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1,308 results for “Vehicle”
Urbanev: An open benchmark dataset for urban electric vehicle charging demand prediction
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Dead mammal walking: a month-long march by a bison (Bison bison) after an ungulate-vehicle collision
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Vehicle pollution is associated with elevated insect damage to street trees
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Data for: Research and application of bag filter system for railway ballast bed coal suction vehicles
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Data from: Engineered nucleocytosolic vehicles for loading of programmable editors
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Transcriptional changes in macaques exposed to Sudan virus and treated with a vehicle controls or obeldesivir for 5 or 10 days
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Data from: US-Mexico second-hand electric vehicle trade: Battery circularity and end-of-life policy implications
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Source Data for Crowdsourcing Bridge Dynamic Monitoring with Smartphone Vehicle Trips
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Vehicles Survey: Twin Cities Household Ecosystem Project
We designed our methods to estimate carbon, nitrogen, and phosphorus fluxes through individual households, and to address two primary questions: 1. How are these fluxes distributed across households? 2. What biophysical and socioeconomic factors contribute to differences in these fluxes across households? Our hybrid approach combines: 1. A mailed survey 2. Energy provider records 3. On-the-ground landscape measurements 4. A computational tool (the Household Flux Calculator) 4. Parcel data (interpreted using GIS) The resulting dataset includes information on biophysical and socioeconomic variables that potentially influence household-level fluxes of elements. Using this method to study element fluxes at the household level allows us to explicitly link consumption choices and element fluxes.
Brainport, Automated valet parking, pickup scenario with DLR vehicle
<p><strong>Scenario description</strong>:</p> <p>The AD-vehicle receives parking command message (AutoPilot.VehicleCommand) containing the destination pickup spot and the free obstacle route and drives from the parking spot and parks to the destination pickup spot.During the collection process the vehicle send two type of messages (AutoPilot.PositionEstimate and AutoPilot.VehicleAVPStatus)</p> <p><strong>Session description</strong>:</p> <p>The pickup scenario with DLR vehicle. DLR vehicle parks autonomously from the parking spot to the destination pickup spot at the parking area on DLR test site in Brunswick.</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_DriverVehicleInteraction</strong>: Data extracted from the CAN of the vehicle</p> <p>Dataset Description This dataset contains e.g. throttlestatus, clutchstatus, brakestatus, brakeforce, wipersstatus, steeringwheel for the vehicle</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_DroneAvpCommand</strong>: Data sent from drone</p> <p>Dataset Description This dataset contains route information for a vehicle to a designated parking spot</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_EnvironmentSensorsAbsolute</strong>: Data extracted from the vehicle environment sensors</p> <p>Dataset Description This dataset contains information about detected object, with absolute coordinates</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_EnvironmentSensorsRelative</strong>: Data extracted from the vehicle environment sensors</p> <p>Dataset Description This dataset contains information about detected object, with relative coordinates</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_IotVehicleMessage</strong>: Data sent between all devices, vehicles and services</p> <p>Dataset Description Each sensor data submission is a Message. A Message has an Envelope, a Path, and optionally (but likely) Path Events and optionally Path Media. The envelope bears fundamental information about the individual sender (the vehicle) but not to a level that owner of the vehicle can be identified or different messages can be identified that originate from a single vehicle.