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405 results for “flight data”
Kitekraft flight data acquired on 27 August 2024
<p>This dataset captures a flight test of the 2.4m wingspan Kitekraft kite, flown with ground station south of Oberbiberg, Germany. The entire system was operated automatically, only with operators giving high level commands such a launch/land. The kite was reeled out, performed trans-in, figure-8 flight, trans-out, hover-landing. A main goal of this flight was to perform this entire sequence, test new control software updates, and record anomalies that may occur. The kite and ground station were equipped with numerous sensors. The dataset includes a photograph and a README file with a detailed description of the data.</p>
Kitekraft flight data acquired on 28 June 2024
<p>This dataset captures a flight test of the 2.4m wingspan Kitekraft kite, flown with ground station south of Oberbiberg, Germany. The entire system was operated automatically, only with operators giving high level commands such a launch/land. The kite was reeled out, performed trans-in, flight of 53 figure-8s, trans-out, hover-landing. A main goal of this flight was to perform this entire sequence, test new control software updates, and record anomalies that may occur e.g. due to wind. The kite and ground station were equipped with numerous sensors. The dataset includes a photograph and a README file with a detailed description of the data.</p>
CHU Time of Flight Data, 8 April 2024 Eclipse - ET Stations
<p>See <a href="https://doi.org/10.5281/zenodo.13293306">10.5281/zenodo.13293306</a> for system documentation.<br>See <a href="https://doi.org/10.5281/zenodo.14004715">10.5281/zenodo.14004715</a> for python code to parse these files. </p>
Cavitation Bubble Data (Parabolic Flights, 2010)
<p>This dataset contains high-speed videos, time-resolved pressure measurements and auxiliary data of 247 isolated single cavitation bubbles produced in variable gravity (0g, 1g, 1.6-2g) aboard the Airbus 300 zero-g on the 53rd ESA parabolic flight campaign in October 2010. The five files contain 247 subdirectories, each of which holds the available data of exactly one cavitation bubble. The experimental setup and data structure are explained in a peer-reviewed publication in Experiments In Fluids (2013, 54:1503, DOI 10.1007/s00348-013-1503-9), titled "The quest for the most spherical bubble: experimental setup and data overview". Most of the data shown in that paper relied on these 247 cavitation bubbles, but many more cavitation bubbles have been recorded using additional sensors (e.g. spectrometer) in later experiments on parabolic flights.</p>
Data and codes from: Flight hampers the evolution of weapons in birds
<p>Birds are a remarkable example of how sexual selection can produce diverse ornaments and behaviors. Specialized fighting structures like deer's antlers, in contrast, are mostly absent among birds. Here, we investigated if the birds' costly mode of locomotion — powered flight — helps explain the scarcity of weapons among members of this clade. Our simulations of flight energetics predicted that the cost of bony spurs — a specialized avian weapon — should increase with time spent flying. Bayesian phylogenetic comparative analyses using a global spur dataset corroborated this prediction. First, extant species with flight-efficient wings (which presumably fly more frequently) tend to have fewer or no bony spurs. Second, this association likely arose because flying more leads to more frequent evolutionary loss of spurs. Together, these findings suggest that, much like pneumatic bones, absence of weaponry may be another feature of the avian body plan that allows birds to efficiently explore the aerial habitat.</p>
Time-resolved ARPES RAW data of bulk WSe2 for a quantitative comparison of time-of-flight momentum microscopes and hemispherical analyzers: RAW MM data
<p>Time- and angle-resolved photoemission spectroscopy data of bulk WSe2 using a laser-based XUV source and a time-of-flight momentum microscope analyzer.</p> <p>The dataset here comprises part of the machine RAW data used to construct the processed data stored in the datasets at</p> <pre>https://doi.org/10.5281/zenodo.4067968</pre> <p>Analysis scripts and conversion tools into NeXus format can be found at</p> <p>https://github.com/nomad-coe/nomad-parser-nexus/tree/master/tests/data/tools/dataconverter/readers/mpes</p>
Data: Thorax Vibration and Force Generation During Non-Flight Behaviors in Carpenter Bees (Xylocopa: Apidae): Implications for Floral Buzzing
<p>Interval data from the manuscript "Thorax Vibration and Force Generation During Non-Flight Behaviors in Carpenter Bees (<em>Xylocopa</em>: Apidae): Implications for Floral Buzzing "</p>
Data from: Age dominates flight distance and duration, while body size shapes flight speed in Bombus terrestris L. (Hymenoptera: Apidae)
