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3,709 results for “urbanization.”
Figure 8 in An assessment of the urban water footprint and blue water scarcity: A case study for Van (Turkey)
Figure 8. Distribution of WF , and WF by years in Van province.
Figure 1 in An assessment of the urban water footprint and blue water scarcity: A case study for Van (Turkey)
Figure 1. The study area.
Figure 4 in An assessment of the urban water footprint and blue water scarcity: A case study for Van (Turkey)
Figure 4. Distribution of WF in Van province.
Figure 9 in An assessment of the urban water footprint and blue water scarcity: A case study for Van (Turkey)
Figure 9. Distribution of WF in Van province when feed crops total are included in WF .
Figure 1 in Prevalence of cestodes infection among school children of urban parts of Lower Dir district, Pakistan
Figure 1. Study sites of the area in Lower Dirdistrict (Khyber, Pakhtunkhwa, province, Pakistan.
"Unlocking the Urban Code. Digital Social Innovation and City Spaces"
<p>DSI in the City is a project funded by MUR-PRIN 2022 led by Chiara Certomà (PI, University Sapienza Rome) and Venere Sanna (University of Siena). The video explore one of the main challenges for contemporary society, i.e. understand and govern the digital revolution and its sociopolitical consequences.The video identifies and deconstructs the principal issues determined by the digitalisation of urban reproduction processes, notably via the diffusion of DSI practices.</p> <p> </p> <p>The project has been funded by the PRIN2022 MUR program, Funded by the European Commission - Next Generation EU, code 2022KTEZPX to the University Sapienza Rome</p>
Dataset for "Aerosol mixing state, new particle formation, and cloud droplet number concentration in an urban environment "
<p>Inverted HTDMA data, parcel model input, and parcel model output associated with the manuscript. HTDMA data are from the TRACER campaign and inverted using the LSQ1 or LSQ2 method described in Petters (2021). Model input is a 4 lognormal aerosol mode representation of the aerosol size distribution during TRACER, obtained by fitting measured size distributions obtained from mobility-based and optical-based sensors. Hygroscopicity is assigned for each mode based on observed size-resolved hygroscopicity from the HTDMA measurements. Model output refers to parcel model simulations using the pyrcel model (Rothenberg and Iompar).</p> <p>Petters, M. D.: Revisiting matrix-based inversion of scanning mobility particle sizer (SMPS) and humidified tandem differential mobility analyzer (HTDMA) data, Atmos. Meas. Tech., 14, 7909–7928, https://doi.org/10.5194/amt-14-7909-2021, 2021. </p> <p>Rothenberg, D. and lompar: darothen/pyrcel: Pyrcel v1.3.2, , https://doi.org/10.5281/zenodo.8378595, 2023.563</p>
Modeling and simulation of a new Urban Lightweight Electric Vehicle concept based on the optimized use of renewable energies and the reduction of CO2 emissions
<p>This work has produced a series of scientifc contributions. This library develops different mathematical expressions and assumptions for the dynamic modelling of an smart-grid located within a solar-powered ULEV are derived. The code was developed using Dymola</p>
SD4EO: AI-based synthetic solar panel dataset on urban areas
<p>This dataset has been created as part of the deliverables for ESA’s <a href="https://eo4society.esa.int/projects/sd4eo/">SD4EO project</a>. It consists of aerial images of urban areas that mimic certain regions inside Poitiers, Bordeaux and Toulouse.<br>The images have been synthetized with a generative diffusion model, conditioned with schematic maps, and later augmented to include solar panels in the most sunlighted portions of the building roofs.</p> <p>Each entry has 4 types of files:</p> <ul> <li>A PNG image containing the binary mask with pixel-by-pixel segmentation of areas where solar panels have been installed.</li> <li>A PNG image which shows a variant of the synthetic image, each featuring differently placed panels with white support structures.</li> <li>A TXT file containing the locations of axis-aligned bounding boxes in YOLOv8 format (also compatible with YOLOv5). These are included only if panels have been added to the image, with one file per panel variant, to facilitate YOLO training without needing to modify its Dataset class for file reading.</li> <li>An NPZ file storing the coordinates of the bounding boxes aligned with the solar panels (not with the axes), intended for use in more advanced object detection models like YOLOv8.1 or Mask R-CNN.</li> </ul> <p>The dataset includes 46,872 synthetic images, which have been further enhanced by adding solar panels in strategic locations. The complete dataset with metadata has been subdivided into 18 ZIP files (each containing a different subfolder), totalling 6.5GB. This division was made to avoid overloading the storage system. By distributing the 156,127 files across multiple folders, we prevent potential issues on users' computers related to exceeding the maximum number of inodes in the file system and/or the operating system.</p> <p>The SD4EO Project is funded by the ESA’s FutureEO programme under contract no. 4000142334/23/I-DT and supervised by ESA Φ-lab.</p>
