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130 results for “Urban environments”
Regional Heat Vulnerability Map and Cooling Solutions: A webtool of the Healthy Urban Environments Initiative
## Regional Heat Vulnerability Map and Cooling Solutions The regional heat vulnerability map and cooling solutions webtool offers two data sources for equitable heat mitigation. The dashboard layers vulnerability data onto land surface temperature regional rankings to identify areas with high and low heat exposure and vulnerability as well as the existing assets in each census block group. Additional layers can be added into the heat vulnerability map to highlight how heat affects critical infrastructures including schools, mobile home parks, parking lots, public transportation stops, pedestrian thoroughfares, and bikeways. The solutions tab showcases a variety of heat mitigation solutions and the research behind them. Heat-related solutions and resources from urban Maricopa County are included, including solutions funded through the Healthy Urban Environment Initiative. The data catalogued here are the underlying data that populate the webtool. ## Healthy Urban Environment (HUE) Initiative - Overview HUE is a solutions-focused research, policy and technology incubator to create healthier communities across Maricopa County (central Arizona, USA) through collaboration between researchers, practitioners and community members. As such, HUE funded rapid development, testing and deployment of heat-mitigation and air-quality improvement strategies and technologies. Heat emerged as the urgent focus, as urban centers across the desert Southwest continue to grow in size and density, aggravating existing challenges posed by the expansion of the built environment. In Phoenix, AZ, this expansion of the built environment creates conditions which magnify the intensity and duration of heat – making it difficult for residents to achieve thermal comfort throughout the day and night. Further, the legacies of urban sprawl and transportation planning in the Phoenix, Arizona metropolitan area have contributed to challenges with atmospheric pollutants. Importantly, urban heat and air qua
Intra-urban variations in land surface phenology in a semi-arid environment
<p>Data repository for 'Intra-urban variations in land surface phenology in a semi-arid environment', ERL</p> <p>Contact: Ben Crawford, University of Colorado Denver (benjamin.crawford@ucdenver.edu)</p> <p>Data description:</p> <ul> <li>NDVI.zip: <ul> <li>MODIS NDVI geotif rasters for Denver study area</li> <li>Additional metadata provided in subdirectories</li> </ul> </li> <li>LST.zip: <ul> <li>Landsat LST geotif rasters for Denver study area</li> </ul> </li> <li>Tair.zip <ul> <li>Seasonal modeled air temperatures for Denver study area (as described in the manuscript and supplemental information)</li> </ul> </li> <li>Den470_LandCover_250m_WGS.tif <ul> <li>Denver study area 2018 land cover fractions, derived from 1 m data at https://data.drcog.org/</li> </ul> </li> </ul> <p> </p>
Challenges of high-fidelity air quality modeling in urban environments - PALM sensitivity study during stable conditions (TURBAN)
<h3>Introduction</h3> <p>This dataset contains the PALM model inputs and the source code used to create the simulations for Prague-Legerova scenarios performed in the scope of the <strong>TURBAN</strong> project (<a href="https://www.project-turban.eu/">https://www.project-turban.eu/</a>). Detailed description of the simulations is provided in the referencing scientific paper.