (For more information on the results, please refer to \dsmlib-zenodo\examples\region4FLEX_dsm_potential\README.md --> section Results). The resulting time series and large-scale input data are not contained, to allow for a fast download. </p> <p><strong>full_download_dsmlib_region4FLEX.zip:</strong><br> This file contains the python dsmlib source code and the full set of input and result data.<br> For more information refer to /dsmlib/examples/region4FLEX_dsm_potential/README.md</p> <p><strong>LICENSES</strong><br> The developed source code is licensed under the GPL v3 License. All result data are licensed under the CC-BY 4.0 License.<br> The input data are licensed under different open licenses. For more information refer to the provided README files, LICENSE files and input_data_overview files.</p> <p><strong>METADATA</strong><br> <strong>Demand sectors:</strong> Residential, commercial trade and services, industry, power-to-heat, power-to-gas, e-mobility<br> <strong>Technologies:</strong> Washing, drying, cooling, ventilation, AC, air separation, cement production, pulping, paper production,<br> recycled paper production, process heat, heat pumps, resistive space heating, resistive DHW heating, power-to-heat in district heating,<br> power-to-methane, power-to-hydrogen, e-mobility <br> <strong>Energy sectors:</strong> Electricity (+ interfaces to heat, gas and transport sector)<br> <strong>Geographical scope:</strong> Germany<br> <strong>Geographical resolution:</strong> Administrative districts (NUTS-3)<br> <strong>Temporal scope:</strong> 2018, 2030<br> <strong>Temporal resolution:</strong> 15min</p> <p><strong>NEWS AND CONTACT</strong><br> This dataset will be used as part of the region4FLEX model. If you wish to receive news or have general questions please contact: wheitkoetter(at)gmail.com</p>
Data from: From welcome culture to welcome limits? Uncovering preference changes over time for sheltering refugees in Germany
Europe recently experienced a large influx of refugees, spurring much public debate about the admission and integration of refugees and migrants into society. Previous research based on cross-sectional data found that European citizens generally favour asylum seekers with high employability, severe vulnerabilities, and Christians over Muslims. These preferences and attitudes were found to be homogeneous across countries and socio-demographic groups. Here, we do not study the general acceptance of asylum seekers, but the acceptance of refugee and migrant homes in citizens' vicinity and how it changes over time. Based on a repeated stated choice experiment on preferences for refugee and migrant homes, we show that the initially promoted "welcome culture" towards refugees in Germany was not reflected in the views of a majority of a sample of German citizens who rather disapproved refugee homes in their vicinity. Their preferences have not changed between November 2015, the peak of "welcome culture," and November 2016, after political debates, media reporting and public discourse had shifted towards limiting admission of immigrants. A minority of one fifth of the sample population, who were initially rather approving of refugee and migrant homes being established in their vicinity, were more likely to change their preferences towards a rather disapproving position in 2016. Experience of contact with refugees and migrants, higher education, and general pro-immigration attitudes explain acceptance of refugee and migrant homes as well as preference stability over time. Country of origin and religion of refugees and migrants are considered less important than decent housing conditions and whether refugee and migrants arrive as families or single persons. In this respect our results highlight the importance of humanitarian aspects of sheltering and integration of refugees and other migrants into society.
