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109 results for “aviation”
Model simulation data used in "The global impact of the transport sectors on atmospheric aerosol in 2030 – Part 2: Aviation" (Righi et al., Atmos. Chem. Phys., 2016)
<p>This dataset contains the output of the EMAC global model simulations analysed and discussed in Righi et al. (<i>Atmos. Chem. Phys.</i>, 2016). For details see the README.md file.</p>
Open-source traffic and CO2 emission dataset for commercial aviation
<p>This record is a global open-source passenger air traffic dataset primarily dedicated to the research community. <br>It gives a seating capacity available on each origin-destination route for a given year, 2019, and the associated aircraft and airline when this information is available. </p> <p>Context on the original work is given in the related articles (<a href="https://doi.org/10.59490/joas.2024.7365">https://doi.org/10.59490/joas.2024.7365,</a> <a href="https://doi.org/10.59490/joas.2023.7201">https://doi.org/10.59490/joas.2023.7201)</a> and on the associated GitHub page (<a href="https://github.com/AeroMAPS/AeroSCOPE/">https://github.com/AeroMAPS/AeroSCOPE/</a>).<br>A simple data exploration interface will be available at <a href="www.aeromaps.eu/aeroscope">www.aeromaps.eu/aeroscope.</a><br>The dataset was created by aggregating various available open-source databases with limited geographical coverage. It was then completed using a route database created by parsing Wikipedia and Wikidata, on which the traffic volume was estimated using a machine learning algorithm (XGBoost) trained using traffic and socio-economical data.<br> </p> <h4><br><strong>1- DISCLAIMER</strong></h4> <p><br>The dataset was gathered to allow highly aggregated analyses of the air traffic, at the continental or country levels. At the route level, the accuracy is limited as mentioned in the associated article and improper usage could lead to erroneous analyses. </p> <p>Although all sources used are open to everyone, the Eurocontrol database is only freely available to academic researchers. It is used in this dataset in a very aggregated way and under several levels of abstraction. As a result, it is not distributed in its original format as specified in the contract of use.</p> <p>As a general rule, we decline any responsibility for any use that is contrary to the terms and conditions of the various sources that are used. In case of commercial use of the database, please contact us in advance.</p> <h4><br><strong>2- DESCRIPTION</strong></h4> <p>Each data entry represents an (Origin-Destination-Operator-Aircraft type) tuple.</p> <p><em>Please </em>refer<em> to </em>the<em> support article for more details (see above).</em></p> <p>The dataset contains the following columns:</p> <ul> <li>"First column" : index</li> <li><strong>airline_iata : </strong>IATA code of the operator in nominal cases. An ICAO -> IATA code conversion was performed for some sources, and the ICAO code was kept if no match was found.</li> <li><strong>acft_icao : </strong>ICAO code of the aircraft type</li> <li><strong>acft_class : </strong>Aircraft class identifier, own classification. <ul> <li>WB: Wide Body</li> <li>NB: Narrow Body</li> <li>RJ: Regional Jet</li> <li>PJ: Private Jet</li> <li>TP: Turbo Propeller</li> <li>PP: Piston Propeller</li> <li>HE: Helicopter</li> <li>OTHER</li> </ul> </li> <li><strong>seymour_proxy: </strong>Aircraft code for Seymour Surrogate (https://doi.org/10.1016/j.trd.2020.102528), own classification to derive proxy aircraft when nominal aircraft type unavailable in the aircraft performance model.