</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_ParkingSpotDetection</strong>: Data sent from drone to parkingService</p> <p>Dataset Description This dataset contains informaton about detected parking spots</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_PositioningSystem</strong>: Data from GPS on the vehicle</p> <p>Dataset Description This dataset contains speed, longitude, latitude, heading from the GPS</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_PositioningSystemResampled</strong>: Data from GPS on the vehicle</p> <p>Dataset Description This dataset contains speed,longitude,latitude,heading from the GPS, resampled to 100 milliseconds</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_Vehicle</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>Dataset Description This dataset contains a.o temperature and battery state of the vehicles</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleAvpCommand</strong>: Data sent from ParkingService to vehicle</p> <p>Dataset Description This dataset contains route to parkingspot, and some other environmental information</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleAvpStatus</strong>: Data sent from vehicle to ParkingService</p> <p>Dataset Description This dataset contains information about the current status and parkingstatus of the vehicle</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleDynamics</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>Dataset Description This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors</p>
Livorno, Urban driving, Automated vehicle and smart traffic light
<p><strong>Scenario description</strong>:</p> <p> </p> <p><strong>Session description</strong>:</p> <p> </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 and connected vehicle and smart traffic light
<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.</p> <p><strong>Session description</strong>:</p> <p>A "smart" traffic light sends SPaT and MAP messages describing the topology, actual status of the traffic light to other connected vehicles and to the oneM2M platform on the cloud.<br> An AD vehicle consumes the information and autonomously adapts its speed in order to cross the intersection without violating the traffic light phases, considering also other vehicles moving in front. 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 Drining 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 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>
Comparing power-system- and user-oriented battery electric vehicle charging representation and its implications on energy system modeling
<p>This supplementary material includes data and code for the research described in the paper "Comparing power-system- and user-oriented battery electric vehicle charging representation and its implications on energy system modeling". The code containts an interface between the output files of the agent-based simulation model CURRENT and the energy system optimization model REMix as well as some scripts for analyzing REMix results. The data folder contains input data for REMix, the complete list of all model runs analyzed in the paper in the GAMS format .gdx as well as Excel files containing annual results of the sensitivity runs and respective pivot tables and figures for respective analysis.</p>
MOTIVE - tiMe-OpTimized contextual Information flow on unmanned VEhicles project experimental results
<p>The experimentation data were collected during the 6th Fed4FIRE+ Open Call (https://www.fed4fire.eu/) using the mobile nodes of the w-iLab.t (link) testbed. During this Open Call we proposed to evaluate the performance of an optimization model for temporal control of the transmission of messages from an IoT mobile device. This mechanism is based on a network condition model that transits from favourable to adverse conditions and vice versa. All these transitions are monitored and validated through our system (change detection and optimal stopping). If a network is performing properly then the transmission control can be relaxed to exploit available resources.The main contribution of MOTIVE is to apply a sequential decision-making process (DMP) on IoT devices that leverages the on-line derived network statistics to efficiently control their telemetry measurements transmission.</p> <p>Our data collected from the experimentation using the testbed's devices consists of network related information, packet error rate and latency. We collected data from a completely functional mobile node that operates in a saturated network and five mobile nodes in the same circumstances. Saturated conditions were generated by data produced by twenty static sensors for the duration of the each experiment. On each run we collected more than 2 * 105 samples. The comparative assessment we did was based on five different policies of decision making: i) no-policy model, ii) the heuristic threshold based model in which the transmission of the messages is paused when a specific threshold of quality network is below a threshold, iii) TOCP model which overviews the quality of network in normal mode and gets in pausing mode when a change is detected; the pausing period lasts for a specific threshold and then it is activated again, iv) TOCP-DRP and the v) fair TOCP-DRP which enters in pausing mode if a change is indicated by TOCP decision making model.</p> <p>The datasets include the packet error rate and the latency of each device, for each experiment, and the critical areas found during the experiments (most saturated areas).</p> <p>The datatset includes for each scenario(experiment) the participating devices and the Latency and Packet Error Rate(PER) per policy( NoPolicy, Heuristic, TOCP, TOCPDRP, TOCPDRPFAIR), apart from scenarioA(single node) where only four policies are present, since the TOCPDRPFAIR applies with more that one participating nodes.</p>
