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1,068 results for “Flight”
BirdVox-ANAFCC: A dataset for American Northeast Avian Flight Call Classification
<p>BirdVox-ANAFCC: A dataset for American Northeast Avian Flight Call Classification<br> ===============================================================<br> Version 2.0, February 2022.</p> <p>https://wp.nyu.edu/birdvox</p> <p><br> Description<br> ---------------</p> <p>BirdVox-ANAFCC is a dataset of short audio waveforms, each of them containing a flight call from one of 14 birds of North America: four American sparrows, one cardinal, two thrushes, and seven New World warblers.<br> * American Tree Sparrow (ATSP)<br> * Chipping Sparrow (CHSP)<br> * Savannah Sparrow (SAVS)<br> * White-throated Sparrow (WTSP)<br> * Red-breasted Grosbeak (RBGR)<br> * Gray-cheeked Thrush (GCTH)<br> * Swainson's Thrush (SWTH)<br> * American Redstart (AMRE)<br> * Bay-breasted Warbler (BBWA)<br> * Black-throated Blue Warbler (BTBW)<br> * Canada Warbler (CAWA)<br> * Common Yellowthroat (COYE)<br> * Mourning Warbler (MOWA)<br> * Ovenbird (OVEN)</p> <p>It also contains other sounds which are often confused for one of the species above. These "confounding factors" encompass flight calls from other species of birds, vocalizations from non-avian animals, as well as some machine beeps.</p> <p>BirdVox-ANAFCC results from an aggregation of various smaller datasets, integrated under a common taxonomy. For more details on this taxonomy, we refer the reader to [1]:</p> <p>[1] Cramer, Lostanlen, Salamon, Farnsworth, Bello. Chirping up the right tree: Incorporating biological taxonomies into deep bioacoustic classifiers. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2020.</p> <p>The second version of the BirdVox-ANAFCC dataset (v2.0) contains flight calls from the BirdVox-full-night dataset. These flight calls were present in the ICASSP 2020 benchmark but did not appear in the initial release of BirdVox-ANAFCC.</p> <p><br> Data Files<br> ------------<br> BirdVox-ANAFCC contains the recordings as HDF5 files, sampled at 22,050 Hz, with a single channel (mono). Each HDF5 file contains flight call vocalizations of a particular species. The name of each HDF5 file follows the format: `<data-source>_<taxonomy-code>_original.h5`. The name of the HDF5 dataset in each file is "waveforms", with the corresponding key for each audio recording varying in format depending on the data source.</p> <p> </p> <p>Metadata Files<br> ---------------<br> `taxonomy.yaml` details the three-level taxonomy structure used in this dataset, reflected in three-number-codes which largely follow "<family>.<order>.<species>". Additionally, at any level of the taxonomy, the numeric code "0" is reserved for "other" and the code "X" refers to unknown. For example, 1.1.0 corresponds to an American Sparrow with a species outside of our scope of interest, and 1.1.X corresponds to an American Sparrow of unknown species. At the top level (family), the "other" codes (0.\*.\*) deviate from the family-order-species in order to capture a variety of other out-of-scope sounds, including anthropophony, non-avian biophony, and biophony of avians outside of the scope of interest.</p> <p><br> Please acknowledge BirdVox-ANAFCC in academic research<br> --------------------------------------------------------------------------</p> <p>When BirdVox-ANAFCC is used for academic research, we would highly appreciate it if scientific publications of works partly based on this dataset cite the following publication:</p> <p>Cramer, Lostanlen, Salamon, Farnsworth, Bello. Chirping up the right tree: Incorporating biological taxonomies into deep bioacoustic classifiers. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2020.</p> <p>The creation of this dataset was supported by NSF grants 1125098 (BIRDCAST) and 1633259 (BIRDVOX), a Google Faculty Award, the Leon Levy Foundation, and two anonymous donors.</p> <p> </p> <p>Conditions of Use<br> ----------------------</p> <p>Dataset created by Aurora Cramer, Vincent Lostanlen, Bill Evans, Andrew Farnsworth, Justin Salamon, and Juan Pablo Bello.<br> <br> The BirdVox-ANAFCC dataset is offered free of charge under the terms of the Creative Commons Attribution International License:<br> https://creativecommons.org/licenses/by/4.0/<br> <br> The dataset and its contents are made available on an "as is" basis and without warranties of any kind, including without limitation satisfactory quality and conformity, merchantability, fitness for a particular purpose, accuracy or completeness, or absence of errors. Subject to any liability that may not be excluded or limited by law, the authors are not liable for, and expressly exclude all liability for, loss or damage however and whenever caused to anyone by any use of the BirdVox-ANAFCC dataset or any part of it.