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2,442 results for “Car”
Livorno, Highway pilot, one automated and connected car and one connected car
<p><strong>Scenario description</strong>:</p> <p>Precondition:</p> <p>1. AD cars with C-eHorizon and V2X OBU devices on board travels on the highway. The highway is equipped with IoT G5 RSUs. All the devices publish and share the information by the oneM2M platform in the cloud.</p> <p>Actions or events:</p> <p>1 The Traffic Control Center publishes the presence of roadway works to the OneM2M platform.</p> <p>2 The RSU (subscribed to the OneM2M platform) receives the information and it broadcasts to the vehicles the DENM message containing information about available lanes, speed limits, geometry, alternative routes etc.</p> <p>3 At the same time the CONTI cloud is subscribed to the oneM2M platform; it receives and share with the FCA cloud the information of the road works, updating dynamically the maps of the Connected e-Horizon installed onboard the CRF AD car</p> <p>4 The in-vehicle application fusing the information from the OBU, the C-eHorizon and on-board sensors, performs speed adaptation and lane change maneuvers</p> <p>Relevant situations: How the AD function interacts with different IoT input: from I2V (DENM, Roadwork position and extension); from V2V (CAM with info from other vehicles).</p> <p><strong>Session description</strong>:</p> <p>Test session with one AD+connected car and one connected car, lap of 11,6 km on the highway.</p> <p><strong>Datasets description</strong>:</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_Vehicle_all</strong>: Data generated from the vehicle sensors</p> <p>This dataset refers to the vehicle datasets generated from the vehicle sensors during Highway Piloting 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_HighwayPilot_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 Highway Piloting in Livorno.</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_IoT_all</strong>: Data extracted from IoT oneM2M platform</p> <p>This dataset refers to messages exchanged by HighwayPilot devices, applications and services across the oneM2M platform.</p>
spanish_used_car_market: Coches de segunda mano a la venta en España
<p>En el siguiente <em>dataset</em> se presentan los datos sobre <strong>coches en venta en el territorio español </strong>obtenidos fruto del desarrollo de la Práctica 1 de la asignatura 'Tipología y Ciclo de Vida de los Datos' del Máster en Ciencia de Datos de la Universitat Oberta de Catalunya, con fin meramente académico.</p> <p>Los datos han sido recolectados utilizando técnicas de <em>web scraping </em>sobre el sitio web www.milanuncios.com.</p> <p>Los atributos del fichero .CSV se definen a continuación:</p> <ul> <li><strong>ad_id</strong>: Identificador del anuncio del coche.</li> <li><strong>ad_type</strong>: Tipo de anuncio. En nuestro caso, siempre va a ser Oferta.</li> <li><strong>ad_time</strong>: Tiempo que llevaba publicado el anuncio cuando se recogió la información, en formato X horas o X días. En el caso en que fuese un anuncio destacado, no tenemos esa información.</li> <li><strong>ad_title</strong>: Título del anuncio de venta, con formato {Marca} – {Modelo}.</li> <li><strong>car_desc</strong>: Preview de la descripción del anuncio de venta del vehículo.</li> <li><strong>car_km</strong>: Kilómetros que tiene recorridos el coche.</li> <li><strong>car_year</strong>: Año de matriculación del vehículo.</li> <li><strong>car_engine_type</strong>: Tipo de transmisión. Posibles valores: Manual | Automático.</li> <li><strong>car_door_num</strong>: Número de puertas de las que dispone el coche.</li> <li><strong>car_power</strong>: Potencia del vehículo, en formato XXX CV.</li> <li><strong>car_price</strong>: Precio en euros por el que se vende el coche.</li> <li><strong>advertizer_type</strong>: Indica cuál es el tipo de vendedor del vehículo. Valores posibles: Profesional | Particular.</li> <li><strong>image_url</strong>: Foto principal del anuncio de venta del coche.</li> <li><strong>ts</strong>: Hora en la que se recogió la información, con formato YYYY-MM-DD hh:mm:ss.ms.</li> <li><strong>region</strong>: Provincia en la que se está vendiendo el vehículo.</li> </ul>
Dataset for the publication: Potential of alcohol fuels in active and passive pre-chamber applications in a passenger car spark-ignition engine
<div> <p>The dataset covers the research data of the publication "Potential of alcohol fuels in active and passive pre-chamber applications in a passenger car spark-ignition engine" in International Journal of Engine Research (DOI: 10.1177/14680874211053168).</p> </div>
Fortalecimiento de Acueductos Veredales en Cundinamarca mediante la Apropiación Social del Conocimiento y Técnicas de Muestreo de Agua según NTC ISO/IEC 17025:2017, por la CAR Cundinamarca.
