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802
datasets available to search
ShareScore release 0.9.0
Dataset results
802 results for “urban data”
Data from: Clinical and laboratory predictors of influenza infection among individuals with influenza-like illness presenting to an urban Thai hospital over a five-year period
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Data from: Urbanization and elevated cholesterol in American crows
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Data from: Species interactions limit the occurrence of urban-adapted birds in cities
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Data from: Differing impacts of two major plant invaders on urban plant-dwelling spiders (Araneae) during flowering season
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Data from: Urban forest fragments as unexpected sanctuaries for the rare endemic ghost butterfly from the Atlantic forest.
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Data from: Urban versus forest ecotypes are not explained by divergent reproductive selection
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Raw data from: Consumer-resource interactions along urbanization gradients drive natural selection
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Data from: Reduced flight-to-light behaviour of moth populations exposed to long-term urban light pollution
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Data from: Local and landscape metrics identify opportunities for conserving cavity-nesting birds in a rapidly urbanizing ecoregion
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Data from: Sleepless in town – drivers of the temporal shift in dawn song in urban European Blackbirds
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Data from: Vocal characteristics of prairie dog alarm calls across an urban noise gradient
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Data from: Influence of mate and nest-site fidelity on a declining, urban avian population
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Data from: Impacts of habitat on butterfly dispersal in tropical forests, parks and grassland patches embedded in an urban landscape
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Data from: Diurnal trends of indoor and outdoor fluorescent biological aerosol particles in a tropical urban area
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Wildland-urban interface in California using remote sensing data
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Data and code for: Explainable machine learning revealed the conditional response of biogenic isoprene to the changes in environmental factors at an urban site in the Yangtze River Delta, China
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Data from MECO(n) model simulations on "Urban greenhouse gas emissions from the Berlin area: A case study using airborne CO2 and CH4 in situ observations in summer 2018"
<p>This tar-files contain the results of the MECO(n) model, which are published in</p> <p>T. Klausner, M. Mertens, H. Huntrieser, M. Galkowski, G. Kuhlmann, R. Baumann, A. Fiehn, P. Jöckel, M. Pühl, and A. Roiger: Urban greenhouse gas emissions from the Berlin area: A case study using airborne CO<sub>2</sub> and CH<sub>4</sub> in situ observations in summer 2018, Elementa: Science of the Anthropocene (Ref.: Ms. No. ELEMENTA-D-19-00074R1), 2019.</p>
Wealth, water and wildlife: landscape aridity intensifies the urban Luxury Effect - data used in meta-analysis
<p>The available Excel file contains all data used in the meta-analysis to analyse the Luxury Effect (i.e. the relationship between urban biodiversity and socioeconomic status) and its moderators (wealth status, species provenance and precipitation). Each column in the data set (tab ‘Data’) is defined as follows:</p> <p><strong>Location</strong>: Location (e.g. city) where a given study took place. If more than one geographical location was considered, they are detailed in the column 'Sample'</p> <p><strong>Sample: </strong>Any separate samples based either on location or temporal sampling period (e.g. geographical location, habitat types, different years) considered in a given paper. If the column is blank, only one location or period was considered.</p> <p><strong>Biodiversity measure: </strong>Defined into either diversity or abundance measures as defined in the text.</p> <p><strong>Response variable: </strong>The precise response variable analysed in the paper.</p> <p><strong>Socioeconomic variable: </strong>The socioeconomic variable analysed in the paper.</p> <p><strong>Provenance: </strong>Native or exotic species, where specified. 'All' refers both to papers where it was explicitly stated that both native and exotic species were considered, and those where no information was given, but we assumed that native and exotic species had been considered.</p> <p><strong>Development status: </strong>Countries with developed economies ('Rich') and countries with developing economies 'Poor') as defined in the text.</p> <p><strong>Gradient length: </strong>Studies including only urbanized areas ('Short') and those also including rural sampling locations ('Long').</p> <p><strong>Precipitation: </strong>in mm.</p> <p><strong>Pearson's r: </strong>Standardized values used in the meta-analysis.</p> <p> </p> <p>Note the above information is also available in the Excel file in the ‘Notes’ tab. </p> <p> </p>
Detectable urbanization effect in observed surface air temperature data series in Pyongyang region, DPR Korea-Supporting Information-data
<p>This is the calculated dataset as supplemental information of a paper entitled "Detectable urbanization effect in observed surface air temperature data series in Pyongyang region, DPR Korea", which will be likely published in Geophysical Research Letters.</p> <p> </p> <p> </p> <p> </p>
Supporting data for "Reducing uncertainties in urban drainage models by explicitly accounting for timing errors in objective functions"
<p>Supporting data for "Reducing uncertainties in urban drainage models by explicitly accounting for timing errors in objective functions" submitted to Water Resources Research.</p> <p>Contains:</p> <ul> <li>SWMM template files.</li> <li>Objective function values for all model runs.</li> <li>Jupyter Notebooks used to create figures and tables for the article.</li> <li>Copy of rainfall runoff data available from <a href="https://doi.org/10.5281/zenodo.3931582">https://doi.org/10.5281/zenodo.3931582</a></li> </ul> <p>For python implementations of the Hydrograph Matching Algorithm (Ewen 2011) see <a href="https://doi.org/10.5281/zenodo.3923792">https://doi.org/10.5281/zenodo.3923792</a></p> <p>Ewen, John. “Hydrograph Matching Method for Measuring Model Performance.” <em>Journal of Hydrology</em> 408, no. 1–2 (September 2011): 178–87. <a href="https://doi.org/10.1016/j.jhydrol.2011.07.038">https://doi.org/10.1016/j.jhydrol.2011.07.038</a>.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.