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3,709 results for “urbanization.”
FIGURE 5 in Effects of urbanization and environmental heterogeneity on fish assemblages in small streams
FIGURE 5 | Relationship between the local contribution to beta diversity (LCBD) and the proportion of impervious surfaces in urban streams in Cuiabá. The line indicates fitted values.
FIGURE 4 in Effects of urbanization and environmental heterogeneity on fish assemblages in small streams
FIGURE 4 | Relationship between rarefied species richness (Srarefied) and the proportion of impervious surfaces in urban streams in Cuiabá. The line indicates fitted values.
FIGURE 3 in Effects of urbanization and environmental heterogeneity on fish assemblages in small streams
FIGURE 3 | Spatial variation in rarefied species richness (Srarefied; A) and the local contribution to beta diversity (LCBD; B) in urban streams in Cuiabá (Mato Grosso, Brazil). The circle sizes in the legend indicate the minimum, mean, and maximum values of Srarefied and the LCBD. The arrow in "A" indicates flow direction.
FIGURE 2 in Effects of urbanization and environmental heterogeneity on fish assemblages in small streams
FIGURE 2 | Principal Coordinate Analysis (PCoA) of local environmental variables of streams from the urban area of Cuiabá, Mato Grosso, midwestern Brazil. The symbol sizes are proportional to the environmental heterogeneity (EH); PlaMat: plant matter; GraRoc: gravel and rocks; CanCov: canopy cover.
FIGURE 1 in Effects of urbanization and environmental heterogeneity on fish assemblages in small streams
FIGURE 1 | Location of the 45 streams sampled for fish in urban area within Cuiabá, Mato Grosso state, midwestern Brazil. The gray area indicates impervious surfaces. The color gradient indicates the proportion of impervious surfaces in a 500 m buffer around each stream; the closer to one the value is, the higher the quantity of urban infrastructure around the stream. The continuous lines indicate the hydrography. The dashed lines indicate the political boundary of Cuiabá with the neighboring municipality of Várzea Grande.
Figure 1 in Seasonality and bait type driving the diversity of dung beetle (Scarabaeidae: Scarabaeinae) communities in urban remnants of the Atlantic Forest
Figure 1. Partial map of the state of Pernambuco, with emphasis on the remnants of the Atlantic Forest (Green) and the urban area (Pink) located on the outskirts of FURB Jaguarana in the municipality of Paulista and APA Aldeia-Beberibe in the municipality of Camaragibe, PE, Brazil.
Figure 1 in Ecological impact and population status of non-native bees in a Brazilian urban environment
Figure 1 Bipartite network and non-native bees sampled in Curitiba. a) Bipartite network, non-native plant and bees colored, b) Anthidium manicatum, female; c) Distributional range of A. manicatum (SpeciesLink); d) Melipona scutellaris worker on Calliandra brevipes; e) Distributional range of M. scutellaris (SpeciesLink), natural records in green.
Figure 3 in Seasonality and bait type driving the diversity of dung beetle (Scarabaeidae: Scarabaeinae) communities in urban remnants of the Atlantic Forest
Figure 3. Canonical Correspondence Analysis (CCA) with group formations related to separation and types of baits used in the collection of dung beetles at FURB Jaguarana (A) and APA Aldeia-Beberibe (B).
Figure 2 in Seasonality and bait type driving the diversity of dung beetle (Scarabaeidae: Scarabaeinae) communities in urban remnants of the Atlantic Forest
Figure 2. Differences between diversity index values (q0, q1, q2) for different types of baits (feces, carrion, millipedes) in the rainy and dry season at FURB Jaguarana (A) and APA Aldeia-Beberibe (B). Meaningfulness: <0.001'***'; <0.01'**'; <0.05'*'.
