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60 results for “Berlin, Germany”
Parsimonious Random-Forest-Based Land-Use Regression Model Using Particulate Matter Sensors in Berlin, Germany
<p>The dataset consists of particulate matter pollution concentration, measured in three localities - Hermsdorf, Charlottenburg and Adlershof, in Berlin, Germany.</p> <p><a href="../api/records/10076056/draft/files/pm25_summer_rd_30s.geojson/content" target="_blank" rel="noopener noreferrer">pm25_summer_rd_30s.geojson</a> shows the observed PM2.5 concentration in a 30 second interval.</p> <p><a href="../api/records/10076056/draft/files/pm25_summer.geojson/content" target="_blank" rel="noopener noreferrer">pm25_summer.geojson</a> shows the concentrations shown is the local concentration (observed concentration - background concentration) in a 30 second interval. The background concentration is calculated as the lowest 5 percentile of the measured concentration for each measurement round. </p> <p><a href="../api/records/10076056/draft/files/PM2.5_lc_max.geojson/content" target="_blank" rel="noopener noreferrer">PM2.5_lc_max.geojson</a> contains the information from <a href="../api/records/10076056/draft/files/pm25_summer.geojson/content" target="_blank" rel="noopener noreferrer">pm25_summer.geojson</a> in a 25m resolution. Additionally, it contains the land use information for each coordinate.</p> <p>The original publication providing all necessary background information on study sites, methodology and data processing is the following: Venkatraman Jagatha, J., T. Sauter, C. Schneider (2024): Parsimonious Random-Forest-Based Land-Use Regression Model Using Particulate Matter Sensors in Berlin, Germany. MDPI Sensors, 24(13), 4193, DOI: 10.3390/s24134193. The paper is fully open access and can be downloaded at <a href="https://doi.org/10.3390/s24134193">https://doi.org/10.3390/s24134193</a>.</p> <p>Information on working with geojson file can be found under <a href="https://geojson.readthedocs.io/en/latest/">GeoJSON</a> .</p>
Organic micropollutants and heavy metals in stormwater runoff of five different catchment types in Berlin (Germany)
<p>This dataset includes concentrations of micropollutants (67), heavy metals (8) and standard parameters (9) for stormwater runoff taken from separated sewers of five catchments between 3 and 37 ha in Berlin (Germany). It also includes rain data of analyzed events as separate file. Samples were taken as part of the OgRe research project of Kompetenzzentrum Wasser Berlin (<a href="https://www.kompetenz-wasser.de/en/project/ogre/">www.kompetenz-wasser.de/en/project/ogre/</a>) in 2014 and 2015. Sampling and analytical methods are detailed in "Concentrations of micropollutants in urban stormwater runoff of different land uses" (<a href="https://doi.org/10.3390/w13091312">https://doi.org/10.3390/w13091312</a>). A dataset with concentrations of the urban stream Panke in Berlin during dry and wet weather (samples were taken as part of the same project) is available separately (<a href="https://zenodo.org/record/4633779">https://zenodo.org/record/4633779</a>).</p> <p><strong>Description of fields (concentrations):</strong></p> <ul> <li><strong>SampleID</strong>: unique sample identifier</li> <li><strong>SiteID</strong>: unique site identifier (catchment type) <ul> <li> 1 - OLD: area with typical five-storey perimeter blocks built between 1870 and 1930 (31 ha)</li> <li> 2 - NEW: newer area of 4-8-storey concrete slab buildings built between 1960 and 1980 (16 ha)</li> <li> 3 - STR: 1.3 km of a busy streeat with intersection with traffic lights and bus stops (3 ha)</li> <li> 4 - OFH: a residential area characterized by one-family houses and villas with gardens (17 ha)</li> <li> 5 - COM: a commercial and industrial area of high imperviousness with large flat-roof buildings and yards (37 ha)</li> <li> 6 - PNK: urban stream Panke (characterized by strong stormwater inputs from separate sewer discharges - available in separate dataset)</li> </ul> </li> <li><strong>LocalDateTime</strong>: start time of sampling (local)</li> <li><strong>DateTimeUTC</strong>: start time of sampling (UTC)</li> <li><strong>UTCOffset</strong>: UTC offset to local time in h</li> <li><strong>SampleType</strong>: either "composite" for volume proportional composite sample (all samples from storm sewers) or "single" for grab sample (all stream samples, separate dataset)</li> <li><strong>VariableName</strong>: name of analysed substance/parameter</li> <li><strong>UnitsAbbreviation</strong>: either "ug/L" (microgram per litre) or "mg/L" (milligram