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1,608 results for “Fixing”
Power Balance Characteristics for Multirotor- and Fixed-Wing-Type UAV-BSs Equipped with RES and RISs
<h2><strong>Overview</strong></h2> <p>The following dataset presents the power balance characteristics for Unmanned Aerial Vehicle Base Stations (UAV-BSs) equipped with Renewable Energy Sources (RES) and Reconfigurable Intelligent Surfaces (RISs). The dataset has been prepared for two different types of UAVs, i.e., multirotor and fixed-wing ones.</p> <h2><strong>Scenario</strong></h2> <p>The considered scenario includes 2 UAV-BSs (each of a different type) equipped with a single RF transceiver and an RIS device and RES — a single photovoltaic panel (PV) and a single wind turbine (WT). The UAV-BSs are placed within the city of Poznan and hover (multirotor) or follow a circular route (fixed-wing) above a single mobile user with fixed traffic demand (100 Mbps downlink — DL, and 50 Mbps uplink — UL). The simulation runs have been performed for 4 dates (vernal equinox, summer solstice, autumn equinox, winter solstice), each one from a different season of the year. The aim of such an approach was to highlight the impact of the time of the day and the year on the energy gain obtained thanks to enabling RES generators as well as on the power consumption of the hardware of each UAV-BS type. The weather conditions assumed within the simulation are typical for the climate in Poland.</p> <h2><strong>Methodology</strong></h2> <p>The power-balance calculations (UAV-BSs' power consumption, renewable energy production) have been based on the mathematical formulas from the scientific literature and performed within the digital simulation runs by using dedicated software developed in Python programming language.</p> <h2><strong>Simulation setup</strong></h2> <p>The setup of the input parameters for used mathematical models (power consumption, energy generation) has been done in accordance with the values attached within the literature positions (cited within the publication included in the <em>Related works</em> section of the following dataset) and adjusted to the considered study. Furthermore, the data used to predict weather conditions are the real data (for the year 2022) collected by the weather stations placed in Poznan. A single simulation run has been performed (which takes into account 2 types of UAV-BS simultaneously and estimates their power balance for 4 seasons of the year), where the time step has been set to 1 hour of the day.</p> <h2><strong>Results</strong></h2> <p>The results of the aforementioned investigations have been included in the attached files (<em>_power_balance_multirotor.csv</em> & <em>_power_balance_fixed_wing.csv</em>). The first column denotes the hour of a particular day. Next, 4 multicolumns have been presented for the following variants — No RES enabled, only PV enabled, only WT enabled, and both types of RES generators enabled. In addition, each multicolumn consists of 4 columns, each of which represents a UAV-BS's hardware power balance (in W) for a different date (season of the year).</p> <h2><strong>Acknowledgment</strong></h2> <p>More details about the conducted study have been described within the attached paper (<em>Related works</em> section). The work (including the following dataset preparation) was realized within project no. 2021/43/B/ST7/01365 funded by the National Science Center in Poland.</p>
Correspondence of the natural oscillation frequencies of perforated plates depending on the type of holes, plate material and thickness, type of fixing (CCCS or CSCS)
<p>The method involved the analysis of oscillations of base plates: solid non-perforated and with round holes, as well as perforated plates with holes of complex geometry in the form of a five-petal epicycloid.</p> <p>As a result of the modeling (Abaqus), the natural oscillations frequencies of the studied plates were obtained depending on the type of perforation, material, thickness and type of their fixing. The use of different materials (steel and aluminium) showed an insignificant influence on the natural oscillation frequency of the plates. It was found that the plate thickness has the greatest influence (31.85– 33.35%), the following are the hole parameters: partition width between holes; pitch between hole centers.</p> <p>Analysis of the results showed that the natural vibrations of plates with holes of complex geometry differ by up to 7% compared to plates with basic round holes. </p>