DIVERSITY OF URBAN REPTILES IN THREE MAJOR AMAZONIAN MUNICIPALITIES: AN ASSESSMENT USING A CITIZEN SCIENCE PLATFORM
<p><span>This dataset describes the distribution of reptiles in three Amazonian cities (Belém, Manaus and Rio Branco) compiled from the citizen science platform Inaturalist. The dataset consist of photographs of reptiles taken in those cities available in the Inaturalist until December 2023. This data supports the scientific manuscript in preparation: Diversity of urban reptiles in three major Amazonian municipalities: an assessment using a citizen science platform. The dataset consist of a excel spreadsheet structured in 15 columns described below:</span></p> <p><span><span>·<span> </span></span></span><span>Seq: a numerical sequence</span></p> <p><span><span>·<span> </span></span></span><span>Inaturalist link: link to each of the photographs on the platform</span></p> <p><span><span>·<span> </span></span></span><span>Photographer: photographer's identity according to the platform</span></p> <p><span><span>·<span> </span></span></span><span>Date: date</span></p> <p><span><span>·<span> </span></span></span><span>City: city</span></p> <p><span><span>·<span> </span></span></span><span>Latitude: geographic coordinate available on the platform</span></p> <p><span><span>·<span> </span></span></span><span>Longitude: geographic coordinate available on the platform</span></p> <p><span><span>·<span> </span></span></span><span>Coordinates precision (m): coordinate precision</span></p> <p><span><span>·<span> </span></span></span><span>Used in spatial analysis: indicates whether that photograph was used (yes) or not (no) in the spatial analyses conducted in the study</span></p> <p><span><span>·<span> </span></span></span><span>Squamate group: identifies the three groups of reptiles studied as lizards, snakes or worm lizard</span></p> <p><span><span>·<span> </span></span></span><span>Species name (Inaturalist): species identification according to the platform</span></p> <p><span><span>·<span> </span></span></span><span>Standard species name (Guedes et al. 2023): standardization of species names following Guedes, T. B., Entiauspe-Neto, O. M., & Costa, H. C. (2023). List of reptiles of Brazil: 2022 update. Herpetologia Brasileira, 12(1), 56-161.</span></p> <p><span><span>·<span> </span></span></span><span># of identifiers: number of platform contributors who suggested the species identity</span></p> <p><span><span>·<span> </span></span></span><span>Environment: indicates whether the photograph was taken in an urban or forest/rural environment</span></p> <p><span><span>·<span> </span></span></span><span>Landscape: indicates whether the animal photographed in an urban environment was inside or in the edge of an urban forest fragment, in an urbanized area less than 300 m from a forest fragment, in an urbanized area more than 300 m from a forest fragment, or outside an urban environment (not applied)</span></p>
Computational results data for the assoziated publication "Network Interdiction Problems in Urban Transportation: Theoretical Insights and Computational Characteristics"
<p>This repository contains two Excel tables with computational results for our paper "Network Interdiction Problems in Urban Transportation: Theoretical Insights and Computational Characteristics". Each Excel table includes multiple worksheets, each representing different scenarios and models evaluated in our study.</p> <p><strong>Worksheets Overview</strong></p> <p>Each Excel table contains the following worksheets:<br>1. <strong>ML</strong>: Results for the "ML" big M values.<br>2. <strong>MH</strong>: Results for the "MH" big M values.<br>3. <strong>MF</strong>: Results for the "MF" big M values.<br>4. <strong>Path</strong>: Results for the path model.<br>5. <strong>FMInstances</strong>: Results from applying our models on the original Fontaine and Minner (2018) instances.</p> <p><strong>Columns Description</strong></p> <p>Each worksheet contains the following columns:</p> <p>- <strong>Name of Instance</strong>: A complex string with the identifier of the used instance. The relevant part is "_RXXX_", where XXX is the random seed used to generate the instance.<br>- <strong>Number users</strong>: The number of users/commodities in the network.<br>- <strong>B:</strong> The budget (always set to infinity in our instances).<br>- <strong>GUROBI_RUNTIME</strong>: The time limit set for the computations.