<p>Este proyecto es crucial para las comunidades rurales de Cundinamarca, ya que fortalece su capacidad técnica en la gestión del agua, un recurso vital. Al capacitar a los operadores en técnicas avanzadas de muestreo y análisis según estándares internacionales, se mejora la calidad del agua consumida y se asegura el cumplimiento de normativas ambientales y de salud pública.</p>
Car Wash Booking System
<p><a href="https://www.trakmeets.com/car-wash-scheduling-software">Car wash booking app</a> is available from <strong>Trakmeets</strong>. Allow consumers to select their chosen time to avoid the inconveniences of standing in line. There was no misunderstanding, no call waiting, and the appointment was set up in a matter of minutes.</p>
cars_dataset
<p>Dataset formado por 320 observaciones de coches con 11 variables. A continuación, se describe cada uno de estos 11 atributos:</p> <p>· Name: nombre del coche anunciado. Clase: carácter.</p> <p>· Location: ubicación del coche anunciado. Clase: carácter (Madrid, Barcelona, Valencia, Alicante, Málaga, Cantabria, Islas Baleares, Granada, Murcia, Vizcaya, Álava, Badajoz, Cáceres, Zamora, Sevilla, Córdoba, Pontevedra, Salamanca, La Coruña, León, Lleida, Cádiz, Asturias, Guipúzcoa, Las Palmas, Albacete, Ávila, Burgos, Valladolid, Almería, Huelva, Sta. C. de Tenerife, Jaén, Huesca, Castellón, Girona, Toledo, Tarragona, Zaragoza, Guadalajara, Ciudad Real, Ceuta).</p> <p>· Year: año de matriculación del coche anunciado. Clase: entero.</p> <p>· Combustible: tipo de combustible del coche anunciado. Clase: carácter (Diésel, Gasolina, Híbrido, Eléctrico, Híbrido Enchufable).</p> <p>· Kilometers: cantidad de kilómetros del coche anunciado. Clase: float.</p> <p>· Transmission: tipo de transmisión del coche anunciado. Clase: carácter (Automático, Manual).</p> <p>· Power [CV]: cantidad de caballos de potencia del coche anunciado. Clase: entero.</p> <p>· Price [€]: precio del coche anunciado. Clase: float.</p> <p>· Financed Price: financiación, o no, del precio del coche anunciado. Clase: carácter (Con financiación, Sin financiación).</p> <p>· Dealer: concesionario del coche anunciado. Clase: carácter.</p> <p>· Dealer Rating (over 5): puntuación sobre 5 del concesionario del coche anunciado. Clase: float.</p>
DATASET: Biobased composites by photoinduced polymerization of car-danol methacrylate with microfibrillated cellulose - FTIR data
<p>Raw FTIR data for the article "Biobased composites by photoinduced polymerization of car-danol methacrylate with microfibrillated cellulose"</p>
GP22: A Car Styling Dataset for Automotive Designers
<p>GP22 is the dataset relevant to the designer's perspective and practices. <br> The dataset involves side profiles of cars produced or introduced from the 1960s to 2021.</p> <p><em>Images folder contains dataset images labeled with: SEGEMENT_BRAND_COUNT.jpg</em><br> <strong>SEGMENT</strong> were labeled in single alphabet: A, B, C, D, E, F, J, S, P<br> <strong>BRAND</strong> were labeled in three alphabet: Alf(AlfaRomeo), Ast(Aston Martin), Aud(Audi), Ben(Bentley), Bmw(BMW), Che(Chevrolet), Chr(Chrysler), Cit(Citroen), Fer(Ferrari), Fia(Fiat), For(Ford), Gen(Genesis), GMC(GMC), Hon(Honda), Hum(Hummer), Hyu(Hyundai), Jag(Jaguar), Jep(Jeep), Kia(Kia), Lan(LandRover), Lex(Lexus), Mas(Maserati), Maz(Mazda), Mer(Mercedes Benz), Mit(Mitsubishi), Nis(Nissan), Ope(Opel), Peu(Peugeot), Pon(Pontiac), Por(Porsche), Ren(Renault), Riv(Rivian), Rol(Rolls Royce), Sko(Skoda), Ssa(Ssang Yong), Vwg(Volkswagen), and Vol(Volvo)</p> <p><em>Labels folder contain labels in text file: SEGEMENT_BRAND_COUNT.txt</em><br> Single text file structured in:<br> <strong>0</strong>(tire1): x1, y1, x2, y2 positions of the single box<br> <strong>0</strong>(tire2): x1, y1, x2, y2 positions of the single box<br> <strong>1</strong>(wheel1): x1, y1, x2, y2 positions of the single box<br> <strong>1</strong>(wheel2): x1, y1, x2, y2 positions of the single box<br> <strong>2</strong>(body): x1, y1, x2, y2 positions of the single box<br> <strong>3</strong>(cabin): x1, y1, x2, y2 positions of the single box<br> <strong>4</strong>(day light opening): x1, y1, x2, y2 positions of the single box<br> <strong>5</strong>(bodyside): x1, y1, x2, y2 positions of the single box</p> <p><em>This dataset also contained the YOLO v5 l based detection model trained on GP22: DetectionModel.pt</em></p>