Possibility of bridge inspection through drive-by vehicles
<p class="midium"><span><span><span>Based on virtual simulations of vehicle-bridge interactions, the possibility of detecting stiffness reduction damages in bridges through vehicle responses has been tested in two dimensional (2D) and three dimensional (3D) settings. Short Time Fourier Transformation (STFT) has been used to process the acceleration data of vehicles. The energy band variation was found strongly related to damage parameters. More important, initial entering conditions of vehicles are critical in obtaining vehicle responses through the vehicle bridge interaction models. Through different levels of road profile roughness, the offset distance needed before executing the vehicle-bridge interaction (VBI) modeling is obtained.</span></span></span></p>
A digital mapping of the literature on vehicle-bridge-wind systems
<p>This dataset is the result of a systematic search and mapping of the literature relevant to the vehicle-bridge-wind system. The search phrase used to search the databases listed below can be found in tabulated form in one of the spreadsheets. The core component to this search phrase is that the title, abstract or keywords should include all three of the words vehicle, bridge and wind. Using one possible syntax for the boolean operators, the search phrase might look like:</p> <ul> <li>("numerical analysis" OR model OR modelling OR modeling OR experiment OR field OR "wind tunnel" OR parameter OR "case study" OR coupled OR interaction OR analysis OR vbi OR safety) AND (("bridge" AND "vehicle" AND "wind")) AND ("long span" OR "long-span" OR floating OR "cable stayed" OR "cable-stayed" OR suspension OR long OR "floating tunnel") NOT ("short span" OR "short-span" OR rail OR train)</li> </ul> <p>Note that the search is specific to long-span bridges and road vehicles. The phrase was used to search the following databases:</p> <ul> <li>Scopus</li> <li>Engineering Village</li> <li>Web of Science</li> <li>Science Direct</li> <li>Oria/NTNU</li> </ul> <p>Following a screening process, data of interest was extracted from the articles through structured searches and readings. A description of this process and a succinct presentation of the findings of the search and mapping will follow in the form of a published journal article.</p> <p>The "Database" sheet gives a structured list of the main contributions from all texts considered relevant for the mapping process. The citation keys used in the spreadsheet can be mapped to the referenced texts through the .bib file. Each text is pre-categorized based on readings of the abstract/conclusion into one of the following areas, describing the main contribution of the piece:</p> <ul> <li>Vehicle Dynamics</li> <li>Bridge Dynamics</li> <li>Vehicle Aerodynamics</li> <li>Bridge Aerodynamics</li> <li>Vehicle-bridge Aerodynamics</li> <li>Vehicle-bridge Interaction</li> </ul> <p>Texts are also classified by research level:</p> <ul> <li>Evaluation: development/demonstration of a novel modelling method</li> <li>Lab Validation: validation of models by experimental data or advanced numerical models in a virtual lab (e.g. computational fluid dynamics)</li> <li>Field Validation: the use of field experiments to verify, validate and/or calibrate existing models</li> </ul> <p>Each column can be filtered by entering a phrase in the appropriate cell in the table at the top of the sheet. Enabling macros will allow the sheet to update each time the content of said cell changes. The "Filtered List" is then a list of all texts that satisfy the filters.</p> <p>This is intended as a living document and any and all comments, concerns and questions are warmly welcomed.</p>
Unmanned Aerial Vehicle (UAV) data acquired over an experimental area of the UFSM campus Frederico Westphalen, at October 20, 2020, Rio Grande do Sul, Brazil
