Find research datasets worth reusing

opencc-by-4.0May 2019View details →

Evaluation Framework for Multiband Image Enhancement and Blending Algorithms in Enhanced Flight Vision Systems - Image Dataset

This dataset contains data used in the research published by MLabs Optronics in the paper: Medina Heierle, Victor, María Tejada Casado, Alberto Briasco González, Hugo Jestes Zoilo, Jesús Martín Tapia, Adeodato Altamirano Aguilar, and Javier Muñoz De Luna Clemente. Evaluation Framework for Multiband Image Enhancement and Blending Algorithms in Enhanced Flight Vision Systems. Proceedings of the 14th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), pp. 274-280. IEEE, 2018. The dataset is classified into 3 folders: - IR_VIS: Contains 28 pairs of images in the IR (some images may be in the NIR spectrum instead) and Visual spectrum, taken from different public repositories off the internet, which are typically used in multispectral fusion research.   - Fusion: Contains 8 sets with the results of applying each of the 4 fusion algorithms described in the paper on some of the images in folder "IR_VIS". - VIS haze filtering: Contains 24 images taken with a CCD camera of a contrast target inside a fog simulation cabin in a laboratory. For comparison purposes, all images have been taken with a similar amount of fog, which is as much as was possible while still being able to see the target with the camera through the fog. Each image has been taken with a different type of filter (filter information is provided in another image inside the folder).   Mlabs Optronics PTA Calle Pierre Laffitte, 8 29590 Málaga (Spain) www.mlabsoptronics.com info@mlabsoptronics.com

opencc-by-4.0Dec 2023View details →

How aphids fly: take off, free flight and implications for short and long distance migration.

We used a Phantom T4040 camera at 9350-13,000 FPS and at 4.2-Mpx resolution (2560 x 1664) . The aspect ratios varied, but were typically 2048 x 1280 pixels - 2560 x 1664. Videos were captured by the Phantom Camera Control software (PCC) as Cine RAW files and converted to MP4 for analysis and viewing in slow motion. A timer recording behaviour in milliseconds is embedded in MP4 files. Filming at high FPS and in HD requires specialist flicker-free high-speed illumination lighting: we used two GSVitec™ MultiLED MX that each produced 12,000 Lux of white light (24,000 total). Videos include Drepanosiphum platanoidis (Schrank), the sycamore aphid, that feeds on Acer sp, a monophyletic group of trees ancestral to Asia, but present in Europe for the last 30 million years (Gao et al. 2020). Myzus persicae (Sulzer), the peach-potato aphid, is a medium sized aphid that is extremely polyphagous and is truly a global pest species.

opencc-by-4.0Sep 2022View details →

Data, scripts, and R Notebook for Carneiro et al 2023. Flight performance and wing morphology in the bat Carollia perspicillata: biophysical models and energetics. Integrative Zoology DOI:10.1111/1749-4877.12707

Files provided as supporting information for the paper by Carneiro et al. 2023. Flight performance and wing morphology in the bat Carollia perspicillata: biophysical models and energetics. Integrative Zoology. DOI:10.1111/1749-4877.12707 File descriptions ArmTA.txt - Temperature and surface areas for arms of C. perspicillata after flight experiment BodyTA.txt - Temperature and surface areas for body of C. perspicillata after flight experiment HeadTA.txt - Temperature and surface areas for head of C. perspicillata after flight experiment WingTA.txt - Temperature and surface areas for wings (patagium) of C. perspicillata after flight experiment WingMorph.txt - Morphological variables measured in the body and wings of C. perspicillata HeatLoss.R - Function to estimate heat loss (Qt) PowFlight.R - Function to estimate minimum power required to fly Script-HeatLoss-FlightPerformance.R - R script with set of analyses performed SupportingInformationFile.docx - R notebook with set of analyses performed, word format SupportingInformationFile.nb.html - R notebook with set of analyses performed, html format SupportingInformationFile.Rmd - R notebook with set of analyses performed (R markdown) For the R scripts (Script-HeatLoss-FlightPerformance.R) and notebook ( SupportingInformationFile.Rmd) to work and be compiled, all files need to be copied to the same folder.