Training and test dataset of STED images of microtubules in fixed cells
<p>Training and test dataset of microtubule used in the manuscript "Denoising diffusion models for high-resolution microscopy image restoration".</p>
Taming the fixed-node error in diffusion Monte Carlo via range separation
<p>Suplementary information.</p> <p>Contains the org-mode computational notebook with all the input data (geometries, basis sets, pseudo-potentials) and output data (computed energies, densities, number of determinants) related to the article.</p> <p>A csv file is created by the notebook and an HTML export of the notebook is also provided.</p>
Fixed-Wing Micro UAV Open Data With Digicam And Raw INS/GNSS - IGN Flight 8
<p>The data set originate from a series of flights conducted with fixed-wing micro UAV carrying high-quality small camera and navigation sensors. This data was previously used in several peer-reviewed publications and will also be used in ISPRS workshop on dynamic networks given during the 2021 ISPRS Congress. This is part of a larger series of data that will be released gradually after incorporating user's feedback (e.g., on formats, description,etc.). The data set contains the sensor measurements from GPS, IMU and Camera.</p>
Automatic learning of hydrogen-bond fixes in an AMBER RNA force field - dataset
<p>Supporting data related to manuscript "Automatic learning of hydrogen-bond fixes in an AMBER RNA force field"</p>
Data from: "Using low-fix rate GPS telemetry to expand estimates of ungulate reproductive success"
<p>Secondary datasets used for analysis in "Using low-fix rate GPS telemetry to expand estimates of ungulate reproductive success". Raw GPS relocation data are not publicly available due to potential ethical implications but are available from the corresponding author (Nathan Hooven, nathan.d.hooven@gmail.com) upon reasonable request. Datasets include:</p> <p>elk_days_part.csv: generated movement metrics and days from parturition for all cow elk for which reproductive success was confirmed</p> <p>elk_np.csv: generated movement metrics for non-parturient cow elk</p> <p>elk_unknowns.csv: generated movement metrics for cows with unknown reproductive status, but were confirmed pregnant in mid-winter</p> <p>elk_thisyear.csv: generated movement metrics for 2020 Vectronic cows monitored in 2021</p> <p>Part_dates.csv: Confirmed and predicted dates of parturition for all elk in training and testing sets</p> <p>all_prob_summary_parturient.csv: Confirmed and predicted dates of parturition and differences for confirmed successful elk</p> <p>Decision rules 1.xlsx: Spreadsheet with classification accuracy based upon varying decision rules</p> <p>Decision rules 2.csv: Plottable summary of classification accuracy based upon varying decision rules</p> <p> </p>
Techno-economic details of fixed-bottom offshore wind projects deployed in the European markets
<p>Version (with all files) - Updated version (research article is accepted).</p> <p>Publishing Date: July 10, 2022</p> <p>This dataset describes the techno-economic information of fixed-bottom offshore wind projects deployed in the North Sea region (DK, NL, BE, DE, and the UK). </p> <p>Contents: </p> <p>1) Offshore wind farm project prices and technical characteristics (farm size, turbine rated power, water depth, etc.,)</p> <p>2) Offshore wind farm capacity factor and cumulative energy generation</p> <p>3) Monopile weight </p> <p>4) Offshore wind farm installation duration </p> <p>5) UK offshore wind farms' transmission system cost</p> <p> </p>
video_fixing_Blowpipe
This is the process of making a carafe Bontemps. The step depicted is called "Annealing"(from the Mingei project).
Replication Package for the Paper Titled "How Well Do Software Practitioners Fix Code Vulnerabilities with Different Types of Explanations?"