Public support for asylum and labor migration policies in Germany
<p>Randomized controlled experiment on public support for asylum and labor migration policies in Germany, embedded in a German representative survey in 2019.</p>
Weight, Temperature and Humidity Sensor Data of Honey Bee Colonies in Germany, 2019 - 2022
<p>Files:</p> <p>bob_publication_data.zip<br>Sensor data in 1-minute, 1-hour and 1-day interval, processed and unprocessed version as described in data paper. Inspection data with beekeepers' meta-information collected using web app.</p> <p>bob_raw_data.zip<br>Raw sensor data with original measurement interval, mostly 5 or 10 seconds.</p> <p>bob_code_publication.zip<br>Code we used to prepare the data. We anonymised sections with personal keys and passwords.</p> <p>Abstract:</p> <p><br>We present sensor data from 78 honey bee colonies in Germany collected as part of a citizen science project. Each honey bee hive was equipped with five temperature sensors within the hive, one temperature sensor for outside measurements, a combined sensor for temperature, ambient air pressure and humidity, and a scale to measure the weight. During the data acquisition period, beekeepers used a web app to report their observations and beekeeping activities.<br>We provide the raw data with a measurement interval of up to 5 seconds as well as aggregated data, with per minute, hourly or daily average values. Furthermore, we performed several preprocessing steps, removing outliers with a threshold based approach, excluding changes in weight that were induced by beekeeping activities and combining the sensor data with the most important meta-data from the beekeepers' observations. The data is organised in directories based on the year of recording. Alternatively, we provide subsets of the data structured based on the occurrence or non-occurrence of a swarming event or the death of a colony.<br>The data can be analysed using methods from time series analysis, time series classification or other data science approaches to form a better understanding of specifics in the development of honey bee colonies.</p> <p>Information:</p> <p>The first three years of the citizen science project were funded by the German Ministry for Education and Research (BMBF).</p>
Figure 45 from: Höcherl A, Shaw MR, Boudreault C, Rabl D, Haszprunar G, Raupach MJ, Schmidt S, Baranov V, Fernández-Triana J (2024) Scratching the tip of the iceberg: integrative taxonomy reveals 30 new species records of Microgastrinae (Braconidae) parasitoid wasps for Germany, including new Holarctic distributions. ZooKeys 1188: 305-386. https://doi.org/10.3897/zookeys.1188.112516
Figure 45 Rasivalva desueta Papp, 1989, male (CNC602554) A lateral and B dorsal views.
Transect-based survey of bird species in Northern Saxony, Germany
<div> <div> <div> <p>Data from 3 bird surveys conducted between April 1st and June 9th in 2022. The surveys occurred in Northern Saxony, Germany, in an area dominated by agriculture. We recorded bird species occurrences along 20 transects, each extending for 3 km. We placed transects along paths, trails, and roads, ensuring continuous walkability while excluding fast transit routes for safety. In this process, we avoided U- and V-shaped transects to minimize detection probability variations due to spatial irregularities.</p> <p>This dataset contains the following:</p> <ul> <li><strong>transects.sqlite</strong> - Spatialite file with survey transects lines. The file contains the following fields:<br> <ul> <li><em>transect_id: </em>Unique transect identifier</li> <li><em>lenght_m: </em>Length of transect in meters</li> </ul> </li> <li><strong>observations.sqlite</strong> - Spatialite file with species observations points. The file contains the following fields: <ul> <li>transect_id: Unique transect identifier</li> <li>date: Surveying date</li> <li>species: Species name.</li> <li>count: Number of individuals detected.</li> </ul> </li> </ul> </div> </div> </div>
urbisphere_de-berlin_UR-3: Gridded residential population data for Berlin and parts of Brandenburg, Germany
