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76 results for “localization and detection”
Detecting local variations across metazoan communities in backreef depressions of Reunion Island (Mascarene Archipelago) through environmental DNA survey
<p>The back-reef depressions, or lagoons, of Reunion Island (western Indian Ocean) host a high abundance of organisms living amongst the coral reefs and are critical sites for artisanal fishing, tourism, and shoreline stability for the island. Over time, increasing degradation of Reunionese reefs has been observed due to overexploitation, beach erosion and eutrophication. Efforts to mitigate the impact of these pressures on aquatic organisms include biodiversity surveys primarily performed through visual censuses that can be logistically complex and may unintentionally overlook organisms. Surveys integrating environmental DNA (eDNA) collections have provided rapid biodiversity assessments, while helping to circumvent some limitations of visual surveys. The present study describes the results of an exploratory eDNA survey, which aims to characterize metazoan communities of four Reunionese lagoons located along the west coast of the island. As eDNA surveys first require deliberate study design and optimization for each new context, we sought to establish a modernized workflow implementing specialized equipment to collect and preserve samples to facilitate future studies in these lagoons. During the austral summer of 2023, samples were pumped directly from surface and bottom depths at each site through self-preserving filters which were then processed for DNA metabarcoding using regions of the 12S ribosomal RNA (12S), small ribosomal subunit 18S (18S) and Cytochrome Oxidase I (COI) genes. The survey detected high species richness that varied by site, and in a single collection period, recovered the presence of 60 teleost families and numerous invertebrate taxa, including members of the coral faunal community that are less studied in Reunion. Distinct biological communities were observed at each site, and within a single lagoon, suggesting that these differences are due to site-specific factors (e.g., environmental variables, geographic distance, etc.). Although continued protocol optimization is needed, the present findings demonstrate the successful application of an eDNA-based survey for biodiversity assessment within Reunionese lagoons.</p>
A subset of the EMARS dataset in MY24 and MY26 converted from the sigma-p hybrid coordinate to the pressure coordinate and a list of local dust storms detected during the MYs in western Arcadia Planitia
<p>This dataset includes a subset of EMARS' background mean data (Greybush et al., 2019) converted from the sigma-p hybrid coordinate to the pressure coordinate. Only MY24 and MY26 were used to generate the figures shown in Ogohara (submitted to JGR Planets). <br>Updates from the original EMARS are:</p> <ul> <li>The vertical coordinate has been converted from the sigma-p hybrid coordinate to the pressure coordinate.</li> <li>The variables expressing the Earth date (e.g., year, month, day, etc.) have been combined into one variable, earth_date.</li> <li>A new variable, emars_date, has been created from emars_sol and mars_hour.</li> </ul> <p>In addition, this dataset provides two lists of local dust storms events during MY24 and MY26 which were detected in western Arcadia Planitia using a deep learning-based method proposed by Ogohara and Gichu (2022). The lists are:</p> <ul> <li>[Data Set S1] List of global image swath files examined. Only file names of MGS/MOC red band images are listed. The list consists of 5 columns indicating image ID, observation date, orbit number, solar longitude, and filter name (RED).</li> <li>[Data Set S2] List of global image swath files containing identified dust storms, as well as some attributes of the detected dust storms. Only file names of red band images are listed. The list consists of 7 columns indicating image ID, observation date, orbit number, solar longitude, center longitude and latitude, and area (km2.)</li> </ul>
Simulated Detection and Localization in LIGO's HL-Configuration for O4a Run, and LIGO/Virgo/KAGRA's HLVK-Configuration for O4b and O5 Runs (November 2023 edition)
<p>We have conducted a simulation of the <strong>HL</strong>-configuration deployed during the ongoing <strong>O4a</strong> run and the <strong>HLVK-</strong>configuration planned for <strong>O4b </strong>and O5, as outlined in the observation scenarios, which can also be accessed at the following <a href=" https://doi.org/10.5281/zenodo.7026209"> https://doi.org/10.5281/zenodo.7026209. </a></p>
Auxiliary data release for "Fast marginalization algorithm for optimizing gravitational wave detection, parameter estimation and sky localization"
<p>This release contains parameter estimation runs on synthetic injections, described in https://arxiv.org/abs/2404.02435 .</p> <p>Important note: The posterior samples provided are weighted, the weights are stored in a column named 'weights' . </p>
Fig. 1 in Thorius narismagnus (Amphibia: Plethodontidae): rediscovery at the type locality and detection of a new population
Fig. 1. Locations of historical collection localities and the new localities of Thorius narismagnus in Los Tuxtlas region, Mexico.