VHR orthomosaic imageries produced by dedicated drone flight campaigns over Lithuania (2021-2022) - Vilnius -AOI 1, Sub-region 1

In the context of the EU-funded project DIONE (No. 870378), targeted drone flight campaigns were conducted in determined Areas of Interest (AOIs) in the Lithuanian territory as a complementary source of information to the spaceborne Earth Observation (EO) open-accessed data. Such an activity attempted to overcome the limited capabilities of EO coarse resolution data to identify small-scale features alongside the agricultural fields, such as the non-productive Ecological Focus Areas (EFAs) and overall to enhance the quality and accuracy (1m or less) of the estimated Land Cover/Land Use elements. The dataset is in fact a collection of drone orthomosaic images delivered in GeoTIFF data format, which were acquired from the drone flight missions over the first subregion of the first AOI located near the capital of Lithuania, (Vilnius). For each area, four flight campaigns were scheduled and conducted on the following dates:      <ul> <li>1st flight: 24-27/5/2021</li> <li>2nd flight: 26-29/7/2021</li> <li>3rd flight: 13-16/9/2021</li> <li>4th flight: 25-28/8/2022</li> </ul>

VHR orthomosaic imageries produced by dedicated drone flight campaigns over Lithuania (2021-2022) - Vilnius -AOI 2

In the context of the EU-funded project DIONE (No. 870378), targeted drone flight campaigns were conducted in determined Areas of Interest (AOIs) in the Lithuanian territory as a complementary source of information to the spaceborne Earth Observation (EO) open-accessed data. Such an activity attempted to overcome the limited capabilities of EO coarse resolution data to identify small-scale features alongside the agricultural fields, such as the non-productive Ecological Focus Areas (EFAs) and overall to enhance the quality and accuracy (1m or less) of the estimated Land Cover/Land Use elements. The dataset is in fact a collection of drone orthomosaic images delivered in GeoTIFF data format, which were acquired from the drone flight missions over the second AOI located near the capital of Lithuania, (Vilnius). For each area, four flight campaigns were scheduled and conducted on the following dates:      <ul> <li>1st flight: 24-27/5/2021</li> <li>2nd flight: 26-29/7/2021</li> <li>3rd flight: 13-16/9/2021</li> <li>4th flight: 25-28/8/2022</li> </ul>

VHR orthomosaic imageries produced by dedicated drone flight campaigns over Cyprus (2021-2022) - Choirokoitia region

In the context of the EU-funded project DIONE (No. 870378), targeted drone flight campaigns were conducted in determined Areas of Interest (AOIs) in the Cypriotic territory as a complementary source of information to the spaceborne Earth Observation (EO) open-accessed data. Such an activity attempted to overcome the limited capabilities of EO coarse resolution data to identify small-scale features alongside the agricultural fields, such as the non-productive Ecological Focus Areas (EFAs) and overall to enhance the quality and accuracy (1m or less) of the estimated Land Cover/Land Use elements. The dataset is in fact a collection of drone orthomosaic images delivered in GeoTIFF data format, which were acquired from the drone flight missions over one of the two determined locations in Cyprus (e.g. Choirokoitia). For each area, four flight campaigns were scheduled and conducted on the following dates:      <ul> <li>1st flight: 29/3/2021- 1/4/2021</li> <li>2nd flight: 30/8/2021-2/9/2021</li> <li>3rd flight: 29/11/2021-03/12/2021</li> <li>4th flight: 17-20/5/2022</li> </ul>

VHR orthomosaic imageries produced by dedicated drone flight campaigns over Cyprus (2021-2022) - Akaki region

In the context of the EU-funded project DIONE (No. 870378), targeted drone flight campaigns were conducted in determined Areas of Interest (AOIs) in the Cypriotic territory as a complementary source of information to the spaceborne Earth Observation (EO) open-accessed data. Such an activity attempted to overcome the limited capabilities of EO coarse resolution data to identify small-scale features alongside the agricultural fields, such as the non-productive Ecological Focus Areas (EFAs) and overall to enhance the quality and accuracy (1m or less) of the estimated Land Cover/Land Use elements. The dataset is in fact a collection of drone orthomosaic images delivered in GeoTIFF data format, which were acquired from the drone flight missions over one of the two determined locations in Cyprus (e.g. Akaki). For each area, four flight campaigns were scheduled and conducted on the following dates:      <ul> <li>1st flight: 29/3/2021- 1/4/2021</li> <li>2nd flight: 30/8/2021-2/9/2021</li> <li>3rd flight: 29/11/2021-03/12/2021</li> <li>4th flight: 17-20/5/2022</li> </ul>

