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9,302 results for “DETECT”
DNA Metabarcoding Enables High-Throughput Detection of Spotted Wing Drosophila (Drosophila suzukii) within Unsorted Trap Catches
<p>Data to support the paper: DNA Metabarcoding Enables High-Throughput Detection of Spotted Wing Drosophila (Drosophila suzukii) within Unsorted Trap Catches</p>
Data for: Speech naturalness detection and language representation in the dog brain
<p>Abstract<br> Family dogs are exposed to a continuous flow of human speech throughout their lives. However, the extent of their abilities in speech perception is unknown. Here, we used functional magnetic resonance imaging (fMRI) to test speech detection and language representation in the dog brain. Dogs (n = 18) listened to natural speech and scrambled speech in a familiar and an unfamiliar language. Speech scrambling distorts auditory regularities specific to speech and to a given language, but keeps spectral voice cues intact. We hypothesized that if dogs can extract auditory regularities of speech, and of a familiar language, then there will be distinct patterns of brain activity for natural speech vs. scrambled speech, and also for familiar vs. unfamiliar language. Using multivoxel pattern analysis (MVPA) we found that bilateral auditory cortical regions represented natural speech and scrambled speech differently; with a better classifier performance in longer-headed dogs in a right auditory region. This neural capacity for speech detection was not based on preferential processing for speech but rather on sensitivity to sound naturalness.<br> Furthermore, in case of natural speech, distinct activity patterns were found for the two languages in the secondary auditory cortex and in the precruciate gyrus; with a greater difference in responses to the familiar and unfamiliar languages in older dogs, indicating a role for the amount of language exposure. No regions represented differently the scrambled versions of the two languages, suggesting that the activity difference between languages in natural speech reflected sensitivity to language-specific regularities rather than to spectral voice cues. These findings suggest that separate cortical regions support speech naturalness detection and language representation in the dog brain.<br> <br> This dataset contains</p> <ul> <li>Raw data (four functional runs and Matlab logs n = 18)</li> <li>Dog brain template</li> <li>Stimuli (natural and scrambled speech in Hungarian and Spanish) </li> <li>MVPA main results maps (Speech detection and Language discrimination, n = 18)</li> <li>GLM All sounds > Silence contrast (n = 18) </li> </ul>
Monthly TSI for "Prospects for Detecting Accelerated Global Warming"
<p>Stitched monthly TSI reconstruction as used in:<br> Richardson M "Prospects for Detecting Accelerated Global Warming"<br> Submitted to: Geophys. Res. Lett. 2021</p> <p>This is constructed from daily PMOD, SORCE and TSIS data, with linear interpolation between missing values before taking the monthly average.<br> <br> Please cite the original data sources:<br> PMOD: ftp://ftp.pmodwrc.ch/pub/data/irradiance/composite/composite_42_65_1709.dat and: https://doi.org/10.1007/s11214-006-9046-5<br> SORCE-TIM: http://lasp.colorado.edu/data/sorce/tsi_data/daily/sorce_tsi_L3_c24h_latest.txt and: https://doi.org/10.1007/s11207-005-7446-4<br> TSIS: http://lasp.colorado.edu/data/tsis/tsi_data/tsis_tsi_L3_c24h_latest.txt and: https://doi.org/10.5067/TSIS/TIM/DATA306</p>
Automatic Detection of Photovoltaic from Sentinel-2 observations by an Enhanced U-Net method - DataSet
<p>Data Availability for <strong>Enhanced U-Net(E-UNET)</strong></p> <p>Thank you for your interest in our dataset.<br> <strong>Repository contents:</strong><br> <em>Sentinel-2 L2A product</em>: The tiles that we selected containing photovoltaic.<br> <em>ImageFusion_Result</em>: The image fusion results of ROI including photovoltaic which we intercepted from the Sentinel-2 data. It contains 10m resolution, 10m and 20m resolution, 10m, 20m and 60m resolution fusion results, and RGB 3-channels fusion results.<br> <em>Image</em>: The RGB images for the ROI containing photovoltaic.<br> <em>Label</em>: The manual annotations for the ROI containing photovoltaic.</p>
Data from: Cyclone-anticyclone asymmetry of eddy detection on gridded altimetry product in the Mediterranean Sea
<p>We perform an Observing System Simulation Experiment that simulates the satellite sampling and the mapping procedure on the sea surface of the high-resolution model CROCO-MED60v40, to investigate the reliability and the accuracy of the eddy detection. The main result of this study is a strong cyclone-anticyclone asymmetry of the eddy detection on the altimetry products AVISO/CMEMS in the Mediterranean Sea. Large-scale cyclones having a characteristic radius larger than the local deformation radius are much less reliable than large-scale anticyclones. We estimate that less than 60% of these cyclones detected on gridded altimetry product are reliable, while more than 85% of mesoscale anticyclones are reliable. Besides, both the barycenter and the size of these mesoscale anticyclones are relatively accurate. This asymmetry comes from