Simulation data and code used for the publication in Magn. Reson. "Time-domain proton-detected local-field NMR for molecular structure determination in complex lipid membranes"
<p>Simulation data used in the publication Magn. Reson. "Time-domain proton-detected local-field NMR for molecular structure determination in complex lipid membranes". The simulation data set, and the code developed to generate such data, are included. Details in the published paper </p>
Research data supporting: "Detecting dynamic domains and local fluctuations in complex molecular systems via timelapse neighbors shuffling"
<p>This repository contains the set of data shown in the paper "Detecting dynamic domains and local fluctuations in complex molecular systems via timelapse neighbors shuffling" published on PNAS (DOI: 10.1073/pnas.2300565120).</p>
The cell adhesion molecule Sdk1 shapes assembly of a retinal circuit that detects localized edges
<p>Nearly 50 different mouse retinal ganglion cell (RGC) types sample the visual scene for distinct features. RGC feature selectivity arises from its synapses with a specific subset of amacrine (AC) and bipolar cell (BC) types, but how RGC dendrites arborize and collect input from these specific subsets remains poorly understood. Here we examine the hypothesis that RGCs employ molecular recognition systems to meet this challenge. By combining calcium imaging and type-specific histological stains we define a family of circuits that express the recognition molecule Sidekick 1 (Sdk1) which include a novel RGC type (S1-RGC) that responds to local edges. Genetic and physiological studies revealed that Sdk1 loss selectively disrupts S1-RGC visual responses which result from a loss of excitatory and inhibitory inputs and selective dendritic deficits on this neuron. We conclude that Sdk1 shapes dendrite growth and wiring to help S1-RGCs become feature selective.</p>
Intersection Monitoring: A dataset for vehicle detection using an infrastructure camera including ground truth vehicle localization
<p><strong>Intersection Monitoring: A dataset for vehicle detection using an infrastructure camera including ground truth vehicle localization.</strong></p> <p>This dataset contains images from a infrastructure camera monitoring an intersection and the ground-truth information for a vehicle crossing it from different directions. The data is aimed to develop and improve image-based vehicle detection algorithms.</p> <p>The dataset includes two different recordings with the same structure. For each of them, the sequence of images is provided in PNG format, along with the ground truth of the vehicle. The ground truth data was captured using a high-precision GNSS receiver installed on the test vehicle, fused with in-vehicle sensors using an Extended Kalman Filter (EKF) .</p> <p><strong>Time considerations</strong></p> <p>The camera and GNSS receiver clocks were synchronized before each test to have the same time base.</p> <p>Each test last about 140 seconds.</p> <p><strong>Image files</strong></p> <p>Image files can be found on the <strong>img/</strong> folder within for each test. The camera was configured to record images at 25 fps:</p> <ul> <li>Test 1: 3704 files</li> <li>Test 2: 3426 files</li> </ul> <p><strong>Vehicle localization ground truth</strong></p> <p>The test vehicle is a prototype of Autonomous Vehicle developed by the <a href="https://autopia.car.upm-csic.es">AUTOPIA</a> research group at the <a href="https://car.upm-csic.es">Centre for Automation and Robotics</a> in Spain.</p> <p>Vehicle location mainly depends on a Trimble BX982 GNSS receiver using RTK inputs from a local station. However, the location algorithm applies an EKF for combining GNSS measurements with different onboard sensors providing yaw rate, longitudinal acceleration and speed, steering wheel position and speed, etc.</p> <p>For each test, the <strong>vehicle.csv</strong> file contains the vehicle information recorded at 20Hz. This file includes:</p> <ul> <li>UTM Time: Time using the format HHMMSSss: <ul> <li>HH: Hours</li> <li>MM: Minutes</li> <li>SS: Seconds</li> <li>ss: Fraction of seconds</li> </ul> </li> <li>UTM East: East coordinate of the GNSS antenna in the UTM frame, in meters.</li> <li>UTMNorth: North coordinate of the GNSS antenna in the UTM frame, in meters.</li> <li>Orientation: Yaw angle of the vehicle, measured from the East axis (x-axis).</li> <li>Speed: Vehicle speed in m/s</li> <li>Acceleration: Vehicle acceleration in m/s^2</li> </ul> <p><strong>Camera info</strong></p> <p>The file <strong>camera_parameters.json</strong> includes all the information about the intrinsic and extrinsic parameters for the camera. The configuration stored on this file appplies to all tests.