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802 results for “urban data”
Data from: Health-related lifestyle behaviors among male and female rural-to-urban migrant workers in Shanghai, China
Background: Lifestyle behaviors significantly impact health, yet remain poorly defined in Chinese rural-to-urban migrants. Methods: In a cross-sectional study of health-related behaviors of 5484 rural-to-urban migrants who had worked in Shanghai for at least six months, we assessed the contribution of demographics and physical and mental health to lifestyle behaviors in male and female participants by multiple stepwise cumulative odds logistic regression. Results: Respondents were 51.3% male. 9.9% exhibited abnormal blood pressure; 27.0% were overweight or obese; 11.2% reported abnormal mental health; 36.9% reported healthy lifestyle. Multiple stepwise cumulative odds logistic regression indicated that men working in manufacturing reported less unhealthy lifestyle than those in hospitality (cumulative odds ratio (COR) = 1.806, 95%CI 1.275–2.559) or recreation/leisure (COR = 3.248, 95%CI 2.379–4.435); and women working in manufacturing and construction reported less unhealthy lifestyle than those in all other sectors. Unhealthy lifestyle was associated with small workplaces for men (COR = 1.422, 95%CI 1.154–1.752), working more than 8 or 11 hours per day for women and men, respectively, and earning over 3500 RMB in women (COR = 1.618, 95%CI 1.137–2.303). Single women and women who had previously resided in three or more cities were more likely to report unhealthy lifestyle (COR = 2.023, 95%CI 1.664–2.461, and COR = 1.311, 95%CI 1.072–1.602, respectively). Abnormal mental status was also correlated with unhealthy lifestyle in men (COR = 3.105, 95%CI 2.454–3.930) and women (COR = 2.566, 95%CI 2.024–3.252). Conclusions: There were different risk factors of unhealthy lifestyle score in male and female rural-to-urban migrants, especially in number of cities experienced, salary, marital status, work place scale. Several demographic groups: employment sectors (e.g. hospitality and recreation/leisure), working conditions (e.g. long hours) and abnormal mental status were associated with unhealthy lifestyle behaviors in Chinese rural-to-urban migrants, and health interventions should be targeted to these groups.
Data from: How weather instead of urbanity measures affects song trait variability in three European passerine bird species
Previous studies detected an influence of urban characteristics on song traits in passerine birds, that is, song adjustments to ambient noise in urban areas. Several studies already described the effect of weather conditions on the behavior of birds, but not the effect on song traits. We investigate, if song trait variability changes along a continuous urbanity gradient in Frankfurt am Main, Germany. We examined, for the first time on a larger scale, the influence of weather on song parameters. We made song recordings of three common passerine species: the blue and great tit (Cyanistes caeruleus (Linnaeus, 1758) and Parus major Linnaeus, 1758) and the European blackbird (Turdus merula Linnaeus, 1758). We measured different song traits and performed statistical analyses and modeling on a variety of variables—among them urbanity and weather parameters. Remarkably, we found only few cases of a significant influence of urbanity parameters on song traits. The influence of weather parameters (air pressure, atmospheric humidity, air and soil temperatures) on song traits was highly significant. Birds in Frankfurt face high noise pollution and might show different adaptations to high noise levels. The song trait variability of the investigated species is affected more by weather conditions than by urban characteristics in Frankfurt. However, the three species react differently to specific weather parameters. Smaller species seem to be more affected by weather than larger species.
Data and Code for: Evaluation of vertically resolved longwave radiation in SPARTACUS-Urban and the sensitivity to urban surface temperatures
<p>This contains the model outputs and analysis code for the article: Evaluation of vertically resolved longwave radiation in SPARTACUS-Urban and the sensitivity to urban surface temperature, Stretton et al. 2022, Submitted to <em>Geoscientific Model Development</em>.</p>
Transferability of 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>
Data produced for "Water Scarcity Challenges across Urban Regions with Expanding Irrigation"
<p>This archive includes the data and codes to produced results for the paper- <strong>Transition from Rain-fed to Irrigation-fed Agriculture in Rural Areas to Exacerbate Urban Water Scarcity</strong>. It contains 3 archive files:</p> <p><strong>1. H08_inputs_org: </strong>All the input files required for the simulation of the global hydrological model, H08 (/H08/map/org). It has the following files (for both scenarios S1, and S2) -</p> <p>a) AQUASTAT- industrial and domestic water use</p> <p>b) C05- GDP information for the countries</p> <p>c) FAO2009_Slop- data on slope</p> <p>d) GMIA5_S1- global map for irrigated areas version 5 for scenario 1</p> <p>e) GMIA5_S2- global map for irrigated areas version 5 for scenario 2</p> <p>f) GRanD- Global Reservoir and Dam (GRanD) database</p> <p>g) GSWP2_Albedo- albedo data</p> <p>h) GSWP23_SoilType- soil type data</p> <p>i) IGRAC- groundwater use for domestic and industrial sector</p> <p>j) IIASA_SSP- socio economic pathways data</p> <p>k) K14- explicit aqueduct data</p> <p>l) M08- crop distribution data</p> <p>m) OneGeology- geology units offshore</p> <p>n) R08- cropland and pastureland data</p> <p>o) WFDEI- flow direction </p> <p>p) DS02- irrigation data</p> <p><strong>2. Codes:</strong> Jupyter notebook consisting of the python codes for post-processing, and a folder named <em>files_req </em>with the required files for the notebook. </p> <p><strong>3. Outputs: </strong>This includes 4 archives</p> <p>a) arcgis_outputs- post-processing model results' shapefiles and rasters; ArcMap documents of CAD difference; and other outputs</p> <p>b) CAD_S1_S2- Raster files of monthly CAD for scenarios S1 (named as S12) and S2; raster files of total water abstraction from renewable water sources; and raster files of total water demand. </p> <p>c) diff_cad_21- Raster files of difference in CAD (CAD_S2 - CAD_S1) for with and without considering environmental flow requirements.</p> <p>e) urban_results_excel- Excel files of CAD comparison in S1 and S2; excel file of monthly water demand and abstraction in S1 and S2. </p> <p> </p>