</li> <li><strong>source: </strong>Original data source for the record, before compilation and enrichment. <ul> <li>ANAC: Brasilian Civil Aviation Authorities</li> <li>AUS Stats: Australian Civil Aviation Authorities</li> <li>BTS: US Bureau of Transportation Statistics T100</li> <li>Estimation: Own model, estimation on Wikipedia-parsed route database</li> <li>Eurocontrol: Aggregation and enrichment of R&D database</li> <li>OpenSky</li> <li>World Bank</li> </ul> </li> <li><strong>seats: </strong>Number of seats available for the data entry, AFTER airport residual scaling</li> <li><strong>n_flights: </strong>Number of flights of the data entry, when available</li> <li><strong>iata_departure</strong>, <strong>iata_arrival : </strong>IATA code of the origin and destination airports. Some BTS inhouse identifiers could remain but it is marginal.</li> <li><strong>departure_lon</strong><em>, </em><strong>departure_lat</strong><em>, </em><strong>arrival_lon</strong><em>, </em><strong>arrival_lat : </strong>Origin and destination coordinates, could be NaN if the IATA identifier is erroneous</li> <li><strong>departure_country, arrival_country</strong>: Origin and destination country ISO2 code. <strong>WARNING: </strong>disable NA (Namibia) as default NaN at import</li> <li><strong>departure_continent, arrival_continent: </strong>Origin and destination continent code. <strong>WARNING: </strong>disable NA (North America) as default NaN at import</li> <li><strong>seats_no_est_scaling: </strong>Number of seats available for the data entry, BEFORE airport residual scaling</li> <li><strong>distance_km: </strong>Flight distance (km)</li> <li><strong>ask: </strong>Available Seat Kilometres</li> <li><strong>rpk: </strong>Revenue Passenger Kilometres (simple calculation from ASK using IATA average load factor)</li> <li><strong>fuel_burn_seymour: </strong>Fuel burn <em>per flight</em> (kg) when seymour proxy available</li> <li><strong>fuel_burn: </strong>Total fuel burn of the data entry (kg)</li> <li><strong>co2: </strong>Total CO2 emissions of the data entry (kg)</li> <li><strong>domestic: </strong>Domestic/international boolean (Domestic=1, International=0)</li> </ul> <p> </p> <h4><strong>3- Citation</strong></h4> <p>Please cite the support paper instead of the dataset itself. </p> <blockquote> <p>Salgas, A., Sun, J., Delbecq, S., Planès, T., & Lafforgue, G. (2024). Compilation and Applications of an Open-Source Dataset on Global Air Traffic Flows and Carbon Emissions. <em>Journal of Open Aviation Science</em>. <a href="https://doi.org/10.59490/joas.2024.7365">https://doi.org/10.59490/joas.2023.7201</a></p> </blockquote>
Analysis of two Methods for Aircraft Fuel Requirement Calculations in the Context of a novel Methodological Framework for LCA of Sustainable Aviation
<p>This Microsoft Excel file contains equations to compare different approaches to calculate fuel efficiency ("energy use" in [MJ/t*km]) of aircraft over a specific distance at a specific payload. </p> <p>Two approaches are compared: A novel approach by <a href="10.1016/j.scitotenv.2023.163881" target="_blank" rel="noopener">Su-ungkavatin et al.</a> and the more established approach well documented by eg. <a href="https://www.fzt.haw-hamburg.de/pers/Scholz/arbeiten/TextBurzlaff.pdf" target="_blank" rel="noopener">Burzlaff</a> or <a href="http://www.aircraftmonitor.com/uploads/1/5/9/9/15993320/aircraft_payload_range_analysis_for_financiers___v2.pdf" target="_blank" rel="noopener">Ackert</a>.</p> <p>This work augments a Letter to the Editor we submitted to the journal <a href="https://www.sciencedirect.com/journal/science-of-the-total-environment" target="_blank" rel="noopener">Science of the Total Environment</a>.</p>
Model simulation data used in "Exploring the uncertainties in the aviation soot-cirrus effect" (Righi et al., Atmos. Chem. Phys., 2021)
<p>This dataset contains the output of the EMAC global model simulations analysed and discussed in Righi et al. (<i>Atmos. Chem. Phys.</i>, 2021). For details see the README.md file and Table 1 in the paper.</p>
Dassault Aviation in flight with Clean Sky
