Find research datasets worth reusing

opencc-by-4.0Nov 2021View details →

Migrating birds real flight V-formation spatial configuration.

Bird real flight V-formation dataset: Arbitrary (pixel) coordinates of migrating birds, probably Geese, flying in V-formation. Photo is taken in an angle so their formation data is only a cross-section in 3-D perspective but with entire pack. However, despite this limitation this V-formation configuration provides a quantitative data for the understanding for the spatial properties, i.e., V-shape characteristics. There are 95 birds in total including the lead bird. Lower V-arm is denoted with tags dXX has 51 birds and upper V-arm is denoted by tags uXX has 43 birds. Lead bird has two entries d00 and u00 for consistency. Annotated image provides boxes and labels. The data is given under bird_arbitrary_coordinates as pixel location on the plane with tags. In coordinate annotation head of the bird is taken as a refrence point.

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Morphological adaptations linked to flight efficiency and aerial lifestyle determine natal dispersal distance in birds

Natal dispersal—the movement from birthplace to breeding location—is often considered the most significant dispersal event in an animal's lifetime. Natal dispersal distances may be shaped by a variety of intrinsic and extrinsic factors, and remain poorly quantified in most groups, highlighting the need for indices that capture variation in dispersal among species. In birds, it is hypothesized that dispersal distance can be predicted by flight efficiency, which can be estimated using wing morphology. However, the use of morphological indices to predict dispersal remains contentious and the mechanistic links between flight efficiency and natal dispersal are unclear. Here, we use phylogenetic comparative models to test whether hand-wing index (HWI, a morphological proxy for wing aspect ratio) predicts natal dispersal distance across a global sample of 114 bird species. In addition, we assess whether HWI is correlated with flight usage in foraging and daily routines. We find that HWI is a strong predictor of both natal dispersal distance and a more aerial lifestyle. Our results support the use of HWI as a valid proxy for relative natal dispersal distance, and also suggest that evolutionary adaptation to aerial lifestyles is a major factor connecting flight efficiency with patterns of natal dispersal.

opencc-zeroApr 2022View details →

opencc-by-4.0May 2022View details →

Nocturnal flight calls dataset: long-term acoustic monitoring of birds migrating at night

General Description: This is a development set used in the experiments in the Ph.D. thesis: "Nowe metody akustycznej identyfikacji ptaków migrujących nocą" ("Novel methods of acoustic identification of birds migrating at night") by Hanna Pamula. The project focuses on the detection (and - partially - classification) of passerine birds' calls from long-term audio recordings collected during bird autumn migration between 2016 and 2019. The dataset consists of >56,5 hours of recordings with annotations of nocturnal flight calls of passerine birds migrating along the Baltic Sea coast, Poland.   Folder Structure Development_Set_3.1.zip |_Development_Set_3.1/     |__Training_Set/             |____*.wav             |____*.txt     |__Validation_Set/             |____*.wav             |____*.txt     |__Testing_Set/             |____*.wav             |____*.txt Training Set: 86 recordings Validation Set: 8 recordings Testing set: 18 recordings (BUT: uploaded 20 recordings, as in the previous version of the dataset - version 3, two additional recordings were used. Then, they were deleted in the final version of development set 3.1. Two additional recordings are: 'BUK5_20161101_002104a and BUK5_20161101_002104b) Names of waveforms and annotations are matching. Waveforms: The whole dataset consists of 114 recordings. One hundred thirteen recordings are about 30 minutes long (29min56s – 29min 59s), one recording is 1min20s. All data were recorded at 44,100 Hz sampling rate, one channel, with SM2 Wildlife Acoustics recorders + SMX-NFC microphone. The recording sessions were performed at night (starting time and date denoted in a file name) on the Baltic Sea coast in Poland (Dąbkowice, near Darłowo). Annotations: Transcriptions were produced using Audacity 2.4.1: https://www.audacityteam.org/ by an experienced birdwatcher, Hanna Pamula. While every effort has been made to ensure the quality and accuracy of the labels, some errors may occur, taking into account the difficulty of nocturnal call recognition and transcription tasks in general. Transcription format: [Starting time (sec)] [Ending time (sec)] [Label] Meaning of the labels: 1. Positive classes – migrating passerine birds: <ul> <li>'s' – song thrush call (Turdus philomelos)</li> <li>'k' – blackbird call (Turdus merula)</li> <li>'d' – redwing call (Turdus iliacus)</li> <li>'r' – robin call (Erithacus rubecula)</li> <li>‘kwiczol’ – fieldfare call (Turdus pilaris)</li> <li>‘skowronek’ – skylark call (Alauda arvensis)</li> <li>Each of the above labels could also have a question mark '?', e.g. 'r?', 'k?' – meaning that it's not a sure label. In a bird call detection task, they are regarded as positive chunks containing bird call(s).</li> <li>'ni' – non identified bird call (distant/quiet/not recognized)</li> </ul> Only the supposed calls of migrating passerine birds were labeled; other sounds of species were ignored (e.g., robin's tik-calling, which can be often heard at dusk, and may be regarded as warning sounds). 2. Negative classes – other marked sound events: <ul> <li>'g' – other bird calls/songs/sounds. Sounds that could confuse the model; for example, sounds of migrating geese, cranes, plovers calls, etc.</li> <li>'gh' – human voices</li> <li>'t' – cracks, clicks, raindrops, other noise</li> <li>‘puszczyk’ – tawny owl voice (Strix aluco)</li> <li>'czapla' – grey heron voice (Ardea cinerea)</li> </ul> Not all occurrences of the negative sounds were labeled – only some chosen examples to represent the possible noises/negative samples. Thus these annotations can't be used for entirely different detection / classification tasks than intended, e.g., detecting migrating cranes or human voices in long-term recordings. 3. Labels to be excluded from analysis: <ul> <li>'???', '??? mysz', '??? high freq' – unknown, not sure if the sound event is a birds' call or not. Uncertainty about belonging to a positive/negative class in the detection task.</li> </ul>