<p>Fábio Marcelo Breunig¹ (author)</p> <p><em>¹</em> <em>Universidade Federal de Santa Maria, Departamento de Engenharia Florestal, Frederico Westphalen, Rio Grande do Sul, Brasil. </em><em>E-mail: </em><em>breunig@ufsm.br</em></p> <p>Title:</p> <p> </p> <p>Unmanned Aerial Vehicle (UAV) data acquired over an experimental area of the UFSM campus Frederico Westphalen, at October 20, 2020, Rio Grande do Sul, Brazil</p> <p>Data description:</p> <p> </p> <p><br> The data were acquired from an aerial survey conducted with an Unmanned Aerial Vehicle (UAV, also <em>Drone</em>) covering an experimental area of the Federal University of Santa Maria – UFSM in the municipality of Frederico Westphalen, in the Rio Grande do Sul, Brazil (Figure 1). The climate of the region is subtropical (Cfa in the Köppen-Geiger classification) with an average annual temperature of 18 °C and annual precipitation of 1919 mm (<a href="https://www.sciencedirect.com/science/article/pii/S0303243419309481#bib0015">Alvares et al., 2013</a>). The rainfall is well distributed throughout the year.</p> <p> </p> <p>Figure 1. Location of the site of data acquisition. Based on Google Earth Pro scenes. The KML and KMZ are appended to the files.</p> <p>UAV and camera settings for the acquisition (Specifications Table):</p> <p> </p> <p><strong>Parameters</strong></p> <p><strong>Specification/value</strong></p> <p>Date (YYYYMMDD):</p> <p>20201020</p> <p>Time of day (BRT = -3)</p> <p>11 h a.m.</p> <p>UAV – Drone - Camera</p> <p>Matrice 100 X3</p> <p>Fly high (meters above ground)</p> <p>80 m</p> <p>View angle</p> <p>90° automatic mode.</p> <p>Sky conditions</p> <p>( x ) Clear sky</p> <p>( ) Low cloud coverage (some clouds)</p> <p>( ) Completely cloudy</p> <p>Wind condition</p> <p>( x ) no wind</p> <p>( ) Low speed</p> <p>( ) High speed wind</p> <p>Approximate data acquisition duration</p> <p>16 minutes</p> <p>Total of photographs acquired</p> <p>224</p> <p>Across track coverage</p> <p>80%</p> <p>Cross-track coverage</p> <p>80%</p> <p>Fly planning software</p> <p>Pix4D Capture</p> <p> </p> <p>For more information contact: Fábio Marcelo Breunig, <a href="mailto:breunig@ufsm.br">breunig@ufsm.br</a></p> <p>An example of the mosaic is showed (Figure 2), referring to a screen capture of Agisoft Metashape (Agisoft LLC, 11 Degtyarniy per., St. Petersburg, Russia, 191144) and, the workflow adopted.</p> <p> </p> <p>Figure 2. The capture of an orthomosaic and processing workflow</p> <p> </p> <p> </p> <p>References to the main project/publications:</p> <p> </p> <p>Breunig, Fabio Marcelo. CONESAT – Monitoring the CONESUL using remote sensing data. Project. Federal University of Santa Maria, Campus of Frederico Westphalen. Brazil. Available at: <https://www.researchgate.net/project/CONESAT-Monitoring-the-CONESUL-using-remote-sensing-data>.</p> <p> </p> <p>Breunig, Fabio Marcelo. Integration of multiscale remote sensing data in the precision agriculture and silviculture (in Portuguese: Integração de dados multiescala de sensoriamento remoto na agricultura e silvicultura de precisão). Project. National Council for Scientific and Technological Development (CNPq). Grant 113769/2018-0</p> <p> </p> <p>Breunig, Fabio Marcelo. Combination of UAV, PlanetScope, Landsat and Sentinel-2 images to precision silviculture and agriculture in a subtropical region (in Portuguese: Combinação de imagens de VANT, PlanetScope, Landsat e Sentinal-2 para a silvicultura e agricultura de precisão em uma região subtropical). Project of the National Council for Scientific and Technological Development (CNPq). Grant 305084/2020-8</p> <p> </p> <p>Acknowledgments:</p> <p> </p> <p>This work was supported by the National Council for Scientific and Technological Development (CNPq) (Grants 113769/2018-0, 312081/2013-8, 478085/2013-3 and, 305084/2020-8) and Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (Grant 23830.388.22048.19092016).</p> <p> </p> <p>Other considerations</p> <p> </p> <p>PS. A pdf file is also attached with this description</p> <p> </p> <p>Declaration of Competing Interest</p> <p> </p> <p>The author declares that he has no competing interests or personal relationships that have or could be perceived to have influenced the work reported in this report.</p> <p> </p> <p>References associated:</p> <p> </p> <p>Breunig, Fábio Marcelo (2017, July 7). Unmanned Aerial Vehicle (UAV) data acquired over a subtropical forest area of the UFSM campus Frederico Westphalen, at July 7, 2017, Rio Grande do Sul, Brazil. Zenodo. http://doi.org/10.5281/zenodo.4327943</p> <p>Alvares, Clayton Alcarde, José Luiz Stape, Paulo Cesar Sentelhas, José Leonardo De Moraes Gonçalves, and Gerd Sparovek, ‘Köppen’s Climate Classification Map for Brazil’, <em>Meteorologische Zeitschrift</em>, 22 (2013), 711–28 <https://doi.org/10.1127/0941-2948/2013/0507></p> <p>Breunig, Fábio Marcelo (2019): UAV images acquired over the UFSM campus in Frederico Westphalen, RS, Brazil. Universidade Federal de Santa Maria,<em> PANGAEA</em>, https://doi.org/10.1594/PANGAEA.897548</p> <p>Breunig, Fábio Marcelo (2019): UAV derived orthomosaic over the “prainha” in the municipality of Iraí, Rio Grande do Sul, Brazil. Universidade Federal de Santa Maria,<em> PANGAEA</em>, https://doi.org/10.1594/PANGAEA.897909</p> <p>Sestari, Geovane (2019): RPAS orthomosaic over the remnant of rainforest on UFSM/IFFar campus in the municipality of Frederico Westphalen, Rio Grande do Sul, Brazil.<em> PANGAEA</em>, https://doi.org/10.1594/PANGAEA.910114</p> <p>Breunig, Fábio Marcelo (2017, July 11). Unmanned Aerial Vehicle (UAV) data acquired over a subtropical forest area of the UFSM campus Frederico Westphalen, at July 11, 2017, Rio Grande do Sul, Brazil. Zenodo. http://doi.org/10.5281/zenodo.4328340</p>
