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”
Dataset for "On a Collision Course: Unveiling Wireless Attacks to the Aircraft Traffic Collision Avoidance System (TCAS)"
<p>The dataset associated with "On a Collision Course: Unveiling Wireless Attacks to the Aircraft Traffic Collision Avoidance System (TCAS)"</p>
Dataset for: Methodology to identify and quantify flight path dependent bird strike scenarios over aircraft
<h1>ScenarioGenerator</h1> <h2>Description</h2> <p>This project is a Python project containing a demonstrations of the methodology developed by J. Bertholdt. <br>The code takes stl files and flight path data in order to create bird strike impact scenarios for each cell. <br>With this data it is possible to approximate the impact intensity and create heat maps over the geometry.</p> <h2>Features</h2> <p>- Data processing: The code creates scenarios (impact vector, angle, velocity and bird data) for hit areas and estimates peak pressure and total impulse. <br>- Data saving: The code saves the data in forms of csv files.<br>- Data reader: The code can read the csv files and recreate the processed data and mesh.<br>- Data visualization: The code contains examples for data filtering and plotting. </p> <h2>Installation</h2> <p>1. Download Code<br>2. Adjust directories in data_reader_demo.py and stl_processing_demo.<br>3. Create a virtual environment:</p> <h3>Required packages:</h3> <p>- numpy<br>- birdpressure<br>- matplotlib<br>- pyvista</p>
Dataset for Securing the Sky: Detecting Aircraft Location Drifting through Cross-Checking Receiver-Based Estimated and Received ADS-B Trajectories
<p>Dataset utilized for our tested data in our accepted paper "Securing the Sky: Detecting Aircraft Location Drifting through Cross-Checking Receiver-Based Estimated and Received ADS-B Trajectories"</p>
Dataset: New Horizon Aircraft Ltd. (HOVRW) 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: New Horizon Aircraft Ltd. (HOVR) 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.
Subset of Opensky Dataset for aircraft trajectories
<p>To reduce the size complexity we have filtered the OpenSky dataset such that it encompasses flight trajectory information from the Nordrhein Westfalen region, with latitude ranging from 50 to 52 degrees north and longitude ranging from 5 to 9 degrees east, capturing the movement of aircraft's from July 1st 2019 to July 31st 2019.</p>
EMAC-L90MA-SD output used in "Stratospheric Injection of Brominated Very Short-Lived Substances: Aircraft Observations in the Western Pacific and Representation in Global Models"
<p>Output of halocarbons, inorganic bromine, and tropopause pressure from EMAC-L90MA-SD used in:</p> <p>Wales et al., Stratospheric Injection of Brominated Very Short-Lived Substances: Aircraft Observations in the Western Pacific and Representation in Global Models." <em>Journal of Geophysical Research: Atmospheres,</em> (2018).</p> <p>The EMAC-L90MA-SD simulation uses ERA-Interim meteorology and was prepared according to: </p> <p>Jöckel, P., Tost, H., Pozzer, A., Kunze, M., Kirner, O., Brenninkmeijer, C. A. M., Brinkop, S., Cai, D. S., Dyroff, C., Eckstein, J., Frank, F., Garny, H., Gottschaldt, K.-D., Graf, P., Grewe, V., Kerkweg, A., Kern, B., Matthes, S., Mertens, M., Meul, S., Neumaier, M., Nützel, M., Oberländer-Hayn, S., Ruhnke, R., Runde, T., Sander, R., Scharffe, D., & Zahn, A.: Earth System Chemistry integrated Modelling (ESCiMo) with the Modular Earth Submodel System (MESSy) version 2.51, <em>Geoscientific Model Development</em>, 9, 1153–1200, doi: 10.5194/gmd-9-1153-2016, URL <a href="http://www.geosci-model-dev.net/9/1153/2016/">http://www.geosci-model-dev.net/9/1153/2016/</a> (2016)</p> <p>For further details, please contact Patrick Joeckel (Patrick.Joeckel@dlr.de) and Phoebe Graf (Phoebe.Graf@dlr.de)</p>
Supplemental dataset for "Weather field reconstruction using aircraft surveillance data and a novel meteo-particle model"
<p>This dataset contains the source data used for the experiments of the paper titled "Weather field reconstruction using aircraft surveillance data and a novel meteo-particle model".</p>
In-Situ Aircraft Observations from North China on May 22, 2017 for AAS
<p>dataset for <span>Airborne Investigation of Riming: Cloud and Precipitation Microphysics Within a Weak Convective System in North China</span></p>
Synthetic Aircraft Trajectory Dataset
<p>This dataset comprises synthetically generated aircraft trajectories for multiple specific airport pairs across Europe. Generated using advanced machine learning techniques, the dataset includes high-resolution spatial and temporal information for each trajectory. It features key flight parameters such as latitude, longitude, altitude, and time, along with synthetic identifiers for each flight. This dataset is ideal for air traffic management research, flight path analysis, and the development of predictive models in aviation.</p>