<p>This is a replication package for the article 'How Well Do Software Practitioners Fix Code Vulnerabilities with Different Types of Explanations?'. The survey questions can be found here, and we encourage the survey to be re-used.</p> <p>We also include survey data (with demographic data and qualitative responses removed for anonymity reasons).</p> <p>The project team consists of Tracy Hall, Emily Winter, Fahad Al Debeyan (Lancaster University) and Lech Madeyski (Wroclaw University of Science and Technology). If you have any questions about the re-use of this survey, feel free to contact Fahad at <a href="mailto:e.winter@lancaster.ac.uk">f.aldebeyan@lancaster.ac.uk</a>.</p>
Rhodamine fluorometry fixed location data Alfacs bay
<p>Rhodamine dye was released from a waste water outfall in Alfacs Bay, Catalonia. This dye has a strong red/pink colour and can be used to trace the movement of the waster water plume. These fluorimetry data were generated using fluorimeters that were in fixed locations and measured the rhodamine concentrations over 96 hours.</p> <p>Times are Central European Time (GMT+1)</p>
AirHeritage Datalake: Multi-site, Multi-season, Multi Unit dataset including Fixed and Mobile Citizen science data from networked Air Quality Low-Cost Multi-Sensors devices and reference stations
<p>This datalake comprises several datasets from <strong>37 networked low cost air quality multisensors</strong> (<strong>30</strong> <strong>mobile</strong> ENEA MONICA(tm) + <strong>7</strong> <strong>fixed</strong>) along with <strong>3</strong> (fixed) + <strong>1</strong> (mobile) <strong>reference stations</strong> operated by Campania Regional Envronmental Protection Agency. The datalake is organized in 3 main directories respectively related to fixed nodes, mobile nodes and nearby reference stations including a mobile laboratory used for colocation campaigns; each subdirectory include its own metadata description file.</p> <p>Data, curated by Energy and Data Science Laboratory of ENEA, include multi-weeks colocation periods when low cost devices have been colocated with reference stations as well as operational periods during which sensors are deployed for fixed or mobile monitoring campaigns. Data have been recorded during 2021 and 2022 in a<strong> pervasive, multi-site, multi-seasonal deployment</strong> in Portici, a densely populated small area city (4km2, 55k + inhabitants) located 7km south of Naples, Italy.</p> <p>The datalake consists in actual sensors and reference intrumentations timeseries along with metadata description files with deployment dates and location data. The dataset files include high sampling frequency raw sensor data of quality-controlled sensor network along with co-located reference stations data sets. Sensor data include electrochemical sensors data (intended target pollutants: NO2, O3, CO), Optical sensor data (PM2.5, PM10, PM1) readings along with meteorological parameters. .</p> <p>Further description of sensors and reference instruments are reported in the accompanying paper (see citation request).</p> <p>The dataset can be used for </p> <ul> <li> <strong>advanced (remote/universal/in field) data driven calibration strategies</strong> test or development including <strong>machine learning </strong>models</li> <li><strong>mobile opportunistic data fusion</strong> methods development</li> <li><strong>geomatics and data assimilation</strong> models studies</li> </ul> <p>as well as low cost sensor characterization performance studies. </p>
Atmospheric sounding of the boundary layer over alpine glaciers using fixed-wing UAVs
<p>Additional code and data for the paper by Groos et al. entitled "Atmospheric sounding of the boundary layer over alpine glaciers using fixed-wing UAVs"</p> <p>Correspondence: Alexander R. Groos (alexander.groos@fau.de)</p> <p><br>The repository contains:<br>(1) The raw data (log files) for each UAV-based atmospheric sounding<br>(2) The postprocessed and reformatted data for each sounding and vertical profile<br>(3) The commented R-Scripts for data processing, analysis and visualisation<br>(4) A subset of the meteorological data from the nearby weather stations</p> <p><br>Description of sub-folders:</p> <p>-aws_data<br>-- aws_fisistock.txt # meteorological