VHR orthomosaic imageries produced by dedicated drone flight campaigns over Lithuania (2021-2022) - Vilnius -AOI 1, Sub-region 2

In the context of the EU-funded project DIONE (No. 870378), targeted drone flight campaigns were conducted in determined Areas of Interest (AOIs) in the Lithuanian territory as a complementary source of information to the spaceborne Earth Observation (EO) open-accessed data. Such an activity attempted to overcome the limited capabilities of EO coarse resolution data to identify small-scale features alongside the agricultural fields, such as the non-productive Ecological Focus Areas (EFAs) and overall to enhance the quality and accuracy (1m or less) of the estimated Land Cover/Land Use elements. The dataset is in fact a collection of drone orthomosaic images delivered in GeoTIFF data format, which were acquired from the drone flight missions over the second subregion of the first AOI located near the capital of Lithuania, (Vilnius). For each area, four flight campaigns were scheduled and conducted on the following dates:      <ul> <li>1st flight: 24-27/5/2021</li> <li>2nd flight: 26-29/7/2021</li> <li>3rd flight: 13-16/9/2021</li> <li>4th flight: 25-28/8/2022</li> </ul>

opencc-by-4.0Oct 2017View details →

BirdVox-full-night: a dataset for avian flight call detection in continuous recordings

BirdVox-full-night: a dataset for avian flight call detection in continuous recordings ====================================================================================== Version 3.0, March 2018. Created By ---------- Vincent Lostanlen (1, 2, 3), Justin Salamon (2, 3), Andrew Farnsworth (1), Steve Kelling (1), and Juan Pablo Bello (2, 3). (1): Cornell Lab of Ornithology (CLO) (2): Center for Urban Science and Progress, New York University (3): Music and Audio Research Lab, New York University https://wp.nyu.edu/birdvox   Description ----------- The BirdVox-full-night dataset contains 6 audio recordings, each about ten hours in duration. These recordings come from ROBIN autonomous recording units, placed near Ithaca, NY, USA during the fall 2015. They were captured on the night of September 23rd, 2015, by six different sensors, originally numbered 1, 2, 3, 5, 7, and 10. Andrew Farnsworth used the Raven software to pinpoint every avian flight call in time and frequency. He found 35402 flight calls in total. He estimates that about 25 different species of passerines (thrushes, warblers, and sparrows) are present in this recording. Species are not labeled in BirdVox-full-night, but it is possible to tell apart thrushes from warblers and sparrrows by looking at the center frequencies of their calls. The annotation process took 102 hours. The dataset can be used, among other things, for the research, development and testing of bioacoustic classification models, including the reproduction of the results reported in [1]. For details on the hardware of ROBIN recording units, we refer the reader to [2]. [1] V. Lostanlen, J. Salamon, A. Farnsworth, S. Kelling, J. Bello. BirdVox-full-night: a dataset and benchmark for avian flight call detection. Proc. IEEE ICASSP, 2018. [2] J. Salamon, J. P. Bello, A. Farnsworth, M. Robbins, S. Keen, H. Klinck, and S. Kelling. Towards the Automatic Classification of Avian Flight Calls for Bioacoustic Monitoring. PLoS One, 2016. @inproceedings{lostanlen2018icassp,   title = {BirdVox-full-night: a dataset and benchmark for avian flight call detection},   author = {Lostanlen, Vincent and Salamon, Justin and Farnsworth, Andrew and Kelling, Steve and Bello, Juan Pablo},   booktitle = {Proc. IEEE ICASSP},   year = {2018},   published = {IEEE},   venue = {Calgary, Canada},   month = {April}, }   Data Files ------------ The BirdVox-full-night_flac-audio folder contains the recordings as FLAC files, sampled at 24 kHz, with a single channel (mono).   Metadata Files -------------- The BirdVox-full-night_csv-annotations folder contains JAMS files, where each row correspond to a different location in the time frequency domain (columns "Time (s)" and "Freq (Hz)"). The approximate GPS coordinates of the sensors (latitudes and longitudes rounded to 2 decimal points) and UTC timestamps corresponding to the start of the recording for each sensor are included as CSV files in the main directory.   Please acknowledge BirdVox-full-night in academic research ---------------------------------------------------------- When BirdVox-full-night is used for academic research, we would highly appreciate it if  scientific publications of works partly based on this dataset cite the following publication: V. Lostanlen, J. Salamon, A. Farnsworth, S. Kelling, J. Bello. BirdVox-full-night: a dataset and benchmark for avian flight call detection, Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2018. 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.   Conditions of Use ----------------- Dataset created by Vincent Lostanlen, Justin Salamon, Andrew Farnsworth, Steve Kelling, and Juan Pablo Bello. The BirdVox-full-night dataset is offered free of charge under the terms of the Creative  Commons Attribution 4.0 International (CC BY 4.0) license: https://creativecommons.org/licenses/by/4.0/ 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, Cornell Lab of Ornithology is not liable for, and expressly excludes all liability for, loss or damage however and whenever caused to anyone by any use of the BirdVox-full-night dataset or any part of it.   Feedback ----------- Please help us improve BirdVox-full-night by sending your feedback to: vincent.lostanlen@gmail.com and af27@cornell.edu In case of a problem, please include as many details as possible.   Acknowledgements ---------------- 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. 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.