</p> <h3>List of simulations</h3> <table> <tbody> <tr> <td><strong>Scenario name</strong></td> <td><strong>Days simulated</strong></td> <td><strong>IBC</strong></td> <td><strong>Configuration changes</strong></td> </tr> <tr> <td>legerovas_s6_sens_base</td> <td>13–15 February 2023</td> <td>ICON</td> <td>-</td> </tr> <tr> <td>legerovas_s6_sens_dtmax</td> <td>13 February 2023</td> <td>ICON</td> <td>dt_max=0.2</td> </tr> <tr> <td>legerovas_s6_sens_heat</td> <td>13 February 2023</td> <td>ICON</td> <td>car anthropogenic heat (custom code)</td> </tr> <tr> <td>legerovas_s6_sens_sgs</td> <td>13 February 2023</td> <td>ICON</td> <td>e_min=0.02</td> </tr> <tr> <td>legerovas_s6_sens_stg</td> <td>13 February 2023</td> <td>ICON</td> <td>STG_PROFILES added</td> </tr> <tr> <td>legerovas_s6_sens_alad</td> <td>13–15 February 2023</td> <td>ALADIN</td> <td>-</td> </tr> <tr> <td>legerovas_s6_sens_alad_heat</td> <td>13 February 2023</td> <td>ALADIN</td> <td>car anthropogenic heat (custom code)</td> </tr> <tr> <td>legerovas_s6_sens_alad_sgs</td> <td>13 February 2023</td> <td>ALADIN</td> <td>e_min=0.02</td> </tr> <tr> <td>legerovas_s6_sens_alad_stg</td> <td>13 February 2023</td> <td>ALADIN</td> <td>STG_PROFILES added</td> </tr> <tr> <td>legerovas_s6_sens_wrf</td> <td>13–15 February 2023</td> <td>WRF</td> <td>-</td> </tr> <tr> <td>legerovas_s6_sens_wrf_heat</td> <td>13 February 2023</td> <td>WRF</td> <td>car anthropogenic heat (custom code)</td> </tr> <tr> <td>legerovas_s6_sens_wrf_sgs</td> <td>13 February 2023</td> <td>WRF</td> <td>e_min=0.02</td> </tr> <tr> <td>legerovas_s6_sens_wrf_stg</td> <td>13 February 2023</td> <td>WRF</td> <td>STG_PROFILES added</td> </tr> </tbody> </table> <h3>Directory structure</h3> <p>The directory inputs contains the model inputs and it is further divided into these subdirectories:</p> <p>- inputs/common: The PALM static driver and the emission drivers for the parent and child domains. These files are common to all simulations</p> <p>- inputs/dynamic/*: These directories contain the dynamic drivers for the parent and child domanis, which contain the initial and boundary conditions (IBC) as well as external radiation data. The three subdirectories aladin, icon and wrf contain IBCs created from the respective mesoscale model outputs. </p> <p>- inputs/legerovas_s6_sens_*: These directories contain the PALM model configuration (p3d) for both domains for each simulation.</p> <p>- inputs/build_config: The included .palm.iofiles configuration file ensures that the files STG_PROFILES are correctly copied from the input directory.</p> <p>The directory palm_sources contains the exact model source used for the simulations. It is derived from the PALM model release 23.04 with additional bugfixes. There are two source archives:</p> <p>- heat.tar.gz: PALM source further modified to include anthropogenic heat from cars, used for the simulations legerovas_s6_sens_*_heat</p> <p>- standard.tar.gz: PALM source used for all other included simulations.</p> <h3>Reproducing the simulations</h3> <p>In order to reproduce the simulations, unpack the respective source code archive and follow the standard installation, configuration and build procedures described in the README.md file within the archive and on the PALM model website http://www.palm-model.org/. Then copy the input files for the respective simulation in the JOBS directory. The common files and the dynamic driver files need to be renamed so that they match the prefix given by the name of the simulation, as is described in the PALM model documentation.</p>
Dataset of IEEE 802.11 probe requests from an uncontrolled urban environment
<p><strong>Introduction</strong></p> <p>The 802.11 standard includes several management features and corresponding frame types. One of them are Probe Requests (PR), which are sent by mobile devices in an unassociated state to scan the nearby area for existing wireless networks. The frame part of PRs consists of variable-length fields, called Information Elements (IE), which represent the capabilities of a mobile device, such as supported data rates.</p> <p>This dataset contains PRs collected over a seven-day period by four gateway devices in an uncontrolled urban environment in the city of Catania.</p> <p>It can be used for various use cases, e.g., analyzing MAC randomization, determining the number of people in a given location at a given time or in different time periods, analyzing trends in population movement (streets, shopping malls, etc.) in different time periods, etc.</p> <p><strong> Related dataset</strong></p> <p>Same authors also produced the <a href="https://zenodo.org/record/7503594">Labeled dataset of IEEE 802.11 probe requests</a> with same data layout and recording equipment.