Figure 7. The mental spaces set up by the sentence If John buys the car, he will drive to Berlin.-Representing Mental Spaces and Dynamics of Natural Language Semantics
<p>In the sentence (4) there are three proper names which constitute elements of the base space<br> or reality space (R). The CNSB if sets up the hypothetical space (H) with elements identical to those<br> of reality space (Figure 7). Every space’s internal structure is presented in the boxes next to them.<br> In the next part, we will see how MultiNet’s built-in meaning representation mechanisms are<br> capable of representing the basic principles of mental space building outlined above.</p>
Driving Data of a Real F1tenth Car
<div> <div>This dataset provides the real driving data of an F1tenth car, which can be used to identify or train a dynamic model to facilitate control and digital twinning purposes.</div> </div>
Dataset: America's Car-Mart, Inc. (CRMT) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Avis Budget Group, Inc. (CAR) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
◂Fig. 3 Gynoecium of C. crenata %yellow frames), C. cf. grandicalyx %blue frames) and C. sinensis %pink frames; A, B stack shot images; C–K light microscopy; G polarised light; TS in horizontal orientation). A, B Anthetic female flower, calyx and corolla partly removed. B LS of gynoecium. C LS of functionally female flower %note strongly stained peripheral tissue of corolla, anther and gynoecium). D LS of gynoecium. E, F TS of functionally female flower %note strongly stained, peripheral tissue). G TS of functionally female flower %note crystal deposition). H LS of ovule %note stalked embryo sac). J TS of functionally male flower with non-functional ovules. K LS of functionally male flower %style lacking, original position indicated by an asterisk) %LS, longisection; TS, transverse section; a,anther; bs, basal septum; c, calyx; car, carpel; co, corolla; db, dorsal bundles; es, embryo sac; fs, false septum; lb, lateral bundles; o, ovule; stg, stigma; sty, style; t, trichomes; tt, transmission tissue; ut, peripheral, strongly stained tissue; vb, ventral bundles; vs, ventral slit) in Observations on flower and fruit anatomy in dioecious species of Cordia (Cordiaceae, Boraginales) with evolutionary interpretations
◂Fig. 3 Gynoecium of C. crenata %yellow frames), C. cf. grandicalyx %blue frames) and C. sinensis %pink frames; A, B stack shot images; C–K light microscopy; G polarised light; TS in horizontal orientation). A, B Anthetic female flower, calyx and corolla partly removed. B LS of gynoecium. C LS of functionally female flower %note strongly stained peripheral tissue of corolla, anther and gynoecium). D LS of gynoecium. E, F TS of functionally female flower %note strongly stained, peripheral tissue). G TS of functionally female flower %note crystal deposition). H LS of ovule %note stalked embryo sac). J TS of functionally male flower with non-functional ovules. K LS of functionally male flower %style lacking, original position indicated by an asterisk) %LS, longisection; TS, transverse section; a,anther; bs, basal septum; c, calyx; car, carpel; co, corolla; db, dorsal bundles; es, embryo sac; fs, false septum; lb, lateral bundles; o, ovule; stg, stigma; sty, style; t, trichomes; tt, transmission tissue; ut, peripheral, strongly stained tissue; vb, ventral bundles; vs, ventral slit)
Figure 10.a. Simulation's display: The characters "\__/" represents the front part of the car that should pass through the string "....==..=.==..==" representing the range of obstacles-Designing a Growing Functional Modules "Artificial Brain"
<p>Once performed the design and configuration, the controller and the local application are<br> executed by pressing the corresponding button. In case of a local simulation, two terminal windows<br> are generated. The first one, localized on the left side of the screen (figure 10.a), displays the<br> behavior of the simulation; the second one, localized on the right side of the screen (figure 10.b),<br> displays the behavior of the controller. Both application run concurrently and their respective<br> contents allow the user to observe and monitor the control session. The last text line of figure 10.b<br> displays the set of commands at cycle 201.</p>
Linked collectors and determiners for: Museo de Ictiología Carlos Arturo Ardila Rodríguez (CAR).
