Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
395
datasets available to search
ShareScore release 0.9.0
Dataset results
395 results for “Aircraft”
Spitfire - Sketchfab Aircraft Painting Contest
 When I was collecting reference photos about **Spitfire**, I came across some interesting stories. It concerns the ***PA944*** crash, and ***people*** associated with this event - It inspired me, If you feel like you want to know more, I invite you to watch this short film here - https://youtu.be/ie3SrjLlcUY , or check the [American Museum](http://www.americanairmuseum.com/media/7505) Based on "[Aircraft Painting Contest – Base](https://sketchfab.com/models/e1b4d01dbd7c42bb95c5a0f922747ac2)" by [Renafox](https://sketchfab.com/kryik1023), licensed under CC Attribution-ShareAlike. Audio: Soud Effect cue thanks to [Audio Production](https://www.youtube.com/watch?v=EUo5LBOP7c4&t=9s) Workflow: * No changes to the model, scene. * 4k pbr material based on delivered Normals and AO Source: Objaverse 1.0 / Sketchfab
Miniature multihole airflow sensor for lightweight aircraft over wide speed and angular range
<p>This repository contains:</p> <ul> <li>the data collected for the paper "Miniature multihole airflow sensor for lightweight aircraft over wide speed and angular range"</li> <li>the python code to extract the wind tunnel and flight data to reproduce the plots in the paper</li> <li>the exact polynomials used for the calibration</li> <li>the 3D object files of the airflow sensor</li> <li>the schematics of the PCB</li> <li>the video of the flown manoeuvres (available on youtube: https://youtu.be/U3nR1v3fbZg)</li> </ul> <p>The data and the plots are included in the repository, but can be reproduced by running the python scripts in the following order:</p> <ol> <li>Code/reduce_data.py</li> <li>Code/plot_averages.py</li> <li>Code/read_tunnel.py</li> <li>Code/Regress.py</li> <li>Code/Validation_plot.py</li> </ol>
Large aircraft landing dataset at 20 large europen airports
<p>This dataset includes metadata of landing trajectories at 20 large European airports for the period from January 2019 to June 2023. The metadata have been sourced from ADS-B flight trajectories sourced from the OpenSky Network and EUROCONTROL's Network Manager. Notably, the dataset labels Go-Arounds, providing valuable insights into these specific flight maneuvers.<br><br>The proposed dataset includes the following:</p><ol><li><strong>stop</strong> [date time]: UTC time of landing.</li><li><strong>icao24</strong> [string]: Unique 24-bit (hexadecimal number) ICAO identifier of the aircraft concerned.</li><li><strong>callsign</strong> [string]: Aircraft identifier in air-ground communications.</li><li><strong>airport</strong> [string]: ICAO airport code where the aircraft is landing.</li><li><strong>ILS</strong> [list]: Designation of runways on which the aircraft performed its landing attempts.</li><li><strong>GoA</strong> [Boolean]: "True" if at least one Go-Around (GA) was performed, otherwise "False".</li><li><strong>n_attempts</strong> [integer]: Number of approaches identified for this flight.</li><li><strong>attempt_times</strong> [list]: List of timestamps at which a landing is attempted.</li><li><strong>market_segment</strong> [string]: Flight market segment.</li><li><strong>n_rwy_approached</strong> [integer]: Number of unique runways approached by this flight.</li><li><strong>AC_CLASS</strong> [string]: Aircraft class.</li><li><strong>C40_CROSS_TIME</strong> [string]: Timestamp at which the aircraft enters the ASMA cylinder.</li><li><strong>C40_BEARING</strong> [float]: Bearing between aircraft and airport when entering the ASMA cylinder.</li><li><strong>C40_CROSS_LAT</strong> [float]: Aircraft latitude when entering the ASMA cylinder.</li><li><strong>C40_CROSS_LON</strong> [float]: Aircraft longitude when entering the ASMA cylinder.</li></ol>
Mobile Anti Aircraft Gun 40mm
A 40mm ofors anti aircraft gun from World War 2 and now located next to HMS Ocelot at Chatham Historic Dockyard, Kent. Date: ? 777 photos taken in November 2021 with a Sony a7R III and processed in Reality Capture. Source: Objaverse 1.0 / Sketchfab
Auralization of amplitude fluctuations in aircraft flyover
Open the record for dataset details and reuse information.
Supporting datasets used in the paper entitled "Aircraft-based observation of mineral dust particles over the western North Pacific in summer using a complex amplitude sensor"
<p>This archive contains datasets used in the paper entitled "Aircraft-based observation of mineral dust particles over the western North Pacific in summer using a complex amplitude sensor."</p>
Aircraft profiles of stable isotope ratios in atmospheric total and condensed water from the NASA ORACLES mission.