</p> <p><br> <strong>Measurement setup</strong> </p> <p>The system for collecting PRs consists of a Raspberry Pi 4 (RPi) with an additional WiFi dongle to capture WiFi signal traffic in monitoring mode (gateway device).<br> Passive PR monitoring is performed by listening to 802.11 traffic and filtering out PR packets on a single WiFi channel.</p> <p>The following information about each received PR is collected:<br> - MAC address<br> - Supported data rates<br> - extended supported rates<br> - HT capabilities<br> - extended capabilities<br> - data under extended tag and vendor specific tag<br> - interworking<br> - VHT capabilities<br> - RSSI<br> - SSID<br> - timestamp when PR was received.</p> <p>The collected data was forwarded to a remote database via a secure VPN connection.<br> A Python script was written using the Pyshark package to collect, preprocess, and transmit the data.</p> <p><br> <strong>Data preprocessing</strong></p> <p><br> The gateway collects PRs for each successive predefined scan interval (10 seconds). During this interval, the data is preprocessed before being transmitted to the database.<br> For each detected PR in the scan interval, the IEs fields are saved in the following JSON structure:</p> <pre><code class="language-json">PR_IE_data = { 'DATA_RTS': {'SUPP': DATA_supp , 'EXT': DATA_ext}, 'HT_CAP': DATA_htcap, 'EXT_CAP': {'length': DATA_len, 'data': DATA_extcap}, 'VHT_CAP': DATA_vhtcap, 'INTERWORKING': DATA_inter, 'EXT_TAG': {'ID_1': DATA_1_ext, 'ID_2': DATA_2_ext ...}, 'VENDOR_SPEC': {VENDOR_1:{ 'ID_1': DATA_1_vendor1, 'ID_2': DATA_2_vendor1 ...}, VENDOR_2:{ 'ID_1': DATA_1_vendor2, 'ID_2': DATA_2_vendor2 ...} ...} }</code></pre> <p><br> Supported data rates and extended supported rates are represented as arrays of values that encode information about the rates supported by a mobile device. The rest of the IEs data is represented in hexadecimal format. Vendor Specific Tag is structured differently than the other IEs. This field can contain multiple vendor IDs with multiple data IDs with corresponding data. Similarly, the extended tag can contain multiple data IDs with corresponding data. <br> Missing IE fields in the captured PR are not included in <em>PR_IE_DATA</em>.</p> <p>When a new MAC address is detected in the current scan time interval, the data from PR is stored in the following structure:</p> <pre><code class="language-json">{'MAC': MAC_address, 'SSIDs': [ SSID ], 'PROBE_REQs': [PR_data] },</code></pre> <p>where <em>PR_data</em> is structured as follows:</p> <pre><code class="language-json">{ 'TIME': [ DATA_time ], 'RSSI': [ DATA_rssi ], 'DATA': PR_IE_data }.</code></pre> <p> </p> <p>This data structure allows to store only 'TOA' and 'RSSI' for all PRs originating from the same MAC address and containing the same 'PR_IE_data'. All SSIDs from the same MAC address are also stored.<br> The data of the newly detected PR is compared with the already stored data of the same MAC in the current scan time interval.<br> If identical PR's IE data from the same MAC address is already stored, only data for the keys 'TIME' and 'RSSI' are appended.<br> If identical PR's IE data from the same MAC address has not yet been received, then the PR_data structure of the new PR for that MAC address is appended to the 'PROBE_REQs' key.<br> The preprocessing procedure is shown in Figure ./Figures/Preprocessing_procedure.png</p> <p>At the end of each scan time interval, all processed data is sent to the database along with additional metadata about the collected data, such as the serial number of the wireless gateway and the timestamps for the start and end of the scan. For an example of a single PR capture, see the <em>Single_PR_capture_example.json</em> file.</p> <p><br> <strong>Folder structure</strong></p> <p>For ease of processing of the data, the dataset is divided into 7 folders, each containing a 24-hour period.<br> Each folder contains four files, each containing samples from that device.</p> <p>The folders are named after the start and end time (in UTC).