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3,709 results for “urbanization.”
Dataset from 'Influence of anthropogenic emissions on the composition of highly oxygenated organic molecules in Helsinki: a street canyon and urban background station comparison'
<p>This dataset supplements the following manuscript:</p> <p>Okuljar, M., Garmash, O., Olin, M., Kalliokoski, J., Timonen, H., Niemi, J. V., Paasonen, P., Kontkanen, J., Zhang, Y., Hellén, H., Kuuluvainen, H., Aurela, M., Manninen, H. E., Sipilä, M., Rönkkö, T., Petäjä, T., Kulmala, M., Dal Maso, M., and Ehn, M.: Influence of anthropogenic emissions on the composition of highly oxygenated organic molecules in Helsinki: a street canyon and urban background station comparison, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-524, 2023.</p>
Fig. 1 in A new record of the Blotched snake Elaphe sauromates Pallas, 1811 (Serpentes: Colubridae) from an urbanized habitat in Bulgaria
Fig. 1. Photos of the dead specimen of E. sauromates, the exact locality of the finding and the position of the locality on the territory of Bulgaria (in UTM-grid 10×10 km).
Urban resilience to extreme natural events and climate change: from Brasil to Europe
<p>Originally published at this link: <a href="https://www.iai.it/en/eventi/urban-resilience-extreme-natural-events-and-climate-change-brasil-europe">Urban resilience to extreme natural events and climate change: from Brasil to Europe | IAI Istituto Affari Internazionali</a></p> <p>The workshop will discuss best practices and innovative methodologies for urban resilience building to extreme natural events and climate change. The focus will be on participatory processes and citizen engagement practices, looking in particular at their relevance for physically and socially vulnerable urban areas. The event is designed as a knowledge-sharing opportunity for cities and will include the participation of researchers and experts working on the field with municipalities.</p> <p>The project “Waterproofing Data: Engaging Stakeholders in Sustainable Flood Risk Governance for Urban Resilience” will be discussed as a successful case study, focusing on data co-production practices and the role of digital tools. The project has been implemented in several Brazilian cities to build resilience to flooding in vulnerable communities by engaging citizens in data generation processes critical to design effective early-warning systems and strategies to reduce the risk of flood-related events. Professor João Porto De Albuquerque (University of Glasgow, UK) and Professor Maria Alexandra Viegas da Cunha (Fundação Getulio Vargas, São Paolo, Brasil) will illustrate the results of Waterproofing Data project and its innovative methodology, discussing opportunities for its implementation in cities both in Brasil and Europe and application to a broad spectrum of extreme events, from flooding to heatwaves and drought.</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>
TranEpiSim: Urban Transportation Epidemic Simulator
<p>This data set is to replicate a case study of disease transmission through micromobility systems and a large synthetic contact network in Cook County.</p>
Bees need larger brains to thrive in urban environments
<p>The rapid conversion of natural habitats to anthropogenic landscapes is threatening insect pollinators worldwide, raising concern on the negative consequences for their fundamental role as plant pollinators. However, not all pollinators are negatively affected by habitat conversion, as certain species find in anthropogenic landscapes appropriate resources to persist and proliferate. The reason why some species thrive in anthropogenic environments while most find them inhospitable remains poorly understood. The cognitive buffer hypothesis, widely supported in vertebrates but untested in insects, offers a potential explanation. This theory suggests that species with larger brains have enhanced behavioural plasticity, enabling them to confront and adapt to novel challenges. To investigate this hypothesis in insects, we measured brains for 89 bee species, and evaluated the association between brain size and habitat preferences. Our analyses revealed that bee species that prefer urban habitats had larger brains relative to their body size than those who prefer forested or agricultural habitats. Additionally, urban bees exhibited larger body sizes and, consequently, larger absolute brain sizes. Our results provide the first empirical support for the cognitive buffer hypothesis in invertebrates, suggesting that a large brain in bees could confer behavioural advantages to tolerate urban environments.</p>
FIGURE 7 in Ptomaphagus thebeatles n. sp., a previously unrecognized beetle from Europe, with remarks on urban taxonomy and recent range expansion (Coleoptera: Leiodidae)
FIGURE 7 Enlarged section of the Maximum-likelihood phylogenetic reconstruction of fig. 6, with all COI sequences shown for the sericatus group, i.e., P. thebeatles, P. medius, and P. sericatus. Colours match those in fig. 5.
FIGURE 4 in Ptomaphagus thebeatles n. sp., a previously unrecognized beetle from Europe, with remarks on urban taxonomy and recent range expansion (Coleoptera: Leiodidae)
FIGURE 4 Dorsal (perpendicular to the surface of the aedeagus between the orifice and the apex) and lateral views of the apices of the aedeagi of members of the Ptomaphagus sericatus complex. a: P. medius from Oegstgeest, the Netherlands, dorsal (a) and lateral (b) views. b: P. sericatus from Les Arnoulats, France, dorsal (c) and lateral (b) views. c: P. thebeatles from Amsterdam, the Netherlands, dorsal (e) and lateral (f) views. (Images derived from topotypical material, not from the holotype itself.).