Sampled Encounters of Low Altitude Manned Aircraft
<p><strong>Summary</strong></p> <p>Paired encounters between two manned aircraft based on samples from Bayesian dynamic models of aircraft behavior. The encounters are uncorrelated in the sense that it is assumed that air traffic control services are not being provided during the encounter.</p> <p><strong>Description</strong></p> <p>For many aviation safety studies, aircraft behavior is represented using encounter models, which are statistical models of how aircraft behave during close encounters. They are used to provide a realistic representation of the range of encounter flight dynamics where an aircraft collision avoidance system would be likely to alert. These models represent aircraft behavior (rates) during the course of the encounter with other aircraft. Encounter models have been developed for many different manned operational contexts. For more details on encounter models, please refer to the overview on the Airspace Encounter Models GitHub organization: <a href="https://github.com/Airspace-Encounter-Models/em-overview">https://github.com/Airspace-Encounter-Models/em-overview</a>.</p> <p>The Bayesian models were sourced from <a href="https://github.com/Airspace-Encounter-Models/em-model-manned-bayes/tree/v1.3">v1.3 of the em-model-manned-bayes </a>repository while<a href="https://github.com/Airspace-Encounter-Models/em-pairing-uncor-importancesampling/tree/v1.2"> v1.2 of em-pairing-uncor-importancesampling</a> contained the software to pair samples from the Bayesian models to create encounters. This dataset contains 1.6 million encounters across 18 different encounter sets, each consisting of 100,000 unique encounters. The encounters were generated in June 2021. Please refer to the software for details on the format and variables for each encounter.</p> <p>The dataset is provided as a single .zip archive with multiple directories. Simply download the .zip file and extract. An excel file with two sheets are document the configuration parameters. The first sheet reports the tradespace and configurations for each encounter set. The model files correspond to Bayesian models from em-model-manned-bayes, while <em>tCPA</em> and <em>sample_time</em> are encounter parameters from em-pairing-uncor-importancesampling. The <em>tCPA </em>variable the desired time at which the closest point of approach occurs and <em>sample_time</em> variable is the desired total duration of the encounter in seconds. The <em>sample_time</em> value must be greater than <em>tCPA</em>. The other sheet, <em>fixed_paramters</em>, documents the fixed encounter parameters. The minimum initial horizontal and vertical separation requirements were defined by <em>R_min</em> and <em>H_min</em>. The bin edges and desired proportions defined the importance sampling criteria for the horizontal miss distance (HMD) and vertical miss distance (VMD) at the closest point of approach. The <em>encIds</em> denote that 100,000 encounters were generated for each set and that the random seed (<em>randSeed</em>) was set to one.</p> <p><strong>Distribution Statement</strong></p> <p>DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited.</p> <p>© 2021 Massachusetts Institute of Technology.</p> <p>Delivered to the U.S. Government with Unlimited Rights, as defined in DFARS Part 252.227-7013 or 7014 (Feb 2014). Notwithstanding any copyright notice, U.S. Government rights in this work are defined by DFARS 252.227-7013 or DFARS 252.227-7014 as detailed above. Use of this work other than as specifically authorized by the U.S. Government may violate any copyrights that exist in this work.</p> <p>This material is based upon work supported by the Federal Aviation Administration under Air Force Contract No. FA8702-15-D-0001. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Federal Aviation Administration.