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300 results for “Urban area”
Figure 1 in Bacterial community associated with Culex quinquefasciatus Say, 1823 (Diptera: Culicidae) from an urban area in the Amazon, Brazil
Figure 1 Bar chart of the relative abundance of each bacterial genus per sample. The black bar comprises the genera that show relative abundance of less than 1%.
Figure. Food preferences of Chinese mole shrew (Anourosorex squamipes) from an urban area. a, b, c, d. Mean relative consumption along six trials for each food set are shown in a, b, c, d, respectively. (see materials and methods for details on the calculation of relative consumption). in Natural animal food preference of Chinese mole shrew (Anourosorex squamipes) from an urban area: a laboratory study
Figure. Food preferences of Chinese mole shrew (Anourosorex squamipes) from an urban area. a, b, c, d. Mean relative consumption along six trials for each food set are shown in a, b, c, d, respectively. (see materials and methods for details on the calculation of relative consumption).
Figs 1, 2 in Nesting biology of the oil-collecting bee Epicharis (Hoplepicharis) fasciata (Hymenoptera: Apidae) in an urban area of Rio de Janeiro, RJ, Brazil
Figs 1, 2. Construction and nests of Epicharis (Hoplepicharis) fasciata Lepeletier & Serville, 1828 at the Jardim Botânico of Rio de Janeiro:1, female constructing her nest; 2, female inside her nest and another flying in the nesting area.
Figs 6, 7 in Nesting biology of the oil-collecting bee Epicharis (Hoplepicharis) fasciata (Hymenoptera: Apidae) in an urban area of Rio de Janeiro, RJ, Brazil
Figs 6, 7. Insects associated with Epicharis (Hoplepicharis) fasciata Lepeletier & Serville, 1828: 6, female of the cleptoparasitic bee Rhathymus bicolor Lepeletier & Serville, 1828 leaving a nest;7, female of Pseudomethoca sp. walking through the nesting area.
Figs 4, 5 in Nesting biology of the oil-collecting bee Epicharis (Hoplepicharis) fasciata (Hymenoptera: Apidae) in an urban area of Rio de Janeiro, RJ, Brazil
Figs 4, 5. Brood cells and larva of Epicharis (Hoplepicharis) fasciata Lepeletier & Serville, 1828: 4, lateral view (scale bar 1 cm); 5, brood cell with pre-defecating larva eating the pollen mass (scale bar 1 cm).
Fig. 3 in Nesting biology of the oil-collecting bee Epicharis (Hoplepicharis) fasciata (Hymenoptera: Apidae) in an urban area of Rio de Janeiro, RJ, Brazil
Fig. 3. Nests of Epicharis (Hoplepicharis) fasciata Lepeletier & Serville, 1828 showing the position of the brood cells.
Fig. 3 in Lymnaeid snails in the city of Salzburg - A malacological and ecological study in the urban area
Fig. 3: Global Marginality Coefficient (GMC; a) and Global Tolerance Coefficient (GTC; b) modeled for the different environmental parameters (Abbreviations: see Table 1).
Fig. 1 in Lymnaeid snails in the city of Salzburg - A malacological and ecological study in the urban area
Fig. 1: Geographic map of the city of Salzburg with all sample locations investigated for the possible colonization by pond snails.
Fig. 2 in Lymnaeid snails in the city of Salzburg - A malacological and ecological study in the urban area
Fig. 2: Urban distribution maps computed for Radix labiata (a) and R. balthica (b). The first gastropod species could be collected at 68% of all sample locations, whereas the second one was restricted to 62% of the studied sites.