per litre)</li> <li><strong>CensorCode</strong>: either "lt" (less than) for concentration below detection limit (value is detection limit) or "nc" (not censored) for concentration above detection limit</li> <li><strong>DataValue</strong>: measured value (if censor code is lt, value indicates detection limit)</li> </ul> <p><strong>Description of fields (rain data):</strong></p> <ul> <li><strong>SampleID</strong>: sample identifier of matching sample (see above)</li> <li><strong>SiteID and SiteName</strong>: unique site identifier and name (catchment type) (see above)</li> <li><strong>tBeg_rain, tEnd_rain</strong>: begin and end of rain event in local time</li> <li><strong>depth.mm</strong>: rain depth of rain event in mm</li> <li><strong>duration_rain.h</strong>: duration of rain event in h</li> <li><strong>intensity_max_10min.mm_h</strong>: maximum rain intensitity of rain event in 10-min interval in mm/h</li> <li><strong>intensity_mean_event.mm_h</strong>: mean rain intensitity of rain event in mm/h</li> <li><strong>ADD.d</strong>: number of antecedent dry days in days</li> </ul> <p>Rain data was collected by rain gauge network of Berlin waterworks (>40 gauges) — gauge with best correlation between rain depth and event volume in storm sewer was chosen (distances to monitoring sites: 2–6 km).</p> <p>Two data files are provided in comma separated format:</p> <ul> <li>"OgRe_drain.csv" contains concentrations of all stormwater runoff samples taken in separate storm sewers</li> <li>"OgRe_rain.csv" contains rain data for all stormwater runoff samples</li> </ul>
Emissions from building materials - concentration of micropollutants and heavy metals in stormwater runoff of two new development areas in Berlin (Germany)
<p>This dataset includes concentrations of micropollutants (27) and heavy metals (7) for stormwater runoff from different sampling points at two test sites (A and B) in Berlin, Germany. Both sites are new development areas of similar size that were both constructed in 2017 (1 – 1.5 years prior to the start of the monitoring campaign). Composite samples of individual rain events were taken at three sampling points of each test site: façade runoff, roof runoff and corresponding stormwater runoff from the catchment area. Samples were taken as part of the research project BaSaR (<a href="http://www.kompetenz-wasser.de/en/forschung/projekte/basar/">www.kompetenz-wasser.de/en/forschung/projekte/basar/</a>) of Kompetenzzentrum Wasser Berlin, Ostschweizer Fachhochschule and Berliner Wasserbetriebe. More information including sampling and analytical methods are detailed in the corresponding journal paper "Emissions from building materials – a thread for the environment?", submitted to the MDPI-journal <em>Water</em>.</p> <p><strong>Description of fields:</strong></p> <ul> <li><strong>SiteID</strong>: site identifier <ul> <li>A: new development site with typical architecture for multi-storey apartment buildings with plastered and painted facades in northern part of Berlin (124 apartments)</li> <li>B: new development site with typical architecture for multi-storey apartment buildings with plastered and painted facades in southeastern part of Berlin (122 appartments)</li> </ul> </li> <li><strong>SamplingPoint</strong> <ul> <li>facade runoff: runoff from plastered facade collected with gutters during individual rain events</li> <li>roof runoff: roof runoff collected from one downpipe during individual rain events</li> <li>storm sewer: stormwater runoff sampled during individual rain events in a manhole receiving runoff from the entire catchment (A or B)</li> </ul> </li> <li><strong>LocalDateTime_StartRain</strong>: start time of sampled rain event (CET / CEST)</li> <li><strong>LocalDateTime_EndRain</strong>: end time of sampled rain event (CET / CEST)</li> <li><strong>CardinalDirection</strong>: only relevant for facade runoff <ul> <li>N: runoff from facade oriented to the north</li> <li>W: runoff from facade oriented to the west</li> </ul> </li> <li><strong>VariableName</strong>: name of analysed substance/parameter</li> <li><strong>CensorCode</strong>: either "lt" (less than) for concentration below detection limit (value is detection limit) or "nc" (not censored) for concentration above detection limit</li> <li><strong>UnitsAbbreviation</strong>: either "ug/L" (microgram per litre) or "mg/L" (milligram per litre)</li> <li><strong>DataValue</strong>: measured value (if censor code is lt, value indicates detection limit)</li> </ul> <p>One data file is provided in comma separated format:<br> "BaSaR_data.csv" contains concentrations of all samples.</p>