opencc-by-4.0Apr 2022View details →

Data from: Operando Proton Transfer Reaction-Time of Flight-Mass Spectrometry of Carbon Dioxide Reduction Electrocatalysis

Seven top-level folders GC-PTR-TOF-MS - Raw data and Jupyter Notebook used for analysis of GC-PTR-TOF-MS data LSV-PTR-TOF-MS - Raw data and Jupyter Notebook used for analysis of PTR-TOF-MS data under linear sweep voltammetry MSCP-PTR-TOF-MS - Raw data and Jupyter Notebook used for analysis of PTR-TOF-MS data under multi-step chronopotentiometry PTR-TOF-MS-Calibration - Raw data and Jupyter Notebook used for analysis of PTR-TOF-MS calibration data SEM - Raw images from scanning electron microscope Stability - Raw data of electrochemical stability TEM - Raw images from transmission electron microscopy

opencc-by-4.0Aug 2022View details →

Large eddy simulation of a quadcopter in forward flight: aeroloads and wake

This dataset contains model information and results of large eddy simulation of a quadcopter in forward flight. The simulations were performed using the Vortex Particle-Mesh method. The study uses a generic model with a blade and airframe geometry inspired from a DJI drone. One flight condition is considered, with the vehicle at a forward speed of 10 m/s and at a pitch angle of 13 degrees. The results of two cases are available: 1) a simulation of the full model (comprising 4 rotors and the airframe); 2) a simulation of the rotors only (no airframe included). The data contains a description of the airframe (CAD files), a description of the blades, the resulting aeroloads on the blades and airframe (for each case), the time-resolved velocity and vorticity field in a cross-section of the wake (for each case), and supporting material for the companion article: <blockquote> D.-G. Caprace, A. Ning, P. Chatelain, G. Winckelmans, Effects of rotor-airframe interaction on the aeromechanics and wakes of a quadcopter in forward flight, 2022 </blockquote>

opencc-by-4.0Oct 2022View details →

Flight demonstration of a miniature atomic scalar magnetometer based on a microfabricated rubidium vapor cell