the difference of stability between cyclonic and anticyclonic eddies. Large mesoscale cyclones often split into smaller sub-mesoscale structures having a rapid dynamical evolution. The numerical model CROCO-MED60v40 shows that this complex dynamic is too fast and too small to be accurately captured by the gridded altimetry products. The spatio-temporal interpolation smoothes out this sub-mesoscale dynamics and tends to generate an excessive number of unrealistic mesoscale cyclones in comparison with the reference field. On the other hand, large mesoscale anticyclones, which are more robust and which evolve more slowly, can be accurately tracked by standard altimetry products. We also confirm that the AVISO/CMEMS products induce a bias on the eddy intensity. The azimuthal geostrophic velocities are always underestimated for large mesoscale anticyclones.</p>
Tomography data for interparticle contact detection analysis in spheroidal granular packings
<p>This collection contains a series of synchrotron XCT scans on a hexagonal close-packed arrangement of soda-glass pellets. The field of view (FOV) diameter is 68.9 mm in diameter, approximately, and the nominal individual pellet diameter is 10 mm. The detector pixel size is 21 microns for all scans. The pellets were arranged in three horizontal lattices (layers). The middle and top lattices were separated by a layer of polyethylene film (cling film), while the bottom and middle layer were fully-contacting. Each file corresponds to a scan of either the bottom contacting or top non-contacting lattice pair. Thus, each filename includes a 'top' and 'bot' identifier. </p> <p>Acquisition parameters (number of projections, exposure time per projection, rotation range and sample position) were varied to achieve different image qualities and are included in 'README.txt'. All but scan A5 were local scans; scan A5 is a full-field scan acquired using the 'half-acquisition' method. </p> <p>Tomographic reconstruction was carried out using filtered back-projection in Savu. After reconstruction, a 3D median filter (kernel size = 2) and an anisotropic diffusion filter (diffusion threshold = 100; iterations = 2) were used to reduce noise.</p> <p>Data was acquired using Beamline I12-JEEP at Diamond Light Source (proposal NT26307-1).</p> <p>Please read README.txt</p> <p>Copyright 2021 Diamond Light Source Ltd. Licensed under the Apache License, Version 2.0.</p>
Early Detection Of Cardiovascular Risk Factors And Definition Of Psychosocial Profile In Women Through A Systematic Approach: The Monzino Women Heart Center's Experience
<p>The raw dataset is related to the analysis of CV risk factors and psychosocial profile in women enrolled in a CV prevention program </p>
Grape bunch and vine trunk dataset for Deep Learning object detection.
<p>Grape bunch and vine trunk dataset containing images and annotations for Deep Learning object detection.</p>
A collection of X-ray projections of 131 pieces of modeling clay containing stones for machine learning-driven object detection
<p><strong>Summary</strong></p> <p>This submission contains a collection of 235800 X-ray projections of 131 pieces of modeling clay (Play-Doh) with various numbers of stones inserted. The submission is intended as an extensive and easy-to-use training dataset for supervised machine learning driven object detection. The ground truth locations of the stones are included. The data is supplementary material to the paper titled "A tomographic workflow enabling deep learning for X-ray based foreign object detection" [Zeegers 2022].</p> <p> </p> <p><strong>Description</strong></p> <p><em>Sample information</em></p> <p>The samples are modeling clay (Play-Doh, Hasbro, RI, USA) with various numbers of pieces of gravel included. In total 131 samples are prepared, of which 20 samples contain 5-8 inserted stones, 3 samples contain three stones, 35 contain two stones, 62 contain one stone and 11 contain no stones. The stones have an average diameter of ca. 7mm (ranging from 3mm to 11mm). The Play-Doh is remolded for every sample.</p> <p><em>Apparatus</em></p> <p>The dataset is acquired in the FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. The CT scanner consists consists of a cone-beam microfocus polychromatic X-ray point source, and a 1944x1536 pixel, 14-bit, flat detector panel (Dexela1512NDT). Full details can be found in [Coban 2020].</p> <p><em>Scanning setup</em></p> <p>For each sample, 1800 radiographs are collected by rotating the sample over 360 degrees in a circular and continuous motion. A peak voltage of 90kV is used, and the target power is set to 20W. The distance between the source and detector is 69.80 cm and the distance between the source and the object is 44.14 cm. An exposure time of 20 ms is used for each projection.</p> <p><em>Experimental plan</em></p> <p>This data is the result of a demonstration of a workflow to collect annotated data for supervised machine learning for X-ray based object detection. The ground truth locations are retrieved by tomographic reconstruction, segmentation and virtual projections with the same acquisition angles. A detailed description for the workflow to obtain a training dataset is given in [Zeegers 2022].