opencc-by-4.0Oct 2018View details →

Third harmonic generation images of the lacuno-canalicular network in bone femoral diaphysis of mice from the BionM1 project (space flight)

Data set for 11 samples in 3 groups of Control, Space Flight and Synchro (ground control with space flight housing and feeding conditions). Contains THG images in tif format of 2D mosaic of selected samples and 3D stacks in selected anatomical regions of interest. See readme file for more information.

opencc-by-4.0Oct 2018View details →

Confocal fluorescence microscopy images of the lacuno-canalicular network in bone femoral diaphysis of mice from the BionM1 project (space flight)

This data set provides complementary measurements to a separate THG data set of the same study: doi: 10.5281/zenodo.1475906 Data set for 1 sample of each of the 3 groups: Control, Space Flight and Synchro (ground control with space flight housing and feeding conditions). Contains confocal fluorescence microscopy images in tif format of 2D mosaic of selected samples and 3D stacks in selected anatomical regions of interest. See readme file for more information.

opencc-by-4.0Apr 2020View details →

BATAL POPS flight (July 2019)

The Printed Optical Particle Spectrometer (POPS) was operated during the BATAL (Balloon Measurements of the Asian Tropopause Aerosol Layer) campaign in 2019 in Hyderabad (17N,78E), India. The primary goal of this campaign is the investigation of the ATAL (Asian Tropopause Aerosol Layer). Here, we use POPS observations from one balloon flight on July 17th 2019 (UT time). The instrument weighs around 800g and uses a 405 mm diode laser. POPS delivers aerosol number concentration and size distribution measurements. Here the file corresponds to concentrations in each size class between 0.15 and ~3 µm.

opencc-by-4.0Dec 2019View details →

Harnessing the power of digitized natural history collections to visualize spatiotemporal patterns in native and non-native bee flight phenology

What time of year are bees flying, where are they flying, and how do biogeographical factors, sex, and native status affect flight phenology? Consistent monitoring along with creating spatially and temporally explicit visualizations using large openly available data sets enhance our understanding of trends in flight time phenology and shape our understanding of bee-plant interactions, including shifts in the phenology of bee pollinators. Species occurrence data from digitized collection networks (iNaturalist, Global Biodiversity Information Faculty (GBIF), Integrated Digitized Biocollections (iDigBio), Symbiota Collections of Arthropods Network (SCAN), and UC Santa Barbara Collection Network) are part of an effort to improve our understanding of bees in coastal Santa Barbara County, including the California Channel Islands. New inventory collections combined with historical data from over 11 natural history museums and 2 observation networks are used in an effort to examine patterns and changes in phenology of native and non-native bee species, and create updated species inventories. Synthesizing species observation data from digitized natural history collections makes use of a wealth of existing data and multiplies the analytical power of isolated observations, but it is not without limitations and challenges. By exploring novel techniques to generate clear and accurate visualizations to communicate bee flight time, we present our key initial findings and identify geographic, temporal, and taxonomic gaps, which will lead to further focused inventory projects of coastal Santa Barbara County, improved data quality for phenological analyses, and reusable methods for visualizing insect phenology data across taxa or geography. The attached files include the R code and some of the .csv files used to produce the figures in my poster that was available on demand at the Entomology Society of America 2020 virtual meeting.