<p>Aircraft in-situ measurements of water concentration and heavy water isotope ratios D/H and 18O/16O of cloud water and total water (water vapor plus condensed water) were collected during the NASA ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) project. Aircraft sampling took place in the southeast Atlantic marine boundary layer and lower troposphere (equator to 22 degrees south) over the months of Sept. 2016, Aug. 2017, and Oct. 2018. Isotope measurements were made using cavity ring-down spectroscopic analyzers integrated into the Water Isotope System for Precipitation and Entrainment Research (WISPER). The WISPER data are processed into mean latitude-altitude curtains and individual vertical profiles for each sampling period.</p> <p> </p> <p>The WISPER data accompanied a suite of other variables including standard meteorological quantities (wind, temperature, moisture), trace gas and aerosol concentrations, radar, and lidar remote sensing, which can be accessed through the DOIs listed further down. The ORACLES campaigns are described by Redemann et al., (2021). The water isotope measurements are further described in Henze et al., (2021). The absolute error with respect to the SMOW-SLAP scale is explained in detail by Henze et al., (2021).</p> <p> </p> <p>Total water concentration and isotope ratios were binned and averaged onto latitude-altitude grids using a kernel estimation approach, with weighting designed to estimate the mean during the approximate month-long duration of each sampling period. Standard deviations for each bin are also computed using kernel density estimation.</p> <p> </p> <p>Time intervals during aircraft vertical profiling are isolated and averaged onto 50-meter vertical levels. The files include water concentration and isotope ratios for both total water and cloud water in addition to temperature, pressure, latitude, and longitude.</p> <p> </p> <p>See included file README.txt for additional details.</p> <p> </p> <p>References</p> <p>---------------</p> <p>Henze, D., Noone, D., and Toohey, D.: Aircraft measurements of water vapor heavy isotope ratios in the marine boundary layer and lower troposphere during ORACLES, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2021-238, in review, 2021.</p> <p> </p> <p>Redemann, J., Wood, R., Zuidema, P., Doherty, S. J., Luna, B., LeBlanc, S. E., Diamond, M. S., Shinozuka, Y., Chang, I. Y., Ueyama, R., Pfister, L., Ryoo, J.-M., Dobracki, A. N., da Silva, A. M., Longo, K. M., Kacenelenbogen, M. S., Flynn, C. J., Pistone, K., Knox, N. M., Piketh, S. J., Haywood, J. M., Formenti, P., Mallet, M., Stier, P., Ackerman, A. S., Bauer, S. E., Fridlind, A. M., Carmichael, G. R., Saide, P. E., Ferrada, G. A., Howell, S. G., Freitag, S., Cairns, B., Holben, B. N., Knobelspiesse, K. D., Tanelli, S., L'Ecuyer, T. S., Dzambo, A. M., Sy, O. O., McFarquhar, G. M., Poellot, M. R., Gupta, S., O'Brien, J. R., Nenes, A., Kacarab, M., Wong, J. P. S., Small-Griswold, J. D., Thornhill, K. L., Noone, D., Podolske, J. R., Schmidt, K. S., Pilewskie, P., Chen, H., Cochrane, S. P., Sedlacek, A. J., Lang, T. J., Stith, E., Segal-Rozenhaimer, M., Ferrare, R. A., Burton, S. P., Hostetler, C. A., Diner, D. J., Seidel, F. C., Platnick, S. E., Myers, J. S., Meyer, K. G., Spangenberg, D. A., Maring, H., and Gao, L.: An overview of the ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) project: aerosol–cloud–radiation interactions in the southeast Atlantic basin, Atmos. Chem. Phys., 21, 1507–1563, https://doi.org/10.5194/acp-21-1507-2021, 2021.</p> <p> </p> <p>The complete archive of ORACLES data are accessible via the digital object identifiers (DOIs) provided under ORACLES Science Team references as follows:</p> <p> </p> <p>ORACLES Science Team: Suite of Aerosol, Cloud, and Related Data Acquired Aboard P3 During ORACLES 2018, Version 3, NASA Ames Earth Science Project Office, https://doi.org/10.5067/Suborbital/ORACLES/P3/2018_V3, 2020a. </p> <p> </p> <p>ORACLES Science Team: Suite of Aerosol, Cloud, and Related Data Acquired Aboard P3 During ORACLES 2017, Version 3, NASA Ames Earth Science Project Office, https://doi.org/10.5067/Suborbital/ORACLES/P3/2017_V3, 2020b. </p> <p> </p> <p>ORACLES Science Team: Suite of Aerosol, Cloud, and Related Data Acquired Aboard P3 During ORACLES 2016, Version 3, NASA Ames Earth Science Project Office, https://doi.org/10.5067/Suborbital/ORACLES/P3/2016_V3, 2020c. </p> <p> </p> <p>ORACLES Science Team: Suite of Aerosol, Cloud, and Related Data Acquired Aboard ER2 During ORACLES 2016, Version 3, NASA Ames Earth Science Project Office, https://doi.org/10.5067/Suborbital/ORACLES/ER2/2016_V3, 2020d.</p>
Animated Aircraft
I'm a multimedia engenieer, specialiced on 3D modeling and rendering. My linkedin: https://www.linkedin.com/in/lothwolf15/ My Behance: https://www.behance.net/lothwolf15 Source: Objaverse 1.0 / Sketchfab
