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3,709 results for “urbanization.”
Livorno, Urban driving, Automated vehicle approaching intersection
<p><strong>Scenario description</strong>:</p> <p>Test session for AD vehicle approaching an intersection with jaywalking at traffic light</p> <p><strong>Session description</strong>:</p> <p>A "smart" traffic light with a stereocamera sends SPaT and MAP messages describing the topology, actual status of the traffic light, presence of pedestrian, and jaywalking occurrence to other connected vehicles (via DENM) and to the oneM2M platform on the cloud. An AD vehicle consumes the information and autonomously adapts its speed in order to cross the intersection without violating the traffic light phases, or even stop to avoid collision with pedestrian. The influence of other vehicles moving in front is considered too.</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_Vehicle_all</strong>: Data generated from the vehicle sensors</p> <p>This dataset refers to the vehicle datasets generated from the vehicle sensors during Urban Driving in Livorno. This includes the data coming from the CAN bus and GPS. It includes following kind of dataset: Vehicle: general data (speed, battery); PositioningSystem: data from GPS; VehicleDynamics: data about dynamic (acceleration...); LateralControl: steering and lane control data</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_V2X_all</strong>: V2V messages during platooning sessions</p> <p>This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Urban Drining in Livorno.</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_IoT_all</strong>: Data extracted from IoT oneM2M platform</p> <p>This dataset refers to messages exchanged by Urban Driving devices, applications and services across the oneM2M platform.</p>
Livorno, Urban driving, autonomous speed adaptation approaching intersection
<p><strong>Scenario description</strong>:</p> <p>Test session for AD+connected car and connected cars approaching an intersection regulated by a "smart" traffic light with a stereocamera able to detect jaywalking.</p> <p><strong>Session description</strong>:</p> <p>A "smart" traffic light (with stereocamera) sends SPaT and MAP messages describing the topology and the actual status of the traffic light. If a jaywalking occurrence is detected (pedestrian crossing with the red light) a DENM message is sent to warn vehicles of the presence of the pedestrian to other connected vehicles. The the hazard warning is also sent to the oneM2M platform on the cloud. An AD vehicle consumes the information and autonomously adapts its speed in order to cross the intersection without violating the traffic light phases, or even stop to avoid collision with pedestrian. The influence of other vehicles moving in front is considered too. Goal is to record data for the technical evaluation.</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_Vehicle_all</strong>: Data generated from the vehicle sensors</p> <p>This dataset refers to the vehicle datasets generated from the vehicle sensors during Urban Driving in Livorno. This includes the data coming from the CAN bus and GPS. It includes following kind of dataset: Vehicle: general data (speed, battery); PositioningSystem: data from GPS; VehicleDynamics: data about dynamic (acceleration...); LateralControl: steering and lane control data</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_V2X_all</strong>: V2V messages during platooning sessions</p> <p>This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Urban Drining in Livorno.</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_IoT_all</strong>: Data extracted from IoT oneM2M platform</p> <p>This dataset refers to messages exchanged by Urban Driving devices, applications and services across the oneM2M platform.</p>
Livorno, Urban driving, Automated car detects pothole
<p><strong>Scenario description</strong>:</p> <p>Test session for connected car detecting potholes using the combination of one or more of the following sensors: smartphone, 6LoWPAN vibration sensor, IMU.<br> The information is sent to the cloud and can be sent back to other connected vehicles for warning.<br> The information is also transmitted via V2V to AD cars that can automatically adapt the speed.</p> <p><strong>Session description</strong>:</p> <p>Test session with only a connected car with smartphone based pothole detector, lap of 1,9 km on the harbour's public road. Goal is to record data for the technical evaluation.</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_Vehicle_all</strong>: Data generated from the vehicle sensors</p> <p>This dataset refers to the vehicle datasets generated from the vehicle sensors during Urban Driving in Livorno. This includes the data coming from the CAN bus and GPS. It includes following kind of dataset: Vehicle: general data (speed, battery); PositioningSystem: data from GPS; VehicleDynamics: data about dynamic (acceleration...); LateralControl: steering and lane control data</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_V2X_all</strong>: V2V messages during platooning sessions</p> <p>This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Urban Drining in Livorno.</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_IoT_all</strong>: Data extracted from IoT oneM2M platform</p> <p>This dataset refers to messages exchanged by Urban Driving devices, applications and services across the oneM2M platform.</p>