opencc-by-4.0Jan 2021View details →

Fixed-Wing Micro UAV Open Data With Digicam And Raw INS/GNSS - IGN Flight 8

The data set originate from a series of flights conducted with fixed-wing micro UAV carrying high-quality small camera and navigation sensors. This data was previously used in several peer-reviewed publications and will also be used in ISPRS workshop on dynamic networks given during the 2021 ISPRS Congress. This is part of a larger series of data that will be released gradually after incorporating user's feedback (e.g., on formats, description,etc.). The data set contains the sensor measurements from GPS, IMU and Camera.

opencc-by-4.0Feb 2024View details →

Data for: First experimental time-of-flight-based proton radiography using low gain avalanche diodes

Data for: First experimental time-of-flight-based proton radiography using low gain avalanche diodes The associated publication can be found on https://iopscience.iop.org/article/10.1088/1361-6560/ad3326. All graphs inside the publication can be recreated with this dataset. Similar to the publication, the data for the timewalk and offset correction are only given for one sensor and one channel as they only serve a representative purpose. The raw data for all other channels can be shared upon request. Furthermore, as in the publication, the data for the water-equivalent-thickness (WET) calibration and proton radiography (pRAD) creation are given by the median and the interquartile range of the measured quantities of the individual graphs. Those data are also calibrated. If required, the raw, unprocessed data of each measurement can be shared upon request. In the following, a description of the individual files and corresponding figures in the publication is given. If not specified otherwise, the physical units are given in brackets next to the name of the corresponding physical quantity (usually first line in file): <ul> <li>Figure 6: <ul> <li> RawToTspectrumrescaledLGAD3.txt: <ul> <li>Describes the re-scaled time-over-threshold (ToT) spectrum measured inside the third LGAD of the time-of-flight-based ion computed tomography (TOF-iCT) demonstrator using 800 MeV protons (Figure 6a). The first column gives the channel number on the LGAD (channelnr[#]), the second column, the ToT value measured in this channel (ToT[ps]) and the third channel, the corresponding occurrence  (counts[#]).</li> </ul> </li> <li>ToTspectrumrescaledLocMaxLGAD3.txt <ul> <li>Describes the re-scaled ToT spectrum measured inside the third LGAD of the TOF-iCT demonstrator using only the local ToT maxima inside each 4D-cluster. The spectrum was obtained using 800 MeV protons (Figure 6b). The first column gives the channel number on the LGAD (channelnr[#]), the second column, the ToT value measured in this channel (ToT[ps]) and the third channel, the corresponding occurrence  (counts[#]).</li> </ul> </li> </ul> </li> <li>Figure 7: <ul> <li>offsetpraecalib.txt: <ul> <li>Describes the raw, uncalibrated time difference spectrum in LGAD3 measured between all channels on LGAD3 and a central reference channel on LGAD4 (figure 7a). The first column represents the detector channel nr in LGAD3, the second column the raw, uncalibrated time difference between LGAD3 and LGAD4 (TDiff[ns]) and the third column the number of corresponding counts (counts[#]).</li> </ul> </li> <li>offsetpraecalib.txt: <ul> <li>Describes the time walk and offset-calibrated time difference spectrum in LGAD3 measured between all channels on LGAD3 and a central reference channel on LGAD4 (figure 7b). The first column represents the detector channel nr in LGAD3, the second column the calibrated time difference between LGAD3 and LGAD4 (TDiff[ns]) and the third column the number of corresponding counts (counts[#]).</li> </ul> </li> <li> praetwdata.txt: <ul> <li>Describes the ToT dependence of the measured time difference between LGAD1 and LGAD2 using the raw ToT of channel 31 in LGAD1 (figure 7c). The first column represents the raw, unscaled and uncalibrated ToT in LGAD 1 (ToT[ns]), the second column the measured time difference (TDiff[ns]) and the last column, the number of corresponding counts (counts[#]). A ToT cut on the reference channel on LGAD2 has been applied in advance to guarantee a correlation between only true particle hits in the second sensor.