Data acquired by the Miniature Absolute Scalar Magnetometer (MASM) on the low-flying sounding rocket of the Twin Rockets to Investigate Cusp Electrodynamics 2 (TRICE-2) mission. The data columns are as follows: epoch time: CDF Epoch Time time: year, month, day, hour, minute, second lfreq [Hz]: Larmor frequency in Hertz Bt [nT]: Measured total magnetic field in nano-Tesla Btm [nT]: Total magnetic field of the IGRF model in nano-Tesla Bxm [nT]: Magnetic field Bx-component of the IGRF model in ECEF coordinates and nano-Tesla Bym [nT]: Magnetic field By-component of the IGRF model in ECEF coordinates and nano-Tesla Bzm [nT]: Magnetic field Bz-component of the IGRF model in ECEF coordinates and nano-Tesla flight time: Flight time with respect to launch in seconds ecef x pos [m]: ECEF X coordinate in meters ecef y pos [m]: ECEF Y coordinate in meters ecef z pos [m]: ECEF Z coordinate in meters ecef x vel [m/s]: ECEF X velocity in meters per second ecef y vel [m/s]: ECEF Y velocity in meters per second ecef z vel [m/s]: ECEF Z velocity in meters per second lat [deg]: Geopgraphic latitude in degrees lon [deg]: Geographics longitude in degrees alt [km]: Altitude in kilometers vcsel temp: VCSEL temperature in engineering units gas cell temp: Rubidium vapor cell temperature in engineering units bx mag: Magnetic field Bx-component measured by the mission's science magnetometer in ECEF coordinates and nano-Tesla by mag: Magnetic field By-component measured by the mission's science magnetometer in ECEF coordinates and nano-Tesla bz mag: Magnetic field Bz-component measured by the mission's science magnetometer in ECEF coordinates and nano-Tesla bt mag: Total magnetic field measured by the mission's science magnetometer in ECEF coordinates and nano-Tesla btf mag: Filtered total magnetic field measured by the mission's science magnetometer in ECEF coordinates and nano-Tesla

opencc-by-4.0Oct 2022View details →

ALIMA and BAHAMAS data for SOUTHTRAC flight ST08

Temperature vertical profiles from the ALIMA lidar during SOUTHTRAC flight ST08. The three components of velocity, pressure and temperature recorded from BAHAMAS instrument along the airplane trajectory.

opencc-by-4.0Dec 2017View details →

Movies on flights dataset

The provided dataset includes movie titles and links, that allows to gather online metadata and trailers. We do not provide the video files because of copyright restrictions. Examples are collected based on movie lists from a major international airline, i.e., KLM Royal Dutch Airlines. The final list of movies is a merged set of movies collected between February and April 2015. The video dataset contains both positive and negative samples, carefully sampled in order to create fair and representative positive and negative class. The data is also split into a trainingset and a testset. In order to collect user judgments, we used an existing system that has been built for the purpose of collecting user feedback of this sort.

opencc-by-4.0Jun 2022View details →

Рис. 1. Αиния маршрута; цифры — места, гΑе быΛи отмечены особи бурого меΑвеΑя во время учетов с вертоΛета 22.05.2018. РезуΛьтаты учетов бурого меΑвеΑя на о. ЗавьяΛова с вертоΛета «Еврокоптер 120». 11:55 выΛет с нефтепирса г. МагаΑана, 12:14 поΑΛет к острову, 12:20 (1) отмечен первый моΛоΑой меΑвеΑь на террасе, 12:52 (2) отмечен оΑин взросΛый меΑвеΑь, 13:06 (3, 4) отмечены Αва взросΛых меΑвеΑя, 13:08 (5, 6, 7) отмечены три взросΛых меΑвеΑя, 13:18 (8) отмечен оΑин взросΛый меΑвеΑь. 13:56 переΛет в гороΑ МагаΑан Fig. 1. Route line; the figures indicate areas where brown bears were seen during the helicopter surveys on 22 May 2018. The results of the brown bear surveys on Zavyalov island from the Eurocopter 120 helicopter. 11:55 departure from the oil pier of Magadan, 12:14 hovering near the island, 12:20 (1) the first young bear identified on the terrace, 12:52 (2) one adult bear identified, 13:06 (3, 4) two adult bears identified, 13:08 (5, 6, 7) three adult bears identified, 13:18 (8) one adult bear identified, 13:56 Flight to Magadan in Brown bear (Ursus arctos) of Zavyalov Island (Sea of Okhotsk): Abundance and possible migration routes

Рис. 1. Αиния маршрута; цифры — места, гΑе быΛи отмечены особи бурого меΑвеΑя во время учетов с вертоΛета 22.05.2018. РезуΛьтаты учетов бурого меΑвеΑя на о. ЗавьяΛова с вертоΛета «Еврокоптер 120». 11:55 выΛет с нефтепирса г. МагаΑана, 12:14 поΑΛет к острову, 12:20 (1) отмечен первый моΛоΑой меΑвеΑь на террасе, 12:52 (2) отмечен оΑин взросΛый меΑвеΑь, 13:06 (3, 4) отмечены Αва взросΛых меΑвеΑя, 13:08 (5, 6, 7) отмечены три взросΛых меΑвеΑя, 13:18 (8) отмечен оΑин взросΛый меΑвеΑь. 13:56 переΛет в гороΑ МагаΑан Fig. 1. Route line; the figures indicate areas where brown bears were seen during the helicopter surveys on 22 May 2018. The results of the brown bear surveys on Zavyalov island from the Eurocopter 120 helicopter. 11:55 departure from the oil pier of Magadan, 12:14 hovering near the island, 12:20 (1) the first young bear identified on the terrace, 12:52 (2) one adult bear identified, 13:06 (3, 4) two adult bears identified, 13:08 (5, 6, 7) three adult bears identified, 13:18 (8) one adult bear identified, 13:56 Flight to Magadan