IoT Sensor Deployment in the Wildland Urban Interface: Leveraging Fire Risk Analysis
<p>Included here are individual burn maps used for evaluating algorithm results in the paper: IoT Sensor Deployment in the Wildland Urban Interface: Leveraging Fire Risk Analysis. This paper will be presented at the IEEE World Forum on Internet of Things in November 2024 and available on IEEE Xplore after that.</p> <p>Also included are maps of fuel load and elevation (geotifs) and the daily weather (in .csv format) for the region of interest used in the burn probability simulator Burn-P3+ to generate the individual burn maps.</p> <p>This paper investigates various algorithms for distributing Internet of Things sensors within the Wildland-Urban Interface to enhance early wildland fire detection. Utilizing geospatial data analysis and a validated wildland fire growth model burn maps were generated to guide sensor placement strategies across a defined region of interest. The algorithms evaluated include an even grid distribution, random distributions, and genetic algorithm-based methods. Each algorithm was tested against 50,000 selected burn maps to assess detection rates, with sensor counts ranging from 50 to 800 across 500 experimental runs. Results indicate that while the even grid distribution yielded the highest detection rates, the practicality of such a method in real-world applications is limited. Genetic algorithms showed promise, but require further exploration to more accurately simulate random distribution used in field deployment. Surprisingly, weighting sensor placement based on wildland fire growth risk did not significantly impact detection effectiveness, suggesting the need for additional research into the representativeness of selected burn maps.</p> <p>Partial code for the sensor deployment algorithms discussed in the above mentioned paper is <a href="https://github.com/richardjpurcell/sensor-deployment-algorithms">available on GitHub</a>.</p>
Dataset to reproduce the paper "A new framework to evaluate urban design using urban microclimatic modelling in future climatic conditions"
<p>This dataset has been generated with the paper " A new framework to evaluate urban design using urban microclimatic<br> modelling in future climatic conditions" (https://doi.org/10.3390/su10041134). A Python notebook is also included to conduct the analysis.</p> <ol> <li>Data analysis - Sustainability paper.ipynb : Python notebook</li> <li>Geneva_Eur11_TDY_2010_2039 : Climate file for the year 2039 obtained from RCA4</li> <li>Geneva_Eur11_TDY_2010_2039_cim : Climate file for the year 2039 obtained from RCA4-CIM</li> <li>Geneva_Eur11_TDY_2010_2039 : Climate file for the year 2069 obtained from RCA4</li> <li>Geneva_Eur11_TDY_2010_2069_cim : Climate file for the year 2069 obtained from RCA4-CIM</li> <li>Geneva_Eur11_TDY_2010_2099 : Climate file for the year 2099 obtained from RCA4</li> <li>Geneva_Eur11_TDY_2010_2099_cim : Climate file for the year 2099 obtained from RCA4-CIM</li> <li>heating_2039 : Heating demand from CitySim for the year 2039</li> <li>heating_2039_cim : Heating demand from CitySim-CIM for the year 2039</li> <li>heating_2069 : Heating demand from CitySim for the year 2069</li> <li>heating_2069_cim : Heating demand from CitySim-CIM for the year 2069</li> <li>heating_2099 : Heating demand from CitySim for the year 2099</li> <li>heating_2099_cim : Heating demand from CitySim-CIM for the year 2099</li> <li>heating_2099_minP : Heating demand from CitySim for the year 2099 with Minergie-P scenario</li> <li>heating_2099__minP_cim : Heating demand from CitySim-CIM for the year 2099 with Minergie-P scenario</li> <li>cooling_2039 : Cooling demand from CitySim for the year 2039</li> <li>cooling_2039_cim : Cooling demand from CitySim-CIM for the year 2039</li> <li>cooling_2069 : Cooling demand from CitySim for the year 2069</li> <li>cooling_2069_cim : Cooling demand from CitySim-CIM for the year 2069</li> <li>cooling_2099 : Cooling demand from CitySim for the year 2099</li> <li>cooling_2099_cim : Cooling demand from CitySim-CIM for the year 2099</li> <li>cooling_2099_minP : Cooling demand from CitySim for the year 2099 with Minergie-P scenario</li> <li>cooling_2099__minP_cim : Cooling demand from CitySim-CIM for the year 2099 with Minergie-P scenario</li> <li>temp_cim : simulated temperature from CIM using Meteonorm</li> <li>temp_meteonorm : temperature from Meteonorm</li> <li>u_cim : simulated wind speedfrom CIM using Meteonorm</li> <li>u_meteonorm : wind speed from Meteonorm</li> </ol> <p> </p>
Vector shapefiles of the rural-urban interface in Portugal estimated for different periods (1990, 2000, 2006, 2012)