Livorno, Urban driving, IMU pothole detection
<p><strong>Scenario description</strong>:</p> <p>Test session for connected car detecting potholes using the combination of one or more of the following sensors: smartphone, 6LoWPAN vibration sensor, IMU.<br> The information is sent to the cloud and can be sent back to other connected vehicles for warning.<br> The information is also transmitted via V2V to AD cars that can automatically adapt the speed.</p> <p><strong>Session description</strong>:</p> <p>Test session with only a connected car with IMU based pothole detector, lap of 1,9 km on the harbour's public road. Goal is to record data for the technical evaluation.</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_Vehicle_all</strong>: Data generated from the vehicle sensors</p> <p>This dataset refers to the vehicle datasets generated from the vehicle sensors during Urban Driving in Livorno. This includes the data coming from the CAN bus and GPS. It includes following kind of dataset: Vehicle: general data (speed, battery); PositioningSystem: data from GPS; VehicleDynamics: data about dynamic (acceleration...); LateralControl: steering and lane control data</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_V2X_all</strong>: V2V messages during platooning sessions</p> <p>This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Urban Drining in Livorno.</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_IoT_all</strong>: Data extracted from IoT oneM2M platform</p> <p>This dataset refers to messages exchanged by Urban Driving devices, applications and services across the oneM2M platform.</p>
Livorno, Urban driving, pothole detection
<p><strong>Scenario description</strong>: Test session for connected car detecting potholes using the combination of one or more of the following sensors: smartphone, 6LoWPAN vibration sensor, IMU. The information is sent to the cloud and can be sent back to other connected vehicles for warning. The information is also transmitted via V2V to AD cars that can automatically adapt the speed.</p> <p><strong>Session description</strong>: Test session with only a connected car with 6LoWPAN based pothole detector, lap of 1,9 km on the harbour's public road. Goal is to record data for the technical evaluation.</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_Vehicle_all</strong>: Data generated from the vehicle sensors</p> <p>This dataset refers to the vehicle datasets generated from the vehicle sensors during Urban Driving in Livorno. This includes the data coming from the CAN bus and GPS. It includes following kind of dataset: Vehicle: general data (speed, battery); PositioningSystem: data from GPS; VehicleDynamics: data about dynamic (acceleration...); LateralControl: steering and lane control data</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_V2X_all</strong>: V2V messages during platooning sessions</p> <p>This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Urban Drining in Livorno.</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_IoT_all</strong>: Data extracted from IoT oneM2M platform</p> <p>This dataset refers to messages exchanged by Urban Driving devices, applications and services across the oneM2M platform.</p>
Vigo,Urban driving, GLOSA and VRU detection
<p><strong>Scenario description:</strong></p> <p>GLOSA and VRU detection. The autonomous vehicle makes several turns on the scenario adapting its speed to the state of the traffic light. At an intersection controlled by a camera connected via IoT, a VRU is introduced into one of the laps so that the autonomous vehicle brakes when it receives the information via IoT.</p> <p><strong>Session description:</strong></p> <p>Human factor tests performed at CTAG proving grounds.</p> <p><strong>Datasets descriptions:</strong><br> <br> <strong>AUTOPILOT_Vigo_UrbanDriving_Vehicle: </strong>Data extracted from the CAN of the vehicle. Contains data regarding the state of the vehicle, such as speed, engine speed or fuel consumption.</p> <p><strong>AUTOPILOT_Vigo_UrbanDriving_VehicleDynamics: </strong>Data extracted from the CAN of the vehicle.Contains data regarding the dynamic state of the vehicle, such as speed limit, acceleration or yaw rate.</p> <p><strong>AUTOPILOT_Vigo_UrbanDriving_DriverVehicleInteraction: </strong>Data extracted from the CAN of the vehicle. Contains all the information about the actions that the driver can perform, such as the state of the brake, the steering wheel and the throttle.</p> <p><strong>AUTOPILOT_Vigo_UrbanDriving_EnviromentSensorsAbsolute: </strong>Data extracted from the sensors of the vehicle in absolute coordinates.</p> <p><strong>AUTOPILOT_Vigo_UrbanDriving__EnviromentSensorsRelative: </strong>Data extracted from the sensors of the vehicle in relative coordinates.</p> <p><strong>AUTOPILOT_Vigo_UrbanDriving_Positioning:</strong> Data from the GPS of the vehicle, such as latitude longitude and heading.</p> <p><strong>AUTOPILOT_Vigo_UrbanDriving_IoT: </strong>Data of the communications via IoT, coming from the OBU of the vehicle, the IoT server and the RSU that controls the camera and the traffic light.<br> </p> <p> </p>