</p> <p>The camera used is an AXIS M1125 with variable focal length. It was installed in a communication tower near the intersection.</p> <p><strong>Vehicle info</strong></p> <p>The file <strong>vehicle_parameters.json</strong> includes information about vehicle dimensions and antenna location. The GNSS antenna is installed near the rear axle of the vehicle, in the middle part of the vehicle. The configuration stored on this file appplies to all tests.</p> <p><strong>Tools</strong></p> <p>Some MATLAB tools can be found in <a href="https://github.com/autopia-car/datasets-tools-intersection-monitoring">https://github.com/autopia-car/datasets-tools-intersection-monitoring</a></p> <p><strong>Disclaimer</strong></p> <p>That the dataset comes "AS IS", without express or implied warranty and/or any liability exceeding mandatory statutory obligations. This especially applies to any obligations of care or indemnification in connection with the dataset. The dataset was created for our research purposes only and no quality assessment was done for the usage in products of any kind. We can therefore not guarantee for the correctness, completeness or reliability of the provided data set.</p>
Validation of FACBC for Detection of Metastasis Among High-risk Prostate Cancer Patients With Presumed Localized Disease
ClinicalTrials.gov study NCT03081884. IPD Sharing: NO. Countries: 1. Publications: 1.
GE-148-002: A Phase 2, Open-label, Single-Center Study to Assess GE-148 (18F) Injection PET Imaging to Detect Localized Prostate Cancer
ClinicalTrials.gov study NCT01176513. IPD Sharing: Not stated. Countries: 1. Publications: 3.
The cell adhesion molecule Sdk1 shapes assembly of a retinal circuit that detects localized edges
Open the record for dataset details and reuse information.
Data from: Reduced-representation sequencing detects trans-Arctic connectivity and local adaptation in polar cod (Boreogadus saida)
Open the record for dataset details and reuse information.
Data from: Outlier loci detect intraspecific biodiversity amongst spring and autumn spawning herring across local scales
Herring, Clupea harengus, is one of the ecologically and commercially most important species in European northern seas, where two distinct ecotypes have been described based on spawning time; spring and autumn. To date, it is unknown if these spring and autumn spawning herring constitute genetically distinct units. We assessed levels of genetic divergence between spring and autumn spawning herring in the Baltic Sea using two types of DNA markers, microsatellites and Single Nucleotide Polymorphisms, and compared the results with data for autumn spawning North Sea herring. Temporally replicated analyses reveal clear genetic differences between ecotypes and hence support reproductive isolation. Loci showing non-neutral behaviour, so-called outlier loci, show convergence between autumn spawning herring from demographically disjoint populations, potentially reflecting selective processes associated with autumn spawning ecotypes. The abundance and exploitation of the two ecotypes have varied strongly over space and time in the Baltic Sea, where autumn spawners have faced strong depression for decades. The results therefore have practical implications by highlighting the need for specific management of these co-occurring ecotypes to meet requirements for sustainable exploitation and ensure optimal livelihood for coastal communities.
Data from: Effects of ambient noise on detectability and localization of avian songs and tones by observers in grasslands
Probability of detection and accuracy of distance estimates in aural avian surveys may be affected by the presence of anthropogenic noise, and this may lead to inaccurate evaluations of the effects of noisy infrastructure on wildlife. We used arrays of speakers broadcasting recordings of grassland bird songs and pure tones to assess the probability of detection, and localization accuracy, by observers at sites with and without noisy oil and gas infrastructure in south-central Alberta from 2012 to 2014. Probability of detection varied with species and with speaker distance from transect line, but there were few effects of noisy infrastructure. Accuracy of distance estimates for songs and tones decreased as distance to observer increased, and distance estimation error was higher for tones at sites with infrastructure noise. Our results suggest that quiet to moderately loud anthropogenic noise may not mask detection of bird songs; however, errors in distance estimates during aural surveys may lead to inaccurate estimates of avian densities calculated using distance sampling. We recommend caution when applying distance sampling if most birds are unseen, and where ambient noise varies among treatments.