</p> <p>This document is derived from work done for the FAA (and possibly others); it is not the direct product of work done for the FAA. The information provided herein may include content supplied by third parties. Although the data and information contained herein has been produced or processed from sources believed to be reliable, the Federal Aviation Administration makes no warranty, expressed or implied, regarding the accuracy, adequacy, completeness, legality, reliability or usefulness of any information, conclusions or recommendations provided herein. Distribution of the information contained herein does not constitute an endorsement or warranty of the data or information provided herein by the Federal Aviation Administration or the U.S. Department of Transportation. Neither the Federal Aviation Administration nor the U.S. Department of Transportation shall be held liable for any improper or incorrect use of the information contained herein and assumes no responsibility for anyone’s use of the information. The Federal Aviation Administration and U.S. Department of Transportation shall not be liable for any claim for any loss, harm, or other damages arising from access to or use of data or information, including without limitation any direct, indirect, incidental, exemplary, special or consequential damages, even if advised of the possibility of such damages. The Federal Aviation Administration shall not be liable to anyone for any decision made or action taken, or not taken, in reliance on the information contained herein.</p>
Syntheses of aircraft noise obtained by computational methods
<p>In ANIMA WP4, where focus is put on toolset development, a benchmark on three partners’ auralization tools was performed.</p> <p>These tools are used to reproduce the sound of an aircraft flyover from either physical modelling of noise, a prediction based on measurement or a combination of both. As the chosen methodologies and the modelling hypotheses are different between partners, a benchmark was performed to assess the impact of these strategies on the produced sound synthesis.</p> <p>The realism of each auralization was evaluated through comparison to experimental recordings. For propriety reasons, only the synthesized sounds are available here, and can be compared between each other.</p> <p>Two of the three tools were further used in the WP3 task dedicated to Virtual Reality, see "<a href="https://doi.org/10.5281/zenodo.5517218">Virtual reality simulated aircraft flyovers: Influence of the landscape on the overall pleasantness of the environment</a>"</p> <p>The sounds represent three flight configurations, one landing, and two take-offs with different engine speeds. Two aircraft are considered, one single-aisle and one double-aisle aircraft. The synthesis is performed at a receiver position below the aircraft trajectory.</p> <p>For more information, please contact:</p> <ul> <li><a href="mailto:Ingrid.legriffon@onera.fr">Ingrid.legriffon@onera.fr</a> (ONERA)</li> <li><a href="mailto:isabelle.boullet@airbus.com">isabelle.boullet@airbus.com</a> (Airbus Aviation)</li> <li><a href="mailto:jean-michel.boiteux@safrangroup.com">jean-michel.boiteux@safrangroup.com</a> (Safran Aircraft Engine)</li> </ul> <p> </p>
HRPlanesv2 - High Resolution Satellite Imagery for Aircraft Detection
<p>The HRPlanesv2 dataset contains 2120 VHR Google Earth images. To further improve experiment results, images of airports from many different regions with various uses (civil/military/joint) selected and labeled. A total of 14,335 aircrafts have been labelled. Each image is stored as a ".jpg" file of size 4800 x 2703 pixels and each label is stored as YOLO ".txt" format. Dataset has been split in three parts as 70% train, %20 validation and test. The aircrafts in the images in the train and validation datasets have a percentage of 80 or more in size.</p>
Estimated stand-off distance between ADS-B equipped aircraft and obstacles
<p><strong>Summary</strong>:</p> <p>Estimated stand-off distance between ADS-B equipped aircraft and obstacles. Obstacle information was sourced from the FAA Digital Obstacle File and the FHWA National Bridge Inventory. Aircraft tracks were sourced from processed data curated from the OpenSky Network. Results are presented as histograms organized by aircraft type and distance away from runways.</p> <p> </p> <p><strong>Description:</strong></p> <p>For many aviation safety studies, aircraft behavior is represented using encounter models, which are statistical models of how aircraft behave during close encounters. They are used to provide a realistic representation of the range of encounter flight dynamics where an aircraft collision avoidance system would be likely to alert. These models currently and have historically have been limited to interactions between aircraft; they have not represented the specific interactions between obstacles and aircraft equipped transponders. In response, we calculated the standoff distance between obstacles and ADS-B equipped manned aircraft.</p> <p>For robustness, this assessment considered two different datasets of manned aircraft tracks and two datasets of obstacles. For robustness, MIT LL calculated the standoff distance using two different datasets of aircraft tracks and two datasets of obstacles. This approach aligned with the <a href="https://doi.org/10.2514/1.D0091">foundational research</a> used to support the <a href="https://doi.org/10.1520/F3442_F3442M-20">ASTM F3442/F3442M-20</a> well clear criteria of 2000 feet laterally and 250 feet AGL vertically.