<p>Vector shapefiles of the rural-urban interface (RUI) in Portugal estimated for different periods (1990, 2000, 2006, 2012). RUI was mapped considering the first and the second level hierarchy of CORINE Land Cover (CLC). A specific geospatial approach was designed to extract the area of intersection between a buffer around the artificial surfaces and the area resulting from the sum of the forest and semi natural areas plus the heterogeneous agricultural areas. We adopted a buffer width of 1 km, corresponding to two times the spatial resolution of CLC inventories. </p> <p>The field “code_yy” refers to CLC class level 3. More about the Corine Land Cover (CLC) programme and datasets can be found at <a href="http://www.eea.eu">http://www.eea.eu</a></p> <p>More about the the RUI maps elaboration can be found at: <a href="https://doi.org/10.5194/nhess-18-1-2018">https://doi.org/10.5194/nhess-18-1-2018</a>. Please, cite data and the related paper if you use them. (cite as: Tonini., M., Parente., J., Pereira., M., Global assessment of rural-urban interface in Portugal related to land cover changes; Nat. Hazards Earth Syst. Sci., 18, 1–18, 2018)</p>
Imaging subsurface structure of an urban area based on Diffuse-Field Theory concept using seismic ambient noise
<p>Ambient noise data for a small urban area of NER India. The data set constitute all the raw files that were used for figures in the paper by Bora et al.</p>
Urban heat island in Geneva city
<p>As an indicator of urbanization impact, urban heat island (UHI) effect brings us the thermal discomfort and detrimental effects on human health. Considering NDVI and population density in this dataset, we aim to find the most influenced UHI area in Geneva city.</p>
Stylized urban landscapes optimized for compactness, climate regulation and vascular plant species richness
<p>The data set provides the output of a genetic algorithm optimizing a stylized urban region with respect to three target functions: urban compactness, climate regulation as an exemplary ecosystem service and vascular plant species richness as a measure of biodiversity.</p> <p>The optimisation varies the spatial allocation of three types of land cover blocks in a stylized urban region: high- and low-density and park blocks which consist of green and/or built-up cells. We systematically vary landscape composition at the block level, but keep city size constant.</p> <p>The data set is related to a publication submitted to Frontiers in Environmental Science.</p>
MAUPP: 20 Years of Urban Expansion in Sub-Saharan Africa
<p>This dataset contains 20 years of urban expansion data for 46 case studies located in Sub-Saharan Africa. For more information, see <a href="https://maupp.ulb.ac.be">the MAUPP website</a>. The dataset can also be explored from your browser in an <a href="https://maupp.ulb.ac.be/page/wp1results/">interactive web map</a>. For each case study, the following files are provided:</p> <pre>Filename: builtup_<year>.tif Format: GeoTIFF [UINT8] Description: Binary built-up data for each year. Legend: 0=Not built-up; 1=Built-up. Filename: builtup_history.tif Format: GeoTIFF [UINT8] Description: Age of the built environment. Legend: 0=Not built-up; 1=Before 1995; 2=1995-2000; 3=2000-2005; 4=2005-2010; 5=2010-2015. Filename: expansion_<year1>_<year2>.tif Format: GeoTIFF [UINT8] Description: Characterization of urban expansion between <year1> and <year2>. Legend: 1=Existing built-up; 2=Infill; 3=Expansion; 4=Leapfrog. Filename: training_samples.tif Format: GeoTIFF [UINT8] Description: Training samples extracted from OpenStreetMap. Legend: 1=Built-up; 2=Not built-up. Filename: aoi.geojson Format: GeoJSON Description: Area of interest (EPSG:4326). Filename: validation.json Format: JSON Description: Validation metrics. Filename: metadata.json Format: JSON Description: Creation date, source data, CRS, country, city.</pre>
Urban stormwater pond water quality and vegetation cover data
<p>These data include measurements of water physical and chemical parameters and littoral vegetation measurements in urban stormwater ponds located in three different urban land use types in Orlando, Florida, USA.</p>
WASHTREET. Runoff velocity data using different Particle Image Velocimetry (PIV) techniques in a full scale urban drainage physical model