<p>The Dasault-Aviation video provides an overview of the activities performed in Clean Sky Programs</p> <p>This project has received funding from the Clean Sky 2 Joint Undertaking (JU) under grant agreement No 807097. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and the Clean Sky 2 JU members other than the Union.<br> The results, opinions, conclusions, etc. presented in this work are those of the author(s) only and do not necessarily represent the position of the JU; the JU is not responsible for any use made of the information contained herein.</p>
Aeronautical Occurrences from the Brazilian Civil Aviation (2010/2019) Dataset
<p>This dataset is originated from the dataset of Aeronautical Occurrences managed by Centro de Investigação e Prevenção de Acidentes Aeronáuticos (CENIPA) and available at the Portal Brasileiro de Dados Abertos website (https://dados.gov.br/dataset/ocorrencias-aeronauticas-da-aviacao-civil-brasileira), which was processed and integrated into this dataset. The dataset was used on the Accidents Severity Classifier Model v1.0 (http://doi.org/10.5281/zenodo.4298791) for a Data Science Project. Metadata is available at: <https://drive.google.com/file/d/1Dpp3uEYI-9B_Gf36j5CwARKrWgtgyDOt/view?usp=sharing>.</p>
PROCRAFT Final Meeting - Educational activities on WWII aircraft at the Montreal Aviation Museum by Jean Desbiens – Montreal Museum
Open the record for dataset details and reuse information.
Dataset: FTAI Aviation Ltd. (FTAIN) 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: FTAI Aviation Ltd. (FTAIM) 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: FTAI Aviation Ltd. (FTAI) 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: Aviat Networks, Inc. (AVNW) 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.
DEPA 2050 aviation emission inventories
<p>The dataset are global aviation emission inventories for the years 2020, 2025, 2030, 2035, 2040, 2045 and 2050.</p> <p>The original data was created for the project "Development Pathways for Aviation up to 2050" (DEPA 2050) which was carried out at the German Aerospace Center (DLR) in the years 2019-2020.</p> <p>The original datasets were converted into the Network Common Data Format (netCDF) and are suitable as input files for the OpenAirClim framework. OpenAirClim models the major responses of the atmosphere by evaluation of the approximate chemistry-climate impact of air traffic emissions. The framework will be released as Open Source software.</p>
Fig. 1 in Complete mitochondrial genome of Nyctalus aviator and phylogenetic analysis of the family Vespertilionidae
Fig. 1. Neighbor Joining (NJ) phylogeny of Vespertilionidae was inferred from concatenated nucleotide sequences of 13 mitogenomic protein-coding genes. Node labels indicate the bootstrap values. GenBank accession numbers for the sequences are indicated next to species designations.
Correlation between Radiation Enhancements at Aviation Altitudes and Energetic Precipitation Electrons
<p>Figures, data, and code used in my paper describing "Correlation between Radiation Enhancements at Aviation Altitudes and Energetic Precipitation Electrons"</p>
The role of mineral dust aerosol particles in aviation soot-cirrus interactions
<p>The files contain the datasets shown in the publication "The role of mineral dust aerosol particles in aviation soot-cirrus interactions" to appear in J. Geophys. Res. Atmos. (revised manuscript submitted) The files are xmgrace plot files containing the research data (ASCII) shown in all figures that appear in the main text (4) and appendix (2).</p> <p> </p>
US Army Aviation air movement request problem instances
<p>Although lacking the same preeminent status of air assault planning, air movement operations comprise a majority of Army utility and cargo helicopter combat aviation operations in terms of volume of customers and the endless appetite for rapid movement of troops across the battlespace. The data provided enabled research and development of a US Army Aviation air movement mission planning model to assist the mission planner by rapidly providing courses of action based on the commander's priorities. Features of the problem and the data provided include priority demand, multi-node refueling, aircraft and passenger time windows, maximum passenger transportation time, and the minimization of unsupported demand, aircraft utilization, and total flight time. The mathematical model provided is an extension of the dial-a-ride problem (DARP) that will coordinate air mission requests (AMRs) at the aviation task force-level or lower to generate courses of action that optimize helicopter fleet resourcing and routing decisions against mission variables, while supporting the optimal number of AMRs that sustain combat power over time. </p>