</p> <p><br> Feedback<br> -------------</p> <p>Please help us improve BirdVox-full-night by sending your feedback to:<br> vincent.lostanlen@gmail.com and auroracramer@nyu.edu</p> <p>In case of a problem, please include as many details as possible.<br> <br> <br> Versions<br> ------------<br> 1.0, May 2020: initial version, paired with ICASSP 2020 publication.<br> 2.0, February 2022: added a missing dataset file (BirdVox-70k), updated name of first author (Aurora Cramer).<br> </p> <p><br> Acknowledgement<br> --------------------------<br> Jessie Barry, Ian Davies, Tom Fredericks, Jeff Gerbracht, Sara Keen, Holger Klinck, Anne Klingensmith, Ray Mack, Peter Marchetto, Ed Moore, Matt Robbins, Ken Rosenberg, and Chris Tessaglia-Hymes.</p> <p>We thank contributors and maintainers of the Macaulay Library and the Xeno-Canto website.</p> <p>We acknowledge that the land on which the data was collected is the unceded territory of the Cayuga nation, which is part of the Haudenosaunee (Iroquois) confederacy.</p>
Airborne EM data (Belgium) from flight line 306025
<p>This dataset contains Airborne EM data from a SkyTEM instrument from the Flanders region, Belgium. </p> <p>Details about the instrument set-up can be found in the data report.</p> <p>Details about the region, geology, saltwater intrusion context can be found in </p> <p>Delsman, J., van Baaren, E., Vermaas, T., Karaoulis, M., Bootsma, H., de Louw, P. G. B., ... & Thofte, S. (2019). TOPSOIL Airborne EM kartering van zoet en zout grondwater in Vlaanderen (FRESHEM Vlaanderen: Deelopdrachten 1 tot en met 3.</p> <p><strong>When using this dataset, always cite the above reference. </strong></p> <p>The actual measured data is in "dat_skytem_306025_flightline.csv". Columns refer to either Low or High moment and time of measurement. Corresponding estimated relative errors can be found in rel_err_skytem_306025_flightline. Distances between soundings/measurment locations and their hieghts above the surface is found in "distances_between_soundings" and "altitudes_per_sounding" respectively.</p> <p>To use this data for the appraisal method, see https://github.com/WouterDls/AEM_appraisal. For more information, write to wouter.deleersnyder@kuleuven.be </p>
Code/data to accompany publication "Using cloud radar to investigate the effect of rainfall on migratory insect flight"
<p>Code/data to accompany publication "Using cloud radar to investigate the effect of rainfall on migratory insect flight". The cloud radar data files contains all the data used in the publication "Using cloud radar to investigate the effect of rainfall on migratory insect flight" by Charlotte E. Wainwright, Sabrina N. Volponi, Phillip M. Stepanian, Don R. Reynolds, and David H. Richter, published in Methods in Ecology and Evolution in 2022. The MATLAB code implements the method described in the paper on the data files included.</p>
Data archive for "Flight behaviour of Red Kites within their breeding area in relation to local weather variables: Conclusions with regard to wind turbine collision mitigation"
<p>The archive contains the data files to reproduce the results presented in the article “Flight behaviour of Red Kites within their breeding area in relation to local weather variables: Conclusions with regard to wind turbine collision mitigation” published in the Journal of Applied Ecology.</p>
Identification of Southeast Asian Anopheles mosquito species with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry using a cross-correlation approach
<p>This is the dataset used in the analysis "Identification of Southeast Asian <em>Anopheles </em>mosquito species with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry using a cross-correlation approach". It consists in 3584 raw mass spectra (mzXML file format) of the head of 359 <em>Anopheles </em>mosquito specimens collected in Karen (Kayin state) in Myanmar between 2020 and 2022 and associated metadata (Rdata file format) including sample information (taxonomy.Rdata) and spectra information (metadata.Rdata).</p>
Multiple DJI drone flights with thermal camera over Lithuanian forests in winter for wild boar detection
<p>5 Flight over multiple days in the evening time for better thermal conditions for boar detection.</p> <p>Flight were conducted with DJI thermal cameras filmed at the speed of about 5m/s. <br>Flights 1, 2, 4 and 5 were filmed at from 90m height with camera pointing straight down.<br>Flight 3 was filmed at 120m height.</p> <p> </p> <p>Link for Dataset download: <a title="Thermal imaging dataset over Lithuanian forests in winter" href="https://art21-icaerus.s3.eu-central-1.amazonaws.com/Boars.zip" target="_blank" rel="noopener">https://art21-icaerus.s3.eu-central-1.amazonaws.com/Boars.zip</a> </p>
Raw data supporting Identifying invertebrates from pitfall, flight interception traps and hand collecting