Crowdsourcing public perceptions of urban green space quality: A case study of Rembrandt park in Amsterdam
<p><strong><em>City-dwellers are realizing the benefits of green spaces and are flocking to urban parks. City planners face the challenge of ensuring that urban green spaces are functional for all citizens. To make informed choices they need the right information and that is where the Mijn Park app can help. </em></strong></p> <p>Research shows that when considering the social functions of urban green spaces, quality is just as important as quantity. It is easy enough to map how much green spaces there are, but how do we measure their quality? How do city planners ensure that the city’s green areas are attractive, accessible and inclusive – for everyone? The Vrije Universiteit Amsterdam in collaboration with the International Institute for Applied Systems Analysis developed Mijn Park, a mobile application that will help city planners do just that. As part of the LandSense Citizen Observatory, the ‘Mijn Park’ (My Park) app asks respondents to go to several locations in a park and give subjective responses to those locations. They are then further questioned about how they use the whole park and how much they would like to see certain changes made in the park. This information provides information that can help to inform decisions about any renovations or improvements to the park.</p> <p>A pilot campaign was conducted in the summer of 2018 in Rembrandt park in Amsterdam and insights from the citizen-driven observations were shared with the Department of Planning and Sustainability of Amsterdam.</p> <p>This dataset includes responses and photographs collected by 129 unique volunteers providing 377 observations in select locations across Rembrandt Park. The following files are available:</p> <ul> <li>_Preview MijnPark-Amsterdam-LandSense.png</li> <li>Attributes-MijnPark-Amsterdam-LandSense.csv</li> <li>MijnPark-Amsterdam-LandSense.csv</li> <li>MijnPark-Amsterdam-LandSense.geoJSON</li> <li>README.txt</li> </ul> <p> </p> <p>This dataset is licensed under a Creative Commons Attribution 4.0 International. It is attributed to the <a href="https://landsense.eu/">LandSense Citizen Observatory</a>, <a href="https://www.vu.nl/">Vrije Universiteit</a> (VU), Amsterdam and the <a href="https://iiasa.ac.at/">International Institute for Applied Systems Analysis</a> (IIASA).</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no 689812.</p>
Crowd and community sourcing to update authoritative LULC data in urban areas
<p>The French National Mapping Agency (Institut National de l'Information Géographique et Forestière - IGN) is responsible for producing and maintaining the spatial data sets for all of France. At the same time, they must satisfy the needs of different stakeholders who are responsible for decisions at multiple levels from local to national. IGN produces many different maps including detailed road networks and land cover/land use maps over time. The information contained in these maps is crucial for many of the decisions made about urban planning, resource management and landscape restoration as well as other environmental issues in France. Recently, IGN has started the process of creating a high-resolution land use land cover (LULC) maps, aimed at developing smart and accurate monitoring services of LULC over time. To help update and validate the French LULC database, citizens and interested stakeholders can contribute using the <a href="https://paysages.ign.fr/">Paysages</a> mobile and web applications. This approach presents an opportunity to evaluate the integration of citizens in the IGN process of updating and validating LULC data.</p> <p><strong>Dataset 1: Change detection validation 2019</strong></p> <p>This dataset contains web-based validations of changes detected by time series (2016 – 2019) analysis of Sentinel-2 satellite imagery. Validation was conducted using two high resolution orthophotos from respectively 2016 and 2019 as reference data. Two tools have been used: <a href="https://paysages.ign.fr/">Paysages</a> web application and <a href="https://laco-wiki.net/">LACO-Wiki</a>. Both tools used the same validation design: blind validation and the same options. For each detected change, contributors are asked to validate if there is a change and if it is the case then to choose a LU or LC class from a pre-defined list of classes.