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802 results for “urban data”
Turner et al., 2021, urban water supply contributions and GAMUT output data
<p>Output from gamut (Geospatial Analytics for Multisectoral Urban Teleconnections) model supporting Turner et al. (2021) - https://www.nature.com/articles/s41467-021-27509-9</p> <p> </p> <p> </p>
Questionnaire for surveys on Urban Green Space use and survey raw data for Brussels (Belgium), Luxembourg-city (Luxembourg) and Rouen (France)
<p>The repository contains the xml files of survey questionnaires on the use of urban green spaces. All survey files are translated into three languages (English, French and German).</p> <p>At the time of this publication, these questionnaires have already been used for conducting face-to-face surveys in 2016 in Brussels (Belgium), in 2017 in Luxembourg-city (Luxembourg) and in 2017 in Rouen (France).</p> <p>The results of these surveys are provided in raw data format (csv files), after anonymisation (home and workplace locations have been removed).</p> <p>Please feel free to contact us for any supplementary info.</p>
Survey questionnaire data on Perceptions, interactions, and responses to urban natural environments through ecological momentary assessment
<p>Data consist of a R file format and contain the cleaned survey quistionaire data where people were asked about their perceptions, interactions, and responses to urban natural environments. For more information please read the following report Published as part of the Regreen Horizon project. </p> <p>Panduro, T.E., Zandersen, M., Guell, C., Lovell, R., Garrett, J., Taylor, T., Fullam, J., Amegbor, P. (2024) Perceptions, interactions, and responses to urban natural environments through ecological momentary assessment (EMA). Deliverable D4.6. REGREEN - Fostering nature-based solutions for smart, green and healthy urban transitions in Europe and China. Horizon2020 Grant No. 821016. https://www.doi.org/10.5281/zenodo.10594764</p>
Integrated Machine Learning model in Early Urban Flooding Warning System - Data
<p>AI_DATA.npy - Inundation data (mm) generated from MIKE+ model that has been converted to numpy array</p> <p>INDEX.npy - The index where inundation is > 0 </p> <p>source.tif - Source tif image for creating map from ML models</p>
Data for "COSMO-BEP-Tree v1.0: a coupled urban climate model with explicit representation of street trees"
<p>In order to represent the interactions between street trees, urban elements and the atmosphere in realistic regional weather and climate simulations, we coupled the vegetated urban canopy model BEPTree and the mesoscale weather and climate model COSMO.</p> <p>The performance and applicability of the coupled model, named COSMO-BEP-Tree, are demonstrated over the urban area of Basel, Switzerland, during the heatwave event of June-July 2015.</p> <p>The data includes:</p> <p>1. <em>datasets</em><br> Datasets of building geometries (Shapefile, WGS84), trees (GeoTiff, WGS84), Landsat 7 scene (GeoTIFF, WGS84) and imperviousness (GeoTIFF, WGS84).</p> <p>2. <em>model outputs</em><br> The processed model outputs (.npy files, generated with Python v3) are provided for all the simulations, in terms of time series at the observation sites and spatial distributions. The full 3D model outputs, 1 TB) can be provided by request by contacting the author (<a href="mailto:mussetti.gianluca@gmail.com">mussetti.gianluca@gmail.com</a>).</p> <p>3. <em>model inputs</em><br> Input namelists for the COSMO-BEP-Tree model and initial/static conditions. The full 3D boundary conditions (60 GB) can be provided by request (<a href="mailto:mussetti.gianluca@gmail.com">mussetti.gianluca@gmail.com</a>).</p> <p>4. <em>observations</em><br> Measurement data (.txt).</p> <p>5. <em>post-processing scripts</em><br> Jupyter (Python 3) Notebook files used to generate the figures and to analyse model results. Tested in Python 3.6.5.</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>
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>
Urban Tree Canopy Cover, environmental and socioeconomic data to São Paulo city, Brazil
<p>Urban Tree Canopy Cover from 2017, elevation and roughness from 2011 and socioeconomic data from 2010, to São Paulo city, Brazil. These data are analyzed in: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.ufug.2024.128497" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.ufug.2024.128497</a></p>
Data from: Using Landsat time-series to investigate nearly 50 years of tree canopy cover change across an urban-rural landscape in southern Ontario
<p><strong>Paper Abstract:</strong></p> <p>Canadian urban and adjacent landscapes have been dynamic over the last 50 years due to land management, land cover alternations, climate change, and disturbances. Remote sensing, particularly the Landsat archive, provides the only means to spatially quantify these long-term dynamics locally. Here, we explore the utility of Landsat, including the often-forgotten MSS sensor, for investigating percent tree canopy cover (TCC) change between 1972 and 2020 in a Canadian urban-rural context. We build a TCC time-series by training random forest models using visually interpreted TCC from high-resolution imagery. Predictors include topographic and yearly LandsatLinkr-harmonized and LandTrendr-fitted tasseled cap indices. Yearly binary TCC maps are built to mask consistently treeless areas and limit noise. To increase