<h2>Files in this archive </h2> <ul> <li>respop_300m_grid_Berlin_Brandenburg.zip <ul> <li>Residential population numbers in 300-m grid-boxes covering Berlin and parts of Brandenburg, Germany</li> <li>Polygons, ESRI shapefile format (*.shp, *.shx, *.cpg, *.dbf, *.prj)</li> </ul> </li> <li>code.zip <ul> <li>Code to produce the dataset (Python3)</li> </ul> </li> <li>urbisphere_de-berlin_UR-3.pdf <ul> <li>Documentation</li> </ul> </li> </ul> <h2>Data purpose </h2> <p>Dataset contains residential population numbers and densities for Berlin and Brandenburg, Germany, aggregated to regular grids of ~300 m horizontal resolution for 2021. The data support <em>urbisphere</em>–Berlin characterisation of observations undertaken in the region and modelling activities. </p> <h3>Linked with</h3> <ul> <li>Glazer et al. 2024. urbisphere_de-berlin_UR-1: <em>urbispher</em>e-Berlin Model grids defined for use with the Unified Model (UM). <em>urbisphere</em>–Berlin Data Release and Technical Documentation [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.10889490</li> <li>Hertwig et al. 2024. urbisphere_de-berlin_UR-2: Gridded building volumes for Berlin and parts of Brandenburg, Germany. <em>urbisphere</em>–Berlin Data Release and Technical Documentation [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.10889609</li> </ul> <p> </p>
urbisphere_de-berlin_UR-2: Gridded building volumes for Berlin and parts of Brandenburg, Germany
<h2>Files in this archive</h2> <ul> <li>buildvolume_300m_grid_Berlin_Brandenburg.zip <ul> <li>Building volumes by function categories in 300-m grid-boxes covering Berlin and parts of Brandenburg, Germany</li> <li>Polygons, ESRI shapefile format (*.shp, *.shx, *.cpg, *.dbf, *.prj)</li> </ul> </li> <li>auxiliary_data.zip <ul> <li>Auxiliary data for the processing (*.csv).</li> </ul> </li> <li>code.zip <ul> <li>Code to produce dataset (Python3)</li> </ul> </li> <li>urbisphere_de-berlin_UR-2.pdf <ul> <li>Documentation</li> </ul> </li> </ul> <h2>Data purpose </h2> <p>This dataset contains aggregated building volumes of residential (i.e., domestic), non-residential (non-domestic) and mixed-use buildings for Berlin and Brandenburg, Germany, in regular grids of ~300 m horizontal resolution. The data support <em>urbisphere</em>–Berlin characterisation of observations undertaken in the region and modelling activities. </p> <h3>Linked with</h3> <ul> <li>Glazer et al. 2024. urbisphere_de-berlin_UR-1: urbisphere-Berlin Model Grids defined for use with the Unified Model (UM). <em>urbisphere</em>–Berlin Data Release and Technical Documentation [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.10889490</li> <li>Hertwig et al. 2024. urbisphere_de-berlin_UR-3: Gridded residential population data for Berlin and parts of Brandenburg, Germany. <em>urbisphere</em>–Berlin Data Release and Technical Documentation [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.10889632</li> </ul>
Figure 1 from: Boudinot BE, Bock BL, Weingardt M, Tröger D, Batelka J, LI D, Richter A, Pohl H, Moosdorf OTD, Jandausch K, Hammel JU, Beutel RG (2024) Et latet et lucet: Discoveries from the Phyletisches Museum amber and copal collection in Jena, Germany. Deutsche Entomologische Zeitschrift 71(1): 111-176. https://doi.org/10.3897/dez.71.112433
Figure 1 Eduard Uhlmann around 1955. Archive of the Phyletisches Museum XXVII.
Figure 5 from: Weber D, Brad T, Stoch F, Flot J-F (2021) Rediscovery and redescription of Niphargus enslini Karaman, 1932 (Amphipoda, Niphargidae) in southern Germany. Subterranean Biology 40: 65-89. https://doi.org/10.3897/subtbiol.40.73017
Figure 5 Niphargus enslini, live male from the Blätterteighöhle (Photo: Bernd Hugger).
Figure 1 in A new genus of Mantispidae (Insecta: Neuroptera) from the Eocene of Germany, with a review of the fossil record and palaeobiogeography of the family
Figure 1. Distribution of extant and fossil Mantispidae.
Non-freely available datasets for the following work: "Comparison of consumption data and phenotypical antimicro-bial resistance in E. coli isolates of human urinary samples and of weaning and fattening pigs from surveillance and monitor-ing systems in Germany."