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Data from: Biomechanical properties of non-flight vibrations produced by bees

Bees use thoracic vibrations produced by their indirect flight muscles for powering wingbeats in flight, but also during mating, pollination, defence, and nest building. Previous work on non-flight vibrations has mostly focused on acoustic (airborne vibrations) and spectral properties (frequency domain). However, mechanical properties such as the vibration's acceleration amplitude are important in some behaviours, e.g., during buzz pollination, where higher amplitude vibrations remove more pollen from flowers. Bee vibrations have been studied in only a handful of species and we know very little about how they vary among species. Here, we conduct the largest survey to date of the biomechanical properties of non-flight bee buzzes. We focus on defence buzzes as they can be induced experimentally and provide a common currency to compare among taxa. We analysed 15,000 buzzes produced by 306 individuals in 65 species and six families from Mexico, Scotland, and Australia. We found a strong association between body size and the acceleration amplitude of bee buzzes. Comparison of genera that buzz-pollinate and those that do not suggests that buzz-pollinating bees produce vibrations with higher acceleration amplitude. We found no relationship between bee size and the fundamental frequency of defence buzzes. Although our results suggest that body size is a major determinant of the amplitude of non-flight vibrations, we also observed considerable variation in vibration properties among bees of equivalent size and even within individuals. Both morphology and behaviour thus affect the biomechanical properties of non-flight buzzes.

opencc-zeroJun 2024View details →

Figure 1 in Effect of Pupal Holding Density on Emergence Rate, Flight Ability, and Yield of Sterile Male Mediterranean Fruit Flies (Diptera: Tephritidae)

Figure 1. Emergence rate (top) and flight ability (bottom) for Hawaii- and Guatemaladerived flies when held at loadings of 250, 350, or 450 ml per eclosion tower tray. Bar heights represent mean values over all trays for a given density (15 dates X 6 trays per loading amount per date = 90 trays total), and error bars represent 1 SE.

Figure 2 in Effect of Pupal Holding Density on Emergence Rate, Flight Ability, and Yield of Sterile Male Mediterranean Fruit Flies (Diptera: Tephritidae)

Figure 2. Estimated numbers of fliers produced per tower (top) and ratios of number of fliers produced to number of pupae placed per tower (bottom) in relation to the pupal loading level per constituent tray. Hawaii-derived flies were used exclusively; towers contained 52 trays. Symbols represent means over 33 towers per loading level (3 towers per test day x 11 test days); error bars represent ± 1 SE.

opencc-by-4.0May 2024View details →

Dataset for: Methodology to identify and quantify flight path dependent bird strike scenarios over aircraft

<h1>ScenarioGenerator</h1> <h2>Description</h2> This project is a Python project containing a demonstrations of the methodology developed by J. Bertholdt.  The code takes stl files and flight path data in order to create bird strike impact scenarios for each cell.  With this data it is possible to approximate the impact intensity and create heat maps over the geometry. <h2>Features</h2> - Data processing: The code creates scenarios (impact vector, angle, velocity and bird data) for hit areas and estimates peak pressure and total impulse.    - Data saving: The code saves the data in forms of csv files. - Data reader: The code can read the csv files and recreate the processed data and mesh. - Data visualization: The code contains examples for data filtering and plotting.  <h2>Installation</h2> 1. Download Code 2. Adjust directories in data_reader_demo.py and stl_processing_demo. 3. Create a virtual environment: <h3>Required packages:</h3> - numpy - birdpressure - matplotlib - pyvista

Fig. 4 in Diel flight activity patterns of the red palm weevil (Coleoptera: Curculionidae) as monitored by smart traps

Fig. 4. Diel pattern for the mean total number of red palm weevils (RPW) captured per trap during the 62 d trapping period. Columns headed by the same letter are not significantly different from each other.