FIGURE 1. Newly detected localities for G in Gomphus kinzelbachi Schneider, 1984, in Iran: identification, habitat and behaviour (Odonata: Gomphidae)
FIGURE 1. Newly detected localities for G. kinzelbachi: (1: 32,401236°N, 48,149589°E, 107 m; 2: 32,286061°N, 48,172997°E, 86 m; 3: 32,281944°N, 48,217228°E, 82 m; 4: 32,195917°N, 48,209356 °E, 74 m), and the two previously reported localities in Iran, visited by us again without finding the species (filled dots, A = Bisheh, Ab-i-Zezar river near the 433 km railway stone; 50 km SE Khoramabad; B = Ahwåz road 4 km S Ahwaz).
Datasets for "An Evaluation of Bayesian Approaches based on Uniformly Most Powerful Tests for Detecting Violations of Local Independence"
<p>These files contain the results of the simulation studies reported in the main text. A detailed documentation is included as pdf file.</p>
Supplementary material 1 from: Erdélyi A, Hartdégen J, Malatinszky Á, Vadász C (2023) Historical reconstruction of the invasions of four non-native tree species at local scale: a detective work on Ailanthus altissima, Celtis occidentalis, Prunus serotina and Acer negundo. One Ecosystem 8: e108683. https://doi.org/10.3897/oneeco.8.e108683
Derived data of Ailanthus altissima, Celtis occidentalis, Prunus serotina and Acer negundo from the National Forestry Database and its archives.
Splicing Detection and Localization for Speech Deepfakes using Audio Novelty
<p>With the rapid progress of artificial intelligence over the last few years, the possibility of generating highly realistic multimedia content is within everyone's reach. This has paved the way for the creation of deepfakes, synthetic data produced using deep learning techniques that realistically represent people in behaviors not belonging to them. <br>In the audio case, speech deepfakes can be used alone or combined with splicing techniques.<br>This consists of replacing portions of authentic speech with synthetic elements, thereby altering the conveyed message and leading to significant threats.<br>In this work, we address the problem of splicing detection and localization in speech deepfakes.<br>We consider spliced audio tracks created by substituting parts of a pristine speech with synthetically generated segments. We design a system able to detect whether a manipulation takes place and localize it in time.<br>The proposed method, trained exclusively on splicing-free audio tracks, extracts embeddings from the input signal through a sliding window. Then, it employs audio novelty to measure the similarity among consecutive signal sections and uses it to detect and localize splicing points.<br>We evaluate our method on a state-of-the-art dataset as well as a novel, bias-free corpus specifically developed and released in this paper. <br>The proposed approach is benchmarked against multiple baselines for both splicing detection and localization tasks.</p>
Sound event localization and detection (SELDnet) results
<p>This package is part of the work - <a href="https://github.com/sharathadavanne/seld-metric">Joint Measurement of Localization and Detection of Sound Events</a> presented in WASPAA 2019.</p> <p>This package consists of results from the <a href="https://arxiv.org/abs/1905.08546">SELDnet method</a> for joint sound event localization and detection. The results corresponding to different training states of 5, 25 and 75 epochs are provided here. These results are for the four cross-validation splits of the <strong>TAU Spatial Sound Events 2019 - Microphone Array </strong>dataset. The sound events in this dataset consist of stationary point sources from multiple sound classes each associated with a temporal onset and offset time, and DOA coordinate represented using azimuth and elevation angle. This <strong>TAU Spatial Sound Events 2019 - Microphone Array </strong>dataset is part of the <a href="https://github.com/sharathadavanne/seld-dcase2019">DCASE 2019 Sound Event Localization and Detection Task</a> and can be downloaded <a href="https://zenodo.org/record/2599196#.XT_RmHUzaCg">here</a>.</p> <p>Each of the results folders consists of 400 files, corresponding to the results of the individual recordings of the <strong>TAU Spatial Sound Events 2019 - Microphone Array </strong>dataset.</p> <p>This data collection received funding from the European Research Council, grant agreement 637422 EVERYSOUND.</p> <p><strong>Download instructions</strong></p> <p>The three files, <strong><em>mic_dev_5</em></strong>,<strong><em> mic_dev_25, </em></strong>and <strong><em>mic_dev_75</em></strong>, correspond to SELDnet results at 5, 25 and 75 epochs.</p> <p>Download the zip files and use your favorite compression tool to unzip these split zip files.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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.