</li> </ul> </li> <li>posttwdata.txt <ul> <li>Describes the time walk-calibrated ToT vs TDiff spectrum using the measured time difference between LGAD1 and LGAD2 and the  ToT of channel 31 in LGAD1 (figure 7d). The first column represents the  ToT in LGAD 1 (ToT[ns]), the second column the measured time difference (TDiff[ns]) and the last column the number of corresponding counts (counts[#]). A ToT cut on the reference channel on LGAD2 has been applied in advance to guarantee a correlation between only true particle hits in the second sensor.</li> </ul> </li> </ul> </li> <li>Figure 8: <ul> <li>tofinaridata.txt: <ul> <li>Describes the measured TOF in air through the scanner w.r.t the TOF measured at 800MeV, i.e. the median TOF value at 800MeV was subtracted from all data points (Figure 8a). The first column describes the beam energy (beamenergy[MeV]), the second column the first quartile of the measured TOF per pixel (TOFperpixelQ1[ps]), the second column the median TOF per pixel (TOFperpixelQ2[ps]) and the last column the third quartile of the measured TOF per pixel (TOFperpixelQ3[ps]).</li> </ul> </li> <li>tofinairtheodata.txt: <ul> <li>Describes the theoretical TOF in air through the scanner w.r.t the theoretical TOF at 800MeV, i.e. the theoretical TOF value at 800MeV was subtracted from all data points (Figure 8a).</li> </ul> </li> <li>intrinsictimeresolution.txt: <ul> <li>Describes the energy dependence of the intrinsic time resolution per channel measured inside LGAD1 (figure 8b). The first column represents the primary beam energy (beamenergy[MeV), the second column the corresponding energy loss in MIPs (relativeenergylossi[MIP]), the third column the first quartile of the intrinsic time resolution per LGAD channel (timeresperpixelQ1[ps]), the fourth column the median of the intrinsic time resolution per LGAD channel and the last column the third quartile of the intrinsic time resolution per LGAD channel (timeresperpixelmedian[ps],timeresperpixelQ3[ps]).</li> </ul> </li> </ul> </li> <li>Figure 9: <ul> <li>wetcalib.txt <ul> <li>Describes the measured TOF increase per pixel w.r.t to the TOF in air (i.e. without a phantom) for a given WET and primary beam energy. The first column represents the WET of the irradiated sample (WET[mm]), the second column the used beam energy (beamenergy[MeV]), the third column the first quartile of the measured TOF distribution (TOFperpixelQ1[ps]), the fourth column the median (TOFperpixelQ2[ps]) and the sixth column the third quartile (TOFperpixelQ3[ps]).</li> <li>For each energy, a fifth-order polynomial was used to fit the WET and the TOF increase (Delta TOF(E)~sum_i a_i*(WET_i )^i, with i in [0,5] ). The fit parameters are given in the following for each beam energy: <ul> <li>83 MeV: a_i=[-4.70496227e-02,4.64323118e-01, -2.71391535e-02,4.23655842e-03, -1.13034255e-04,1.23725678e-06]</li> <li>100.4 MeV: a_i=[-3.28976022e-02,-3.68818468e-02,1.96339858e-02,7.31585040e-04, -4.38697681e-05 ,7.52163384e-07]</li> </ul> </li> </ul> </li> </ul> </li> <li>Figure 10: <ul> <li>wetsperpixel83MeV.txt <ul> <li>Describes the proton radiography (pCR) for 83 MeV (Figure 10a). The first column represents the x position of the pixel (x[mm]), the second column the y position of the pixel (y[mm]) and the last column the corresponding WET (WET[mm]).</li> </ul> </li> <li>wetsperpixel83MeV.txt <ul> <li>Describes the proton radiography (pCR) for 100.4 MeV (Figure 10b). The first column represents the x position of the pixel (x[mm]), the second column the y position of the pixel (y[mm]) and the last column the corresponding WET (WET[mm]).</li> </ul> </li> </ul> </li> <li>Figure 11: <ul> <li>wetdistrdata83MeV.txt <ul> <li>Describes the measured TOF per pixel inside the ROI for 83 MeV protons (Figure 11a). The first column represents the lower boundary of each WET bin (WETlowerbinboundary[mm]), the second column the upper boundary of each WET bin (WETupperbinboundary[mm) and the last column the corresponding counts per bin (counts[#]).</li> </ul> </li> <li>wetdistrdata100MeV.txt <ul> <li>Describes the measured TOF per pixel inside the ROI for 100.4 MeV protons (Figure 11b). The first column represents the lower boundary of each WET bin (WETlowerbinboundary[mm]), the second column the upper boundary of each WET bin (WETupperbinboundary[mm) and the last column the corresponding counts per bin (counts[#]).</li> </ul> </li> </ul> </li> </ul>

opencc-by-4.0Dec 2021View details →

BirdVox-296h: a large-scale dataset for detection and classification of flight calls