confidence in observed TCC change without historical reference imagery, we investigate multiple temporal validation options. Our TCC time-series (R2: 0.89, RMSE: 10.7%), quantifies TCC dynamics while limiting erroneous change and predictor space extrapolation. We explore TCC changes across landscapes, revealing periods of gain and loss associated with agricultural reforestation (1978-1996), housing development (on-going), drought (late 1990s), emerald ash borer (2010s), an ice storm (2013), and other drivers. Results demonstrate how long-term Landsat time-series can be used to better understand historical tree canopy change at local-regional scales. </p> <p> </p> <p><strong>Dataset details:</strong></p> <p>See paper. </p> <ul> <li>cc_72to20.tif: Yearly tree CC predictions (1972-2020)</li> <li>always_nonforest10_nowater.tif: continuous-non-canopy mask</li> <li>water.tif: water mask</li> <li>Yearly.zip: Annual predictors (including CC10) and asc outputs</li> </ul> <p> </p> <p>See code on GitHub: <a href="https://github.com/ZZMitch/PredictTreeCC_Landsat_1972to2020">ZZMitch/PredictTreeCC_Landsat_1972to2020: Code from the portion of my PhD about using Landsat time-series to predict tree canopy cover from 1972 - 2020. Code will be released as papers are published. (github.com)</a></p>
Multi-disciplinary survey data for the assessment of regulating and recreational ecosystem services in urban parks under heat and drought conditions
<p>The research group GreenEquityHEALTH provides quantified knowledge on how urban green spaces contribute to the mitigation of climate change induced challenges and challenges from urbanization to improve health, well-being and environmental justice. The project identifies the mediating pathways or direct effects of divers urban green spaces that act to either promote health, encourage healthy behaviours like social interaction or physical activity, or to decrease risk factors such as air pollution or urban heat.</p> <p>Here we present core data of our interdisciplinary multi-method campaigns that included in-situ stationary and aerial (remote sensing-based) environmental measurements, mobile air quality measurements, and social science-informed surveys, namely, park vistor observations and countings and a questionnaire survey.</p> <p><strong>List of data and content</strong></p> <ul> <li>Aarial_survey: digital surface model, orthophoto and thermal infrared images as raster files (*.tif); flight and processing report (*.pdf)</li> <li>Air_quality: stationary PM measurement data (*.csv); coordinates (*.txt)</li> <li>Meteorology: stationary air temperature and humidity data (*.csv), sensor meta data (*.csv)</li> <li>ParkVisitor_Surveys: survey data and questionnaire replies (*.csv), survey sheets (*.docx), questionnaire form (*.pdf)</li> </ul> <p><strong>Data acquisition and processing</strong></p> <p>For details on the data (e.g. sensors, calibration, survey settings) please refer to the linked publication, incl. Supplementary Material.</p> <p><strong>Acknowledgments</strong><br> We would like to thank the City of Leipzig, Department for Urban Green and Waters, for supporting the project. We would like to thank Henrique Miguel Pereira (Head of Research Group Biodiversity Conservation of the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig) for providing equipment for the meteorological field campaigns. We also thank Anhalt University of Applied Sciences, Institute of Geoinformation and Surveying with Lutz Bannehr for conducting the airborne campaigns, Marco Pohle and Helko Kotas (both Helmholtz Centre for Environmental Research - UFZ) for technical support, and Judith Rakowski for support during the field surveys. This work was carried out within the research project ‘Environmental-health Interactions in Cities<br> (GreenEquityHEALTH) – Challenges for Human Wellbeing under Global Changes’ (2017 to 2022) funded by the German Federal Ministry of Education and Research (BMBF), funding code: 01LN1705A.</p> <p><strong>Related publication</strong><br> Kabisch, N. et al. (2021). A methodological framework for the assessment of regulating and recreational ecosystem services in urban parks under heat and drought conditions. <em>Ecosystems and People</em>. <a href="https://doi.org/10.1080/26395916.2021.1958062">doi:10.1080/26395916.2021.1958062</a></p>
Scientific data: Laboratory modelling of urban flooding
<p>These datasets include experimental data obtained in the hydraulic laboratory. A detailed description of the datasets is available in the article. </p>
Potentials and perspectives of food self-sufficiency in urban areas – data and code
<p>Script and input data files (consumption and production data) for the publication "Potentials and perspectives of food self-sufficiency in urban areas – a case study from Leipzig". The script provides a tool for 1) the calculation of the self-sufficiency level (ssl), regional and non-regional agricultural area demand for the region of interest and 2) the calculation of share of area demand and total consumption of single commodities and food groups. Input data files include production yields and consumption quantities for single commodities.</p>
Dataset for the paper "Data for Distribution of Vascular Plants (Tracheophytes) of urban forests and floodplains in the Tyumen city (Western Siberia)"