Hopping on: Conspecific traveller density within a vehicle regulates parasitic hitchhiking between ephemeral microcosms
<p>Hitchhikers (phoretic organisms) identify their vehicles using species-specific visual, chemical and vibrational cues. However, what factors influence their choice between vehicles of the same species has rarely been investigated.</p> <p>Hitchhikers must not only avoid overcrowded vehicles but may also need to travel with conspecifics to ensure mates at their destination. Hence, a trade-off between overcrowding and presence of conspecifics likely determines choice of a vehicle especially when destination sites are distant, ephemeral and unique.</p> <p>Here, we investigate whether a trade-off between the presence of conspecifics vs overcrowding by conspecifics or heterospecifics on a vehicle affects hitchhiker choice. We also investigate the sensory modality responsible for this choice. We experimentally examine these questions using a phoretic nematode community (containing plant- and animal-parasitic taxa) obligately associated with a brood-site pollination mutualism. In this model system nematodes co-travel with conspecifics and heterospecifics on pollinators as vehicles, between ephemeral plant brood-sites to complete their developmental life cycle. In this system, hitchhiker overcrowding has proven negative impacts on vehicle and plant fitness. We expected nematodes to respond to conspecifics and heterospecific density on offered vehicles when making their choice.</p> <p>We found that animal-parasitic nematodes preferred vehicles containing some conspecifics within a certain density range. However, plant-parasitic nematodes preferentially boarded vehicles that were devoid of conspecifics or had few conspecifics. Plant parasites that preferred empty vehicles likely hitchhiked in pairs. Both nematode types employed volatile cues to discriminate between vehicles with different conspecific nematode densities. Our results suggest that vehicle overcrowding by conspecifics, most likely, guaranteed access to mates at the destination determined hitchhiker choice. Surprisingly, and contrary to our expectations, plant- and animal-parasitic nematodes did not respond to heterospecific crowding on vehicles and did not discriminate between vehicles with different heterospecific nematode densities. The reason for this lack of response to heterospecific presence is unknown.</p> <p>This study not only shows that phoretic organisms use different strategies while choosing a vehicle but also confirms that density-dependent effects can ensure the stability and persistence of phoretic interactions in a mutualism by balancing overcrowding against reproductive assurance.</p>
Data from: A few large roads or many small ones? How to accommodate growth in vehicle numbers to minimise impacts on wildlife
Roads and vehicular traffic are among the most pervasive of threats to biodiversity because they fragmenting habitat, increasing mortality and opening up new areas for the exploitation of natural resources. However, the number of vehicles on roads is increasing rapidly and this is likely to continue into the future, putting increased pressure on wildlife populations. Consequently, a major challenge is the planning of road networks to accommodate increased numbers of vehicles, while minimising impacts on wildlife. Nonetheless, we currently have few principles for guiding decisions on road network planning to reduce impacts on wildlife in real landscapes. We addressed this issue by developing an approach for quantifying the impact on wildlife mortality of two alternative mechanisms for accommodating growth in vehicle numbers: (1) increasing the number of roads, and (2) increasing traffic volumes on existing roads. We applied this approach to a koala (Phascolarctos cinereus) population in eastern Australia and quantified the relative impact of each strategy on mortality. We show that, in most cases, accommodating growth in traffic through increases in volumes on existing roads has a lower impact than building new roads. An exception is where the existing road network has very low road density, but very high traffic volumes on each road. These findings have important implications for how we design road networks to reduce their impacts on biodiversity.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.