</p> <p>The two datasets of processed tracks of ADS-B equipped aircraft curated from the OpenSky Network. It is likely that rotorcraft were underrepresented in these datasets. There were also no considerations for aircraft equipped only with Mode C or not equipped with any transponders. The first dataset was used to train the <a href="https://github.com/Airspace-Encounter-Models/em-model-manned-bayes/releases/tag/v1.3">v1.3 uncorrelated encounter models</a> and referred to as the “Monday” dataset. The second dataset is referred to as the “aerodrome” dataset and was used to train the v2.0 and v3.x terminal encounter model. The Monday dataset consisted of 104 Mondays across North America. The other dataset was based on observations at least 8 nautical miles within Class B, C, D aerodromes in the United States for the first 14 days of each month from January 2019 through February 2020. Prior to any processing, the datasets required 714 and 847 Gigabytes of storage. For more details on these datasets, please refer to "Correlated Bayesian Model of Aircraft Encounters in the Terminal Area Given a Straight Takeoff or Landing" and “Benchmarking the Processing of Aircraft Tracks with Triples Mode and Self-Scheduling.”</p> <p>Two different datasets of obstacles were also considered. First was point obstacles defined by the FAA digital obstacle file (DOF) and consisted of point obstacle structures of antenna, lighthouse, meteorological tower (met), monument, sign, silo, spire (steeple), stack (chimney; industrial smokestack), transmission line tower (t-l tower), tank (water; fuel), tramway, utility pole (telephone pole, or pole of similar height, supporting wires), windmill (wind turbine), and windsock. Each obstacle was represented by a cylinder with the height reported by the DOF and a radius based on the report horizontal accuracy. We did not consider the actual width and height of the structure itself. Additionally, we only considered obstacles at least 50 feet tall and marked as verified in the DOF.</p> <p>The other obstacle dataset, termed as “bridges,” was based on the identified bridges in the FAA DOF and additional information provided by the National Bridge Inventory. Due to the potential size and extent of bridges, it would not be appropriate to model them as point obstacles; however, the FAA DOF only provides a point location and no information about the size of the bridge. In response, we correlated the FAA DOF with the National Bridge Inventory, which provides information about the length of many bridges. Instead of sizing the simulated bridge based on horizontal accuracy, like with the point obstacles, the bridges were represented as circles with a radius of the longest, nearest bridge from the NBI. A circle representation was required because neither the FAA DOF or NBI provided sufficient information about orientation to represent bridges as rectangular cuboid. Similar to the point obstacles, the height of the obstacle was based on the height reported by the FAA DOF. Accordingly, the analysis using the bridge dataset should be viewed as risk averse and conservative. It is possible that a manned aircraft was hundreds of feet away from an obstacle in actuality but the estimated standoff distance could be significantly less. Additionally, all obstacles are represented with a fixed height, the potentially flat and low level entrances of the bridge are assumed to have the same height as the tall bridge towers. The attached figure illustrates an example simulated bridge.</p> <p>It would had been extremely computational inefficient to calculate the standoff distance for all possible track points. Instead, we define an encounter between an aircraft and obstacle as when an aircraft flying 3069 feet AGL or less comes within 3000 feet laterally of any obstacle in a 60 second time interval. If the criteria were satisfied, then for that 60 second track segment we calculate the standoff distance to all nearby obstacles. Vertical separation was based on the MSL altitude of the track and the maximum MSL height of an obstacle.</p> <p>For each combination of aircraft track and obstacle datasets, the results were organized seven different ways. Filtering criteria were based on aircraft type and distance away from runways. Runway data was sourced from the FAA runways of the United States, Puerto Rico, and Virgin Islands <a href="https://adds-faa.opendata.arcgis.com/datasets/4d8fa46181aa470d809776c57a8ab1f6_0/about">open dataset</a>. Aircraft type was identified as part of the <a href="https://github.com/Airspace-Encounter-Models/em-processing-opensky">em-processing-opensky</a> workflow.