</p> <p><em>Technical details</em></p> <p>All projections have been corrected with flatfield images (averaged over 10 pre and 10 post radiographs) and darkfield images (averaged over 10 pre and 10 post images). Both the X-ray projections and the ground truth images are resized to 128x128 pixels. The raw data is made available in another (larger) submission for complete reproduction (<a href="https://zenodo.org/record/5866228">https://zenodo.org/record/5866228</a>). All images are stored in .tif format. The data for samples with 5-8 stones are put in a separate folder from the data with 0-3 stones. The size of the completely unpacked dataset is 19.6 GB.</p> <p><strong>NOTE</strong>: Because the dataset consists of 471600 files, fully extracting the dataset may take a while. Therefore, an additional and significantly smaller zip-file is included for previewing the data, with one X-ray projection for each sample.</p> <p> </p> <p><strong>Additional Links</strong><br> These datasets are produced by the Computational Imaging group at Centrum Wiskunde & Informatica (CI-CWI) in Amsterdam, The Netherlands: <a href="https://www.cwi.nl/research/groups/computational-imaging">https://www.cwi.nl/research/groups/computational-imaging</a></p> <p> </p> <p><strong>Contact details</strong><br> zeegers [at] cwi [dot] nl</p> <p> </p> <p><strong>Acknowledgments</strong><br> The authors would like to acknowledge the funding from the Netherlands Organisation for Scientific Research (NWO), project number 639.073.506. The authors also acknowledge TESCAN-XRE NV for their collaboration and support of the FleX-ray laboratory.</p> <p><br> <strong>References</strong><br> [Zeegers 2022] M. T. Zeegers, T. van Leeuwen, D. M. Pelt, S. B. Coban, R. van Liere, K. J. Batenburg, "A tomographic workflow enabling deep learning for X-ray based foreign object detection", 2022 (in preparation)<br> [Coban 2020] S. B. Coban, F. Lucka, W. J. Palenstijn, D. Van Loo, and K. J. Batenburg, “Explorative imaging and its implementation at the FleX-ray Laboratory,” J. Imaging, vol. 6, no. 18, 2020, doi: 10.3390/jimaging6040018.</p> <p>If you use (parts of) this data in a publication, we would appreciate it if you would refer to the first article.</p>
Unsupervised Blink Detection Using Eye Aspect Ratio Values
<p>This datastore supports an open-source blink detection software which acts as both an implementation of methods proposed in the (unpublished) paper "Unsupervised Blink Detection Using Eye Aspect Ratio Values" and a validation of the results provided.</p> <p>The software can be accessed at the <strong>GitHub repository</strong>: <a href="https://github.com/mi3nts/tobiiBlinkDetection">https://github.com/mi3nts/tobiiBlinkDetection</a></p> <p>Although the study results can be displayed readily from the files provided at the GitHub, this datastore contains the required resources to apply the proposed blink detection algorithm to new data. Simply download this file and replace the resources folder of the cloned repository.</p>
Miniaturization eliminates detectable impacts of drones on bat activity
<p>A new way to survey wildlife populations may be possible with advancements in drones, or unmanned aerial vehicles (UAVs) that render aerial technology more accessible and promote surveying in inapproachable habitats. However, it remains unclear whether UAV disturbance deters animals, which would make this method inaccurate for data collection and hazardous to wildlife welfare. This study addresses the viability of UAV use for wildlife research by measuring the effects of UAV flight on acoustic bat detection and comparing bat activity in response to varying UAV models. Depending on the way UAVs effect bat detection rate, it may be possible to identify whether wildlife surveys should be done with UAVs and the drone models best suited for this purpose. The results reveal that larger and louder UAVs deterred significantly more bats, and the smallest and quietest model had no effect on bat detection. Indeed, drone noise was positively correlated with drone size, but drone size had little effect on the range of frequencies emitted. While detecting bats with small and quiet UAVs may be possible, complications still arise with acoustic detection and the species-specific effects of drone flight. The reliability of automatic identification with the acoustic detecting software is limited, as over a quarter of detections were triggered by non-bat noises yet still classified as bats (25.99%). Overall, using drones for wildlife detection should be approached with caution, as this study illustrates that some drones deter and disturb wild bats. If drones are used in wildlife habitat, consider flying smaller and quieter models, which are significantly less disturbing. Otherwise, large and loud drones will likely deter more animals and skew the results of the survey.</p>
No Allee effect detected during the natural recolonization by a large carnivore despite low growth rate