Data from: Frequency-dependent tolerance to aircraft disturbance drastically alters predicted impact on shorebirds
<p>This data package includes data and R script belonging to the publication "Frequency-dependent tolerance to aircraft disturbance drastically alters predicted impact on shorebirds" by van der Kolk et al. (2024) in Ecology Letters.</p> <p>The script analysis_figures_vanderKolketal2024.R can be used to reproduce all analysis and figures in the manuscript. The Figures are stored in the Output folder.</p> <p>The data folder includes an excel file with metadata explaining all columns in the csv files.</p>
Dataset: 2023 Aircraft traffic and GPS anomalies aggregated per hexbins
<p>We divided the globe into hexbins, each with an average area of 385 square kilometers. Once the hexbin grid was established, data from the GPS gaps, GPS deviations, and Traffic Density datasets were used to populate these hexbins with relevant information. On average, each hexbin has around 23,478 flights passing through it.</p> <ul> <li><strong>Total Records</strong>: 14,117 - total number of hexbin on a map, where number of flights > 0</li> <li><strong>Columns</strong>:</li> <ul> <li><strong>id:</strong></li> <li><strong>WKT</strong>: Well-Known Text representation of a POINT (senter of each hexbin) in the CSV file, or a geometry field in the DPKG file.</li> <li><strong>flights</strong>: number of flights traveled trough that hexbin in 2023</li> <li><strong>gaps</strong>: number of GPS gap incidents registered in that hexbin in 2023</li> <li><strong>deviations</strong>: number of GPS deviation incidents started in that hexbin in 2023</li> </ul> </ul>
Supporting information for the paper "Evidence of a new population of weak Terrestrial Gamma-ray Flashes observed from aircraft altitude" by I. Bjørge-Engeland et al.
<p>Supporting data for the paper "Evidence of a new population of weak Terrestrial Gamma-ray Flashes observed from aircraft altitude" by I. Bjørge-Engeland et al. </p>
Muti-type Aircraft of Remote Sensing Images: MTARSI
<p>MTARSI has a total of 9'385 remote sensing images acquired from Google Earth satellite imagery and manually expanded, including 20~different types of aircraft covering 36~airports.<br> The new data set is made up of the following 20 aircraft types: B-1, B-2, B-29, B-52, Boeing, C-130, C-135, C-17, C-5, E-3, F-16, F-22, KC-10, C-21, U-2, A-10, A-26, P-63, T-6, T-43.<br> All the sample images are carefully labeled by seven specialists in the field of remote sensing images interpretation. <br> Each image contains one and only one complete aircraft.</p>
Multi-type Aircraft of Remote Sensing Images: MTARSI 2
<p>Multi-Type Aircraft of Remote Sensing Images (MTARSI 2) dataset of aircraft on runways. The dataset has had some reclassification into 42 classifications, and extra data augmentation in those classifications. It is an example of an unbalanced dataset, with challenges of different light and viewing angles. Originated from https://zenodo.org/record/3464319#.YNwk3-hKiUk. (MTARSI)</p>
Tracking wildlife energy dynamics with unoccupied aircraft systems and 3-dimensional photogrammetry
<p>We present a novel application using unoccupied aircraft systems (UAS; drones) for structure-from-motion three-dimensional (3-D) photogrammetry of multiple, free-ranging animals simultaneously. Pinnipeds reliably haul-out on shore for pupping and breeding each year, accompanied by dramatic female-to-pup mass transfer over a short lactation period and males lose mass while defending mating territories. This provides a tractable study system for validating the use of UAS as a non-invasive tool for tracking energy dynamics in wild populations.</p> <p>UAS imagery of grey seals (<i>Halichoerus grypus</i>) was collected at Saddle Island, Nova Scotia. A multirotor UAS was piloted in 360-degree orbits around relatively dense animal aggregations and georeferenced images were used for construction of a 3-D point cloud, orthomosaic, and Digital Surface Model for animal volumetric measurements. Directly following UAS survey, a subset of adult females were hand-measured (morphometrics, blubber depth, n=21 handlings [15 were unique animals]) and female-pup pairs were weighed (adult females: n=32 [24]; pups: n=33 [23]) to validate that UAS 3-D photogrammetric models provided accurate animal volume and mass estimates.</p> <p>UAS two-dimensional body length measurements were sensitive to animal recumbency and posture. The new UAS 3-D photogrammetric method overcame these constraints, and aerial-derived body volume measurements were equivalent to those collected from the ground. UAS body volume measurements precisely predicted 'true' body mass (mean-absolute-error, adult female: 8 kg, 2.1% body mass; pup: 4.1 kg, 9.8%), and exhibited a stronger relationship with total body mass than with blubber volume.</p> <p>The method was applied to 673 free-ranging animals to characterize volume and mass dynamics across lactation and breeding for a much larger sample size than would be possible using traditional ground methods. Indeed, 1-46 animals (mean±SE: 9.2±1.2) were modeled concurrently within the focal area of a UAS flight. Application of the method also captured significant inter-annual variation in body volume/mass dynamics, and female-to-pup energy transfer efficiencies were lower when there was low sea-ice extent. The UAS 3-D photogrammetric method presented in this study is likely to be broadly applicable to other species, and the ability to measure whole groups of free-ranging animals at once makes strides towards 'weighing populations'.</p>