Natural history specimen data linked to collectors and determiners held within, "Museo de Ictiología Carlos Arturo Ardila Rodríguez (CAR)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/4fa187d0-563f-42ec-b496-99e5ad747750">https://bionomia.net/dataset/4fa187d0-563f-42ec-b496-99e5ad747750</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/4fa187d0-563f-42ec-b496-99e5ad747750">https://gbif.org/dataset/4fa187d0-563f-42ec-b496-99e5ad747750</a>. Formatted as a Frictionless Data package.
KU Car, a young massive eclipsing binary system in NGC 3324
<p>Massive stars are needed engines in the galactic clockwork for the chemical evolution of the Galaxy and the energetic input to the ISM. These stars suffer endemic multiplicity, and thus, studies of the origin and evolution of massive stars is tied to the study of binaries. Therefore, eclipsing binary systems are indispensable objects because absolute parameters (e.g. masses, radii, etc.) can be derived combining the study and modeling of radial velocity and light curves. Such absolute parameters derived from direct observations help to test models about the structure and evolution of massive stars. In this talk, we are presenting an analysis of the light and radial velocity curves of the eclipsing binary system KU Car (P=2.96 d). This binary, composed by a B0.5-0.7 V primary and an unseen companion, shows a remarkable light-curve with a sharp and deep primary minimum and a shallow secondary minimum with strong "reflection effect". Using PHOEBE 2.1 code for modeling, we have derived absolute parameters for both stars proposed the evolutionary stage. This Algol-like system dominated by a young early-B type star, is located in the center of NGC 3324, a bubble-like star-forming region also known as "Gabriela Mistral" Nebula. The photometric analysis is based on multi-band observations obtained using LCOGTN telescopes, and data gathered from ASAS-3, ASAS-SN, TESS, and GDS projects spanning a period of about 18 years. Radial velocities were determined using spectroscopic observations obtained at LCO/duPont and ESO-LSO/2p2 telescopes in the framework of "OWN Survey" and "B-SUGOS" projects.</p>
TRA-DE 2022 German OAP survey data on mobility patterns, car ownership and use
<p>Online Access Panel-based survey data collected Jan-May 2022 in Germany within the research project <a href="https://energysufficiency.de/">Energy Sufficiency</a>. N=3422 (+N=510 for city of Flensburg). Data available as coded and labelled version, incl. codebook and method report from data collection contractor (all in German). Supplementary files: questionnaire (for survey implemented as online questionnaire), codebook and method report (German).</p>
Future role of the car
<p><em><strong>In the fourth Rebalance Dialogue, <a href="https://rebalancemobility.eu/andrea-ricci/">Andrea Ricci</a> moderated the dialogue between <a href="https://rebalancemobility.eu/jens-schade/">Jens Schade</a> and <a href="https://rebalancemobility.eu/stefan-gossling/">Stefan Gössling</a> about the role of the car in the future and how private car use is recognized as having significant negative impacts on environment, health, and safety. The car generates congestion, consumes significant urban space, and affects the quality of life in general. Access regulation, initiatives to foster active mobility and micro-mobility, and to make public transport more attractive address some of these concerns. But private car ownership and use is deeply ingrained in our culture, and car sales keep growing.</strong></em></p> <p>REBALANCE explores the congruity of a perspective consisting of a radical shift away from cars and the “key values” that should be promoted in order to achieve it. Driving a car is perceived as an expression of freedom: the driver is “in control”, and can decide where, when, at what speed, and with what itinerary to move around. Although, this is sometimes a delusion as drivers suffer frustrating congestion, time losses to find parking spots, impotence in avoiding accidents provoked by others, along with high economic costs.</p> <blockquote> <p><strong>“Agency” is arguably a fundamental determinant of mobility choices. Nevertheless, it is worth researching how more and better evidence of the true costs and benefits, both individual and collective, could help deconstruct the current paradigm, and convince people that their real need is to move, not to drive.</strong></p> </blockquote> <p>It is often stated that the main reason behind the prevalence of private car use is the “status symbol” notion attached to the car, and the subsequent reticence to give up ownership. The emergence of shared vehicles and shared rides schemes can be optimistically interpreted as a promising signal in this regard. To become prevalent, “soft spots” in peoples’ values and perceptions must be addressed for this to happen, also considering the “generation gap” and differentiated perspectives across age groups. On the other hand, if it is sustained, the generalized acceptance of shared schemes could lead car use to become an integral part of more sustainable, multimodal, and seamless mobility patterns. This would then further rule out any perspective of phasing out cars as such! Ultimately, a radical shift to shared schemes could not be enough to curb the dramatic impacts of automobility.