<p>Raw data supporting identifying invertebrates from pitfall, flight interception traps and hand collecting: comparing metabarcoding with traditional methods.</p> <p>Two step PCRs were performed on each sample replicate using modified primers mICOIintF and jgHCO2198 followed by the Nextera XT index kit v2 Set A (Illumina).</p> <p>The pool was loaded onto an illumina MiSeq using a MiSeq Reagent Kit v2 500 cycle kit (Illumina), with 10% Phi-X to generate 250-bp paired-end reads.</p>
Multiple DJI drone flights with hyperspectral camera over forests in Lithuania #3
<p>Hyperspectral data in all of the datasets consist of processed hyperspectral data cubes to radiance values.<br>all data cubes with _radiance.dat and ._radiance.hdr files are for ENVI format data file. While _rect.dat and _rect.hdr files are ENVI files with georectification applied.</p> <p>Hyperspectral data also contain calculated RGB and NDVI png and tiff images. Png images are generated from _radiance.dat files and tiff images are generated from _rect.dat files that have georectification.</p> <p>All hyperspectral data was collected using the Specim hyperspectral pushbroom camera.</p> <p> </p> <p>### Hyper 3</p> <p>https://art21-icaerus.s3.eu-central-1.amazonaws.com/Hyper_3.zip</p> <p>Dataset consists of 7 flight lines filmed over an infected forest.</p> <p>Each flight folder denoted by fl# contains processed hyperspectral data cubes and "processed" folder with RGB and NDVI images. </p> <p>Dataset zip size: 33.6 GB</p>
Multiple DJI drone flights with hyperspectral camera over forests in Lithuania #2
<p>Hyperspectral data in all of the datasets consist of processed hyperspectral data cubes to radiance values.<br>all data cubes with _radiance.dat and ._radiance.hdr files are for ENVI format data file. While _rect.dat and _rect.hdr files are ENVI files with georectification applied.</p> <p>Hyperspectral data also contain calculated RGB and NDVI png and tiff images. Png images are generated from _radiance.dat files and tiff images are generated from _rect.dat files that have georectification.</p> <p>All hyperspectral data was collected using the Specim hyperspectral pushbroom camera.</p> <p> </p> <p>### Hyper 2</p> <p><a href="https://art21-icaerus.s3.eu-central-1.amazonaws.com/Hyper_1.zip/Hyper_2.zip">https://art21-icaerus.s3.eu-central-1.amazonaws.com/Hyper_2.zip</a></p> <p>Dataset consists of 7 flight lines filmed over an infected forest.</p> <p>Each flight folder denoted by fl# contains processed hyperspectral data cubes and "processed" folder with RGB and NDVI images. </p> <p>Dataset zip size: 23.4 GB</p>
Multiple DJI drone flights with hyperspectral camera over forests in Lithuania #1
<p>Hyperspectral data in all of the datasets consist of processed hyperspectral data cubes to radiance values.<br>all data cubes with _radiance.dat and ._radiance.hdr files are for ENVI format data file. While _rect.dat and _rect.hdr files are ENVI files with georectification applied.</p> <p>Hyperspectral data also contain calculated RGB and NDVI png and tiff images. Png images are generated from _radiance.dat files and tiff images are generated from _rect.dat files that have georectification.</p> <p>All hyperspectral data was collected using the Specim hyperspectral pushbroom camera.</p> <p> </p> <p><a title="Hyperspectral imaging dataset over Lithuanian forests " href="https://art21-icaerus.s3.eu-central-1.amazonaws.com/Hyper_1.zip">https://art21-icaerus.s3.eu-central-1.amazonaws.com/Hyper_1.zip</a></p> <p>Dataset consists of 6 flight lines filmed over a stated healthy forest.</p> <p>Each flight folder denoted by fl# contains processed hyperspectral data cubes and "processed" folder with RGB and NDVI images. </p> <p>Dataset zip size: 25.9 GB</p>
Data for paper "Parametric schedulability analysis of a launcher flight control system under reactivity constraints"
<p>This is the data set (models, sources and results) for the paper "Parametric schedulability analysis of a launcher flight control system under reactivity constraints" published in Informatica Fundamentae in 2021.</p>
Data for the "Does prognostic seeding along flight tracks produce the desired effects of cirrus cloud thinning?" manuscript
<p>Tar file of the data used to prepare the plots and write the text in: "Does prognostic seeding along flight tracks produce the desired effects of cirrus cloud thinning?" manuscript for submission to ACP.</p> <p>A description of each netcdf file is provided in the README file. The format of each file is in netcdf4</p>
Ryanair: All december flight departures from Spain and their average prices
<p>In this dataset, we collected the average prices of all flight departures in Spain projected in December by Ryanair. Although we are uploading only raw data, this dataset can be useful to study the connectivity of Spain, and its accessibility by the people (comparing frequencies and prices in different airports).</p> <p>We searched for the December period to focus on the relative fluctuations of prices in holidays versus the rest of the month. Also, the prices may change between holidays lengths in days, and the day of departure. Specifically, we searched for 2 to 4 days of vacation, and departures from Thursday, Friday and Saturday. We only searched for the average prices of two adults because Ryanair prices are averages per person.</p> <p>All in all, this dataset has 11 attributes and 9794 rows.</p>