<p>Flight plays crucial role in the fitness of insect pollinators, such as bumble bees. Despite their relatively large body size compared to their wings, bumble bees can fly under difficult ambient conditions, such as cooler temperatures. While their body size is often positively linked to their foraging range and flight ability, the influence of age remains less explored. Here, we studied the flight performance (distance, duration, and speed) of aging bumble bee workers using tethered flight mills. Additionally, we measured their intertegular distance (ITD) and dry mass as proxies for their body size. We found that flight distance and duration was predominantly influenced by age, challenging assumptions that age does not play a key role in foraging and task allocation. From the age of 7 to 14 days, flight distance and duration increased six-fold and five-fold, respectively. Conversely, body size primarily impacted the maximum and average flight speed of workers. Our findings indicate that age substantially influences flight distance and duration in bumble bee workers, affecting foraging performance and potentially altering task allocation strategies. This underscores the importance of considering individual age and physiological changes alongside body size/mass in experiments involving bumble bee workers.</p>
Thermal Flight Data for Forchheim, July 20, 2022, OPTRIS PI-450
<p>Georeferenced orthomosaic of the thermal flight campaign in raster format</p>
Data from: Reduced palatability, fast flight, and tails: Decoding the defence arsenal of Eudaminae skipper butterflies in a Neotropical locality
<p>Prey often rely on multiple defences against predators, such as flight speed, attack deflection from vital body parts, or unpleasant taste, but our understanding on how often and why they are co-exhibited remains limited. Eudaminae skipper butterflies use fast flight and mechanical defences (hindwing tails), but whether they use other defences like unpalatability (consumption deterrence), and how these defences interact, has not been assessed.</p> <p>We tested the palatability of 12 abundant Eudaminae species in Peru, using training and feeding experiments with domestic chicks. Further, we approximated the difficulty of capture explained by flight speed and quantified by wing loading. We performed phylogenetic regressions to find any association between multiple defences, body size, and habitat preference.</p> <p>We found a broad range of palatability in Eudaminae, within and among species. Contrary to current understanding, palatability was negatively correlated with wing loading, suggesting that faster butterflies tend to have lower palatability.</p> <p>The relative length of hind wing tails did not explain the level of butterfly palatability, showing that attack deflection and consumption deterrence are not mutually exclusive. Habitat preference (open or forested environments) did not explain the level of palatability either, although butterflies with high wing loading tended to occupy semi-closed or closed habitats.</p> <p>Finally, the level of unpalatability in Eudaminae is size dependent. Larger butterflies are less palatable, perhaps because of higher detectability/preference by predators. Altogether, our findings shed light on the contexts favouring the prevalence of single vs. multiple defensive strategies in prey.</p>
Data for Origins of UTLS Turbulence: Insights from the RRJ-ClimCORE Mesoscale Reanalysis - The ACCLIP Flight Over the Super Typhoon Hinnamnor (2022)
<p>Data used in our paper entitled "Origins of UTLS Turbulence: Insights from the RRJ-ClimCORE Mesoscale Reanalysis - The ACCLIP Flight Over the Super Typhoon Hinnamnor (2022)".</p> <p>The user may use these data only for the purpose of scientific evaluation of this paper and secondary distribution is prohibited. See README file for more details.</p>
Wandering abatross flight data
<p>Wandering albatrosses exploit wind shear by dynamic soaring, enabling rapid, efficient, long-range flight. To explore this flight mode, we compared the ability of a nonlinear dynamic soaring model and a linear empirical model to explain observed variation of the airspeeds of GPS-tracked albatrosses in across-wind flight. In fast winds (> 8 m/s), maximum observed airspeeds reach an asymptote at ~ 20 m/s, whereas the dynamic soaring model predicts much faster airspeeds, up to around 50 m/s. We hypothesize that the birds actively limit airspeed by making fine-scale adjustments to turn angles and soaring heights. Predicted dynamic soaring airspeeds do not extend down to the slowest winds (< 3.2 m/s) of observed flight. We hypothesize that in slow winds wandering albatrosses obtain additional energy from updrafts over water waves. The dynamic soaring model predicts that the minimum wind speed necessary to support dynamic soaring at a cruise airspeed of 16 m/s is 3.2 m/s, achieved via a flight trajectory of linked 137° turns. In reality, observed turn angles are typically ~ 60°. Our simulations suggest that birds may necessarily use smaller turns angles than the theoretical optimum for fast flight in order to limit aerodynamic force on their wings.</p>