Data for "A Shifting Lens: Urban Evolution Brings Exaptation into Focus"
<p>Data from literature search of number of citations per year including terms related to adaptation.</p>
The process data used in paper 'Urbanization Alters Atmospheric Dryness through Land Evapotranspiration'
<p>The process data used in a journal paper ' <strong>Urbanization Alters Atmospheric Dryness through Land Evapotranspiration </strong>', including data used for each plot.</p> <p>The weather observation data are available online (http://data.cma.cn/en). The 20 urban sites dataset can be accessed from https://doi.org/10.5281/zenodo.7104984 under a Creative Commons Attribution licence (CC-BY-4.0). Impervious surface data set are free available online (http://data.starcloud.pcl.ac.cn/zh); and USGS FEWS NET SSEBop Actual Evapotranspiration Products (Version 5.0) are free available online (https://earlywarning.usgs.gov/fews/product/458). Global Land Surface Satellite (GLASS) LAI data sets (version 3.0) (http://www.glass.umd.edu/Download.html) are free available online; digital elevation model (DEM) (www.geodata.cn) are free available online.</p>
In-situ relative humidity and air temperature urban microclimate data
<p>Monitoring and understanding the variability of heat within cities is important for urban planning and public health, and there has been a growth in the number of studies measuring intra-urban temperature variability. Recognizing that the physiological effects of heat depend on humidity as well as temperature, some of these measurement campaigns have included measurements of relative humidity alongside temperature. Reported analyses, however, have not reported whether spatial structure in humidity, independent from temperature, contributes significantly to intra-urban heat variability. Here we use summer temperature and humidity from networks of stationary sensors in multiple cities in the USA to examine this issue. It is shown that although there are spatial variations in relative humidity there are only very weak spatial variations in the absolute humidity within these cities. This variability in absolute humidity plays an insignificant role in the spatial variability of the heat index and humidex, and the spatial variability of the heat metrics is dominated by temperature variability. A practical consequence of this is that a network of sensors that only measure temperature is sufficient to quantify the spatial variability of heat across these cities when combined with humidity measured at a single location, allowing for lower-cost heat monitoring networks.</p>
Data from: Microbial diversity in the floral nectar of Linaria vulgaris along an urbanization gradient
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Data from: Urban green roofs provide habitat for migrating and breeding birds and their arthropod prey
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Data from: Outcomes of cataract surgery in urban and rural population in the South Indian State of Andhra Pradesh: Rapid Assessment of Visual Impairment (RAVI) Project
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Data from: Impact of point-of-care ultrasound on clinical decision-making at an urban emergency department in Tanzania
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Data from: Health-related lifestyle behaviors among male and female rural-to-urban migrant workers in Shanghai, China
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Data from: Hierarchical multi-taxa models inform riparian vs. hydrologic restoration of urban streams in a permeable landscape
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Data from: A worldwide model for boundaries of urban settlements
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Data from: How weather instead of urbanity measures affects song trait variability in three European passerine bird species
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In-situ relative humidity and air temperature urban microclimate data
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Spontaneous urban flora of Los Angeles: Transect data
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Global Urban Heat Island (UHI) Data Set, 2013
The Urban Heat Island (UHI) effect represents the relatively higher temperatures found in urban areas compared to surrounding rural areas owing to higher proportions of impervious surfaces and the release of waste heat from vehicles and heating and cooling systems. Paved surfaces and built structures tend to absorb shortwave radiation from the sun and release long-wave radiation after a lag of a few hours. The Global Urban Heat Island (UHI) Data Set, 2013, estimates the land surface temperature within urban areas in degrees Celsius (average summer daytime maximum and average summer nighttime minimum) as well as the difference between those temperatures and the temperatures in surrounding rural areas, defined as a 10km buffer around the urban extent. Urban extents are from SEDAC�s Global Rural-Urban Mapping Project, Version 1 (GRUMPv1), and land surface temperatures are from SEDAC�s Global Summer Land Surface Temperature (LST) Grids, 2013, which are derived from the Aqua Level-3 Moderate Resolution Imaging Spectroradiometer (MODIS) Version 5 global daytime and nighttime Land Surface Temperature (LST) 8-day composite data (MYD11A2). For most regions, the UHI data set provides the average daytime maximum (1:30 p.m. overpass) and average nighttime minimum (1:30 a.m. overpass) temperatures in urban and rural areas, and the urban-rural temperature differences, derived from LST data representing a 40-day time-span during July-August (Julian days 185-224) in the northern hemisphere and January-February (Julian days 001-040) in the southern hemisphere. LST grid cells with missing values resulting from high cloud cover in tropical regions were filled with daytime maximum and nighttime minimum LST values from April-May 2013 in the northern hemisphere and December 2013-January 2014 in the southern hemisphere, where available. Some data gaps remain in areas where data were insufficient (e.g., Central Africa).
mRNA expression data from A549 exposed to fresh or aged urban air mixtures
GEO Series GSE23735. Homo sapiens. 8 samples. Type: Expression profiling by array.
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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.