</p> <p>The dataset has the following characteristics:</p> <ul> <li>Time period of the change detection: 2016-2019.</li> <li>Time period of data collection: February 2019-December 2019</li> <li>Total number of contributors: 105</li> <li>Number of validated changes: 1048; each change was validated by between 1 to 6 contributors.</li> <li>Region of interest: Toulouse and surrounding areas</li> </ul> <p>Associated files: 1- Change validation locations.png, 1-Change validation 2019 – Attributes.csv, 1-Change validation 2019.csv, 1-Change validation 2019.geoJSON</p> <p>This dataset is licensed under a Creative Commons Attribution 4.0 International. It is attributed to the <a href="https://landsense.eu/">LandSense Citizen Observatory</a>, <a href="http://www.ign.fr/">IGN-France</a>, and <a href="https://www.geoville.com/">GeoVille</a>.</p> <p><strong>Dataset 2: Land use classification 2019</strong></p> <p>The aim of this data collection campaign was to improve the LU classification of authoritative LULC data (<a href="https://geoservices.ign.fr/documentation/diffusion/telechargement-donnees-libres.html#ocs-ge">OCS-GE 2016</a> ©IGN) for built-up area. Using the Paysages web platform, contributors are asked to choose a land use value among a list of pre-defined values for each location. </p> <p>The dataset has the following characteristics:</p> <ul> <li>Time period of data collection: August 2019</li> <li>Types of contributors: Surveyors from the production department of IGN</li> <li>Total number of contributors: 5</li> <li>Total number of observations: 2711</li> <li><a href="https://geoservices.ign.fr/ressources_documentaires/Espace_documentaire/BASES_VECTORIELLES/OCS_GE/DC_OCS_GE_1-1.pdf">Data specifications of the OCS-GE</a> ©IGN</li> <li>Region of interest: Toulouse and surrounding areas</li> </ul> <p>Associated files: 2- LU classification points.png, 2-LU classification 2019 – Attributes.csv, 2-LU classification 2019.csv, 2-LU classification 2019.geoJSON</p> <p>This dataset is licensed under a Creative Commons Attribution 4.0 International. It is attributed to the <a href="https://landsense.eu/">LandSense Citizen Observatory</a>, <a href="http://www.ign.fr/">IGN-France</a> and the <a href="https://iiasa.ac.at/">International Institute for Applied Systems Analysis</a>.</p> <p><strong>Dataset 3: In-situ validation 2018</strong></p> <p>The aim of this data collection campaign was to collect in-situ (ground-based) information, using the Paysages mobile application, to update authoritative LULC data. Contributors visit pre-determined locations, take photographs, of the point location and in the four cardinal directions away from the point and answer a few questions with respect with the task. Two tasks were defined: </p> <ul> <li>Classify the point by choosing a LU class between three classes: industrial (US2), commercial (US3) or residential (US5).</li> <li>Validate changes detected by the LandSense Change Detection Service: for each new detected change, the contributor was requested to validate the change and choose a LU and LC class from a pre-defined list of classes.</li> </ul> <p>The dataset has the following characteristics </p> <ul> <li>Time period of data collection: June 2018 – October 2018</li> <li>Types of contributors: students from the School of Agricultural and Life Sciences and citizens</li> <li>Total number of contributors: 26</li> <li>Total number of observations: 281</li> <li>Total number of photos: 421</li> <li>Region of interest: Toulouse and surrounding areas</li> </ul> <p>Associated files: 3- Insitu locations.png, 3- Insitu validation 2018 – Attributes.csv, 3- Insitu validation 2018.csv, 3- Insitu validation 2018.geoJSON</p> <p>This dataset is licensed under a Creative Commons Attribution 4.0 International. It is attributed to the <a href="https://landsense.eu/">LandSense Citizen Observatory</a>, <a href="http://www.ign.fr/">IGN-France</a>.</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no 689812.</p>
Exploring and promoting green urban spaces in Vienna - can data about public perception help drive change?