<p>Human and animal databases used for the creation of the work "Comparison of consumption data and phenotypical antimicrobial resistance in E. coli isolates of human urinary samples and of weaning and fattening pigs from surveillance and monitoring systems in Germany."</p>
Urban Green Raster Germany 2018
<p><strong>Abstract</strong></p> <p>The Urban Green Raster Germany is a land cover classification for Germany that addresses in particular the urban vegetation areas. The raster dataset covers the terrestrial national territory of Germany and has a spatial resolution of 10 meters. The dataset is based on a fully automated classification of Sentinel-2 satellite data from a full 2018 vegetation period using reference data from the European LUCAS land use and land cover point dataset.<br> The dataset identifies eight land cover classes. These include Built-up, Built-up with significant green share, Coniferous wood, Deciduous wood, Herbaceous vegetation (low perennial vegetation), Water, Open soil, Arable land (low seasonal vegetation).<br> The land cover dataset provided here is offered as an integer raster in GeoTiff format. The assignment of the number coding to the corresponding land cover class is explained in the legend file.</p> <p><strong>Data acquisition</strong></p> <p>The data acquisition comprises two main processing steps: (1) Collection, processing, and automated classification of the multispectral Sentinel 2 satellite data with the “Land Cover DE method”, resulting in the raw land cover classification dataset, NDVI layer, and RF assignment frequency vector raster. (2) GIS-based postprocessing including discrimination of (densely) built-up and loosely built-up pixels according NDVI threshold, and creating water-body and arable-land masks from geo-topographical base-data (ATKIS Basic DLM) and reclassification of water and arable land pixels based on the assignment frequency.</p> <p><strong>Data collection</strong></p> <p>Satellite data were searched and downloaded from the Copernicus Open Access Hub (https://scihub.copernicus.eu/).</p> <p>The LUCAS reference and validation points were loaded from the Eurostat platform (https://ec.europa.eu/eurostat/web/lucas/data/database).</p> <p>The processing of the satellite data was performed at the DLR data center in Oberpfaffenhofen.</p> <p>GIS-based post-processing of the automatic classification result was performed at IOER in Dresden.</p> <p><strong>Value of the data</strong></p> <p>The dataset can be used to quantify the amount of green areas within cities on a homogeneous data base [5].</p> <p>Thus it is possible to compare cities of different sizes regarding their greenery and with respect to their ratio of green and built-up areas [6].</p> <p>Built-up areas within cities can be discriminated regarding their built-up density (dense built-up vs. built-up with higher green share).</p> <p><strong>Data description</strong></p> <p>A Raster dataset in GeoTIFF format: The dataset is stored as an 8 bit integer raster with values ranging from 1 to 8 for the eight different land cover classes. The nomenclature of the coded values is as follows: 1 = Built-up, 2=open soil; 3=Coniferous wood, 4= Deciduous wood, 5=Arable land (low seasonal vegetation), 6=Herbaceous vegetation (low perennial vegetation), 7=Water, 8=Built-up with significant green share. Name of the file ugr2018_germany.tif. The dataset is zipped alongside with accompanying files: *.twf (geo-referencing world-file), *.ovr (Overlay file for quick data preview in GIS), *.clr (Color map file).</p> <p>A text file with the integer value assignment of the land cover classes. Name of the file: Legend_LC-classes.txt.</p> <p><strong>Experimental design, materials and methods</strong></p> <p>The first essential step to create the dataset is the automatic classification of a satellite image mosaic of all available Sentinel-2 images from May to September 2018 with a maximum cloud cover of 60 percent. Points from the 2018 LUCAS (Land use and land cover survey) dataset from Eurostat [1] were used as reference and validation data. Using Random Forest (RF) classifier [2], seven land use classes (Deciduous wood, Coniferous wood, Herbaceous vegetation (low perennial vegetation), Built-up, Open soil, Water, Arable land (low seasonal vegetation)) were first derived, which is methodologically in line with the procedure used to create the dataset "Land Cover DE - Sentinel-2 - Germany, 2015" [3]. The overall accuracy of the data is 93 % [4].</p> <p>Two downstream post-processing steps served to further qualify the product. The first step included the selective verification of pixels of the classes arable land and water. These are often misidentified by the classifier due to radiometric similarities with other land covers; in particular, radiometric signatures of water surfaces often resemble shadows or asphalt surfaces. Due to the heterogeneous inner-city structures, pixels are also frequently misclassified as cropland.