Estimates of high tide flooding on roadways within urban areas along the United States Atlantic coast
<p>Estimates of high tide flooding (HTF) on roadways in urban areas along the US Atlantic Coast. These estimates were calculated using NOAA HTF areal extent estimates, OpenStreetMap roadway data, and 2010 census-designated urban area and census block data. See the corresponding manuscript (Gold et al., 2021 - link coming soon) and <a href="https://github.com/acgold/HTF-on-roads">GitHub repository</a> for additional information about these data.</p>
Potentials and perspectives of food self-sufficiency in urban areas – data and code
<p>Script and input data files (consumption and production data) for the publication "Potentials and perspectives of food self-sufficiency in urban areas – a case study from Leipzig". The script provides a tool for 1) the calculation of the self-sufficiency level (ssl), regional and non-regional agricultural area demand for the region of interest and 2) the calculation of share of area demand and total consumption of single commodities and food groups. Input data files include production yields and consumption quantities for single commodities.</p>
Fig. 1 in Diet Composition Of The Austral Pygmy Owl In A Peri-Urban Protected Area In South-Central Chile
Fig. 1. Trophic isoclines for Austral Pygmy Owl, Glaucidium nana, in the study area: A. l. — Abrothrix longipilis; A. o. — Abrothrix olivaceus; Art — Arthropods; Bd. — Birds; D. g. — Dromiciops gliroides; R. n. — Rattus norvegicus; R. r. — Rattus rattus.
Supplementary Material - Marine animal forests in turbid environments are overlooked seascapes in urban areas
<p>Figures S1, S2, and S3 of the manuscript "Marine animal forests in turbid environments are overlooked seascapes in urban areas" accepted in the open-access journal Ocean and Coastal Research (Soares et al. 2023)</p>
Datasets of sap flow, meteorological, leaf gas measurements in urban green areas in Helsinki
<p>Datasets of sap flow, meteorological, leaf gas measurements used in the manuscript “Sap flow and leaf gas exchange response to drought and heatwave in urban green spaces in a Nordic city”. Data contains cleaned half-hourly sap flow data and half-hourly meteorological datasets (Tair, Tsoil, RH, soil temperature, soil moisture) at four different urban green areas in Helsinki.</p> <p>Manual measurements of leaf gas exchanges using GFS instruments. Datasets contains mainly the Amax parameters derived from curve fitting and instantaneous values of G and E at PAR 1100 W m<sup>-2</sup>.</p> <p>Folders contain:</p> <ul> <li>Leaf gas exchange data <ul> <li>Leaf_gas_data_all_v2.csv</li> <li>Metadata_leaf gas exchange data.csv</li> </ul> </li> <li>Meteo data <ul> <li>Meteo_Forest_data_30min.csv</li> <li>Meteo_Orchard_data_30min.csv</li> <li>Meteo_Park_data_30min.csv</li> <li>Meteo_Street_data_30min.csv</li> <li>Metainfo_meteo.xlsx</li> </ul> </li> <li>Sap flow data <ul> <li>Sap_Forest_30min_cleaned.csv</li> <li>Sap_Orchard_30min_cleaned.csv</li> <li>Sap_Park_30min_cleaned.csv</li> <li>Sap_Street_30min_cleaned.csv</li> <li>Metadata_info_sap.csv</li> </ul> </li> </ul>
Rapid decline of aerosol absorption coefficient and aerosol optical properties effects on radiative forcing in urban areas of Beijing from 2018 to 2021
<p>data for Rapid decline of aerosol absorption coefficient and aerosol optical properties effects on radiative forcing in urban areas of Beijing from 2018 to 2021</p>
PoqueiraOccupancy: Dataset and metrics from occupancy sensors of urban areas and establishments in the region of Barranco del Poqueira in the Alpujarra Granadina
<p>This dataset is linked to the analysis of different aspects related to the conservation of the Sierra Nevada National Park through advanced digital systems. The devices have been deployed in the municipalities of Pampaneira and Capileira in the Alpujarra region of the province of Granada. The data is collected by 11 BOSCH fixed cameras 11.00 387.4900 Interior IR 5.3 MP and 4 TURRET type cameras Interior IR Lens 2.8 mm 5.3 MP 100º H.265 multi-streaming (H.265; H.264; M-JPEG).</p> <p>The devices have been installed as follows: 3 devices have been placed in establishments in Capileira, 8 in establishments in Pampaneira, and 4 in urban passage areas in the municipality of Pampaneira. All devices are capable of measuring the entry and exit to the establishment or area they are designated for. In some cases, there is also a metric which measures the number of people present within that area. The information related to the establishments has been anonymized to ensure the privacy of the collaborating companies in the project and the flow of customers during the studied period.