</p> <p>The participants agreed that the car is part of one’s personality, showing the real economic cost and impact will not change anything. Many costs do not steer behaviour because they are indirect and invisible. However, giving immediate feedback on your behaviour does have an effect. Possession has its value. Ownership will be more determinant than electric or autonomous. In this case, safety and self-protection discourses could help change minds.</p> <p>Powerful automotive industries in some countries (Germany, Italy) convey a message of car dependency, much more than the reality. Changes are easier in countries without a car industry (The Netherlands, Denmark). Car sharing of AVs could increase traffic and create additional problems. Perhaps the future of sharing will depend on convenience (and availability).</p> <p>The discussants agreed that societal change will not be fast, but resistance can grow quickly. To avoid that, awareness of wrong policy direction must be considered. Western societies do not accept restrictions well. Indirect measures (price, parking…) could work but will not lead to structural changes. The participants wondered why the vast majority of the population accepted COVID restrictions but show reluctance in other cases. Education is important to give information and awareness, including subsidies and internalisation of external costs and benefits and even the need to be open to addressing taboos like speed limits, driving licences renewal of elder people.</p> <p>The conversation addressed some issues related to urban space, mainly focused on cycling and how every bicycle creates space and needs improving by creating safe spaces to allow people (who are willing) to switch from cars to bicycles. This change could be based at first on progressive city dwellers and concentrate on the progressive part of big cities. Cities are the place to start implementing alternatives but politicians (and the electoral agenda) are one of the main barriers for car-free cities. Nevertheless, big cities are closer to achieving this change.</p> <p>Technological innovation is mostly presented as a promising pathway towards sustainability: cleaner propulsion, vehicle automation, “smart”, ICT-based mobility can undoubtedly bring – if only incremental – improvements in environmental and safety related performances of transport systems and services. This could simply mean a further step towards the consolidation of the car-based mobility culture, one that in the end definitively precludes a radical shift away from automobility. And even worse,</p> <blockquote> <p><strong>technological progress could generate perverse rebound effects that would ultimately mainly serve the interests of the automotive industry rather than those of society.</strong></p> </blockquote> <p>The urge to avoid crowded transport solutions due to COVID-19 has at least partially overshadowed what was observed, only a few years ago, as a progressive disenchantment with car ownership and use, especially among the young. To minimize contact with others, many people are traveling in their own vehicles. Since the outbreak of COVID-19, the use of public transportation and carpools has declined significantly worldwide. Whether COVID-related concerns are an alibi to sticking to private car use, where all collective modes, but also shared cars, are perceived as a health risk or – especially among the younger generations – is the fall back on private car mobility only a “minor evil”, to get rid of as swiftly as possible has yet to be seen. However, the participants agreed that young people are becoming more conservative and could become more attached to cars and car ownership.</p>
Supplementary material - Full-spectrum CARS Microscopy Of Cells And Tissues With Ultrashort White-light Continuum Pulses
<p>Supplementary material containing the raw Broadband CARS data presented in the manuscript entitled "Full-spectrum CARS Microscopy Of Cells And Tissues With Ultrashort White-light Continuum Pulses".</p>
Car-EWaves: Extreme waves in the Caribbean Sea from 1958 to 2017
<p>Maximum monthly aggregate value of significant wave height for the Caribbean Sea from 1958 to 2017. The mean wave period, mean wave direction and the wind velocity (U10 and V10) related to the date of the maximum aggregation significant wave height value, are included in this dataset.</p>
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.