FIGURE 2 in Ptomaphagus thebeatles n. sp., a previously unrecognized beetle from Europe, with remarks on urban taxonomy and recent range expansion (Coleoptera: Leiodidae)
FIGURE 2 Neotype of P. sericatus from Kiev, Ukraine: a, habitus; b, aedeagus, dorsal view (angle of view somewhat more rostral than in fig. 4); c, aedeagus, lateral view; d, genital segment; e, labels.
FIGURE 3 Antennomeres 1-7 in Ptomaphagus thebeatles n. sp., a previously unrecognized beetle from Europe, with remarks on urban taxonomy and recent range expansion (Coleoptera: Leiodidae)
FIGURE 3 Antennomeres 1-7 for members of the Ptomaphagus sericatus complex. a: P. sericatus, female (a) and male (b) from Les Arnoulats, France. b: P. thebeatles, female (c) from Middelburg, the Netherlands, and male (d) from Schiedam, the Netherlands. After Schilthuizen, 1989. c: P. medius, female (e) from Nieuw & St. Joostland, the Netherlands, and male (f) from Oostvoorne, the Netherlands. After Schilthuizen, 1989.
FIGURE 5 in Ptomaphagus thebeatles n. sp., a previously unrecognized beetle from Europe, with remarks on urban taxonomy and recent range expansion (Coleoptera: Leiodidae)
FIGURE 5 Distribution map of the specimens studied for this paper (the extralimital specimens of P. medius in Canada have not been included). The white arrows point at records that we interpret as recent range expansions.
FIGURE 6 in Ptomaphagus thebeatles n. sp., a previously unrecognized beetle from Europe, with remarks on urban taxonomy and recent range expansion (Coleoptera: Leiodidae)
FIGURE 6 Maximum-likelihood phylogenetic reconstruction, based on COI sequences, of P. thebeatles, P. medius, P. sericatus, and other members of the subgenus Ptomaphagus s. str., rooted with P. (Adelops) hatchi. Multiple sequences per species have been collapsed. Support values are given only for branches with>80% bootstrap percentage.
Data from: Urbanization alters the spatiotemporal dynamics of plant-pollinator networks in a tropical megacity
<p><span>Urbanization is a major driver of biodiversity change but how it interacts with spatial and temporal gradients to influence the dynamics of plant-pollinator networks is poorly understood, especially in tropical urbanization hotspots. Here, we analyzed the drivers of environmental, spatial, and temporal turnover of plant-pollinator interactions (interaction β-diversity) along an urbanization gradient in Bengaluru, a South Indian megacity. The compositional turnover of plant-pollinator interactions differed more between seasons and with local urbanization intensity than with spatial distance, suggesting that seasonality and environmental filtering were more important than dispersal limitation for explaining plant-pollinator interaction β-diversity. Furthermore, urbanization amplified the seasonal dynamics of plant-pollinator interactions, with stronger temporal turnover in urban compared to rural sites, driven by greater turnover of native non-crop plant species (not managed by people). Our study demonstrates that environmental, spatial, and temporal gradients interact to shape the dynamics of plant-pollinator networks and urbanization can strongly amplify these dynamics. </span></p>
A dynamic von Mises-based model to evaluate the impact of urbanization and climate change on flood timing in Yangtze and Huaihe River Basins, China
<p>The daily streamflow data extracted from 8 selected stations from the Huaihe and Yangtze River Basins, China.</p>
Urbanization impacts short- but not long-distance natal dispersal in a common orb web spider
<p>Urban environments represent a theatre for life history evolution. Species able to survive in cities can adapt to the local and often divergent environmental conditions compared to rural or (semi-)natural environments. Dispersal determines establishment, gene flow, and thus the potential for local adaptation. Since habitats in urban environments are highly fragmented, and show substantial turnover, contrasting adaptive effects on dispersal are expected. Fragmentation selects against dispersal while patch turn-over is expected to promote the evolution of dispersal. We here show both processes to act in concert when different scales are considered. Dispersal behavior of juvenile, lab-reared garden spiders from three mid-sized European cities were tested under standardized conditions. While long-distance dispersal showed to be overall rare, short-distance dispersal strategies increased with urbanization at small scales, but declined when urbanization was quantified at large scales. We discuss the putative drivers behind these differences in natal dispersal and highlight its importance for urban evolution and ecology.</p>
Urban Heat: Forward-Looking Climate Modeling for Nis, Serbia