</p> <p>The data attached in the CSV files DATA_OCCUPANCY_2022 and DATA_OCCUPANCY_2023 contain information about individuals detected by the cameras in the years 2022 (from February to December) and 2023 (from January to August). The calculation of the number of people is done cumulatively in hourly intervals. The collected variables include:</p> <ul> <li> <p>device_ID: The name of the device recording the value.</p> </li> <li> <p>type: The metric measuring the recording, which can be ENTRADA (entry), SALIDA (exit), or AFORO (occupancy).</p> </li> <li> <p>date: The date and time at which the cumulative people count is recorded for the specific metric.</p> </li> <li> <p>counter: The number of people counted for a specific metric in that time period.</p> </li> </ul>
Rapid evolutionary divergence of a songbird population following recent colonization of an urban area
Open the record for dataset details and reuse information.
RESCCUE (RESilience to cope with Climate Change in Urban arEas) EU Project - WP1 Data
<p>These files contain the data generated for the Work Package 1 of the RESCCUE project. RESCCUE project was devised to analyse future urban impacts due to climate change so as to improve resilience of three target cities: Barcelona, Bristol and Lisbon. To achieve that, future climate projections and changes in extreme events were obtained at a local scale for the Work Package 1. Several past studies were analysed to identify all the climate variables and extreme events that could affect urban areas, e.g. heavy rainfall, heat waves and storm surge. All available meteorological observations in the considered areas were collected and filtered through several tests (general consistency, outliers and inhomogeneities) in order to handle datasets long enough and of good quality. As a way to obtain the best input possible, every valid station was extended in time by downscaling process with the ERA-Interim reanalysis.</p> <p>Future climate projections were obtained for ten different global climate models considering two of the main Representative Concentration Pathways (RCP4.5 and RCP8.5) established in the last IPCC report. These models were downscaled through a sophisticated statistical methods (analogous stratification and transfer functions among others) to project local climate according to the identified climate drivers: temperature, precipitation, wind, relative humidity, sea level pressure, potential evapotranspiration, snowfall, wave height and sea level; and for both climate and decadal timescales. Already downscaled models were first validated for the method and afterwards verified, obtaining small errors and good coherent simulations.</p> <p>Extreme events of the main climate drivers were obtained and analysed for both historical and future scenarios through the combination of several statistical methods as well as through the analysis of several teleconnectionpatterns. Derived events such as heat waves, drought, snowstorms, storm surges, wave height among others were afterwards inferred for climate, decadal and seasonal scale.</p> <p><strong>FILES</strong></p> <p>The data generated have been grouped into three different files, one for each studied area: the hydrological basin of the rivers Ter and Llobregat (the area that influences Barcelona), the geographical area between England and South Wales (the area that influences Bristol) and the Lisbon area.</p> <p>Each of the files contains a self-explanatory file detailing the structure of the information contained and the way in which it is provided.</p> <p><strong>ABOUT THE RESCCUE PROJECT</strong></p> <p>The RESCCUE project, Resilience to cope with Climate Change in Urban Areas, –a multisectorial approach focusing on water– aims to provide practical and innovative models and tools to end-users facing climate change challenges to build more resilient cities.</p> <p>The project provides tools to assess urban resilience from a multisectorial approach, for current and future climate scenarios and including multiple hazards. This holistic approach to urban resilience will enable city managers and urban systems operators to decide the optimal investments to cope with future situations.</p> <p>For more information, please visit <a href="http://www.resccue.eu/">www.resccue.eu</a></p>