Geospatial data on indicators for parks in the city of Berlin, Germany
<p>The data contains features and indicators for 224 parks (at least 2 ha in size) in the city of Berlin and overall scores (indices) for natural elements, built elements (infrastructure) and spatial context (e.g. distance to public transport). All data is supplement to linked online web map.</p> <p><strong>List of data and content</strong></p> <ul> <li>Park_Berlin_Indicators: vector files (*.shp, *.geojson)</li> <li>Park_Berlin_Indicators: excel files (*.xlsx)</li> </ul> <p><strong>Spatial reference</strong><br> All data is projected in ETRS 1989 UTM Zone 33N (<a href="https://spatialreference.org/ref/epsg/25833/">EPSG:25833</a>)</p> <p><strong>Web-GIS</strong><br> View data and explore interactively using the <a href="https://arcg.is/5a9me">online application.</a></p> <p><strong>Data sources and processing</strong><br> For details on underlying data sources (e.g. availabilty, spatial resolution, time reference) and on data processing please refer to the linked publication, incl. Appendix 1</p> <p><strong>Acknowledgments</strong><br> We thank the City of Berlin for providing data. We greatly acknowledge OpenStreetMap (OSM) and contributers for providing important parts of the used data. This work was supported by the research project “Environmental‐Health Interactions in Cities (GreenEquityHEALTH) ‐ Challenges for Human Well‐Being under Global Changes” (project duration 2017–2022), funded by the German Federal Ministry of Education and Research (BMBF; no.01LN1705A).</p> <p><strong>Based on related original publication</strong><br> Kraemer, R., & Kabisch, N. (2021). Parks in context: Advancing citywide spatial quality assessments of urban green spaces using fine-scaled indicators. Ecology and Society, 26(2). <a href="https://doi.org/10.5751/ES-12485-260245">https://doi.org/10.5751/ES-12485-260245 </a></p>
Ultrafine particle number concentration observations perpendicular to a main street in Berlin, Germany, in summer 2017
<p>The data set was recorded in summer 2017 in Berlin, Germany. It was the basis of the paper:</p> <p>Fritz, S., Schubert, S., and Schneider, C. (2021). Measurements of spatial variability of sub-micron particle number concentrations perpendicular to a main road in a built-up area. metz. 30, 315–331. doi:10.1127/metz/2021/1058</p> <p>The measurement setup, study site, and devices used are described more detailed in the paper.</p> <p>The data sets uploaded include observations of particle number concentrations, wind direction and wind speed at ground level as well as traffic count data. The time stamp is in local time for Berlin/Germany.</p> <p>The data sets:</p> <p>coordinates.csv contains the coordinates of the measurement points used in file obs_pnc_wind. The column ‘distance’ links these two files. The values in the column ‘distance’ are the distance from the curb of the main road. The distance ‘-10’ represents the measurement point on the central strip of the main road. The two columns ‘latitude’ and ‘longitude’ provide the coordinates of the measurement points.</p> <p>obs_pnc_wind contains the observation data for particle number concentration (PNC), wind speed, and wind direction. PNC was recorded in #/cm³ with a TSI 3007 CPC at a 1-second resolution. Wind speed `ws’ [m/s] was recorded with a KESTREL 5000 with a 2-second resolution. Wind direction ‘wdir’ [°] was manually recorded as the most prominent wind direction within the 3-minute measurements per measurement point in angles of 10°. Wind direction was changed to NA (wdir_comment > 0), if it could not be determined either due to too calm wind (wdir_comment = 1) or with continuously changing wind direction (wdir_comment = 2). Along the measurement route, sometimes additional sources occurred and were recorded in the variable ‘source_type’ at the time they passed by the measurement device. Variable ‘source_comment’ provides additional information about ‘source_type’ where necessary.</p> <p>traffic_count contains the vehicles counted on the main street (Straße des 17. Juni) for each of the 72 runs. It was calculated as [vehicles/hour] based on 5-minute traffic counts. During the traffic counts, we differentiated between smaller (light duty) vehicles (<3.5 t, cars, vans, motorcycles, scooters) and bigger (heavy duty) vehicles (> 3.5 t, trucks and busses).</p>
GECCO Industrial Challenge 2017 Dataset: A water quality dataset for the 'Monitoring of drinking-water quality' competition at the Genetic and Evolutionary Computation Conference 2017, Berlin, Germany.