Data from: Sensitivity analysis of collision risk at wind turbines based on flight altitude of migratory waterbirds
<p>This dataset contains information on the distribution of geese and swans and the three-dimensional flight trajectories. The former was obtained through vehicle field surveys, interviews, and a literature review. The latter was obtained using ornithodolites.</p>
Data from: What makes a good pollinator? Abundant and specialized insects with long flight periods transport the most strawberry pollen
<ol> <li>Despite the importance of insect pollination to produce marketable fruits, insect pollination management is limited by insufficient knowledge about key crop pollinator species. This lack of knowledge is due in part to: 1) the extensive labour involved in collecting direct observations of pollen-transport, 2) the variability of insect assemblages over space and time, and 3) the possibility that pollinators may need access to wild plants as well as crop floral resources.</li> <li>We address these problems using strawberry in the UK as a case study. First, we compare two proxies for estimating pollinator importance: flower visits and pollen transport. Pollen-transport data might provide a closer approximation of pollination service, but visitation data are less time-consuming to collect. Second, we identify insect parameters that are associated with high importance as pollinators, estimated using each of the proxies above. Third, we estimated insects' use of wild plants as well as the strawberry crop.</li> <li>Overall, pollinator importances estimated based on easier-to-collect visitation data were strongly correlated with importances estimated based on pollen loads. Both frameworks suggest that bees <em>Apis</em> and <em>Bombus</em> and hoverflies <em>Eristalis</em> are likely to be key pollinators of strawberries, although visitation data underestimate the importance of bees.</li> <li>Moving beyond species identities, abundant, relatively specialised insects with long active periods are likely to provide more pollination service. </li> <li>Most insects visiting strawberry plants also carried pollen from wild plants, suggesting that pollinators need diverse floral resources.</li> <li>Identifying essential pollinators or pollinator parameters based on visitation data will reach the same general conclusions as those using pollen transport data, at least in monoculture crop systems. Managers may be able to enhance pollination service by preserving habitats surrounding crop fields to complement pollinators' diets and provide habitats for diverse life stages of wild pollinators.</li> </ol>
DLC networks from: Application of a novel deep learning based 3D videography workflow to bat flight data
<p>Studying the detailed biomechanics of flying animals relies on producing accurate three-dimensional coordinates for key anatomical landmarks. Traditionally, this is achieved through manual digitization of animal videos, a labor-intensive task that grows more so with increasing frame rates and numbers of cameras. In this study, we present a workflow that combines deep learning-powered automatic digitization with intelligent filtering and correction of mislabeled points using 3D information. We tested our workflow using a particularly challenging scenario – bat flight. First, we documented bats flying steadily in a wind tunnel. We compared the results from manually digitizing bats with markers applied to anatomical landmarks against using our automatic workflow on the same bats without markers. In our second test case, we compared manual digitization against our automated workflow for bats exhibiting complex maneuvers in a large flight arena. We found that the variation between the 3D coordinates from our workflow and those from manual digitization was less than a millimeter larger than the variation between 3D coordinates resulting from two different human digitizers. The reduced reliance on manual digitization stemming from this work has the potential to significantly increase the scalability of studies into the detailed biomechanics of animal flight.</p>
DLC networks from: Application of a novel deep learning based 3D videography workflow to bat flight data
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Flight data from: Acrobatics at the insect-scale: A durable, precise, and agile micro-aerial-robot
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Data from: What makes a good pollinator? Abundant and specialized insects with long flight periods transport the most strawberry pollen
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Fatty acid and carbon isotopic data for: Use of essential vs. non-essential fatty acids during flight in monarch butterflies: Implications for the importance of nectaring during migration
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Data from: Reduced palatability, fast flight, and tails: Decoding the defence arsenal of Eudaminae skipper butterflies in a Neotropical locality
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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.