</p> <ul> <li><em>All</em>: No filter, all observations that satisfied encounter conditions</li> <li><em>nearRunway</em>: Aircraft within or at 2 nautical miles of a runway</li> <li><em>awayRunway</em>: Observations more than 2 nautical miles from a runway</li> <li><em>glider</em>: Observations when aircraft type is a glider</li> <li><em>fwme</em>: Observations when aircraft type is a fixed-wing multi-engine</li> <li><em>fwse</em>: Observations when aircraft type is a fixed-wing single engine</li> <li><em>rotorcraft</em>: Observations when aircraft type is a rotorcraft</li> </ul> <p><strong>License</strong></p> <p>This dataset is licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International(CC BY-NC-ND 4.0).</p> <p>This license requires that reusers give credit to the creator. It allows reusers to copy and distribute the material in any medium or format in unadapted form and for noncommercial purposes only. Only noncommercial use of your work is permitted. Noncommercial means not primarily intended for or directed towards commercial advantage or monetary compensation. Exceptions are given for the not for profit standards organizations of ASTM International and RTCA.</p> <p>MIT is releasing this dataset in good faith to promote open and transparent research of the low altitude airspace. Given the limitations of the dataset and a need for more research, a more restrictive license was warranted. Namely it is based only on only observations of ADS-B equipped aircraft, which not all aircraft in the airspace are required to employ; and observations were source from a crowdsourced network whose surveillance coverage has not been robustly characterized.</p> <p>As more research is conducted and the low altitude airspace is further characterized or regulated, it is expected that a future version of this dataset may have a more permissive license.</p> <p><strong>Distribution Statement</strong></p> <p>DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited.</p> <p>© 2021 Massachusetts Institute of Technology.</p> <p>Delivered to the U.S. Government with Unlimited Rights, as defined in DFARS Part 252.227-7013 or 7014 (Feb 2014). Notwithstanding any copyright notice, U.S. Government rights in this work are defined by DFARS 252.227-7013 or DFARS 252.227-7014 as detailed above. Use of this work other than as specifically authorized by the U.S. Government may violate any copyrights that exist in this work.</p> <p>This material is based upon work supported by the Federal Aviation Administration under Air Force Contract No. FA8702-15-D-0001. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Federal Aviation Administration.</p> <p>This document is derived from work done for the FAA (and possibly others); it is not the direct product of work done for the FAA. The information provided herein may include content supplied by third parties. Although the data and information contained herein has been produced or processed from sources believed to be reliable, the Federal Aviation Administration makes no warranty, expressed or implied, regarding the accuracy, adequacy, completeness, legality, reliability or usefulness of any information, conclusions or recommendations provided herein. Distribution of the information contained herein does not constitute an endorsement or warranty of the data or information provided herein by the Federal Aviation Administration or the U.S. Department of Transportation. Neither the Federal Aviation Administration nor the U.S. Department of Transportation shall be held liable for any improper or incorrect use of the information contained herein and assumes no responsibility for anyone’s use of the information. The Federal Aviation Administration and U.S. Department of Transportation shall not be liable for any claim for any loss, harm, or other damages arising from access to or use of data or information, including without limitation any direct, indirect, incidental, exemplary, special or consequential damages, even if advised of the possibility of such damages. The Federal Aviation Administration shall not be liable to anyone for any decision made or action taken, or not taken, in reliance on the information contained herein.</p> <p><strong>Download and Format:</strong></p> <p>The dataset is provided as a single .zip archive with multiple directories. The directories indicate the aircraft track dataset and types of obstacles. Within each directory are csv files corresponding to different the filtering criteria. These files are the total counts of observations (histogram) where columns corresponding to lateral (range) distance between the simulated obstacle and aircraft and rows correspond to the relative vertical separation. The files centers_x_ft.csv and centers_y_ft.csv are the center of each bin.</p> <p>Simply download the .zip file and extract. The MD5 checksum of the .zip file prior to uploading to Zenodo was 03e5aed725a4cf393683730e05521ba5.</p> <p><strong>MIT Lincoln Laboratory LL Group and Division this dataset is associated with</strong>:</p> <ul> <li>Group 42 / Division 4</li> <li>Topic: collision avoidance</li> <li>R&D Area: Air Traffic Control</li> <li>R&D Group: Surveillance Systems</li> </ul>