<br>- <strong>Modelkind</strong>: The type of model used. Possible values are:<br> - INDICATOR: Compact model.<br> - FMbenders: Benders model from Fontaine and Minner (2018).<br> - ICM: Benders-like cuts.<br> - PathModel: Path enumeration model.<br>- <strong>BigM computation</strong>: Time required to compute all the big M values used (not included in the time limit).<br>- <strong>runtime</strong>: Runtime of the selected model.<br>- <strong>BBnodes</strong>: Number of nodes in the Branch & Bound tree.<br>- <strong>gap</strong>: Gap reported by Gurobi after reaching the time limit.<br>- <strong>Cuts BLC</strong>: Number of Benders-like cuts included.<br>- <strong>Time BLC</strong>: Time required for separating Benders-like cuts.<br>- <strong>M improve BLC</strong>: Frequency of improvements to a big M when using the improved big M term in Benders-like cuts.<br>- <strong>Mcutoff_AVE</strong>: Average (non-zero) improvement of a big M when using the improved big M term in Benders-like cuts.<br>- <strong>Cuts FMBenders</strong>: Number of Benders cuts generated in the Fontaine and Minner (2018) model.<br>- <strong>Time FMBenders</strong>: Time required to generate the Benders cuts in the Fontaine and Minner (2018) model.<br>- <strong>Runtime path enum</strong>: Time required to enumerate all paths for the path-based model (not included in the time limit).<br>- <strong>Average Num Path</strong>: Average number of paths generated for a single commodity/user. Multiply this value by the number of users to obtain the absolute number of paths generated.</p> <p><strong>Note on FCP</strong></p> <p>All the results found for the instances already had integer flow solutions. Additionally, we conducted experiments where we explicitly forced the solutions to be integer for the Benders-like cuts model. We observed that the runtimes remained the same, with only some natural insignificant hardware-induced fluctuations. Therefore, we omit reporting these results again.</p> <p><br>For further information or questions, please refer to our paper "Network Interdiction Problems in Urban Transportation: Theoretical Insights and Computational Characteristics" or contact the authors.</p>
Figure 3 in Observations on the urban ecology of the Neotropical stingless bee Tetragonisca angustula (Hymenoptera: Apidae: Meliponini)
Figure 3. Daily foraging activity of Tetragonisca angustula (Latreille) in a forested area (Reserva San Sebastián [RSS]) near the city of Medellín, Colombia, and at four study sites within Medellín: Universidad Nacional (UNAL), Universidad de Antioquia (UdeA), Cerro el Volador (CV), and Jardín Botánico 'Joaquín Antonio Uribe' (JBJAU).
Figures 1–2 in Observations on the urban ecology of the Neotropical stingless bee Tetragonisca angustula (Hymenoptera: Apidae: Meliponini)
Figures 1–2. Nest of Tetragonisca angustula (Latreille) in the city of Medellín, Colombia. 1. Unmarked nest. 2. Marked nest with black ring to make the nest more visible to people.
Urban Growth Strategy in Greater Sydney Leads to Unintended Social and Environmental Challenges
<p>A dataset of the manuscript "Urban Growth Strategy in Greater Sydney Leads to Unintended Social and Environmental Challenges", paper published in Nature Cities. The NVivo files contain all variables and causal relationships among the variables supported by quotations coded from the 1968, 2005 and 2018 urban planning reports in Greater Sydney. (1) <em>Sydney region: outline plan, 1970-2000 A.D – a strategy for development</em><em> </em>published by the NSW State Planning Authority<em>; </em>(2)<em> City of Cities. A plan for Sydney's Future</em> published by the NSW Department of Planning<em>; and </em>(3)<em> A Metropolis of Three Cities – connecting people</em> published by Greater Sydney Commission.</p>
Plant responses to urban gradients: extinction, plasticity, adaptation
<p><span>Individual functional traits (LMA – leaf mass per area, plant height and flower length), population performance traits (seed mass and germination rate), as well as species frequency in the plant community, of four herbaceous plant species present in the metropolitan area of Strasbourg: Dactylis glomerata, Medicago lupulina, Plantago lanceolata, Trifolium pratense. Traits were measured <em>in situ</em> at 60 mesophilic herbaceous sites, and <em>ex situ</em> in a common garden experiment in the Botanical Garden of the University of Strasbourg. Environmental data include mowing frequency, soil characteristics (composition and structure), air temperature, air humidity, and surrounding landscape variables: mean building height, population density, NDVI, road density, and distance to the city center. <br></span></p>