data from AWS Fisistock for the period of the campaign<br>-- aws_gandegg.txt # meteorological data from AWS Gandegg for the period of the campaign<br>-- aws_sackhorn.txt # meteorological data from AWS Sackhorn for the period of the campaign</p> <p>- processed_data<br>-- kanderfirn_2021-06-16_10:45_p1_pprz.tab # meteorological data for first profile/descent at about <br>-- kanderfirn_2021-06-16_10:45_p2_fr.tab # flight recorder data for second profile/descent at about 10:45 CEST<br>-- kanderfirn_2021-06-16_10:45_p2_pprz.tab # meteorological data for second profile/descent at about 10:45 CEST<br>-- kanderfirn_2021-06-16_10:45_pprz.tab # meteorological data for the entire sounding (first and second profile/descent) at about 10:45 CEST<br>-- .<br>-- .<br>-- .<br>-- kanderfirn_2021-06-16_16:50_p1_pprz.tab # meteorological data for first profile/descent at about 16:50 CEST<br>-- kanderfirn_2021-06-16_16:50_p2_fr.tab # flight recorder data for second profile/descent at about 16:50 CEST<br>-- kanderfirn_2021-06-16_16:50_p2_pprz.tab # meteorological data for second profile/descent at about 16:50 CEST<br>-- kanderfirn_2021-06-16_16:50_pprz.tab # meteorological data for the entire sounding (first and second profile/descent) at about 16:50 CEST<br>-- kanderfirn_soundings_2021-06-16.csv # summary table of vertical profiles (1 m height intervals): one column for each profile/descent and variable<br>-- kanderfirn_turbulence_2021-06-16.csv # summary table of vertical turbulence profiles (1 m height intervals): one column for each profile/descent</p> <p>- raw_data<br>-- fr_kanderfirn_2021-06-16_10:45.LOG # flight recorder data from the sounding at about 10:45 CEST (binary file)<br>-- .<br>-- .<br>-- .<br>-- fr_kanderfirn_2021-06-16_16:50.LOG # flight recorder data from the sounding at about 16:50 CEST (binary file)<br>-- pprz_kanderfirn_2021-06-16_10:45.LOG # meteorological data from the sounding at about 10:45 CEST (human readable text file)<br>-- .<br>-- .<br>-- .<br>-- pprz_kanderfirn_2021-06-16_16:50.LOG # meteorological data data from the sounding at about 16:50 CEST (human readable text file)</p> <p>- R_scripts<br>-- figures.R # Script to create Figures 5, 6, 8, 9, 10, 11, 12<br>-- lapse_rate.R # Script to calculate lapse rates and surface-based inversions (includes code for Figures 7 and B1)<br>-- postprocessing.R # Script to reformat preprocessed and preselected pprz-files<br>-- turbulence.R # Script for the calculation of the turbulence proxy from the recorded roll rate</p>
Experimental gust response and flutter test of a wing with a fixed and a hinged wingtip
<p>Gust responses and flutter test of a wing with a hinged and a fixed wingtip. The experiments were performed at the Swansea University wind tunnel by Davide Balatti as part of his research. Additional information in:</p> <p>[1] D. Balatti, H.H. Khodaparast, M.I. Friswell, & M. Manolesos (2022). Aeroelastic model validation through wind tunnel testing of a wing with hinged wingtip. In International Forum on Aeroelasticity and Structural Dynamics (IFASD), Madrid (https://www.researchgate.net/publication/361418631_AEROELASTIC_MODEL_VALIDATION_THROUGH_WIND_TUNNEL_TESTING_OF_A_WING_WITH_HINGED_WINGTIP)</p> <p>[2] Balatti, D., Khodaparast, H. H., Friswell, M. I., Manolesos, M., & Castrichini, A. Improving gust load alleviation performance of hinge wingtip using validated aeroelastic models. <em>Available at SSRN 4258795</em>.(https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4258795)</p>
Studying Bug-Fixing Commits in the WoC Dataset: Replication Package
<p>A replication package for the MSR 2023 Challenge submission titled "Studying Bug-Fixing Commits in the WoC Dataset".</p>
Deconvolved STED nanoscopy images of the nuclear phosphatidylinositol 4,5-bisphosphate and nuclear speckle marker SON together with deconvolved confocal images of DAPI stained nuclei in human formalin-fixed paraffin-embedded skin warts sections