<p>We produced actionable data on heat stress in cities to inform analysis and client dialogue on the part of World Bank teams. We applied an urban-scale climate modeling framework to generate datasets describing modeled heat stress exposure for <strong>present-day and future conditions</strong> under selected climate scenarios (<strong>present, SSP1-1.9, SSP3-7.0</strong>). The study domain focuses on Nis, Serbia.</p> <p>More details about the dataset: </p> <ul> <li>The dataset includes calculations for each indicator across three scenarios (<strong>present, SSP1-1.9, SSP3-7.0</strong>) and three twenty-year periods (<strong>2001-2020, 2021-2040, and 2041-2060</strong>). The present period refers to 2001-2020, while the other two periods correspond to the two SSP scenarios.</li> <li>All indicators are available in both <strong>NetCDF</strong> and <strong>GeoTiff</strong> formats.</li> <li>The indicators are calculated at a resolution of <strong>150 m</strong>, consistent with the UrbClim and WBGT simulations. Additionally, downscaled versions of the indicators are provided at a resolution of <strong>30 m</strong>.</li> <li>The UrbClim and WBGT simulations, as well as the postprocessing, are conducted using the regional projection <strong>E</strong><strong>PSG 32634</strong>. The NetCDF and GeoTiff data also adopt this projection. Furthermore, a GeoTiff data file with <strong>EPSG 4326</strong> projection is included.</li> <li>All indicators are calculated as <strong>yearly averages</strong>. Some indicators also have additional calculations for <strong>seasonal averages</strong>, including Spring (MAM), Summer (JJA), Autumn (SON), and Winter (DJF).</li> <li>Images for <strong>quick viewing</strong> <strong>in</strong> <strong>png</strong> format visualizing the results for each indicator. Present denotes the period 2001-2020; 2030 denotes the period 2021-2040; & 2050 denotes the period 2041-2060.</li> <li>The NetCDF and GeoTiff data can be found in the data.zip; The PNG files for quick viewing can be found in quickview.zip; more information about the dataset, including the methodology, all available data list, contact information, etc. can be found in the Technical_Annex_Nis.docx</li> </ul>
Terrestrial laser scanning data of urban trees in Milton Keynes, UK: individual trees and Treegraph model outputs
<p><strong>TLS_point_clouds:</strong></p> <p>- Data collection: Terrestrial laser scanning data acquired in leaf-off condition in April 2021.</p> <p>- Scanning instrument: We used a RIEGL VZ-400 with a wavelength of 1550 nm, 0.35 mrad beam divergence and 0.04˚ angular resolution.</p> <p>- Locations: The data were collected from three urban sites in Milton Keynes, UK: Avebury Blvd (Ave), Dansteed Way (Dan), and Overgate (Ove).</p> <p>- File information: Each file is an individual tree point cloud in Polygon File Format (.ply), which can be viewed in software such as CloudCompare.</p> <p> </p> <p><strong>Treegraph_outputs:</strong></p> <p>- Description: Model outputs from <em>Treegraph</em> for individual trees.</p> <p>- File naming convention: [TreeID]-[downsample_voxel_length]-[tip_diameter_if_known].*</p> <ul> <li>*.centres.ply: Skeleton nodes of individual trees.</li> <li>*.mesh.ply: Cylinder model of individual trees.</li> <li>*.json: Geometrical and topological attributes at varying scales, from internode and branch to the whole tree.</li> <li>*.txt: Summary of input parameters, logs of intermediate steps, and a statistical overview of whole-tree structural attributes.</li> </ul>
Sentinel-2 urban green training dataset
<p>Training dataset for urban green land cover and land use detection for Sentinel-2 satellite images. Samples are pixel-wise labelled scenes over the city of Prague, including bigger parks and smaller vegetation patches within high-density urban areas.</p> <p> </p> <p>Contains four classes:</p> <p>* 0: Non-vegetated pixels<br> * 1: Low recreational vegetation<br> * 2: High recreational vegetation<br> * 3: Non-recreational vegetation</p>
Inequalities in noise will affect urban wildlife
<p>Understanding the extent to which systemic biases influence local ecological communities is essential for developing just and equitable environmental practices. With over 270 million people across the United States living in urban areas, understanding the socio-ecological consequences of racially-targeted zoning, such as redlining, provides crucial information for urban planning. There is a growing body of literature documenting the relationships between redlining and disparities in the distribution of environmental harms and goods, including inequities in green space cover and pollutant exposure. Yet, it remains unknown whether noise pollution is also inequitably distributed, and whether inequitable noise is an important driver of ecological change in urban environments. We conducted 1) a spatial analysis of urban noise to determine the extent to which noise overlaps with the distribution of redlining categories and 2) a systematic literature review to summarize the effects of noise on wildlife in urban landscapes. We found strong evidence that noise is inequitably distributed in cities across the United States, and that inequitable noise may drive complex biological responses across diverse urban wildlife. These findings lay a foundation for future research that advances acoustic and urban ecology by centering equity and challenging systems of oppression.</p>