Datasets of studying Isoprene oxidation in urban and suburban areas
<p>Observational dataset including functionalized isoprene oxidation products, VOCs, trace gases, MET and organic aerosol composition measured at SORPES, Nanjing (118.9E,32.1N) and ECUST, Shanghai (121.5E,30.9N) during summer of 2018. The measurement period is July 13th- August 9th and June 21th-June 26th, respectively. The isoprene functionalized oxidation products were measured by NO3- CI-API-TOF, VOCs were measured by PTR-TOF-MS and ogrganic aerosol composition was measured by TOF-ACSM.</p>
Urban Green Area Accessibility Prioritization in Helsinki Metropolitan Area, Finland
<p>R scripts and Zonation input and output files for the research article Jalkanen, Fabritius, Vierikko, Moilanen & Toivonen (2020), “Analyzing fair access to urban green areas using multimodal accessibility measures and spatial prioritization”, <em>Applied Geography</em> (doi:10.1016/j.apgeog.2020.102320). See also the GitHub page for possible updates: <a href="https://github.com/DigitalGeographyLab/urban-green-area-accessibility-prioritization/">https://github.com/DigitalGeographyLab/urban-green-area-accessibility-prioritization/</a></p> <p><strong>R scripts</strong></p> <ul> <li> <p>01_Distance-decays_of_travel_modes.r: Code for defining distance-decay functions for travels from home to a recreational area. Functions are defined separately for different travel modes (walking, biking, public transport) and they are based on a travel survey by Helsinki Region Transport Authority (Brandt et al. 2019).</p> </li> <li> <p>02_Green_area_accessibility_layers_from_cell-specific_travel_times.r: Code for creating raster layers depicting the accessibility of green areas in the Helsinki Metropolitan Area, separately from the point of view of all the metropole’s districts. Accessibility is based on modeled travel times (Tenkanen & Toivonen 2020) and distance-decay functions (previous code).</p> </li> <li> <p>03_Green_area_buffer_analysis_for_comparison.r: Code for calculating the number of people living within 500m buffer around different green area pixels in the Helsinki Metropolitan Area.</p> </li> </ul> <p><strong>Zonation files</strong></p> <p>Each folder contains standard Zonation input and output files for different analysis versions described in the article. The .bat files that execute each Zonation run are located in the corresponding folders. The “input” subfolders include the features_list.spp and settings.dat files for each run. The “output” subfolders include all files generated and named automatically by the Zonation software. For instance, the priority rank maps shown in the article are found in these subfolders. See the Zonation manual (Moilanen et al. 2014) for details about e.g. the usage, naming, or structure of the different files.</p> <p>Zonation analysis versions are named as follows:</p> <ul> <li>walk = Analysis includes the accessibility of all green areas based on walking.</li> <li>bike = Analysis includes the accessibility of all green areas based on biking.</li> <li>pt = Analysis includes the accessibility of large forests based on public transportation.</li> <li>weights = The population-weighted version of the analysis. Here, each input raster layer (showing the accessibility of green areas from different city districts) is weighted by the population of the corresponding district.</li> </ul> <p><strong>References</strong></p> <p>Brandt E, Kantele S & Räty P (2019). Liikkumistottumukset Helsingin seudulla 2018 (Travel habits in the Helsinki region in 2018). HSL Publications 9/2019. <a href="https://www.hsl.fi/sites/default/files/hsl_julkaisu_9_2019_netti.pdf">https://www.hsl.fi/sites/default/files/hsl_julkaisu_9_2019_netti.pdf</a></p> <p>Tenkanen, H & Toivonen T (2020). Longitudinal spatial dataset on travel times and distances by different travel modes in Helsinki Region. Scientific Data 7: 1–15. <a href="https://doi.org/10.1038/s41597-020-0413-y">https://doi.org/10.1038/s41597-020-0413-y</a></p> <p>Moilanen, Pouzols FM, Meller L, Veach V, Arponen A, Leppänen J, Kujala H (2014) Zonation Version 4 user manual. C-BIG, University of Helsinki, Helsinki.</p>
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.