<p>Citizen Science has become a vital source for data collection when the spatial and temporal extent of a project makes it too expensive to send experts into the field. However, involving citizens can go further than that – participatory projects focusing on subjective parameters can fill in the gap between local community needs and stakeholder approaches to tackle key social and environmental issues.</p> <p>The Horizon 2020 project, <a href="https://landsense.eu/">LandSense</a>, is building a modern citizen observatory for Land Use & Land Cover (LULC) monitoring, by engaging citizens to transform current approaches to environmental decision making. Citizen Observatories are community-driven mechanisms to complement existing environmental monitoring systems and can be fostered through mobile and web applications, allowing citizens to play a key role in environmental monitoring. Within this project, the City Oases mobile application, focused on the city of Vienna, has been developed that aims not only to stimulate civic engagement to monitor changes within the urban environment, but also to enable users to drive improvements by providing city planners with information about the public perception of urban spaces. <a href="https://play.google.com/store/apps/details?id=com.iiasa.cityoases">City Oases</a> was launched in March 2019.</p> <p>Where are the best places for a romantic date? Where can you skate? Where is it cool on a hot summer’s day? Open urban spaces can be used in many ways. Pick an activity in the CityOases app and we show the spots where you can do them, including the rating of previous users and pictures from the location. If you visit the spot you can rate it as well based on a few selected subjective criteria. If you know a cool spot that is openly accessible but not marked in our map yet? Just add it with a list of activities and some pictures. Additionally, you can input your perceptions including whether it is noisy, clean or if the infrastructure is attractive. The picture module within the application promotes you to take photos in the four cardinal directions. Users can search for specific activities, visit one of the points indicated on the map and then evaluate this point.</p> <p>Time period of data collection: 19/03/2019 - 10/11/2019</p> <p>Types of contributors: General Public, Students<br> Number of contributors: 50<br> Number of observations: 788<br> Number of photos: 1846</p> <p>Associated files: City Oases Vienna 2019.csv, City Oases Vienna 2019.geoJSON, City Oases Vienna 2019 Attributes.csv</p> <p>This dataset is licensed under a Creative Commons Attribution 4.0 International. It is attributed to the <a href="https://landsense.eu/">LandSense Citizen Observatory</a>, <a href="https://www.umweltbundesamt.at/en/">Umweltbundesamt</a> (Environment Agency Austria) and the <a href="https://iiasa.ac.at/">International Institute for Applied Systems Analysis</a>.</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no 689812.</p>
Supporting dataset for "Numerical insights into the effects of model geometric distortion in laboratory experiments of urban flooding"
<p>The supporting datasets includes:</p> <p>(1) All the Figures in the manuscript in .fig format </p> <p> - Figures in main text</p> <p> - Figures in SI</p> <p>(2) Original matrice data for the results of each run</p> <p> - A file that explains detailed data content in tree structure;</p> <p> - 4 sub-repositories which contain data for models in different scales</p>
FIGURE 4 in Description of larva and pupa of Phylloicus cressae Prather 2003 (Trichoptera Calamoceratidae) from a montane forest stream in the peri-urban area of Caracas Venezuela
FIGURE 4: Phylloicus cressae, larva; right thoracic legs I, II, III, posterior. 4A, foreleg; 4B, midleg; 4C, hind leg. Legs chaetotaxy: Coxa: 1 = posterior, dorsal, 3 = anterior, dorsal, proximal, 4 = anterior, dorsal, distal, 5 = anterior, median, 6 = anterior, median. Trochanter: 1 = posterior, median, 2 = posterior, ventral, distal. Femur: 1 = dorsal, median, 2 = dorsal, distal, 3 = posterior, distal, 4 = posterior, ventral, 5 = anterior, ventral, proximal, 6 = anterior, distal. Tibia: 2 = posterior, median, distal, 3 = posterior, dorsal, distal. Tarsus: 1 = posterior, ventral, distal, 2 = dorsal, distal, 3 = dorsal, distal.
FIGURE 3 in Description of larva and pupa of Phylloicus cressae Prather 2003 (Trichoptera Calamoceratidae) from a montane forest stream in the peri-urban area of Caracas Venezuela
FIGURE 3: Phylloicus cressae, larva; thorax, dorsal. 3A, pronotum; 3B, mesonotum; 3C, metanotum. Chaetotaxy of mesonotum and metanotum: DSa1 = tactile seta in dorsal Sa1, DSa2 = tactile setae (2 or 3) in dorsal Sa2, DSa3 = group of long and short tactile setae in dorsal Sa3.
TAU Urban Acoustic Scenes 2020 Mobile, Development dataset
<p>TUT Urban Acoustic Scenes 2020 Mobile development dataset consists of 10-seconds audio segments from 10 acoustic scenes:</p> <ul> <li>Airport - <em>airport</em></li> <li>Indoor shopping mall - <em>shopping_mall</em></li> <li>Metro station - <em>metro_station</em></li> <li>Pedestrian street - <em>street_pedestrian</em></li> <li>Public square - <em>public_square</em></li> <li>Street with medium level of traffic - <em>street_traffic</em></li> <li>Traveling by a tram - <em>tram</em></li> <li>Traveling by a bus - <em>bus</em></li> <li>Traveling by an underground metro - <em>metro</em></li> <li>Urban park - <em>park</em></li> </ul> <p>The dataset contains in total 64 hours of audio. Version 2 fixes synchronization between 118 segments from devices A, B, and C, and removes 5 incorrect audio segments.</p>
DATABASE: RUSSIAN LARGE URBAN REGIONS 2020