Auralizations of Current and Future Aircraft Concepts
<p>The noise of the four flyovers in this video are purely synthetic sound - so called auralizations.</p> <p>In the Horizon 2020 research project ARTEM (Aircraft noise Reduction Technologies and related Environmental iMpact: <a href="https://cordis.europa.eu/project/id/769350">https://cordis.europa.eu/project/id/769350</a>) funded by the European Union, these four presented and 40 more auralizations were used in a psychoacoustic laboratory experiment, conducted at Empa Dübendorf, to investigate the noise annoyance to aircraft flyovers of a future aircraft design compared to a current commercial aircraft.</p> <p> </p> <p>[1] R. Pieren, I. LeGriffon, L. Bertsch, A. Heusser, F. Centracchio, D. Weinstraub, C. Lavandier, and B. Schäffer, "Perception-based noise assessment of a future blended wing body aircraft concept using synthesized flyovers in an acoustic VR environment – the ARTEM study", Aerosp. Sci. Technol., vol. 144, 2024, doi.org/10.1016/j.ast.2023.108767</p> <p>[2] B. Schäffer, L. Bertsch, I. Le Griffon, A. Heusser, C. Lavandier, and R. Pieren, "Evaluation of flyover auralizations of today's and future long-range aircraft concepts", International Congress and Exposition on Noise Control Engineering (InterNoise), Glasgow, 21-24 August 2022.</p> <p>[3] R. Pieren and D. Lincke, "Auralization of aircraft flyovers with turbulence-induced coherence loss in ground effect", J. Acoust. Soc. Am., vol. 151, no. 4, pp. 2453-2460, 2022.</p> <p>[4] R. Pieren, L. Bertsch, D. Lauper, and B. Schäffer, "Improving future low-noise aircraft technologies using experimental perception-based evaluation of synthetic flyovers", Sci. Total Environ., vol. 692, pp. 68-81, 2019.</p>
Data for: Multi-campaign ship and aircraft observations of marine cloud condensation nuclei, and droplet concentrations
<p class="MsoNormal"><span>In-situ marine cloud droplet number concentrations (CDNCs), cloud condensation nuclei (CCN), and CCN proxies, based on particle sizes and optical properties, are accumulated from seven field campaigns, ACTIVATE, NAAMES, CAMP2EX, ORACLES, SOCRATES, MARCUS, and CAPRICORN2. Each campaign involves aircraft measurements, ship-based measurements, or both. Measurements are collected over the North and Central Atlantic, Indo-Pacific, and Southern Oceans, representing a range of clean to polluted conditions in various climate regimes. With the large range of environmental conditions sampled, this collection of data is ideal for testing satellite remote detection methods of CDNC and CCN in marine environment. Remote measurement methods are key to expanding the available data, in these difficult to reach regions of the Earth, and improving our understanding of aerosol-cloud interactions. Additional particle composition and continental tracers are included to identify potential contributing CCN source. Several of these campaigns, include both High Spectral Resolution Lidar and polarimetric imaging measurements that will be the basis for the next generation of space-based remote sensors and, thus, can be utilized as satellite surrogates.</span></p>
Data for figures in "Next-generation ice nucleating particle sampling on aircraft: Characterization of the High-volume flow aERosol particle filter sAmpler (HERA)"
<p>Atmospheric ice nucleating particle (INP) concentration data from the free troposphere are sparse, but urgently needed to understand vertical transport processes of INPs and their influence on cloud formation and properties. Here, we introduce the new High-volume flow aERosol particle filter sAmpler (HERA) which was specially developed for installation on research aircraft and subsequent offline INP analysis. HERA is a modular system constisting of a sampling unit and a powerful pump unit and has several features which were integrated specifically for INP sampling. Firstly, the pump unit enables sampling at flow rates exceeding 100 L min<sup>−1</sup>, which is well above typical flow rates of aircraft INP sampling systems described in the literature (~10 L min<sup>−1</sup>). Consequently, required sampling times to