<p><strong>WASHTREET - Runoff velocity data using different Particle Image Velocimetry (PIV) techniques in a full scale urban drainage physical model.</strong></p> <p>This dataset contains raw data and runoff velocities results obtained using seeded and unseeded Particle Image Velocimetry (PIV) techniques in an urban drainage physical model, which is placed in the Hydraulic Laboratory of the Centre for Technological Innovation in Construction and Civil Engineering (CITEEC) at the University of A Coruña (Spain). The objective of this work is to obtain an accurate representation of the surface velocity distribution as part of the <a href="https://zenodo.org/communities/washtreet">WASHTREET project</a>, where a series of high-resolution experiments were performed measuring urban surface wash-off and sediment transport through gully pots and pipes under laboratory-controlled conditions. The experimental facility is a 36 m<sup>2</sup> full-scale street section and consists of a rainfall simulator placed over a concrete street surface with two gully pots that drain runoff into an underground pipe system. The dataset was used in the work developed in Naves et al. (2019) (DOI: <a href="https://doi.org/10.1016/j.jhydrol.2019.05.003">https://doi.org/10.1016/j.jhydrol.2019.05.003</a>).</p> <p>A detailed description of experimental setup, procedure, postprocessing and results can be consulted in ‘<em>1_TestsDescription.pdf’. </em>4K resolution and 25 fps raw videos from which frames are extracted for the PIV analysis are provided for each experiment performed in separated zip files (named as <em>‘2.</em>(test ID)<em>_RawVideos_</em>(configuration)<em>.zip’</em>). Experiments includes three different steady rainfalls of 30, 50 and 80 mm/h of rain intensity and were recorded with and without added fluorescent traces. Data to orthorectify frames from videos are provided in ‘<em>3_SpatialCalibration.zip</em>’. In addition, 60 seconds of steady conditions are extracted for each test and the frames are processed to obtain velocities from a PIV analysis. ‘<em>4_ProcessedFrames_SteadyFlow.zip’ </em>includes the 1500 rectified and processed frames for each experiment to perform the PIV analysis. Results of runoff velocity distributions are included in ‘<em>5_VelocityResults.zip’</em>.</p> <p>Further details of the rainfall simulator, physical model geometry and more hydraulic and sediment transport results can be consulted in <a href="http://doi.org/10.5281/zenodo.3233918"><em>WASHTREET hydraulic, wash-off and sediment transport experimental data</em></a>. In addition, data regarding the use of photogrammetry to obtain the elevation map of this physical model is included in <a href="http://www.doi.org/10.5281/zenodo.3241337">WASHTREET Structure from Motion data</a>.</p> <p>The WASHTREET project is being developed in the scope of the PhD thesis of the first author, which is in receipt of a Spanish Ministry of Science, Innovation and Universities predoctoral grant [FPU14/01778]. The project also receive funding from the Spanish Ministry of Science, Innovation and Universities under POREDRAIN project RTI2018-094217-B-C33 (MINECO/FEDER-EU)</p> <p>Derived publications:</p> <ul> <li>Naves, J., Anta, J., Puertas, J., Regueiro-Picallo, M., & Suárez, J. (2019). Using a 2D shallow water model to assess Large-Scale Particle Image Velocimetry (LSPIV) and Structure from Motion (SfM) techniques in a street-scale urban drainage physical model. <em>Journal of Hydrology</em>, <em>575</em>, 54-65. <a href="https://doi.org/10.1016/j.jhydrol.2019.05.003">https://doi.org/10.1016/j.jhydrol.2019.05.003</a></li> <li>Naves, J., Anta, J., Suárez, J., & Puertas, J. (2020). Hydraulic, wash-off and sediment transport experiments in a full-scale urban drainage physical model. <em>Scientific Data</em>, <em>7</em>(1), 1-13. <a href="https://doi.org/10.1038/s41597-020-0384-z">https://doi.org/10.1038/s41597-020-0384-z</a></li> <li>Naves, J., García, J. T., Puertas, J., & Anta, J. (2021). Assessing different imaging velocimetry techniques to measure shallow runoff velocities during rain events using an urban drainage physical model. <em>Hydrology and Earth System Sciences</em>, <em>25</em>(2), 885-900. <a href="https://doi.org/10.5194/hess-25-885-2021">https://doi.org/10.5194/hess-25-885-2021</a> </li> </ul>
Dominance by a single species structures multiple ecological functions in urban landscapes
<p>Dataset used in the analysis of the manuscript "Dominance by a single species structures multiple ecological functions in urban landscapes"</p>
Automated Truck Lanes in Urban Area for Through and Cross Border Traffic
<p>Autonomous trucks will soon be operational across our nation’s roadways. Yet, it is unclear how the design of our highway infrastructure should be modified to accommodate autonomous trucks, to enable them to operate in such a way to maximize the economic, capacity and safety benefits. The University of Texas at El Paso (UTEP) is proposing to develop and demonstrate, through microscopic traffic simulations, the concept of operations of autonomous truck lanes along the interstate freeways. Using the I-10 Freeway in the El Paso, TX region as the testbed, the research team will: (i) assess the existing structural, geometric and traffic designs in handling fully automated trucks, including the entrances, exits, and connectors; (ii) perform microscopic traffic simulations at critical locations to demonstrate design issues and the recommended design improvements; (iii) conduct a preliminary cost estimation on such infrastructure improvements.</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.