How to make climate-neutral aviation fly
<p>This repository gathers all the necessary data and scripts to reproduce the results presented in:</p> <p> </p> <p><strong>How to make climate-neutral aviation fly</strong></p> <p><em>Romain Sacchi*<sup>$1</sup>, Viola Becattini*<sup>2</sup>, Paolo Gabrielli<sup>2</sup>, Brian Cox<sup>3</sup>, Alois Dirnaichner<sup>4</sup>, Christian Bauer<sup>1</sup>, Marco Mazzotti<sup>2</sup></em></p> <p>Corresponding authors: email <a href="mailto:romain.sacchi@psi.ch">romain.sacchi@psi.ch</a></p> <p>1 Technology Assessment group, Laboratory for Energy Systems Analysis, Paul Scherrer Institut, Villigen, Switzerland</p> <p>2 Institute of Energy and Process Engineering, ETH Zurich, Zurich, Switzerland</p> <p>3 INFRAS, Bern, Switzerland</p> <p>4 Potsdam Institute for Climate Impact Research, Potsdam, Germany</p> <p> </p> <p>“Supplementary data 1.xlsb”: spreadsheet model to calculate aviation fleet emissions.</p> <p>“Supplementary data 2.xlsx”: data generated by “Supplementary data 1.xlsb”, used to produce Figures 2 and 3.</p> <p>“Supplementary data 3.xlsx”: data representing emission from DAC operation, required by “Supplementary script 1.ipynb” to produce Figures 2 and 3.</p> <p>“Supplementary script 1.ipynb”: Script to generate Figures 2, 3 and 4 in manuscript.</p> <p>“Supplementary script 2.ipynb”: Script to generate sensitivity analysis figure S4 in Supplementary Information file.</p>
Synthesis of National projects on Aviation noise gathered by ANIMA National Focal Points
<p>This bundle gathers synthetic informations on more than hundred projects led at national level in EU countries and their neighbourhood (Switzeland, Ukraine, Serbia...) on aviation noise. This projects are partaking to the EU efforts to update the European Research Roadmap on such aviation noise issues.</p> <p>The bundle has been used for elaboration deliverable ANIMA D6.10 - National Focal Points projects</p>
US Army Aviation air movement request problem instances
Open the record for dataset details and reuse information.
Weekly NOx aviation emissions changes due to COVID-19: modified SSP2-4.5 to account for sector activity level
<p>Weekly NOx aviation emissions estimates for 2020 until 21/07/2020, modified by the country-specific impacts of COVID-19 lockdown. </p> <p>This repository holds the netcdf files for NOx emissions projected by the scenario SSP2-4.5, from the Scenario4MIPs database (<a href="https://esgf-node.llnl.gov/search/input4mips/">https://esgf-node.llnl.gov/search/input4mips/</a>), modified by the country and sector activity levels associated with lockdown for 2020, with observation-based data up until the 5th of July and a fixed estimate thereafter. This is the weekly equivalent of the aviation file in <a href="https://zenodo.org/record/3951601#.XxYBsihKhPY">https://zenodo.org/record/3951601#.XxYBsihKhPY</a> for a shorter time period, although the version number is different since we have more information available and the normalisation process has been improved. </p> <p>Funding was provided by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement nos. 820829 (CONSTRAIN) <a href="http://constrain-eu.org/">http://constrain-eu.org/</a> </p> <p>see <a href="https://github.com/Priestley-Centre/COVID19_emissions">https://github.com/Priestley-Centre/COVID19_emissions</a> for more details on the methodology.</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.