Fig. 3 in Diel flight activity patterns of the red palm weevil (Coleoptera: Curculionidae) as monitored by smart traps

Fig. 3. Mean temporal distributions of adult male (A), female (B), and total (C) red palm weevils captured in STs over the 24 h diel cycle. Each circle represents 4, 16, and 19 weevils in A, B, and C, respectively.

Fig. 3. Tallies within each Tennessee county indicate the total buprestid species recorded from specimen label records spanning 1934 in Seasonal flight activity and distribution of metallic woodboring beetles (Coleoptera: Buprestidae) collected in North Carolina and Tennessee

Fig. 3. Tallies within each Tennessee county indicate the total buprestid species recorded from specimen label records spanning 1934 to 2013. When presented across Tennessee, species yields indicate areas of greatest and least collection activity and highlight regions of future collection interest. This figure is displayed in color online at http://purl.fcla.edu/fcla/entomologist/browse

Fig. 1B in Seasonal flight activity and distribution of metallic woodboring beetles (Coleoptera: Buprestidae) collected in North Carolina and Tennessee

Fig. 1B. Seasonal flight activities recorded for Agrilinae (Coraebini and Trachyini), Polycestinae, and Chrysocrhoinae metallic woodboring beetle species collected across North Carolina (1901–2013) and Tennessee (1934–2013). Collection records frequently included trapping yield results from multi-day deployments; thus, data for individual specimens were pooled within 1 of 4 weeks for each month. Dated records for specimens with labels that noted specimen emergence from infested trunk, stem, firewood, and branch sections are not included within the seasonal ranges presented. Dashed vertical lines (at May and Jul) indicate approximate flight activity period (in North Carolina) for Cerceris fumipennis (Say) (Hymenoptera: Crabronidae) wasps. Asterisks indicate species collected by the wasps during biosurveillance in North Carolina (Nalepa et al. 2013; Swink et al. 2013, 2014). This figure is displayed in color online at http://purl.fcla.edu/fcla/entomologist/browse

Fig. 1C in Seasonal flight activity and distribution of metallic woodboring beetles (Coleoptera: Buprestidae) collected in North Carolina and Tennessee

Fig. 1C. Seasonal flight activities recorded for Buprestinae metallic woodboring beetle species collected across North Carolina (1901–2013) and Tennessee (1934–2013). Collection records frequently included trapping yield results from multi-day deployments; thus, data for individual specimens were pooled within 1 of 4 weeks for each month. Dated records for specimens with labels that noted specimen emergence from infested trunk, stem, firewood, and branch sections are not included within the seasonal ranges presented. Dashed vertical lines (at May and Jul) indicate approximate flight activity period (in North Carolina) for Cerceris fumipennis (Say) (Hymenoptera: Crabronidae) wasps. Asterisks indicate species collected by the wasps during biosurveillance in North Carolina (Nalepa et al. 2013; Swink et al. 2013, 2014). This figure is displayed in color online at http://purl.fcla.edu/fcla/entomologist/browse

Fig. 1A in Seasonal flight activity and distribution of metallic woodboring beetles (Coleoptera: Buprestidae) collected in North Carolina and Tennessee

Fig. 1A. Seasonal flight activities recorded for Agrilinae: Agrilini metallic woodboring beetle species collected across North Carolina (1901–2013) and Tennessee (1934–2013). Collection records frequently included trapping yield results from multi-day deployments; thus, data for individual specimens were pooled within 1 of 4 weeks for each month. Dated records for specimens with labels that noted specimen emergence from infested trunk, stem, firewood, and branch sections are not included within the seasonal ranges presented. Dashed vertical lines (at May and Jul) indicate approximate flight activity period (in North Carolina) for Cerceris fumipennis (Say) (Hymenoptera: Crabronidae) wasps. Asterisks indicate species collected by the wasps during biosurveillance in North Carolina (Nalepa et al. 2013; Swink et al. 2013, 2014). This figure is displayed in color online at http://purl.fcla.edu/fcla/entomologist/browse

Fig. 2. Tallies within each North Carolina county indicate the total buprestid species recorded from specimen label records spanning 1901 in Seasonal flight activity and distribution of metallic woodboring beetles (Coleoptera: Buprestidae) collected in North Carolina and Tennessee

Fig. 2. Tallies within each North Carolina county indicate the total buprestid species recorded from specimen label records spanning 1901 to 2013. When presented across North Carolina, species yields indicate areas of greatest and least collection activity and highlight regions of future collection interest. This figure is displayed in color online at http://purl.fcla.edu/fcla/entomologist/browse