Data from multi-sensor devices and reference station to monitoring urban air quality
<p>Data from electrochemical and optical sensors.</p> <h3>Files names</h3> <ul> <li>ECT01, ECT02, ECT06, ECT07 = device name</li> <li>ISSEP = reference station <ul> <li>"c" = calibration data</li> <li>"v" = validation data</li> </ul> </li> </ul> <h3>Variable description</h3> <table> <tbody> <tr> <td><strong>Electrochemical sensor</strong></td> <td><strong>Optical sensor</strong></td> <td><strong>Probe</strong></td> <td><strong>Reference</strong></td> </tr> <tr> <td> <p>AE = auxiliary electrode (mV)</p> <p>WE = working electrode (mV)</p> <p>N = temperature correction </p> <ul> <li>ch0 = CO sensor</li> <li>ch1 = OX sensor</li> <li>ch2 = NO2 sensor</li> <li>ch3 = NO sensor</li> </ul> </td> <td> <p>PM1, PM2.5 and PM10 in µg/m³</p> </td> <td> <p>Prs_mbar = pressure (mbar)</p> <p>Temp = temperature (°C)</p> <p>RH = relative humidity (%)</p> </td> <td> <p>DV30 = wind direction @ 30m (°)</p> <p>HR = relative humidity (%)</p> <p>NO, NO2, O3, PM10 and PM2.5 (µg/m³)</p> <p>Precipita = precipitation (mm)</p> <p>TC3 = temperature @ 3m (°C)</p> <p>VV30 = wind speed @ 30m (m/s)</p> </td> </tr> </tbody> </table> <p> </p>
Micro-urban environment experimental dataset to validate performance of different Computational Fluid Dynamics methodologies.
<p><span><span>This dataset enclosed wind 3D geolocated wind flow and air concentrations </span><span>5-minutal </span><span>data </span><span>collected in </span><span>El Prat del Llobregat (Spain) </span><span>between January and August 2022 in the context of the experiment 1012-ibam of the FF4EuroHPC European project. The intention of this dataset is to provide a </span><span>resource to do performance benchmark of micro-urban chemical – dispersion models to assess their performance</span><span>. To do so, we enclose experimental data collected by </span><span>Bettair</span><span> Mk2 Series Air quality monitors, 2 Air Quality Monitoring stations equipped with reference instruments f</span><span>rom “La </span><span>Xarxa</span><span> de </span><span>Vigilància</span> <span>i</span> <span>Previsió</span><span> de la </span><span>Contaminació</span> <span>Atmosfèrica</span><span> (XVPCA)”</span><span>, and different data from the repository of the ECMWF Era-5 land and CAMS. We also provide the </span><span>3D watertight geometry model of the </span><span>el</span><span> Prat de Llobregat (Spain) in step file format</span><span> (layout from 2020)</span><span>.<br></span></span></p>
Supplementary dataset for "Global Climatology of the Daytime Surface Cooling of Urban Parks Using Satellite Observations"
<p>This dataset supplements the paper "Global Climatology of the Daytime Surface Cooling of Urban Parks Using Satellite Observations" in Geophysical Research Letters by Agathangeldis et al.</p> <ul> <li>The file "parks.gpkg" contains the boundaries of the parks used in the study, along with the Surface Park Cool Island (SPCI) intensity and additional metadata as attributes.</li> <li>The file "parks.csv" includes the same information as "parks.gpkg" but without the geospatial information.</li> <li>The file "seasonal_values" provides the SPCI intensity for each park, broken down by season.</li> </ul>
Urban Air Pollution and Children's Brain Development: A Systematic Bibliometric Review
<p>The dataset for this research was compiled through an advanced PubMed search targeting publications from a one-year period. Keywords focused on air pollution, neurodevelopment, and associated disorders. From an initial pool of 450 publications, filtering based on co-occurrence of relevant keywords reduced this to approximately 50 papers. VOSviewer was employed to analyze co-occurrences and generate a visual map of relationships between air pollution and child neurodevelopment. The thesaurus was applied to standardize terminology, refining the final network for detailed analysis of keyword clusters and their interactions.</p>
City's Low Hanging Fruit: Exploring Policy Alternatives for Exploiting Urban Fruit Trees
<p>City's Low Hanging fruit:<br><br>Map, Thematic analysis, Three quistionnaires, questionnaire answers<br><br>מפה, ניתוח תמטי, שלושת השאלונים, תשובות לשאלונים<br><br></p> <p>Abstract + methods - below<br>תקציר - למטה</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.