<p>Dataset of the 'Industrial Challenge: Monitoring of drinking-water quality' competition hosted at The Genetic and Evolutionary Computation Conference (GECCO) July 15th-19th 2017, Berlin, Germany</p> <p> </p> <p>The task of the competition was to develop an anomaly detection algorithm for a water- and environmental data set.</p> <p> </p> <p>Included in zenodo: </p> <p>- dataset of water quality data</p> <p>- additional material and descriptions provided for the competition</p> <p> </p> <p>The competition was organized by:</p> <p>M. Friese, J. Stork, A. Fischbach, M. Rebolledo, T. Bartz-Beielstein (TH Köln)</p> <p> </p> <p>The dataset was provided and prepared by:</p> <p>Thüringer Fernwasserversorgung,</p> <p>IMProvT research project (S. Moritz)</p> <p><br> </p> <p>Industrial Challenge: Monitoring of drinking-water quality</p> <p> </p> <p>Description:</p> <p>Water covers 71% of the Earth's surface and is vital to all known forms of life. The provision of safe and clean drinking water to protect public health is a natural aim. Performing regular monitoring of the water-quality is essential to achieve this aim.</p> <p>Goal of the GECCO 2017 Industrial Challenge is to analyze drinking-water data and to develop a highly efficient algorithm that most accurately recognizes diverse kinds of changes in the quality of our drinking-water.</p> <p> </p> <p>Submission deadline:</p> <p>June 30, 2017</p> <p>Official webpage:</p> <p><a href="http://www.spotseven.de/gecco-challenge/gecco-challenge-2017/">http://www.spotseven.de/gecco-challenge/gecco-challenge-2017/</a></p>
Figures 12-16. 12 in The types of Calyptratae (Diptera) preserved in the Museum fϋr Naturkunde, Leibniz-Institute for Evolution and Biodiversity Science, Berlin, Germany, collected from Indian Sub continent
Figures 12-16. 12. Mydaea morose Stein Holotype and Original labels, 13(a&b). Pygophora tricincta Enderlein Type and Original labels, 14(a&b). Chrysomyia nigripes Aubertin Paratype and labels, 15(a,b&c). Lipoptena efovea Speiser Type and Original labels, 16(a&b). Gasterophilus elephantis Cobbold Paratype and labels.
Figures 6b-11. 6b in The types of Calyptratae (Diptera) preserved in the Museum fϋr Naturkunde, Leibniz-Institute for Evolution and Biodiversity Science, Berlin, Germany, collected from Indian Sub continent
Figures 6b-11. 6b.Dichaetomyia splendida (Stein) Holotype with labels 7 (a&b). Limnophora tinctipennis Stein Syntypes and labels, 8. Lispa mirabilis Stein Original type and Syntype labels, 9(a&b). Lispa sericipalpis Stein Holotype and Original labels, 10. Lispocephala tinctipennis (Stein) Original and Syntype labels, 11. Mydaea attenta Stein Holotype Original labels.
Figures 1-6. 1. Coenosia angustifrons Stein Type with labels, 2a. Coenosia capitulate Stein Syntype, 2b. Original plus Syntype labels, 3 in The types of Calyptratae (Diptera) preserved in the Museum fϋr Naturkunde, Leibniz-Institute for Evolution and Biodiversity Science, Berlin, Germany, collected from Indian Sub continent
Figures 1-6. 1. Coenosia angustifrons Stein Type with labels, 2a. Coenosia capitulate Stein Syntype, 2b. Original plus Syntype labels, 3(a&b). Coenosia ceylonica Enderlein Type and Paratype labels, 4. Coenosia indica Enderlein Original and Type labels, 5. Mydaea pallens Stein Original and Paralectotype labels, 6a. Dichaetomyia splendida (Stein) Holotype with labels.
