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1,618 results for “City”
Dissolved organic carbon concentrations for seasonal synoptic sampling of 100 urban streams in Salt Lake City, Utah (USA) from 2022-2023
This dataset contains dissolved organic carbon concentrations from surface water samples collected at 100 urban stream locations in the greater Salt Lake City, Utah metropolitan area. Samples were collected four times (July 2022, October 2022, February 2023, and May 2023) to capture spatial and seasonal variation in DOC concentrations. Filtered stream samples were analyzed for dissolved organic carbon concentration. These data were collected as part of the Carbon in Urban Rivers Biogeochemistry (CURB) Project. Detailed field data and site data are published separately and can be linked using the “curbid” and “synoptic_event” columns in each dataset.
Field data for seasonal synoptic sampling of 100 urban streams in Salt Lake City, Utah (USA), 2023-2024
This dataset contains field measurements taken during water sampling from 100 urban stream locations in the greater Salt Lake City, Utah (USA) metropolitan area. Field collection took place during four synoptic sampling events (July 2022, October 2022, February 2023, and May 2023) to capture spatial and seasonal variation in stream conditions (specific conductivity, water temperature, dissolved oxygen, pH, ORP). Filtered stream samples were analyzed for dissolved organic carbon concentration and characteristics, available in a separate dataset. These data were collected as part of the Carbon in Urban Rivers Biogeochemistry (CURB) Project. Detailed field data and site data are published separately and can be linked using the “curbid” and “synoptic_event” columns in each dataset.
Dissolved organic matter characterization for seasonal synoptic sampling of 100 urban streams in Salt Lake City, Utah (USA) from 2023-2024
This dataset contains dissolved organic matter (DOC) characteristics from surface water samples collected at 100 urban stream locations in the greater Salt Lake City, Utah metropolitan area. Samples were collected four times (July 2023, October 2023, January 2024, and May 2024) to capture spatial and seasonal variation in DOC concentrations. Fluorescent optical properties were measured on filtered water samples to understand the chemical composition of dissolved organic matter. Excitation-Emission Matrices (EEMs) were measured using a Horiba Aqualog spectrometer. DOM characteristics were quantified using both standard fluorescence and absorbance metrics as well as through parallel factor (PARAFAC) analysis. These data were collected as part of the Carbon in Urban Rivers Biogeochemistry (CURB) Project. Detailed field data and site data are published separately and can be linked using the “curbid” and “synoptic_event” columns in each data
Mosquito Ovitrap Data from Baltimore City and County (2011-2016)
Mosquitoes are an important component of insect biodiversity across all ecosystems. As invertebrates, they are sensitive to abiotic conditions during both aquatic juvenile and terrestrial adult stages. The data here were collected to identify how mosquito species composition, phenology, and peak population abundances are influenced by changes in abiotic and biotic conditions along an urbanization gradient from residential Baltimore City to forested Baltimore County. Many of the sample sites were aligned with the LTER's stream sampling along the Gwynns Falls, with additional sites located in community gardens in residential neighborhoods near Watershed 263.
A Land-use/Land Cover Classification of Baltimore City in 1927
Land-use and land cover classifications are typically created using automated methods to analyze modern, spatially explicit color aerial imagery. However, creating classifications from black and white historical aerial imagery presents a number of challenges that require a combination of more traditional, manual techniques and approaches. A georectified mosaic of 93 aerial images was digitized in ArcGIS to create a land-use/land cover classification. The analyzed area covered 585 km2 (226 mi2) including all of Baltimore City, and an area immediately adjacent to the city known at the time as the Metropolitan District of Baltimore County. A combination of 8 land-use and land cover classes were used: Agriculture, Barren, Built (Other), Forest, Grass/Shrubland, Industrial, Residential, and Water. This geospatial data set captures a moment of dynamic expansion in the city, just prior to the Great Depression and can be used to examine relationships between property ownership and forest patch dynamics across time. These insights may help inform future environmental planning, conservation, management, and stewardship goals for Baltimore City forest patches, and other cities throughout the region.