<br> For example, the folder [2022-09-22T22-00-00_2022-09-23T22-00-00](2022-09-22T22-00-00_2022-09-23T22-00-00) contains samples collected between <em>23th of September 2022 00:00 local time</em>, until <em>24th of September 2022 00:00</em> local time.</p> <p>Files representing their location via mapping:<br> - 1.json -> location 1<br> - 2.json -> location 2<br> - 3.json -> location 3<br> - 4.json -> location 4</p> <p><strong>Environments description</strong> </p> <p>The measurements were carried out in the city of Catania, in Piazza Università and Piazza del Duomo<br> The gateway devices (rPIs with WiFi dongle) were set up and gathering data before the start time of this dataset.<br> As of September 23, 2022, the devices were placed in their final configuration and personally checked for correctness of installation and data status of the entire data collection system.<br> Devices were connected either to a nearby Ethernet outlet or via WiFi to the access point provided.</p> <p>Four Raspbery Pi-s were used:<br> - location 1 -> Piazza del Duomo - Chierici building (balcony near Fontana dell’Amenano)<br> - location 2 -> southernmost window in the building of Via Etnea near Piazza del Duomo<br> - location 3 -> nothernmost window in the building of Via Etnea near Piazza Università<br> - location 4 -> first window top the right of the entrance of the University of Catania</p> <p>Locations were suggested by the authors and adjusted during deployment based on physical constraints (locations of electrical outlets or internet access)<br> Under ideal circumstances, the locations of the devices and their coverage area would cover both squares and the part of Via Etna between them, with a partial overlap of signal detection. The locations of the gateways are shown in Figure ./Figures/catania.png.</p> <p> <strong>Known dataset shortcomings</strong></p> <p>Due to technical and physical limitations, the dataset contains some identified deficiencies.</p> <p>PRs are collected and transmitted in 10-second chunks.<br> Due to the limited capabilites of the recording devices, some time (in the range of seconds) may not be accounted for between chunks if the transmission of the previous packet took too long or an unexpected error occurred.</p> <p>Every 20 minutes the service is restarted on the recording device.<br> This is a workaround for undefined behavior of the USB WiFi dongle, which can no longer respond.<br> For this reason, up to 20 seconds of data will not be recorded in each 20-minute period.</p> <p>The devices had a scheduled reboot at 4:00 each day which is shown as missing data of up to a few minutes.</p> <p> <strong> Location 1 - Piazza del Duomo - Chierici</strong></p> <p> The gateway device (rPi) is located on the second floor balcony and is hardwired to the Ethernet port. This device appears to function stably throughout the data collection period.<br> Its location is constant and is not disturbed, dataset seems to have complete coverage.</p> <p> <strong>Location 2 - Via Etnea - Piazza del Duomo</strong></p> <p> The device is located inside the building.<br> During working hours (approximately 9:00-17:00), the device was placed on the windowsill. However, the movement of the device cannot be confirmed.<br> As the device was moved back and forth, power outages and internet connection issues occurred.<br> The last three days in the record contain no PRs from this location.</p> <p> <strong> Location 3 - Via Etnea - Piazza Università</strong></p> <p> Similar to Location 2, the device is placed on the windowsill and moved around by people working in the building.<br> Similar behavior is also observed, e.g., it is placed on the windowsill and moved inside a thick wall when no people are present.<br> This device appears to have been collecting data throughout the whole dataset period.<br> <br> <strong> Location 4 - Piazza Università</strong></p> <p> This location is wirelessly connected to the access point.<br> The device was placed statically on a windowsill overlooking the square.<br> Due to physical limitations, the device had lost power several times during the deployment.<br> The internet connection was also interrupted sporadically.</p> <p><strong>Recognitions</strong></p> <p>The data was collected within the scope of <a href="https://www.resilocproject.eu/">Resiloc project</a> with the help of City of Catania and project partners.</p>