<p>Dataset associated with the manuscript “Data for Distribution of Vascular Plants (Tracheophytes) of urban forests and floodplains in the Tyumen city (Western Siberia)” submitted to the journal Data.</p>
Data from: Metabarcoding of trap nests reveals differential impact of urbanization on cavity-nesting bee and wasp communities
<p><span>Urbanization is affecting arthropod communities worldwide, for example by changing the availability of food resources. However, the strength and direction of a community's response are species-specific and depend on the species' trophic level. Here, we investigated interacting species at different trophic levels in nests of cavity-nesting bees and wasps along two urbanization gradients in four German cities using trap nests. We analyzed bee and wasp diversity and their trophic interaction partners by metabarcoding the DNA of bee pollen and preyed arthropods found in wasp nests. We found that the pollen richness increased with increasing distance from city centers and at sites characterized by a high percentage of impervious and developed surfaces, while the richness of pollinators was unaffected by urbanization. In contrast, species richness of wasps, but not their arthropod prey, was highest at sites with low levels of urbanization. However, the community structure of wasp prey changed with urbanization at both local and regional scales. Throughout the study area, the community of wasps consisted of specialists, while bee species were generalists. Our results suggest that Hymenoptera and their food resources are negatively affected by increasing urbanization. However, to understand the distribution patterns of both, wasps and bees in urban settings other factors besides food availability should be considered.</span></p>
Data from: Insects in the city: Determinants of a contained aquatic microecosystem across an urbanized landscape
<p>Cities can have profound impacts on ecosystems, yet our understanding of these impacts is currently limited. First, the effects of socioeconomic dimensions of human society are often overlooked. Second, correlative analyses are common, limiting our causal understanding of mechanisms. Third, most research has focused on terrestrial systems, ignoring aquatic systems that also provide important ecosystem services. Here we compare the effects of human population density and low-income prevalence on the macroinvertebrate communities and ecosystem processes within water-filled artificial tree holes. We hypothesized that these human demographic variables would affect tree holes in different ways via changes in temperature, water nutrients, and the local tree hole environment. We recruited community scientists across Greater Vancouver (Canada) to provide host trees and tend 50 tree holes over 14 weeks of colonization. We quantified tree hole ecosystems in terms of aquatic invertebrates, litter decomposition, and chlorophyll-a. We compiled potential explanatory variables from field measurements, satellite images, or census databases. Using structural equation models, we showed that invertebrate abundance was affected by low-income prevalence but not human population density. This was driven by cosmopolitan species of Ceratopogonidae (Diptera) with known associations to anthropogenic containers. Invertebrate diversity and abundance were also affected by environmental factors, such as temperature, elevation, water nutrients, litter quantity, and exposure. By contrast, invertebrate biomass, chlorophyll-a, and litter decomposition were not affected by any measured variables. In summary, this study shows that some urban ecosystems can be largely unaffected by human population density. Our study also demonstrates the potential of using artificial tree holes as a standardized, replicated habitat for studying urbanization. Finally, by combining community science and urban ecology, we were able to involve our local community in this pandemic research pivot. </p> <p>This abstract is quoted from the original article "Insects in the city: Determinants of a contained aquatic microecosystem across an urbanized landscape" in Ecology (2023) by DS Srivastava et al.</p>
Dataset of AI4ER MRes titled "Improving Urban Tree Management Using High-Resolution Satellite Data"
<p>This repository contains the data used in the Master's thesis titled "Improving Urban Tree Management Using High-Resolution Satellite Data" by Andrés C. Zúñiga-González as part of the AI4ER MRes 1st year project at the University of Cambridge.</p> <p>The folders are split into large and small training and testing datasets. These folders contain the tiles (in png and tif formats) used in the models. In addition, it includes the crowns in ESRI Shapefile format for the training and testing datasets. Finally, it contains the best model from the project (named urban_trees_Cambridge_20230630.pth).</p>
Data used in the PhD dissertation entitled "Hidden beneath the surface: Microbial methane cycling in Dutch urban canals"
<p>Data used for the figures presented in the PhD dissertation of KAJ Pelsma, entitled "Hidden beneath the surface: Microbial methane cycling in Dutch urban canals". The chapters to which each file corresponds is indicated in each file name.</p>
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>
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>
Required data for simulating a typical large-scale urban traffic network
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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.