Georectified Mosaic of Aerial Images of Baltimore City in 1953
Landscape analyses are typically done using spatially explicit color aerial imagery. However, working with non-spatial black and white historical aerial photographs presents several challenges that require a combination of techniques and approaches. We analyzed 113 aerial images covering approx. 700 km2 (270 mi2) including all of Baltimore City, and a portion of Baltimore County surrounding the City. The images were taken between August 23rd 1952 and February 14th 1953. High-resolution scans were georeferenced and georectified against modern satellite imagery of the area and then combined to create a single raster mosaic. This process converted the images from a disparate set of photographs into a spatially explicit GIS data set that can be used to observe changes in land patches over time—and ultimately integrated with other long-term social, economic, and ecological data.
Spiders in a Desert City: What the Behavior and Microclimate of Western Black Widows Can Teach Us About the Impacts of Urbanization
With the planet rapidly urbanizing, understanding the ecological effects of urbanization is a grand challenge for modern biology. For example, increased city temperatures known as the urban heat island effect, disproportionately impact nocturnal taxa and this consideration is widely overlooked. Slight shifts in the thermal microclimate have a cascade of ramifications that directly impact species density and distribution. Animal behavior is a trait that may explain why some species thrive after urbanization when others go locally extinct. In this study we followed 22 adult females of the western black widow, Latrodectus hesperus, from both urban and undisturbed Sonoran Desert habitats. We began looking for differences between urban and desert spiders under field conditions: boldness, voracity, web size and body condition. Both urban and desert spiders were then brought to the laboratory to see how their behavior changed. We found no behavioral differences between urban and desert spiders in the field or the laboratory. We did find that spider behavior differed between the field and the laboratory. Specifically, boldness in the laboratory was significantly lower compared to the field. Voracity was more repeatable in the laboratory versus the field, and boldness was strongly positively correlated with voracity in the laboratory, but not in the field. These behavioral shifts from the field to the laboratory favor the conclusion that black widow behavior is highly plastic and context dependent. Lastly, we monitored web temperature of black widow microhabitat continuously for an entire year using thermochron data loggers. We found microhabitat temperatures differences between urban and desert sites were greatest at night and absent during the daytime. We uncovered a seasonal effect with the highest magnitude temperature difference occurring during the springtime. Additionally, behavior was significantly correlated with field temperatures; the boldest spiders come from the
Bikerack datasets of the city of Trento
<p>This data package contains:</p> <ol> <li>Three source datasets with bike racks in Trento's ZTL <ol> <li>trento.nt: collected by employees of the municipality of Trento</li> <li>osm.nt: from Open Street Maps, via the Linked Geodata intiative</li> <li>qrowd.nt: collected with crowdworkers using the QROWD developed <a href="https://zenodo.org/record/3540843#.XfON4HH7Q5k">Virtual City Explorer</a>. This dataset in FiWare data model has a <a href="https://zenodo.org/record/3560089#.XfOO2nH7Q5k">separate record</a>. It is added here to facilitate reproducibility.</li> </ol> </li> <li>fused.nt: Fused dataset, including provenance information. Consult the<a href="https://zenodo.org/record/3404796#.XfONo3H7Q5k"> interlinking framework</a> developed in QROWD for more information.</li> </ol>
Demographic, economic, geospatial data for municipalities of the Central Federal District in Russia (excluding the city of Moscow and the Moscow oblast) in 2010-2016