Ground penetrating radar (GPR) measurements in the Lower Muschelkalk of a limestone quarry in Rüdersdorf near Berlin, Germany 2023
<p>Surface Ground Penetrating Radar (GPR) was used on the exposed limestone of a quarry. Several measurements were carried out using a 200 MHz antenna, including a test field with densely spaced profiles suitable for 3D visualization. The measured features are oriented along boreholes and the actively mined demolition edge. Photographs of the wall face are provided at different stages of mining, which extended into the previously measured test field, revealing its cross section.</p>
Ground penetrating radar (GPR) monitoring of a densely gridded survey field in the Lower Muschelkalk of a limestone quarry in Rüdersdorf near Berlin, Germany 2023/24
<p>Surface Ground Penetrating Radar (GPR) was used on the exposed limestone of a quarry to monitor a survey field of densely spaced profiles on three dates (in October 2023, December 2023 and February 2024). The different moisture conditions of these survey dates can be evaluated with linked detailed weather data (<span>10.5281/zenodo.13867069</span>). A time-depth conversion using CMP data to calculate the EM wave velocity suggested a GPR penetration depth of approximately 4 metres. The measurements were planned, carried out and analysed in the context of a Master's thesis on the potential of GPR to investigate the hydrodynamics of carbonate rocks relevant to groundwater recharge processes.</p>
Precipitation and Temperature data from the on-site weather station at the Rüdersdorf limestone quarry near Berlin, Germany, from January 2023 to February 2024
<p>The weather data from the on site station is provided as Temperature [°C] every 5 minutes and the daily sums of Precipitation [mm]. </p>
Monthly and annual evapotranspiration maps in Berlin (Germany)
<p>Monthly and annual evapotranspiration (ET) maps of Berlin, Germany at a 10-m resolution are provided. This dataset is related to the manuscript "City-wide, high-resolution mapping of evapotranspiration to guide climate-resilient planning" (under review).</p> <p>The monthly and annual ET sums are provided as rasters (.tif files). The monthly ET sums are given in the files named "ETmonthly_2019_(month).tif" The annual ET sum for 2019 is named "ETannual_2019.tif". The coordinate reference system (CRS) is "+proj=longlat +datum=WGS84 +no_defs."</p> <p>For access to daily ET maps in 2019, please contact Stenka Vulova (stenka.vulova@tu-berlin.de).</p> <p>The abstract of the manuscript is given for background information on the dataset:</p> <p>"The impacts of global change, including extreme heat and water scarcity, are threatening an ever-growing urban world population. Evapotranspiration (ET) mitigates the urban heat island, reducing the effect of heat waves. It can also be used as a proxy for vegetation water use, making it a crucial tool to plan resilient green cities. To optimize the trade-off between urban greening and water security, reliable and up-to-date maps of ET for cities are urgently needed. Despite its importance, few studies have mapped urban ET accurately for an entire city in high spatial and temporal resolution. We mapped the ET of Berlin, Germany in high spatial (10-m) and temporal (hourly) resolution for the year of 2019. A novel machine learning (ML) approach combining Sentinel-2 time series, open geodata, and flux footprint modeling was applied. Two eddy flux towers with contrasting surrounding land cover provided the training and testing data. Flux footprint modeling allowed us to incorporate comprehensive land cover types in training the ML models. Open remote sensing and geodata used as model inputs included Normalized Difference Vegetation Index (NDVI) from Sentinel-2, building height, impervious surface fraction, vegetation fraction, and vegetation height. NDVI was used to indicate vegetation phenology and health, as plant transpiration contributes to the majority of terrestrial ET. Hourly reference ET (RET) was calculated and used as input to capture the temporal dynamics of the meteorological conditions. Predictions were carried out using Random Forest (RF) regression. Weighted averages extracted from hourly ET maps using flux footprints were compared to measured ET from the two flux towers. Validation showed that the approach is reliable for mapping urban ET, with a mean R2 of 0.76 and 0.56 and a mean RMSE of 0.0289 mm and 0.0171 mm at the more vegetated site and the city-center site, respectively. Lastly, the variation of ET between Local Climate Zones (LCZs) was analyzed to support urban planning. This study demonstrated the capacity to map urban ET at an unprecedented high spatial and temporal resolution with a novel methodology, which can be used to support the sustainable management of green infrastructure and water resources in an urbanizing world facing climate change."</p>
Heat Stress Exposure Maps - Urban Planning Scenario 2026 - 2045: Berlin, Germany