Datasets for figures in Implementation and evaluation of Wet Bulb Globe Temperature within non-urban environments in the Community Land Model version 5
<p>The files contain 4 scripts and 6 netcdf files. </p> <p>"laborCap_200400.ncl" uses "Lancet_LRF.nc" to create Figure 1.</p> <p>Script "world_plot_ensemble_Avg.I2000.csh", drives a NCL script, "plot_modern.I2000.WBGT.ncl" to make figures 3 and 4, using the netcdf files, "I2000_PR_22_x1_60_5.exceed.WBGT.20yrs.75_99.nc," "I2000_PR_22_x1_60_5.exceed.WBGT_BG_R.20yrs.75_99.nc," "I2000_PR_22_x1_60_5.exceed.WBGT_BC_R.20yrs.75_99.nc," and "I2000_PR_22_x1_60_5.exceed.WBGT_AC_R.20yrs.75_99.nc."</p> <p>"heatmap.wbgt.v4.org.ncl" uses netcdf "I2000_PR_23_Chicago_x1_60_1.11-17.Chicago.allvars.nc" to create figures 5-7. </p>
TURDATA: a database of low-cost air quality and remote sensing measurements for the validation of micro-scale models in the real Prague urban environments
<p><strong>README</strong></p> <p>TURDATA is a supplementary data set for the TURBAN project Prague observation campaign described in the manuscript Bauerová et al. 2024 (submitted for publication). The measurement campaign was focused on air pollution and meteorological measurement, including vertical profiles in selected part of Prague city centre called here as Legerova domain. Within this area, one professional meteorological station (MS) Prague Karlov and one reference traffic air quality monitoring (AQM) station Prague 2-Legerova (classified as traffic hotspot) are located. To gain high spatial and temporal resolution data, the supplementary measurement network was established, which consisted of:</p> <p>- 20 combined low-cost sensor (LCS) stations for monitoring of PM<sub>10</sub>, PM<sub>2.5</sub>, NO<sub>2</sub> and O<sub>3</sub> concentrations (using Plantower PMS7003 particle counters and Envea Cairsense electrochemical sensors) placed in different sites and different height levels AGL (higher = H, lower = L),</p> <p>- 1 mobile telescopic meteorological mast for measuring temperature, relative humidity, wind velocity and direction and air pressure (using 2D ultrasonic anemometer Gill WindSonic 60 and weather station Gill MetConnect THP),</p> <p>- 1 MTP-5-He microwave radiometer (MWR; Attex) for temperature vertical profile,</p> <p>- 1 StreamLine XR Doppler LIDAR (HALO Photonics) for wind vertical profile. </p> <p>The main Legerova campaign lasted from 30 May 2022 to 28 March 2023 with some exceptions (see <em>TURDATA_metadata.xlsx</em> with all details). Because LCSs are known for their highly variable measurement quality, before their deployment the Legerova campaign, a sufficiently long-term initial field comparative measurement of all LCSs at RM Prague 4-Libuš was carried out (lasting from 16/12/2021 to 30/5/2022). The results showed that most of the LCSs were in raw measurement differently zero-shifted against each other and against gaseous reference or aerosol optical equivalent monitors (RMs or EMs). Therefore, the Multivariate Adaptive Regression Splines (MARS) method was applied to calculate corrected LCS concentrations based on initial field comparative measurement complemented by meteorological data from MS Prague Libuš. To check the quality of raw and MARS corrected LCS concentrations at the end of the measurement campaign, the final comparative field measurement of all LCSs at Prague 4-Libuš RM station was performed.</p> <p>Therefore, in case of LCSs measurement (both raw and corrected) the important columns of location (measurement placement: RM_Prague_4-Libus and Legerova_domain) and measurement_program (Initial_comparative_measurement, Legerova_campaign and Final_comparative_measurement) were added.