BirdVox 296 hours dataset (BirdVox-296h) ==================================== Version 2.1, May 2022. Created By ---------- Andrew Farnsworth (1), Benjamin Mark Van Doren (1), Steve Kelling (1), Vincent Lostanlen (2), Justin Salamon (3), Aurora Cramer (4), Juan Pablo Bello (4) (1): Cornell Lab of Ornithology (CLO) (2): Laboratoire des Sciences du Numérique de Nantes (LS2N), CNRS (3): Adobe Research (4): New York University https://wp.nyu.edu/birdvox   Description --------------- The BirdVox-296h dataset contains 148 audio recordings, each two hours in duration. These recordings come from ROBIN autonomous recording units, placed near Ithaca, NY, USA during the fall 2015. They were captured by nine different sensors, originally numbered 1, 2, 3, 4, 5, 6, 7, 8, and 10. Ornithologist Andrew Farnsworth used the Raven software to pinpoint and label every avian flight call in time and frequency. He found 26138 sound events, of which 21546 are flight calls from Passeriformes. Of those, 13385 are identifiable in terms of family, and 8669 are identifiable in terms of both family and species. The annotation process took over 600 hours. The dataset can be used, among other things, for the research, development and testing of machine listening models for bird migration monitoring.   Data Files ------------ The BirdVox-296h_wav folder contains 148 recordings as WAV files, sampled at 24 kHz, with a single channel (mono). Each recording lasts exactly two hours and is named according to the following format: YYYY-MM-DD_hh-mm-ss_unitUU.wav Where Y means Year, M means Month, D means Day, h means hour, m means minute, and s means second. This date format corresponds to the start time of the recording file, expressed in Coordinated Universal Time (UTC). The field UU contains two digits corresponding to the identifier of the autonomous recording unit (i.e., bioacoustic sensor). UU is either equal to 01, 02, 03, 04, 05, 06, 07, 08, or 10. Note that 09 is absent from the list because sensor 09 failed during the acquisition campaign.   Metadata Files ------------------- The BirdVox-296h_csv-annotations folder contains CSV files, one for each audio file. The columns of each CSV file are: ID,Time (s),Frequency (Hz),Taxonomy Code,Fine Label,Medium Label,Coarse Label "Taxonomy Code" is compliant with the BirdVoxClassify software: github.com/BirdVox/BirdVoxClassify "Fine Label", "Medium Label", and "Coarse Label" most often correspond to species, family and order respectively.   The BirdVox-296h_gps-coordinates.csv file contains the approximate GPS coordinates of the sensors (latitudes and longitudes rounded to 2 decimal points) of all nine sensors.     Conditions of Use ----------------- Dataset created by Andrew Farnsworth, Steve Kelling, Vincent Lostanlen, Justin Salamon, Aurora Cramer, and Juan Pablo Bello. The BirdVox-full-night dataset is offered free of charge under the terms of the Creative  Commons Attribution 4.0 International (CC BY 4.0) license: https://creativecommons.org/licenses/by/4.0/ 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, Cornell Lab of Ornithology is not liable for, and expressly excludes all liability for, loss or damage however and whenever caused to anyone by any use of the BirdVox-full-night dataset or any part of it.   Feedback ------------- Please help us improve BirdVox-296h by sending your feedback to: vincent.lostanlen@ls2n.fr and af27@cornell.edu In case of a problem, please include as many details as possible.   Acknowledgements -------------------------- 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. 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.