<p>This database provides construction of Large Urban Regions (LUR) in Russia. A Large Urban Region (LUR) can be defined as an aggregation of continuous statistical units around a core that are economically dependent on this core and linked to it by economic and social strong interdependences. The main purpose of this delineation is to make cities comparable on the national and world scales and to make comparative social-economic urban studies. Aggregating different municipal districts around a core city, we construct a single large urban region, which allows to include all the area of economic influence of a core into one statistical unit (see Rogov & Rozenblat, 2020 for more details) thus, changing a city position in a global urban hierarchy. In doing so we use four principal urban concepts (Pumain et al., 1992): political definition, morphological definition, functional definition and conurbation that we call Large Urban Region. We constructed Russian LURs using criteria such as population distribution, road networks, access to an airport, distance from a core, presence of multinational firms. In this database, we provide population data for LURs and their administrative units.</p>
TAU Urban Acoustic Scenes 2020 Mobile, Evaluation dataset
<p>TUT Urban Acoustic Scenes 2020 Mobile development dataset consists of 10-seconds audio segments from 10 acoustic scenes:</p> <ul> <li>Airport - <em>airport</em></li> <li>Indoor shopping mall - <em>shopping_mall</em></li> <li>Metro station - <em>metro_station</em></li> <li>Pedestrian street - <em>street_pedestrian</em></li> <li>Public square - <em>public_square</em></li> <li>Street with medium level of traffic - <em>street_traffic</em></li> <li>Travelling by a tram - <em>tram</em></li> <li>Travelling by a bus - <em>bus</em></li> <li>Travelling by an underground metro - <em>metro</em></li> <li>Urban park - <em>park</em></li> </ul>
Data from: Microplastic prevalence in 4 Oregon rivers along a rural to urban gradient applying a cost‐effective validation technique
<p>Microplastics are ubiquitous in our environment and are found in rivers, streams, oceans, and even tap water. Riverine microplastics are relatively understudied compared to those in marine ecosystems. In Oregon, we sampled eight sites along four freshwater rivers spanning rural to urban areas to quantify microplastics. Plankton tow samples from sites along the Columbia, Willamette, Deschutes, and Rogue Rivers were analyzed using traditional light microscopy for initial microplastic counts. Application of Nile Red dye to validate microplastics improved microplastic identification, particularly for particles (Wilcox Test; p-value=0.001). Nile Red-corrected microfiber abundance was correlated with human population within five kilometers of the sample site (R²=0.554), though no such relationship was observed between microparticles and population (R²=0.183). This study finds plastics present in all samples from all sites, despite the range from undeveloped, remote stretches of river in rural areas to metropolitan sites within Portland, demonstrating the pervasive presence of plastic pollution<span> </span><span><span>in freshwater environments.</span></span></p>
Sigfox and LoRaWAN Datasets for Fingerprint Localization in Large Urban and Rural Areas
<p><strong>INTRODUCTION</strong></p> <p>The goal of these LPWAN datasets is to provide the global research community with a benchmark tool to evaluate fingerprint localization algorithms in large outdoor environments with various properties. An identical collection methodology was used for all datasets: during a period of three months, numerous devices containing a GPS receiver periodically obtained new location data, which was sent to a local data server via a Sigfox or LoRaWAN message. Together with network information such as the receiving time of the message, base station IDs' of all receiving base stations and the Received Signal Strength Indicator (RSSI) per base station, this location data was stored in one of the three LPWAN datasets: </p> <ul> <li> <p><strong>lorawan_dataset_antwerp.csv</strong></p> </li> <li> <p>130 430 LoRaWAN messages, obtained in the city center of Antwerp</p> </li> <li> <p><strong>sigfox_dataset_antwerp.csv</strong></p> </li> <li> <p>14 378 Sigfox messages, obtained in the city center of Antwerp</p> </li> <li> <p><strong>sigfox_dataset_rural.csv</strong></p> </li> <li> <p>25 638 Sigfox messages, obtained in a rural area between Antwerp and Ghent</p> </li> </ul> <p>As the rural and urban Sigfox datasets were recorded in adjacent areas, many base stations that are located at the border of these areas can be found in both datasets. However, they do not necessarily share the same identifier: e.g. ‘BS 1’ in the urban Sigfox dataset could be the same base station as ‘BS 36’ in the rural Sigfox dataset. If the user intends to combine both Sigfox datasets, the mapping of the ID's of these base stations can be found in the file:</p> <ul> <li> <p><strong>sigfox_bs_mapping.csv</strong></p> </li> </ul> <p>The collection methodology of the datasets, and the first results of a basic fingerprinting implementation are documented in the following journal paper:<br> <a href="http://www.mdpi.com/2306-5729/3/2/13">http://www.mdpi.com/2306-5729/3/2/13</a></p> <p> </p> <p><strong>UPDATES IN VERSION 1.3</strong></p> <p>We added the file <strong>lorawan_antwerp_gateway_locations.json.txt. </strong>As the filename suggests, this file contains the coordinates of the LoRaWAN gateways in Antwerp. (The .txt file type had to be appended, otherwise the file could not be uploaded to Zenodo).