capture rare, high-temperature INPs (≥-15 °C) are reduced in comparison to other systems and potential source regions of INPs can be confined more precisely. Secondly, the sampling unit is designed as a seven-way valve, enabling switching between six filter holders and a bypass with one filter being sampled at a time. In contrast to other aircraft INP sampling systems, the valve position is controlled remotely via software so that manual filter changes in-flight are eliminated and the potential for sample contamination is decreased. This design is compatible with a high degree of automation, i.e., triggering filter changes depending on parameters like flight altitude, geographical location, temperature, or time. In addition to the design and principle of operation of HERA, this paper presents laboratory characterization experiments with size-selected test substances, i.e., SNOMAX® and Arizona Test Dust. The particles were sampled on filters with HERA, varying either particle diameter (300 nm to 800 nm) or flow rate (10 L min<sup>−1</sup> to 100 L min<sup>−1</sup>) between experiments. The subsequent offline INP analysis showed good agreement with literature data and comparable sampling efficiencies for all investigated particle sizes and flow rates. Furthermore, the deposition efficiency of atmospheric INPs in HERA was compared to a straightforward filter sampler and good agreement was found. Finally, results from the first campaign of HERA on the High Altitude and LOng range research aircraft (HALO) demonstrate the functionality of the new system in the context of aircraft application.</p> <p>The given csv files contain the data for reproducing the figures in the publication. The data structure of the csv files is explained in the README file.</p>
Data for: Multi-campaign ship and aircraft observations of marine cloud condensation nuclei, and droplet concentrations
Open the record for dataset details and reuse information.
Deconstruction of tropospheric chemical reactivity using aircraft measurements: the Atmospheric Tomography Mission (ATom) data
Open the record for dataset details and reuse information.
Data from: Spatiotemporal variation in disturbance impacts derived from simultaneous tracking of aircraft and shorebirds
<p>1. Assessing impacts of disturbance over large areas and long time periods is crucial for nature management, but also challenging since impacts depend on both wildlife responses to disturbance and on the spatiotemporal distribution of disturbance sources. Combined tracking of animals and disturbance sources enables quantification of wildlife responses as a function of the distance to a disturbance source. We provide a framework to derive such distance-response curves and combine those with disturbance source presence data to quantify energetic costs of disturbance at a landscape scale. 2. We tracked 90 Eurasian Oystercatchers Haematopus ostralegus and all aircraft in a military training area in the Dutch Wadden Sea. We quantified distance-response curves estimating flight probability and additional displacement for five types of aircraft activities, by comparing bird movement prior to aircraft presence with movement during aircraft presence. We then used the distance-response curves to map mean and variation in additional daily energy expenditure due to cumulative aircraft disturbance across the landscape for a 700-day period. 3. Flight probability and displacement responses differed strongly among aircraft activities and decreased from transport airplanes, through bombing jets, helicopters, jets to small civil airplanes. Since the most disturbing aircraft activities were also the rarest ones, mean additional daily energy expenditure did not exceed 0.25%. However, days with substantial (>1%) additional expenditure occurred between 0.1% and 3.7% of all days across high tide roosts in the tidal basin. Notably, expenditure particularly spiked on days with transport airplane activity (up to 8.5%). 4. Synthesis and applications. Cumulative energetic flight costs due to aircraft disturbance are low and unlikely to impact survival of oystercatchers in our study area. Our results provide evidence that the legal minimum flight height of 450m for small civil airplanes effectively limits disturbance of oystercatchers. Mitigation should focus on limiting the number of days when disturbance has a high impact by reducing rare but highly disturbing activities, especially transport airplanes. Our approach can be applied to other species and disturbance sources that are automatically tracked, e.g. boats and walkers, ultimately to quantify the entire anthropogenic disturbance landscape.</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.