<p>Eurasian lynx (Lynx lynx) have recently naturally recolonized southern Sweden. The first documented reproduction of lynx in recent times occurred in 2003, and the population increased from two to 48 family groups (the unit of measurement in Swedish monitoring) during its first 18 years (2003/04 – 2020/21). We did not detect any Allee effect, i.e., lower growth rate at low population density, during the recolonization of southern Sweden, although our population simulations revealed a non-negligible (30 %) chance that population observed development could include an Allee effect. The probable absence of an Allee effect was likely because colonizing females did not lack mating partners, as a larger number of wide-ranging males were established in the area before documented reproduction took place. Despite the absence of an Allee effect, the growth rate during recolonization was lower in southern Sweden (lambda = 1.20) than in central Sweden (lambda = 1.29). We have no evidence of higher mortality, including that from poaching, or lower reproduction in southern Sweden could explain the lower growth rate. Instead, we suggest that the lower growth rate during the recolonization of southern Sweden was explained by fewer immigrants arriving from central Sweden due to areas of less suitable habitat between central and southern Sweden, partially preventing immigration southward. From a conservation point of view, it is positive that this small population could recover without being negatively influenced by an Allee effect, as small populations with an Allee effect experience lower viability than those without.</p>
ULTRA: Ultimate Rootkit Detection over the Air
<p>Data used in the paper <em><a href="https://dl.acm.org/doi/10.1145/3545948.3545962">"ULTRA: Ultimate Rootkit Detection over the Air"</a> </em>presented at RAID-2022. The archive "traces.tar.gz" contains the following directories:</p> <ul> <li> <p><strong>traces_ARM: </strong>extracted bandwidth of spectrograms from ARM EM-traces, </p> </li> <li> <p><strong>traces_MIPS: </strong>extracted bandwidth of spectrograms from MIPS EM-traces (usual probe: H-Field Langer RF-U 5-2 and position 0),</p> </li> <li> <p><strong>traces_MIPS_relocation: </strong>extracted bandwidth of spectrograms from ARM EM-traces (usual probe: H-Field Langer RF-U 5-2 and position 1),</p> </li> <li> <p><strong>traces_MIPS_relocation_cheap: </strong>extracted bandwidth of spectrograms from ARM EM-traces (hand crafted probe and position 2),<br> </p> </li> </ul> <p>The code used for the analysis of this data is available <a href="https://gitlab.com/ultra-RK/ultra">here</a>.</p>
New Zealand Cattle Detection
<p>Remote sensing dataset for detecting cows from high resolution aerial images.</p> <p>Images: Aerial RGB images with spatial resolution of 0.1m. 500 x 500 pixels corresponding to 50m x 50m<br> Label: point annotation of each visible cattle in the image<br> Image Sources: Land Information New Zealand 2016-2019<br> Total number of images: 655<br> Total number of cows in all images: 29803</p> <p><em>Contains data sourced from the <a href="https://data.linz.govt.nz/">LINZ Data Service</a> licensed for reuse under <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a></em></p>
NMR assignment of methyl groups in solid-state using 1H-detection and fast MAS - NMR raw and processed data
<p>This data set contains raw NMR data in Bruker format for experimental series on (1) 2,3-13C-labelled microcrystalline alanine, (2) U-13C,15N-labelled N-fomylated microcrystalline tripeptide Met-Leu-Phe, and (3,4) two differently labelled (ILV-C4 and ILV-C5) microcrystalline chicken-alpha-spectrin SH3 domain. Measurements were performed at 14.4 T and 55.5 kHz MAS (alanine), 18.8 T and 55.5 and 98 kHz MAS (fMLF), 23.5 T and 55.5 kHz (SH3 C5), 18.8T and 55.5 and 94.5 kHz MAS (SH3 C5) and 18.8T and 55.5 and 94.5 kHz MAS (SH3 C4). The data set also contains Fourier processed data (spectra) in UCSF format, Sparky project, save and peak list files. Pulse programs for Bruker spectrometers are provided. The data set is complemented with SIMPSON scripts for simulation of spin dynamics under aformentioned conditions.</p>
Review-based Comparison of Design Pattern Detection Tools
<p>Replication package containing the data obtained from the Systematic Literature Review of design pattern detection tools and the instances obtained from the execution of the tools available.</p> <p>Contains a README file introducing each of the files in this replication package.</p>
Dataset and processing scripts for manuscript titled "Broadband polarization insensitivity and high detection efficiency in high-fill-factor superconducting microwire single-photon detectors"
<p>The manuscript "Broadband polarization insensitivity and high detection efficiency in high-fill-factor superconducting microwire single-photon detectors" contains results for high-efficiency, low-polarization-sensitivity superconducting microwire single-photon detectors. All of the data gathered and the python scripts used for processing is being provided as a zip archive, along with a digital signature file. The dataset contains crucial procedures regarding the equipment calibrations used for accurate device efficiency measurements.</p>
HisVis: Scene Detection Pilot Training Set