</p> <p> </p> <p><strong>UPDATES IN VERSION 1.2</strong></p> <p>In this version of the LPWAN dataset, only the LoRaWAN set has been updated. The Sigfox datasets remain identical to version 1.0 and 1.1. The main updates in the LoRaWAN set are the following:</p> <ul> <li> <p><strong>New data</strong>: the LoRaWAN messages in the new set are collected 1 year after the previous dataset version. To be consistent with the previous versions, the new LoRaWAN set is uploaded in the same .CSV format as before. This upload can still be found in this repository as ‘<strong>lorawan_dataset_antwerp.csv</strong>’.</p> </li> <li> <p><strong>More gateways</strong>: Compared to the previous dataset, 4 gateways were added to the LoRaWAN network. The RSSI of these gateways are shown in columns ‘BS 69’, ‘BS 70’,‘BS 71’ and ‘BS 72’. All other ‘BS’ columns are in the same order as in previous dataset versions.</p> </li> <li> <p><strong>More metadata: </strong>In the previous LoRaWAN dataset, metadata was limited to 3 receiving gateways per message. In the new dataset version, metadata from all receiving gateways is included in every message. Moreover, some gateways provide a timestamp with nanosecond precision, which can be used to evaluate Time Difference of Arrival localization methods with LoRaWAN.</p> </li> <li> <p><strong>2 file formats: </strong>As more metadata becomes available, we find it important to share the dataset in a clearer overview. This also allows researchers to evaluate the performance of LoRaWAN in an urban environment. Therefore, we publish the new LoRaWAN dataset as a .CSV file as described above, but also as a .JSON file (<strong>lorawan_antwerp_2019_dataset.json.txt,</strong> the .txt file type had to be appended, otherwise the file could not be uploaded to Zenodo) An example of one message in this JSON format can be seen below:</p> <ul> <li> <p>JSON format description:</p> <ul> <li> <p><strong>HDOP</strong>: Horizontal Dilution of Precision</p> </li> <li> <p><strong>dev_addr: </strong>LoRaWAN device address</p> </li> <li> <p><strong>dev_eui: </strong>LoRaWAN device EUI</p> </li> <li> <p><strong>sf: </strong>Spreading factor</p> </li> <li> <p><strong>channel: </strong>TX channel (EU region)</p> </li> <li> <p><strong>payload: </strong>application payload</p> </li> <li> <p><strong>adr: </strong>Adaptive Data Rate (1 = enabled, 0= disabled)</p> </li> <li> <p><strong>counter: </strong>device uplink message counter</p> </li> <li> <p><strong>latitude: </strong>Groundtruth TX location latitude</p> </li> <li> <p><strong>longitude: </strong>Groundtruth TX location longitude</p> </li> <li> <p><strong>airtime: </strong>signal airtime (seconds)</p> </li> <li> <p><strong>gateways:</strong></p> <ul> <li> <p><strong>rssi: </strong>Received Signal Strength</p> </li> <li> <p><strong>esp: </strong>Estimated Signal Power</p> </li> <li> <p><strong>snr: </strong>Signal-to-Noise Ratio</p> </li> <li> <p><strong>ts_type: </strong>Timestamp type. If this says "GPS_RADIO", a nanosecond precise timestamp is available</p> </li> <li> <p><strong>time: </strong>time of arrival at the gateway</p> </li> <li> <p><strong>id: </strong>gateway ID</p> </li> </ul> </li> </ul> </li> <li> <p>JSON example</p> <pre><code class="language-json">{ "hdop": 0.7, "dev_addr": "07000EFE", "payload": "008d000392d54c4284d18c403333333f04682aa9410500e8fd4106cabdbc420f00db0d470ce32ac93f0d582be93f0bfa3f8d3f", "adr": 1, "latitude": 51.20856475830078, "counter": 31952, "longitude": 4.400575637817383, "airtime": 0.112896, "gateways": [ { "rssi": -115, "esp": -115.832695, "snr": 6.75, "rx_time": { "ts_type": "None", "time": "2019-01-04T08:59:53.079+01:00" }, "id": "08060716" }, { "rssi": -116, "esp": -125.51497, "snr": -9.0, "rx_time": { "ts_type": "GPS_RADIO", "time": "2019-01-04T08:59:53.962029179+01:00" }, "id": "FF0178DF" } ], "dev_eui": "3432333853376B18", "sf": 7, "channel": 8 }</code></pre> <p> </p> </li> </ul> </li> </ul> <p> </p>
Effects of Vegetation Urban Street Canyons. Summer 2017 and Winter 2018 Experimental Campaigns