<p>The database contains demographic, economic, geospatial data for 452 municipalities of the 16 administrative units of the Central Federal District in Russia (excluding the city of Moscow and the Moscow oblast) for 2010-2016.</p> <p>The sources of data are the municipal-level statistics of Rosstat, Google Maps data and calculated indicators. The statistical data were arranged by the year, the data on municipalities for which there were administrative and territorial transformations for the period under study were excluded (in some cases, the data were provided in accordance with the administrative-territorial demarcation as of 2016).</p> <p>Municipalities' websites were used to fill the lack of population information in individual municipalities for some years.</p> <p>Calculated variables were made to estimate a number of indicators per capita, to introduce additional demographic indicators (e.g. migration inflow rate), to bring price economic indicators to base year prices (2010). For example, indicators of income of the local budget, volumes of investments in fixed assets (excluding budgetary funds), level of wages are modified to a comparable form (to 2010 prices).</p> <p>The distances on roads in different units of measurement from the geographical center of municipalities to the center of the capital of the region are calculated using the Google Maps database.</p> <p>Data mapping was performed using ArcGIS software.</p> <p>The data set consists of</p> <p>1) Municipalities_CFD_Russia_2010_2016_ENG.xlsx - The database of demographic, economic, geospatial data for 452 municipalities of the 16 administrative units of the Central Federal District in Russia (excluding the city of Moscow and the Moscow oblast) for 2010-2016,</p> <p>2) MUNICIPALITIES_CFD_RUSSIA_SHAPE.rar - The shape-files for maps construction,</p> <p>3) Fig.1. Municipalities ENG.jpg - The map of studied administrative units and municipalities of the Central Federal District in Russia .</p>
Hubei STEC Data through CORS stations for DOY 059 and 061 of the year 2018 which used in (Using Real GNSS Data for Ionospheric Disturbance Remote Sensing Associated with Strong Thunderstorm over Wuhan City, manuscript submitted to Earth and Space Science Journal AGU)
<p>Manuscript submitted to Earth and Space Science AGU entitled with <br> (Using Real GNSS Data for Ionospheric Disturbance Remote Sensing Associated with Strong Thunderstorm over Wuhan City)<br> by: Mohamed Freeshah, Xiaohong Zhang, Xiaodong Ren, Jun Chen, and Zhibo Zhao</p> <p>The STEC data inside two compressed folders named as stec059 and stec061, respectively.<br> The STEC file name has the CORS station name for the first forth letters and next three numbers epresent the Day of the year.<br> For example:<br> ES010590.18STEC<br> ES01 is the station name<br> 059 is the day of year (DOY), 2018</p>
Vegetation cover fractions for the City of Berlin dervided from Landsat 5, Landsat 7 and Landsat 8 data between 1988 and 2018
<p>Fractional vegetation cover dataset used in the study "Green growth? On the relation between population density, land use and vegetation cover fractions in a city using a 30-years Landsat time series".</p> <p>Landsat satellite imagery (Landsat-5 TM (Thematic Mapper), Landsat-7 ETM+ (Enhanced Thematic Mapper) and Landsat-8 OLI (Operational Land Imager)) was acquired for seven years between 1988 and 2018. Imagery was pre-processed and a regression-based unmixing approach was performed in order to generate fraction maps of vegetated and non-vegetated surfaces. For complete method description please see:</p> <p>Wellmann, T., Schug, F., Haase, D., Pfulgmacher, D., van der Linden, S. (2020). Green growth? On the relation between population density, land use and vegetation cover fractions in a city using a 30-years Landsat time series. <em>Landscape and Urban Planning</em></p>
List of cities
<p>This dataset provides a list of all cities, towns, and villages in the world, according to the data extracted from the OpenStreetMap. Along with the city name, the location, country and continent are also provided. The data come in a single-file format, with fields separated by tabs.</p> <p> </p> <p>Please refer to README.md for further information.</p>
Socioeconomic disparities in subway use and COVID-19 outcomes in New York City
<p>Using data from New York City, we found that there was an estimated 28-day lag between the onset of reduced subway use and the end of the exponential growth period of SARS-CoV-2 within New York City boroughs. We also conducted a cross-sectional analysis of the associations between human mobility (i.e., subway ridership), sociodemographic factors, and COVID-19 incidence as of April 26, 2020. Areas with lower median income, a greater percentage of individuals who identify as non-white and/or Hispanic/Latino, a greater percentage of essential workers, and a greater percentage of healthcare essential workers had greater mobility during the pandemic. When adjusted for the percent of essential workers, these associations do not remain, suggesting essential work drives human movement in these areas. Increased mobility and all sociodemographic variables (except percent older than 75 years old and percent of healthcare essential workers) was associated with a higher rate of COVID-19 cases per 100k, when adjusted for testing effort. Our study demonstrates that the most socially disadvantaged are not only at an increased risk for COVID-19 infection, but lack the privilege to fully engage in social distancing interventions.</p>