<p>The <em>De Boer</em> photo collection, provided by the Noord-Hollands Archief, consists of approximately two million negatives and metadata for the period 1945-2004. This training set consists of a subset of 2,545 images from the NHA online repository, an early batch of digitized images, and digitized small negatives.</p> <p>We took the categorization scheme for Places-365 as a starting point. We combined this scheme with information from the catalog cards that De Boer used. In making these categories, we kept in mind whether there remained a historical and visual consistency in the categories and whether the category would be of use for users of the collection. To gauge the interest of the users, we conducted several interviews.</p> <p>Together with archivists and cultural historians, we studied the images in the training set and settled on a categorization scheme of 159 categories. During annotation, we noticed the difficulties in distinguishing between scenes that were characterized by a particular object and scenes defined by the location or the action performed in the image.</p>
Environmental differences explain subtle yet detectable genetic structure in a widespread pollinator
<p><strong>Background</strong><br>The environment is a strong driver of genetic structure in many natural populations, yet often neglected in population genetic studies. This may be a particular problem in vagile species, where subtle structure cannot be explained by limitations to dispersal. Consequently, these species might falsely be considered quasi-panmictic and hence potentially mismanaged. A species this might apply to, is the buff-tailed bumble bee (<em>Bombus terrestris</em>), an economically important and widespread pollinator, which is considered to be quasi-panmictic at mainland continental scales. Here we aimed to (i) quantify genetic structure in 21+ populations of the buff-tailed bumble bee, sampled throughout two Eastern European countries, and (ii) analyse the degree to which structure is explained by environmental differences, habitat permeability and geographic distance. Using 12 microsatellite loci, we characterised populations of this species with Fst analyses, complemented by discriminant analysis of principal components and Bayesian clustering approaches. We then applied generalized dissimilarity modelling to simultaneously assess the informativeness of geographic distance, habitat permeability and environmental differences among populations in explaining divergence.</p> <p><br><strong>Results</strong><br>Genetic structure of the buff-tailed bumble bee quantified by means of Fst was subtle and not detected by Bayesian clustering. Discriminant analysis of principal components suggested insignificant but still noticeable structure that slightly exceeded estimates obtained through Fst analyses. As expected, geographic distance and habitat permeability were not informative in explaining the spatial pattern of genetic divergence. Yet, environmental variables related to temperature, vegetation and topography were highly informative, explaining between 33 and 39% of the genetic variation observed.</p> <p><br><strong>Conclusions</strong><br>In contrast to previous studies reporting quasi-panmixia in continental populations of this species, we demonstrated the presence of subtle population structure related to environmental heterogeneity. Environmental data proved to be highly useful in unravelling the drivers of genetic structure in this vagile and opportunistic species. We highlight the potential of including these data to obtain a better understanding of population structure and the processes driving it in species considered to be quasi-panmictic.</p>
Does scent attractiveness reveal women's ovulatory timing? Evidence from signal detection analyses and endocrine predictors of odor attractiveness
<p>Odor cues associated with shifts in ovarian hormones indicate ovulatory timing in females of many nonhuman species. Although prior evidence supports women's body odors smelling more attractive on days when conception is possible, that research has left ambiguous how diagnostic of ovulatory timing odor cues are, as well as whether shifts in odor attractiveness are correlated with shifts in ovarian hormones. Here, 46 women each provided 6 overnight scent and corresponding day saliva samples spaced 5 days apart, and completed luteinizing hormone tests to determine ovulatory timing. Scent samples collected near ovulation were rated more attractive, on average, relative to samples from the same women collected on other days. Importantly, however, signal detection analyses showed that rater discrimination of fertile window timing from odor attractiveness ratings was very poor. Within-women shifts in salivary estradiol and progesterone were not significantly associated with within-women shifts in odor attractiveness. Between-women, mean estradiol was positively associated with mean odor attractiveness. Our findings suggest that raters cannot reliably detect women's ovulatory timing from their scent attractiveness. The between-women effect of estradiol raises the possibility that women's scents provide information about overall cycle fecundity, though further research is necessary to rigorously investigate this possibility.</p>
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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.