<p> </p> <p>This dataset contains the observations of air quality collected within two experimental campaigns conducted in summer 2017 (07 August 2017-26 September 2017) and winter 2018 (15 January 2018-15 February 2018) in two street canyons in the city of Bologna, to verify the effectiveness of vegetation in altering ventilation levels and pollutant concentration in real-world street canyons. The dataset also contains the meteorological data collected during non-synoptic periods, representing the ideal condition to assess the behavior of the local circulation and turbulence. The experimental field campaigns were setup under WP3 (“Planning and evaluation of PCS solutions”) and conducted under WP5 (“Monitoring and Evaluation of the Interventions”) of the iSCAPE project. Briefly, two parallel urban street canyons characterized by a different occurrence of vegetation, i.e. one almost free of vegetation (Marconi St.) and one with a tree line along both sides of the street (Laura Bassi St.), were instrumented for measurements of meteorology and turbulence variables at three height levels, while pollutant concentrations were monitored at ground level inside both canyons. This dataset is linked to the paper “Disentangling the effect of vegetation on ventilation and air quality in urban street canyons: the Bologna iSCAPE experimental campaigns” by Di Sabatino et al., which will be submitted for publication on “Science of the Total Environment” edited by Elsevier, and to the paper “Characteristic Scales for Turbulent Exchange Processes in a Real Urban Canopy” by Barbano et al., which is under review for publication on “Boundary-Layer Meteorology” edited by Springer. If you use this dataset for your research or work purposes, we ask that you acknowledge us in the use of your data, which can be done by including a proper citation in any documents or publications using these data.</p>
Rainfall-runoff data from an urban catchment in Luleå, Sweden.
<p>Description and map of the catchment:<br> Broekhuizen, Ico, Günther Leonhardt, Jiri Marsalek, and Maria Viklander. “Event Selection and Two-Stage Approach for Calibrating Models of Green Urban Drainage Systems.” Hydrology and Earth System Sciences 24, no. 2 (February 26, 2020): 869–85. https://doi.org/10.5194/hess-24-869-2020.</p> <p>Rain gauge: Geonor T200B weighing bucket . Alter-type wind shield.</p> <p>Flow gauge: Teledyne ISCO 2150 Area-Velocity: acoustic Doppler for flow velocity, pressure transducer for water level.</p> <p>Additional information on field calibration checks of the rain gauge and laboratory testing of the same type of flow sensor:<br> Broekhuizen, Ico. “Uncertainties in Rainfall-Runoff Modelling of Green Urban Drainage Systems: Measurements, Data Selection and Model Structure.” Licentiate thesis, Luleå University of Technology, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-73367.</p> <p>Notes:<br> - The observations in this data set cover the snow-free parts of 2016-2019.<br> - For flow data, only observations >= 1 l/s are provided.</p> <p> </p> <p>The authors would like to thank Helen Galfi, Ralf Rentz and Karolina Berggren for their work in the initial setup and maintenance of the field measurements.</p>
Data from: Metropolitan lizards? Urbanization gradient and the density of lagartixas (Tropidurus hispidus) in a tropical city
Urbanization, with its cohort of environmental stressors, has a dramatic effect on wildlife, causing loss of biodiversity and decline in population abundance customarily associated with increasing levels of impervious surface and fragmentation of native habitats. Some studies suggest that faunal species from open habitats, and with higher abundance in natural environments, seem more likely to tolerate and live in urban environments. Here I evaluate how the level of urbanization affects lagartixas (Tropidurus hispidus) one of the most common lizards found in open vegetation ecosystems in NE Brazil. I surveyed a total of 47 transects across sites that differed in proportion of impervious surface (high, mild, peri-urban and rural). I also collected specific biotic (herbaceous cover, tree and arthropod abundance) and abiotic (amount of shelters and impervious surfaces) factors that could affect lagartixas abundance. Ants were the most common arthropod both in the rural and urban environment. Lagartixas thrive in urban environments and trees and shelter were key predictors of their abundance. Lagartixas show a clear association with use of artificial structures. The low densities of lagartixas in rural areas and higher density in urbanized areas suggests that they colonized urban areas due to the hard surfaces and they probably are not exploiting a novel habitat, but somewhat responding to conditions resembling those in which they evolved. Finally, lagartixas are extremely common in tropical cities, they have a suite of features that are associated with selective pressures in cities and they might play a key functional role in urban ecosystems making this lizard an excellent system for the study of ecology and adaptation to the urban environments.
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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.