<p>The collection and analysis of formalin-fixed paraffin-embedded (FFPE) human skin sections was approved by the local ethics-committee at the Department of Pathology, University of Cologne, Germany. Written informed consentwas obtained from all patients in accordance with the Declaration of Helsinki. For biopsy materials from archival paraffin blocks of human skin, an informed consent was obtained from all the subjects and ethical approval obtained from the Ethics Committee at the University of Cologne. Surgically removed human FFPE skin biopsies were sectioned into 4 µm sections. Sections were dewaxed, and indirectly immunofluorescently labeled against nuclear phosphatidylinositol 4,5-bisphosphate (nPI(4,5)P2) using 5 µg/mL rabbit primary polyclonal antibody (Echelon Biosciences Inc. Z-A045, clone 2C11). The primary antibody against nPI(4,5)P2 was recognized by the goat secondary antibody conjugated with Abberrior Star 635P (Abberior 2-0002-007-5). Sections were indirectly immunofluorescently labeled against nuclear speckle marker SON using 1 µg/mL rabbit primary polyclonal antibody (Abcam ab121759). The primary antibody against SON was recognized by the goat secondary antibody conjugated with Abberrior Star 580 (Abberrior ST580-1002). Sections were co-stained by DAPI 1:1000 in PBS for 5 min.</p> <p>Imaging of nPI(4,5)P2-635P channel was performed on Leica TCS SP8 STED 3x inverted DMi8 microscope with pulsed white light laser 470-640 nm 1.5 mW and 775 nm pulse STED laser >1.5 W controlled by Leica Application Suite X software and equipped with HC PL APO CS2 100x/1.40 OIL objective used with Leica Type F immersion oil n=1.518. Unidirectional xyz scanning speed was 400 Hz, line accumulation 8. Pixel size was 20 nm in X and Y. Channel settings: 7% 633 nm laser; 775 Notch filter; 50% 775 nm STED laser; 30% 3D STED; HyD 639-698 nm, photon-counting mode, gain 100, gating 0.3-10 ns. Imaging of SON-580 channel was performed on Leica TCS SP8 STED 3x inverted DMi8 microscope with pulsed white light laser 470-640 nm 1.5 mW and 775 nm pulse STED laser >1.5 W controlled by Leica Application Suite X software and equipped with HC PL APO CS2 100x/1.40 OIL objective used with Leica Type F immersion oil n=1.518. Unidirectional xyz scanning speed was 400 Hz, line accumulation 8. Pixel size was 20 nm in X and Y. Channel settings: 10% 585 nm laser; 775 Notch filter; 80% 775 nm STED laser, 30% 3D STED; Hybrid detector (HyD) 589-616 nm, photon-counting mode, gain 100, gating 0.4-10 ns.</p> <p>Z-stacks of STED images were deconvolved using Huygens Professional 22.10 software (Scientific Imaging B.V.). Data sets were processed using Workflow Processor. The workflow consisted of selecting images, setting up the microscopy and deconvolution parameters and saving deconvolved images as 8-bit TIFF single files for individual channels (which were later used for the quantitative analyses; see below). Microscopy parameters were optimized and set as follows. Sampling intervals were ≤20 nm in X and Y and ≤20 nm in Z. Numerical aperture was 1.4; refractive indexes of the lens immersion oil was 1.518 and of the embedding media 1.458; objective quality was good, coverslip position was 0 µm and imaging direction was downward. For nPI(4,5)P2-635P STED channel the backprojected pinhole was 216 nm; excitation (ex.) and emission (em.) wavelengths (λ) were 633 and 651 nm, resp., ex. fill factor 2. STED depletion mode was pulsed, saturation factor 25, STED λ = 775, STED immunity factor 10 and STED 3X was 30%. Classic MLE algorithm with stabilization of Z-slices was used and signal-to-noise ratio was 5.1. For SON-580 STED channel the backprojected pinhole was 195 nm; excitation (ex.) and emission (em.) wavelengths (λ) were 585 and 602 nm, resp., ex. fill factor 2. STED depletion mode was pulsed, saturation factor 20, STED λ = 775, STED immunity factor 10 and STED 3X was 30%. Classic MLE algorithm with stabilization of Z-slices was used and signal-to-noise ratio was 4.</p>
ifilot/microkinetic-datasets-methanation-fts: Fix error in description