</p> <p>In case of PM<sub>10</sub> and PM<sub>2.5</sub> measurement the maximum raw and MARS-corrected concentrations were influenced by temporary pollution episode on 26 July 2022 around 4 a.m. and 9 p.m. (both UTC) caused by aerosol pollution transported from large forest fire in Hřensko (the northern part of the Czech Republic). </p> <p> </p> <p>TURDATA includes the following files:</p> <p>1. <strong>TURDATA_metadata_and_photos.zip</strong> containing:</p> <p>- "<em>TURDATA_metadata.xlsx</em>" with the important list of metadata about devices placement, locations parameters and measurement periods</p> <p>- Folder "<em>Photos_from_Legerova_campaign</em>" with photos from Legerova measurement campaign</p> <p>2. <strong>AQ_LCSs_raw_measurement_TURDATA.zip</strong> containing:</p> <p>- "<em>NO2_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of NO<sub>2</sub> raw measured concentrations by all LCSs</p> <p>- "<em>O3_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of O<sub>3</sub> raw measured concentrations by all LCSs</p> <p>- "<em>PM10_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>10</sub> raw measured concentrations by all LCSs</p> <p>- "<em>PM2_5_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>2.5</sub> raw measured concentrations by all LCSs</p> <p>- "<em>AQ_LCSs_raw_measurement_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>3. <strong>AQ_data_RM_stations_Prague_TURDATA.zip</strong> containing:</p> <p>- "<em>AQ_data_Prague_RM_stations_TURDATA_12-2021_06-2023.xlsx</em>" with air quality data measured by reference AQM stations in Prague</p> <p>- "<em>AQ_data_RM_stations_Prague_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>4. <strong>Meteo_data_Prague_MS_TURDATA.zip</strong> containing:</p> <p>- "<em>Meteo_data_Prague_MS_TURDATA_12-2021_06-2023.xlsx</em>" with meteorological data measured by professional meteorological stations in Prague</p> <p>- "<em>Meteo_data_Prague_MS_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>5. <strong>AQ_LCSs_MARS-corrected_measurement_TURDATA.zip</strong> containing:</p> <p>- "<em>NO2_COR_LCSs_TURDATA.xlsx</em>" with complete data set of NO<sub>2</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>O3_COR_LCSs_TURDATA.xlsx</em>" with complete data set of O<sub>3</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>PM10_COR_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>10</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>PM2_5_COR_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>2.5</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>AQ_LCSs_MARS-corrected_measurement_TURDATA_readme.txt</em>" with all necessary information for correct data use and brief description of MARS correction method</p> <p>6. <strong>Meteo-mast_PVK_measurement_TURDATA.zip</strong> containing:</p> <p>- "<em>Meteo-mast_PVK_TURDATA_06-2022_06_2023.xlsx</em>“ with non-referential meteorological data measured by mobile meteo-mast</p> <p>- "<em>Meteo-mast_data_PVK_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>7. <strong>MWR_temperature_profile_TURDATA.zip</strong> containing:</p> <p>- "<em>MWR_5min_temperature_TURDATA_02-2022_03-2023.xlsx</em>" with raw temperature vertical profile measurement from microwave radiometer</p> <p>- "<em>MWR_1hour_temperature_TURDATA.xlsx</em>" with 1-hour averaged temperature vertical profile from microwave radiometer</p> <p>- "<em>MWR_1hour_TMP_gradient_TURDATA.xlsx</em>" with 1hour temperature gradient calculated from raw temperature profiles measured by microwave radiometer</p> <p>- "<em>MWR_temperature_profile_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>8. <strong>LIDAR_wind_profile_TURDATA.zip</strong> contains:</p> <p>- Individual folders "yyyymm“ -> "yyyymmdd"</p> <p>- Each daily folder "yyyymmdd" contains files:</p> <p>a) "<em>Processed_Wind_Profile_188_yyyymmdd_hhmmss.hpl</em>" with processed WV and WS data</p> <p>b) "<em>Wind_Profile_188_yyyymmdd_hhmmss.hpl</em>" with non-processed Doppler wind profile data</p> <p>- "<em>LIDAR_wind_profile_TURADATA_readme.txt</em>" with all necessary information for correct data use</p>
Figure 3 in Shell size differences in Helix lucorum Linnaeus, 1758 (Mollusca: Gastropoda) between natural and urban environments
Figure 3. Error bars of mean values of SOS and AOS variables are shown with 99% confidence intervals.