opencc-by-4.0Jan 2022View details →

BirdVox-25SD: a dataset of flight calls with species annotations

<pre>BirdVox 25 Species Dataset (BirdVox-25SD) ============= Version 1.0, Jan 2021. Created By ---------- Andrew Farnsworth (1), Benjamin Mark Van Doren (1), Steve Kelling (1), Vincent Lostanlen (2), Justin Salamon (3), Aurora Cramer (4), Juan Pablo Bello (4) (1): Cornell Lab of Ornithology (CLO) (2): Laboratoire des Sciences du Numérique de Nantes (LS2N), CNRS (3): Adobe Research (4): New York University https://wp.nyu.edu/birdvox Description ----------- The BirdVox 25 Species Dataset (BirdVox-25SD) contains 26,124 audio clips of avian flight calls, each ranging from about 150 ms to 500 ms in duration. The clips are extracted from the <a href="http://https://doi.org/10.5281/zenodo.4603643">BirdVox-296h</a> dataset using the corresponding annotations. The recordings come from ROBIN autonomous recording units, placed near Ithaca, NY, USA during the 2015 migration season (August - November). The dataset can be used, among other things, for the research, development and testing of bioacoustic classification models. For details on the hardware of ROBIN recording units, we refer the reader to [1]. [1] J. Salamon, J. P. Bello, A. Farnsworth, M. Robbins, S. Keen, H. Klinck, and S. Kelling. Towards the Automatic Classification of Avian Flight Calls for Bioacoustic Monitoring. PLoS One, 2016. Changes from BirdVox 14-SD ---------------------------- This dataset builds upon the <a href="http://https://doi.org/10.5281/zenodo.3667094">BirdVox 14 Species Dataset (BirdVox-14SD)</a>, adding ~12,000 audio clips and annotations. The annotation taxonomy has been expanded to add a new order, a new family, and 11 new species. Additionally, the audio clips are more accurately aligned to the annotation times. For backwards compatibility with the BirdVox-14SD taxonomy, we include the file `birdvox25sd-to-birdvox14sd-taxonomy-code-map.csv` which maps BirdVox-25SD taxonomy codes to BirdVox-14SD taxonomy codes. Taxonomic Annotations ----------------------- Classification annotations for each flight call are given at three taxonomic levels: order, family, and species. These annotations are condensed into a three-number-code which largely follow "..". The specific numeric codes are: * Order * 1.\*.\* - Passeriformes * 2.\*.\* - Pelecaniformes * Family * 1.1.\* - American Sparrow * 1.2.\* - Cardinals * 1.3.\* - Thrushes * 1.4.\* - New World warblers * 2.1.\* - Herons * Species * 1.1.1 - American tree sparrow (ATSP) * 1.1.2 - Chipping sparrow (CHSP) * 1.1.3 - Savannah sparrow (SAVS) * 1.1.4 - White-throated sparrow (WTSP) * 1.1.5 - Song sparrow (SOSP) * 1.2.1 - Rose-breasted grosbeak (RBGR) * 1.3.1 - Gray-cheeked thrush (GCTH) * 1.3.2 - Swainson's thrush (SWTH) * 1.3.3 - Hermit thrush (HETH) * 1.3.4 - Veery (VEER) * 1.3.5 - Wood thrush (WOTH) * 1.4.1 - American redstart (AMRE) * 1.4.2 - Bay-breasted warbler (BBWA) * 1.4.3 - Black-throated blue warbler (BTBW) * 1.4.4 - Canada warbler (CAWA) * 1.4.5 - Common yellowthroat (COYE) * 1.4.6 - Mourning warbler (MOWA) * 1.4.7 - Ovenbird (OVEN) * 1.4.8 - Black-and-white warbler (BAWW) * 1.4.9 - Cape May warbler (CMWA) * 1.4.10 - Chestnut-sided warbler (CSWA) * 1.4.11 - Northern Parula (NOPA) * 1.4.12 - Wilson's warbler (WIWA) * 1.4.13 - Yellow-rumped warbler (YRWA) * 2.1.1 - Green heron (GRHE) 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. Please refer to `<a href="https://zenodo.org/record/5856260/files/BirdVox-296h_taxonomy.yaml">BirdVox-296h_taxonomy.yaml</a>` in <a href="http://https://doi.org/10.5281/zenodo.5856260">BirdVox-296h</a> for the details of this taxonomy structure. Data Files ------------ BirdVox-25SD 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: `BirdVox-25SD-v1pt0_{taxonomy_code}_original.h5`. The name of the HDF5 dataset in each file is "waveforms", with the corresponding key for each audio recording following the format: `unit-{unit_num}`. Conditions of Use ---------------------- Dataset created by Andrew Farnsworth, Steve Kelling, Vincent Lostanlen, Justin Salamon, Aurora Cramer, and Juan Pablo Bello. The BirdVox-25SD dataset is offered free of charge under the terms of the Creative Commons Attribution 4.0 International License. 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, CLO is not liable for, and expressly excludes all liability for, loss or damage however and whenever caused to anyone by any use of the BirdVox-25SD dataset or any part of it. Feedback ----------- Please help us improve BirdVox-25SD by sending your feedback to: vincent.lostanlen@gmail.com and auroracramer@nyu.edu In case of a problem, please include as many details as possible. Acknowledgements ------------------------ 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. 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. The creation of this dataset was supported by NSF grants 1633259 (BIRDVOX).</pre>