Impact of urban heat islands on human mortality risk in European cities
<p>These data contain estimates of temperature-related human mortality, as well as the associated economic assessments, related to urban heat islands for 85 European cities over the years 2015-2017. They are based on temperature-mortality relationships from Masselot et al. 2023 and 100m resolution UrbClim urban climate model simulations of near-surface air temperature (De Ridder et al. 2015, Hooyberghs et al. 2019), re-gridded to 500m resolution.</p> <p> </p> <p>Details of the methodology are provided in the associated paper:</p> <p>Huang, W.T.K. et al. Economic valuation of temperature-related mortality attributed to urban heat islands in European cities. <em>Nat Commun</em> <strong>14</strong>, 7438 (2023). <a href="https://doi.org/10.1038/s41467-023-43135-z">https://doi.org/10.1038/s41467-023-43135-z</a></p> <p>And associated core analysis code is available on GitHub at https://github.com/hkatty/Paper_UHI_mortality_Europe (doi:10.5281/zenodo.8429209). </p> <p> </p> <p> </p> <p>The content of the files are as follows:</p> <p><strong>spatial_timeseries</strong> zip files: These contain the most unprocessed attributable fraction estimates, with the exposure-response relationships applied to the modelled temperature, prior to any further processing.</p> <p><strong>uhi</strong> csv files: These are tables of the average mortality and years of life lost, as well as associated economic assessment, related to urban heat islands for each city. They are identical to Tables S4-S11 in the supplementary materials of the above paper.</p> <p><strong>spatial_maps_time_averaged_diff_from_rural.zip</strong>: Spatial maps showing the difference from the rural average for each day and grid box, then averaged over time. </p> <p><strong>data_urbanruralavg_timeseries.nc</strong>: Time series of urban and rural averages, as well as the difference between the two (i.e. the urban heat island effect).</p> <p><strong>avg_diff_from_rural_urbanrural.nc</strong>: The above timeseries file temporally aggregated.</p> <p><strong>simulated_urbanruraldiff_timeseries.zip</strong>: Time series of urban-rural difference in attributable fraction for 1000-member ensembles representing uncertainties in the exposure-response relationships as captured by Monte Carlo simulations.</p> <p><strong>simulated_urbanruraldiff_averaged.zip</strong>: The above simulated timeseries temporally aggregated.</p> <p> </p> <p><strong>Some variables explained:</strong></p> <p>fAF = forward attributable fraction (i.e. fraction of total mortality associated with a single day's temperature, cumulative over lag time)</p> <p>fAD = forward attributable deaths (i.e. equivalent to fAF but for number of deaths)</p> <p>tas = temperature</p> <p>heat_ex = average over heat extreme days (i.e. the warmest 2% days in 2015-2017 for the city)</p> <p>cold_ex = average over cold extreme days (i.e. as heat_ex but for the coldest 2% days)</p> <p>heat = average over days warmer than the age-dependent optimal temperature</p> <p>cold = average over days colder than the age-dependent optimal temperature</p> <p>heat_count = number of days warmer than the optimal for the age group, note that for combined 2085.1 and 2085.5 age groups, days are counted if it is considered warm for at least one age group (therefore heat_count + cold_count ≠ total days over period)</p> <p>cold_count = number of days colder than the optimal for the age group</p> <p>rural = rural average</p> <p>imd = land imperviousness</p> <p>popden = population density</p> <p>age groups: </p> <p>20 = 20 to 44<br>45 = 45 to 64<br>65 = 65 to 74<br>75 = 75 to 84<br>85 = 85 and over<br>2085.1 = all above age groups combined, weighted by the local population age structure<br>2085.5 = all above age groups combined, weighted by the 2013 European standard population age structure</p> <p> </p> <p>References:</p> <p>De Ridder, K., Lauwaet, D., and Maiheu, B., (2015): UrbClim – A fast urban boundary layer climate model. Urban Climate, 12, 21–48. <a href="https://doi.org/10.1016/J.UCLIM.2015.01.001">https://doi.org/10.1016/J.UCLIM.2015.01.001</a>.</p> <p>Hooyberghs, H., Berckmans, J., Lauwaet, D., Lefebre, F., and De Ridder, K., (2019): Climate variables for cities in Europe from 2008 to 2017. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). <a href="https://doi.org/10.24381/cds.c6459d3a">https://doi.org/10.24381/cds.c6459d3a</a>.</p> <p>Masselot et al. (2023): Excess mortality attributed to heat and cold: a health impact assessment study in 854 cities in Europe, The Lancet Planetary Health, <a href="https://doi.org/10.1016/S2542-5196(23)00023-2">https://doi.org/10.1016/S2542-5196(23)00023-2</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.