Lappeenranta City Bus Cycle
<p><strong>Report of Lappeenranta city bus cycle:</strong></p> <p><strong>Lappeenranta Route 1</strong></p> <p><strong>Address of Description document:</strong></p> <p>http://www.doria.fi/bitstream/handle/10024/93685/NBNfi-fe201311117322.pdf?sequence=3</p>
Air pollution in a tropical city: the relationship between wind direction and lichen bio-indicators in San José, Costa Rica
<p>Lichens are good bio-indicators of air pollution, but in most tropical countries there are few studies on the subject; however, in the city of San José, Costa Rica, the relationship between air pollution and lichens has been studied for decades. In this article we evaluate the hypothesis that air pollution is lower where the wind enters the urban area (Northeast) and higher where it exits San José (Southwest). We identified the urban parks with a minimum area of approximately 5 000m² and randomly selected a sample of 40 parks located along the passage of wind through the city. To measure lichen coverage, we applied a previously validated 10 x 20cm template with 50 random points to five trees per park (1.5m above ground, to the side with most lichens). Our results (years 2008 and 2009) fully agree with the generally accepted view that lichens reflect air pollution carried by circulating air masses. The practical implication is that the air enters the city relatively clean by the semi-rural and economically middle class area of Coronado, and leaves through the developed neighborhoods of Escazú and Santa Ana with a significant amount of pollutants. In the dry season, the live lichen coverage of this tropical city was lower than in the May to December rainy season, a pattern that contrasts with temperate habitats; but regardless of the season, pollution follows the pattern of wind movement through the city</p>
Air pollution in a tropical city: the relationship between wind direction and lichen bioindicators in San Jose, Costa Rica
<p>Lichens are good bio-indicators of air pollution, but in most tropical countries there are few studies on the subject; however, in the city of San José, Costa Rica, the relationship between air pollution and lichens has been studied for decades. In this article we evaluate the hypothesis that air pollution is lower where the wind enters the urban area (Northeast) and higher where it exits San José (Southwest). We identified the urban parks with a minimum area of approximately 5 000m² and randomly selected a sample of 40 parks located along the passage of wind through the city. To measure lichen coverage, we applied a previously validated 10 x 20cm template with 50 random points to five trees per park (1.5m above ground, to the side with most lichens). Our results (years 2008 and 2009) fully agree with the generally accepted view that lichens reflect air pollution carried by circulating air masses. The practical implication is that the air enters the city relatively clean by the semi-rural and economically middle class area of Coronado, and leaves through the developed neighborhoods of Escazú and Santa Ana with a significant amount of pollutants. In the dry season, the live lichen coverage of this tropical city was lower than in the May to December rainy season, a pattern that contrasts with temperate habitats; but regardless of the season, pollution follows the pattern of wind movement through the city</p>
Air pollution in a tropical city: the relationship between wind direction and lichen bio-indicators in San José, Costa Rica
<p>Lichens are good bio-indicators of air pollution, but in most tropical countries there are few studies on the subject; however, in the city of San José, Costa Rica, the relationship between air pollution and lichens has been studied for decades. In this article we evaluate the hypothesis that air pollution is lower where the wind enters the urban area (Northeast) and higher where it exits San José (Southwest). We identified the urban parks with a minimum area of approximately 5 000m² and randomly selected a sample of 40 parks located along the passage of wind through the city. To measure lichen coverage, we applied a previously validated 10 x 20cm template with 50 random points to five trees per park (1.5m above ground, to the side with most lichens). Our results (years 2008 and 2009) fully agree with the generally accepted view that lichens reflect air pollution carried by circulating air masses. The practical implication is that the air enters the city relatively clean by the semi-rural and economically middle class area of Coronado, and leaves through the developed neighborhoods of Escazú and Santa Ana with a significant amount of pollutants. In the dry season, the live lichen coverage of this tropical city was lower than in the May to December rainy season, a pattern that contrasts with temperate habitats; but regardless of the season, pollution follows the pattern of wind movement through the city</p>