Figure 4 in Shell size differences in Helix lucorum Linnaeus, 1758 (Mollusca: Gastropoda) between natural and urban environments
Figure 4. Individual scores of the Helix lucorum along the first 2 PCA axes defined by size variables and 2 ratios.
Figure 2 in Shell size differences in Helix lucorum Linnaeus, 1758 (Mollusca: Gastropoda) between natural and urban environments
Figure 2. Measurements of the shells of Helix lucorum used in the analysis: SH, shell height; SW, shell width; AH, aperture height; AW, aperture width.
"An experimental investigation of a roof-mounted horizontal-axis wind turbine in an idealized urban environment"
<p>Dar, Arslan Salim, Guillem Armengol Barcos, and Fernando Porté-Agel. "An experimental investigation of a roof-mounted horizontal-axis wind turbine in an idealized urban environment." <em>Renewable Energy</em> (2022).</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>
Figs 1-4 in Report of the nest of Megachile (Moureapis) benigna (Hymenoptera: Megachilidae) from an urban environment, Rio de Janeiro, Brazil
Figs 1-4. Megachile (Moureapis) benigna Mitchell, 1930: 1, female spawned from the nest (habitus, lateral view); 2, nest collected in the trap-nest; 3, brood cell and round-shaped fragments of leaf forming the cell opercula; 4, different shapes of leaf fragments employed by the female foundress for nest building.
Figs 5, 6 in Report of the nest of Megachile (Moureapis) benigna (Hymenoptera: Megachilidae) from an urban environment, Rio de Janeiro, Brazil
Figs 5, 6. Pollen grains found in the pollen slides analysed from the nest: 5, Cyrtocymura sp. (Asteraceae); 6, Type Fabaceae pollen grain. Pollen grains magnified 40X. Scale bars = 10 µm.
Behavioural changes in the city: the common black garden ant defends aphids more aggressively in urban environments
<p>Data and R code to analyse changes in aphid and ant populations and behaviour along a gradient of urbanisation in Berlin, Germany. This release is associated to a publication in preparation and includes the updated R code used for publication:</p> <p>Gaber, H., Ruland, F, Jeschke, J. & Bernard-Verdier, M. (2024) Behavioural changes in the city: the common black garden ant defends aphids more aggressively in urban environments. <em>Ecology & Evolution</em> (publication details will soon be added)</p>
Fig. 1 in Living with the enemy: activity rhythms of the red fox Vulpes vulpes (Carnivora, Canidae) and some potential preys in an urban environment
Fig. 1 - Satellite view of the municipal area of Padua and location of the camera traps. / Immagine satellitare del territorio comunale di Padova e posizione delle trappole fotografiche (Image/Immagine: Google Satellite).
Fig. 2 in Living with the enemy: activity rhythms of the red fox Vulpes vulpes (Carnivora, Canidae) and some potential preys in an urban environment
Fig. 2 - Temporal activity overlap between the red fox and its potential preys. The shaded area under the two density estimates represents the overlap coefficient. / Sovrapposizione dell'attività temporale tra la volpe rossa e le sue potenziali prede. L'area ombreggiata sotto le due stime di densità rappresenta il coefficiente di sovrapposizione.
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.
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>
Fig. 2 in Larval Development And Habitat Usage Of Stream-Breeding Fire Salamanders In An Urban Environment
Fig. 2. Changes in the number of salamander larvae and rainfall. The bold black continuous line shows the mean number of salamander larvae detected during the surveys in the three 10 day intervals of months in Hűvös-ér stream, 2011–2014. The bar graph shows the mean amount (and SD) of precipitation (mm) during the three 10-day intervals of months
Fig. 3 in Larval Development And Habitat Usage Of Stream-Breeding Fire Salamanders In An Urban Environment
Fig. 3. Mean density of salamander larvae (number of larvae/m2) detected in the 16 segments during surveys every 10 days in "Hűvös-ér" stream, 2011–2014 (2011: thin line, 2012:
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.