Flight dataSet, madrid to any national destination
<p>This is a dataset that have the flights that departs from Madrid and go to any National destination. This dataset is extracted from google flights.</p>
High Energy Lightning Emission Network (HELEN) Flight 7a 06/19/2023
<p>Data gathered during the flight of the High Energy Lightning Emission Network (HELEN) on June 19th, 2023. The first file, 1-Flight7a-Raw Data.zip, is the raw data taken directly from the payloads' micro SD cards. In the second file, 2-Flight7a-Data to Process.zip, the data has been cleaned and is suitable for further processing. The third file, 3-Flight7a-Processed Data.zip, contains data that has been combined and temporally synced. This data can be easily read into MATLAB and used to reproduce results.</p>
THz driven field emission: energy and time-of-flight spectra of ions (DATASET)
<p>We present an experimental and numerical study of ion field evaporation from LaB6 nanotips using single-cycle terahertz (THz) transients and a static bias voltage. Varying the amplitude and phase of the THz pulses and the value of the<br> bias, we explore the THz-induced reshaping of the ions energy and their time-of-flight spectra. These results prove that short THz transient of about 1 ps can induce ionization and emission of ions from LaB6 samples by a field effect: the THz<br> transient acts as an ultra-short electrical pulse. Moreover, comparing numerical and experimental results, we prove that the response time of surface atoms to the THz transient is shorter than 1 ps, corresponding to the vibration times of acoustic phonons<br> in LaB6.</p> <p>In the following dataset, you can find data from THz-APT obtained from LaB6 sample and results of simulation of ions under THz Field done with Lorentz</p>
Stream network from 1997 survey and 2008 LiDAR flight, Andrews Experimental Forest
HJ Andrews stream network from 1994 base prepared from Lienkaemper's 1976 stream survey field data and contours generated from GSC Digital Elevation Model (DEM) maps, and generated from 2008 LiDAR 1 meter bare earth DEM. The 1994 base matches the 30 and 10 meter DEMs and the 2008 LiDAR version matches the LiDAR base layers. Stream order was added to hf01302.
Time-of-flight neutron tomography
<p>This dataset contains the wavelength-resolved neutron tomography of a contrast sample imaged with the time-of-flight (TOF) transmission imaging method at the IMAT beamline at the ISIS pulsed neutron source. The sample is made of several polycrystalline materials: nickel, iron, titanium, lead, copper and aluminium</p> <p>The data are pre-processed for the detector event overlap correction, binned in the TOF axis and cropped. The datasets can be used for replicating the tomographic reconstruction as described in (Carminati et al 2020, https://doi.org/10.1107/S1600576720000151), or for further analysis.</p>
Fight or flight? Behaviour and experiences of laypersons in the face of an incipient fire
<p>This dataset contains raw data collected in an experimental study by the University of Muenster, Germany, in cooperation with the German Fire Protection Association and the State Fire Service Institute NRW, Germany. The study is part of a larger research project and examined the behavior of laypersons when confronted with an incipient fire.</p> <p>Within minutes, an incipient fire can develop into a life-threatening full fire. Consequently, it should be fought as early as possible. But are laypersons capable of doing this? In such a situation, how do they behave and feel? These questions are addressed in the current study. Persons without any professional firefighting training (N=64) were confronted in two experimental runs with a real incipient fire in the form of a burning pillow.</p> <p>The study was approved by the ethics committee of the Department 7 of the University of Münster (ID 2018-16-MT) and pre-registered with AsPredicted.org under the number 20436 (https://aspredicted.org/8py33.pdf). The study was supported by the German Federal Ministry of Education and Research (funding code FKZ 13N14208).</p> <p>In addition to the raw data, the codebook as well as the R- analysis script is included here for better comprehensibility. The raw data contains only the information of persons who were included in the analysis and have agreed to it. Some demographic information was deleted to ensure anonymity.</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>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.