Data set for design for bird strike crashworthiness using a building block approach applied to the Flying-V aircraft
<p>Data set for the manuscript:</p> <p>Chen SY, van de Waerdt W, Castro SGP (2022). Design for bird strike crashworthiness using a building block approach applied to the Flying-V aircraft. Preprint. DOI: <a href="https://doi.org/10.31224/2559">https://doi.org/10.31224/2559</a></p>
Data for: Single-blind determination of methane detection limits and quantification accuracy using aircraft-based LiDAR
<p>Methane detection limits, emission rate quantification accuracy, and potential cross-species interference are assessed for Bridger Photonics' Gas Mapping LiDAR (GML) system utilizing data collected during laboratory testing and single-blind controlled release testing. Laboratory testing identified no significant interference in the path-integrated methane measurement from the gas species tested (ethylene, ethane, propane, n-butane, i-butane, and carbon dioxide). The controlled release study, comprised of 650 individual measurement passes, represents the largest dataset collected to date to characterize GML with respect to point-source emissions. Binomial regression is utilized to create detection curves illustrating the likelihood of detecting an emission of a given size under different wind conditions and for different flight altitudes. Wind-normalized methane detection limits (90% detection rate) of 0.25 (kg/h)/(m/s) and 0.41 (kg/h)/(m/s) are observed at a flight altitude of 500 feet and 675 feet above ground level, respectively. Quantification accuracy is also assessed for emissions ranging from 0.15 to 1400 kg/h. When emission rate estimates were generated using wind from High-Resolution Rapid Refresh (HRRR) model (the primary wind source that Bridger uses for their commercial operations), linear regression indicates bias of 8.1% (R2 = 0.89). For 95% of controlled releases above Bridger's stated production-sector detection sensitivity (3 kg/h with 90% probability of detection), accuracy of individual emission rate estimates produced using HRRR wind ranged from -64.1% to 87.0%. Across all controlled releases 38.1% of estimates had error within +/- 20%, and 87.3% of measurements were within a factor of two (-50% to +100% error). At low wind speed (less than 2 m/s) and low emission rates (less than 3 kg/h) emission estimates are biased high; however, when removed do not impact the regression significantly. The aggregate quantification error including all detected emission events was +8.2% using the HRRR wind source. The resulting detection curves and quantification accuracy illustrate important implications which must be considered when using measurements from GML or other remote emission measurement techniques to inform or validate inventory models, or to audit reported emission levels from oil and gas systems.</p>
DC-8 NASA aircraft
NASA uses a McDonnell Douglas DC-8 aircraft as a flying science laboratory.
ER-2 NASA aircraft
The NASA ER-2 is a high-altitude, moderate-speed aircraft. With a maximum performance altitude of 70,000 feet and a nominal performance altitude of 65,000 feet, the NASA ER-2 travels outside 95 percent of the Earth's atmosphere at approximately 410 knots with a range of 3,000 nautical miles.
CEDS Version 2021-04-21 Aircraft Emissions Fix
<p>This data release contains global CEDS aircraft emissions gridded over a 0.5° latitude x longitude grid, with 25 altitude levels of 0.61 km thickness. The center of these altitude levels range from 0.305 to 14.495 km above sea level. Grids are provided over the years 1950-2019 on a monthly time scale, with the exception of methane, which ranges from 1970-2019. </p>
Stress Testing CPS - Lightweight Aircraft dataset
<p>This dataset contains the test traces generated for the Lightweight Aircraft case study of the paper "Stress Testing Control Loops in Cyber Physical Systems". Simply copy the unpacked folder in the code repository of the testing approach matlab implementation and instead of re-executing the tests, the code will use the pre-generated traces.</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.