</p> <p>To mitigate these errors, all pixels classified as water and arable land were matched with another data source. This consisted of binary land cover masks for these two land cover classes originating from the Monitor of Settlement and Open Space Development (IOER Monitor). For all water and cropland pixels that were outside of their respective masks, the frequencies of class assignments from the RF classifier were checked. If the assignment frequency to water or arable land was at least twice that to the subsequent class, the classification was preserved. Otherwise, the classification strength was considered too weak and the pixel was recoded to the land cover with the second largest assignment frequency.</p> <p>Furthermore, an additional land cover class "Built-up with significant vegetation share" was introduced. For this purpose, all pixels of the Built-up class were intersected with the NDVI of the satellite image mosaic and assigned to the new category if an NDVI threshold was exceeded in the pixel. The associated NDVI threshold was previously determined using highest resolution reference data of urban green structures in the cities of Dresden, Leipzig and Potsdam, which were first used to determine the true green fractions within the 10m Sentinel pixels, and based on this to determine an NDVI value that could be used as an indicator of a significant green fraction within the built-up pixel. However, due to the wide dispersion of green fraction values within the built-up areas, it is not possible to establish a universally valid green percentage value for the land cover class of Built-up with significant vegetation share. Thus, the class essentially serves to the visual differentiability of densely and loosely (i.e., vegetation-dominated) built-up areas.</p> <p><strong>Acknowledgments</strong></p> <p>This work was supported by the Federal Institute for Research on Building, Urban Affairs and Spatial Development (BBSR) [10.06.03.18.101].The provided data has been developed and created in the framework of the research project “Wie grün sind bundesdeutsche Städte?- Fernerkundliche Erfassung und stadträumlich-funktionale Differenzierung der Grünausstattung von Städten in Deutschland (Erfassung der urbanen Grünausstattung)“ (How green are German cities?- Remote sensing and urban-functional differentiation of the green infrastructure of cities in Germany (Urban Green Infrastructure Inventory)). Further persons involved in the project were: Fabian Dosch (funding administrator at BBSR), Stefan Fina (research partner, group leader at ILS Dortmund), Annett Frick, Kathrin Wagner (research partners at LUP Potsdam).</p> <p><strong>References</strong></p> <p>[1] Eurostat (2021): Land cover / land use statistics database LUCAS. URL: <a href="https://ec.europa.eu/eurostat/web/lucas/data/database">https://ec.europa.eu/eurostat/web/lucas/data/database</a></p> <p>[2] L. Breiman (2001). Random forests, Mach. Learn., 45, pp. 5-32</p> <p>[3] M. Weigand, M. Wurm (2020). Land Cover DE - Sentinel-2—Germany, 2015 [Data set]. German Aerospace Center (DLR). doi: 10.15489/1CCMLAP3MN39</p> <p>[4] M. Weigand, J. Staab, M. Wurm, H. Taubenböck, (2020). Spatial and semantic effects of LUCAS samples on fully automated land use/land cover classification in high-resolution Sentinel-2 data. Int J Appl Earth Obs, 88, 102065. doi: <a href="https://doi.org/10.1016/j.jag.2020.102065">https://doi.org/10.1016/j.jag.2020.102065</a></p> <p>[5] L. Eichler., T. Krüger, G. Meinel, G. (2020). Wie grün sind deutsche Städte? Indikatorgestützte fernerkundliche Erfassung des Stadtgrüns. AGIT Symposium 2020, 6, 306–315. doi: 10.14627/537698030</p> <p>[6] H. Taubenböck, M. Reiter, F. Dosch, T. Leichtle, M. Weigand, M. Wurm (2021). Which city is the greenest? A multi-dimensional deconstruction of city rankings. Comput Environ Urban Syst, 89, 101687. doi: 10.1016/j.compenvurbsys.2021.101687</p>
Text-fig. 1. Position of the outcrops of the Wischgrund leaf-bearing clay. in New Leaf Species From The Upper Miocene Flora Of The Leaf-Bearing Wischgrund Clay (Lower Lusatia, Brandenburg, Germany)
Text-fig. 1. Position of the outcrops of the Wischgrund leaf-bearing clay.
Fig. 3. Doliops bakeri Heller, 1924 in Type specimens of the genera Doliops Waterhouse, 1841 and Lamprobityle Heller, 1923 (stat. nov.) (Coleoptera: Cerambycidae) and description of two new species deposited in Senckenberg Natural History collections Dresden, Germany
Fig. 3. Doliops bakeri Heller, 1924 (A – dorsal view, B – lateral view, C – labels)
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.