<p><strong>Berlin heat stress exposure map: average number of heatwave days per year versus socio economic data - urban planning scenario (2026-2045).</strong></p> <p>Heat stress exposure maps for Berlin representing the average number of heatwave days per year versus socio economic data per statistical unit. The average number of heatwave days per year has been modeled over the reference period 2026-2045 using the present land use / cover situation for the city but combined with urban planning projects information until 2030. Hence, the urban morphology has been updated accordingly.</p> <p><strong>Scenario: </strong>Urban planning scenario (situation LULC today + integrated urban planning projects 2030)</p> <p><strong>Exposure mapping variable: </strong><br /> Total population 2030<br /> Population density inhabitants per hectare 2030</p>
Heat Stress Maps - Urban Planning Scenarios 1986-2005 / 2026 - 2045: Berlin, Germany
<p>Heat stress maps for Berlin representing<br /> * the average number of heatwave days<br /> (1986 – 2005 | 2026 – 2045 | 2081 – 2100)<br /> per statistical unit or per grid</p> <p>The heat stress parameter considered has been modelled over the reference period 2026 - 2045 using the present land use / cover situation for the city but combined urban planning projects information until 2030. Hence, the urban morphology has been updated accordingly.</p> <p>Scenario: Urban Planning</p>
Heat Stress Maps - Base Scenarios 1986-2005 / 2026 - 2045 / 2081 - 2100: Berlin, Germany
<p>Heat stress maps for Berlin representing<br /> * the average number of heatwave days<br /> (1986 – 2005 | 2026 – 2045 | 2081 – 2100)<br /> per statistical unit or per grid</p> <p>* The Urban Heat Island effect at 11pm per year<br /> (1986 - 2005) per statistical unit or per grid</p> <p>The heat stress parameter considered has been modelled over the reference period using the present land use / cover situation for the city.</p> <p>Please note that only the base scenario 1986-2005 has got maps with the 2 heat stress parameters:<br /> * Average number of heat wave days per year<br /> * Urban Heat Island effect at 11pm per year</p> <p>Scenario: Base scenario (situation LULC today)</p>
Heat Stress Exposure Maps - Base Scenario 1986 - 2005: Berlin, Germany (Map-2 & Map-3)
<p>Heat Stress Exposure Maps - Base Scenario 1986 - 2005: Berlin, Germany</p> <p>Map-2 & Map-3 (zip.file) ref. to DOI: 10.5281/zenodo.45015</p>
Heat Stress Exposure Maps - Base Scenario 1986 - 2005: Berlin, Germany
<p><strong>Average number of heatwave days per year versus socio economic data - base scenario (1986-2005)</strong></p> <p>Heat stress exposure maps for the city of Berlin representing the average number of heatwave days per year versus socio economic data per statistical unit. The average number of heatwave days per year has been modelled over the reference period 1986-2005 using the present land use / cover situation for the city.</p> <p><strong>Exposure mapping variable include the following:</strong></p> <p>Total population 2013</p> <p>Population density inhabitants per hectare 2013</p> <p>Number of inhabitants aged 0 to 17 years 2013</p> <p>Number of inhabitants aged 18 to 65 years 2013</p> <p>Number of inhabitants aged +65 years 2013</p> <p>Number of schools 2014</p> <p>Number of childcare centers 2014</p> <p>Number of hospitals 2014</p> <p>Number of elderly stay facilities 2014</p>
Comparison of mosquitoes and fly derived DNA as a tool for sampling vertebrate biodiversity in suburban forests in Berlin, Germany
<p><span>The use of invertebrate-derived DNA (iDNA) is a promising non-invasive tool to monitor wildlife. While most studies have been carried out in dense tropical and sub-tropical forests and have focused on the use of a single category of invertebrates, this study compares the use of flies- and mosquito-derived DNA to assess vertebrate diversity in semi-urban environments. We conducted our sampling in four different forest plots in Berlin, Germany. Pools of flies and non-bloodfed mosquitoes were metabarcoded using 108 bp vertebrate-specific 12S rRNA (12S-V5) and 94 bp mammal-specific 16S rRNA (16Smam) mitochondrial markers, and individual bloodfed mosquitoes were sequenced using the 340 bp vertebrate-specific 12S rRNA fragment (Mam-12S-340). From the fly pools, we detected 10 mammal species using 16Smam, while 12S-V5 detected four bird, two amphibian and six mammal species. From the non-bloodfed mosquito pools, we only amplified putative contaminant DNA, indicating that mosquito females without visual signs of a blood meal carry no traces of vertebrate DNA. Finally, in the bloodfed mosquitoes, we identified four mammal and one bird species. Focusing on mammal species, we did not find significant differences in the proportion of species detected regarding the total available number of species between sampling localities. Fly samples were easier to obtain and more abundant over the sampled localities compared to mosquito samples. We conclude that, while there are a few advantages in using mosquito blood meals, the use of flies in the detection of wildlife in a suburban environment is more effective in terms of collection of samples and detection of vertebrates, although this technique is limited to few mammal species in the urban environment. </span></p>
Towards transferable data-driven models to predict urban pluvial flood water depth in Berlin, Germany
<p>The attached files include the predictive features and the water depth from 2D hydrodynamic simulations that were used to train data driven models to predict water depth in Berlin.</p>
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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.