<p>This repository contains three datasets for performing microkinetic simulations.</p> <ul> <li>CO2 methanation over Co(1121) lattice [1]</li> <li>CO2 methanation over a NiMn catalyst [2]</li> <li>Fischer-Tropsch synthesis over a dual-site Co(0001)xCo(1121) lattice [3]</li> </ul> <p>These datasets are based on the following publications</p> <p>1. W. Chen; R. Pestman; B. Zijlstra; I.A.W. Filot; E.J.M. Hensen, Mechanism of cobalt-catalyzed co hydrogenation: 1. methanation, <br> ACS Catal., 2017, 7, 8061-8071.<br> 2. W.L. Vrijburg; E. Moioli; W. Chen; M. Zhang; B.J.P. Terlingen; B. Zijlstra; I.A.W. Filot; A.Zuttel; E.A. Pidko; E.J.M. Hensen, Efficient Base-Metal NiMn/TiO2 Catalyst for CO2 Methanation, ACS Catal., 2019, 9, 7823-7839.<br> 3. B. Zijlstra; R. J. P. Broos; W. Chen; G. L. Bezemer; I. A. W. Filot; E. J. M. Hensen, The vital role of step-edge sites for both co activation and chain growth on cobalt fischer-tropsch catalysts revealed through first-principles-based microkinetic modeling including lateral interactions, ACS Catal., 2020, 10, 9376-9400.</p> <p>For more information on the formatting of these files, please consult the<br> <a href="https://wiki.mkmcxx.nl/index.php/Main_Page">MKMCXX wiki</a>.</p>
CLIC Calorimeter 3D images: Electron showers at Fixed Angle
<p>Energy deposits from single-particle showers in the ECAL+HCAL calorimeters of the CLIC detector</p> <p>Simulation performed with GEANT4 (https://geant4.web.cern.ch) and DD4HEP software (https://dd4hep.web.cern.ch/dd4hep/)</p> <p>Electrons entering the detector at variable energy and fixed direction (perpendicular to the ECAL inner surface)</p> <p>See https://arxiv.org/abs/1912.06794 for details</p>
CLIC Calorimeter 3D images: Photon showers at Fixed Angle
<p>Energy deposits from single-particle showers in the ECAL+HCAL calorimeters of the CLIC detector</p> <p>Simulation performed with GEANT4 (https://geant4.web.cern.ch) and DD4HEP software (https://dd4hep.web.cern.ch/dd4hep/)</p> <p>Photons entering the detector at variable energy and fixed direction (perpendicular to the ECAL inner surface)</p> <p>See https://arxiv.org/abs/1912.06794 for details</p>
Brainport, Urban driving, fixed route, VRU detection
<p><strong>Scenario description</strong>:</p> <p>Only GeoFenching VRU detection with 3 smartphone detection<br> Test detection of multiple VRUs close to each other and compare with camera detections.<br> Test different size GeoFence area (20m wide x 50m long) of detection with different pedestrian walking paths (for pedestrian prediction) </p> <p><strong>Session description</strong>:</p> <p>Route is fixed, vehicle drives north - south. Underway 1 group of 3 VRU crosses the road.</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_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_UrbanDriving_EAI2Mobile</strong>: Data from the service to the mobile</p> <p>Dataset Description This dataset contains information sent to the mobile about the Estimated Arrival time and position</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_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_UrbanDriving_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_UrbanDriving_IOT_CEMA_Message</strong>: Data from the service to the vehicle</p> <p>Dataset Description This dataset contains information from the Crowd Estimation and Mobility Analytics service</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_IOT_FlowRadar_Message</strong>: Data from the vehicle to the service</p> <p>Dataset Description This dataset contains the GPS informaton (speed,position,heading) from the vehicle</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_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_UrbanDriving_IOT_VehicleStatus</strong>: Data sent from the vehicle to the service</p> <p>Dataset Description This dataset contains the current status of the vehicle</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_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_UrbanDriving_SmartphoneGPS</strong>: Data sent by the mobile to the service</p> <p>Dataset Description This dataset contains the GPS informaton (speed,position,heading) from the mobile</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_SmartphoneStatus</strong>: Data sent from the mobile to the service</p> <p>Dataset Description This dataset contains the current status of the mobile</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_TaxiRequest</strong>: Data sent from the mobile to the service</p> <p>Dataset Description This dataset contains the requests for a taxi from the mobile phones</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_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_UrbanDriving_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>
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.