Urban Redevelopment by Census Tract in New York City (2000 -2020)
<p>The annual urban redevelopment map for NYC was produced using the classification method proposed in this experiment to highlight the spatial and temporal distribution of urban reconstructions. The time-series gentrification risk maps illustrate areas that have faced gentrification risk since 2000. The data was aggregated to the Census tract level for displaying a visually friendly result. The raw building-level data is also provided.</p>
Data repository for Global wood harvest is sufficient for climate-friendly transitions to timber cities
<p><strong>Supplementary Information S2</strong></p> <p><strong>Global wood harvest is sufficient for climate-friendly transitions to timber cities | <a href="https://doi.org/10.1038/s41893-025-01605-w" target="_blank" rel="noopener">Nature Sustainability</a></strong></p> <p>Alperen Yayla <sup>1,a</sup>; Adam R. Mason <sup>1,b</sup>; Junyang Wang <sup>1,2,c</sup>; Stijn van Ewijk <sup>3,d</sup>; Rupert J. Myers <sup>1,e,*</sup></p> <p><sup>1</sup> Department of Civil and Environmental Engineering, Imperial College London, London SW7 2AZ, United Kingdom</p> <p><sup>2</sup> Department of Mathematics, Imperial College London, London, SW7 2AZ, United Kingdom</p> <p><sup>3</sup> Department of Civil, Environmental & Geomatic Engineering, University College London, London, WC1E 6BT, United Kingdom</p> <p>* Corresponding author</p> <p><sup>a </sup><a href="mailto:a.yayla22@imperial.ac.uk">a.yayla22@imperial.ac.uk</a>, <sup>b</sup> <a href="mailto:a.mason19@imperial.ac.uk">a.mason19@imperial.ac.uk</a>, <sup>c</sup> <a href="mailto:junyang.wang21@imperial.ac.uk">junyang.wang21@imperial.ac.uk</a>, <sup>d</sup> <a href="mailto:s.vanewijk@ucl.ac.uk">s.vanewijk@ucl.ac.uk</a>, <sup>e</sup> <a href="mailto:r.myers@imperial.ac.uk">r.myers@imperial.ac.uk</a>.</p>
UCLARIS – urban thermo-hygrometric gridded dataset for Iasi city, Romania
<h4>This dataset contains 6 daily gridded climate variables derived from the measurements made during 10 years at 11 screen-level monitoring points for air temperature (T) and relative humidity (RH), distributed over the city of Iași, a medium-sized city in north-eastern Romania. Additionally, T and RH data from 3 air quality monitoring points of the Environmental Protection Agency (EPA) [1], and from the single National Meteorological Administration (NMA) official weather station of Iasi were used. The monitoring points cover the entire urban area of Iasi, sampling the most important local climate zones inside the city. The data were firstly quality controlled and homogenized using the CLIMATOL package [2], and afterwards the spatial distribution was obtained through residual kriging method with the digital elevation model (DEM) as predictor [3]. </h4><p><strong>Climate variables: </strong>Maximum air temperature – <strong>Tmax</strong>; Mean air temperature – <strong>Tavg</strong>; Minimum air temperature - <strong>Tmin</strong>; Maximum relative humidity - <strong>RHmax</strong>; Mean relative humidity - <strong>RHavg</strong>; Minimum relative humidity – <strong>RHmin</strong>.<strong> </strong></p><p><strong>Spatial extent:</strong> from 27.44167 to 27.84167 °E and 47.05833 to 47.25833 °N</p><p><strong>Temporal resolution</strong>: daily </p><p><strong>Temporal coverage</strong>: 2013/01/01 – 2022/12/31</p><p><strong>Spatial resolution</strong>: 0.008°</p><p><strong>File format: </strong>netCDF, CF-1.4-compliant format using netCDF4 compression</p><p><strong>Coordinate system: </strong>WGS 84 (EPSG: 4326)</p><p><strong>Other Institutions: </strong>National Meteorological Administration of Romania, Environmental Protection Agency of Romania</p><p><strong>Acknowledgement:</strong> This work was supported by a grant of the Ministry of Research, Innovation and Digitization, CNCS - UEFISCDI, project number PN-III-P1-1.1-TE-2021-0882, within PNCDI III.</p><p><strong>References:</strong></p><p>[1] https://www.calitateaer.ro/</p><p>[2] Guijaro, J., 2023. Package "Climatol", CRAN, https://climatol.eu/</p><p>[3] European Digital Elevation Model (https://www.eea.europa.eu/en/datahub/datahubitem-view/d08852bc-7b5f-4835-a776-08362e2fbf4b)</p><p> </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.