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9,302 results for “DETECT”
Listening and watching: do camera traps or acoustic sensors more efficiently detect wild chimpanzees in an open habitat?
<p>1. With one million animal species at risk of extinction, there is an urgent need to regularly monitor threatened species. However, in practice this is challenging, especially with wide-ranging, elusive and cryptic species or those that occur at low density.<br> 2. Here we compare two non-invasive methods, passive acoustic monitoring (n=12) and camera trapping (n=53), to detect chimpanzees (Pan troglodytes) in a savanna-woodland mosaic habitat at the Issa Valley, Tanzania. With occupancy modelling we evaluate the efficacy of each method, using the estimated number of sampling days needed to establish chimpanzee absence with 95% probability, as our measure of efficacy.<br> 3. Passive acoustic monitoring was more efficient than camera trapping in detecting wild chimpanzees. Detectability varied over seasons, likely due to social and ecological factors that influence party size and vocalization rate. The acoustic method can infer chimpanzee absence with less than ten days of recordings in the field during the late dry season, the period of highest detectability, which was five times faster than the visual method.<br> 4. Synthesis and applications: Despite some technical limitations, we demonstrate that passive acoustic monitoring is a powerful tool for species monitoring. Its applicability in evaluating presence/absence, especially but not exclusively for loud call species, such as cetaceans, elephants, gibbons or chimpanzees provides a more efficient way of monitoring populations and inform conservation plans to mediate species-loss.</p>
Data from: Steller's jays assess and communicate about predator risk using detection cues and identity
Predators can vary in the risk they pose, depending upon the factors such as body size, maneuverability, hunting strategy, and diet. Prey can also detect predators with different senses, such as seeing, hearing, or smelling them. We presented wild Steller's jays (Cyanocitta stelleri annectens) with visual cues (robotic raptors) or acoustic cues (call playbacks) of 4 different raptors to test how they assess risk and how this influences their alarm calls. The assessment of risk from different predator cues varied with different species of raptors: Jays responded to sharp-shinned hawks (Accipiter striatus) with an increase in latency to resume foraging regardless of whether they were seen or heard, whereas latency responses to northern goshawks (Accipiter gentilis) were longer if they were seen versus if they were heard. Furthermore, Steller's jays altered the acoustic structure of their alarm calls depending on the species of raptor and whether they saw or heard them. These results demonstrate that Steller's jay's assessment of risk involves an interaction between predator identity and predator detection cue and in response, they alter their acoustically-simple alarm calls in surprisingly nuanced ways.
Accounting for imperfect detection in data from museums and herbaria when modeling species distributions: Combining and contrasting data-level versus model-level bias correction
The digitization of museum collections as well as an explosion in citizen science initiatives has resulted in a wealth of data that can be useful for understanding the global distribution of biodiversity, provided that the well-documented biases inherent in unstructured opportunistic data are accounted for. While traditionally used to model imperfect detection using structured data from systematic surveys of wildlife, occupancy models provide a framework for modelling the imperfect collection process that results in digital specimen data. In this study, we explore methods for adapting occupancy models for use with biased opportunistic occurrence data from museum specimens and citizen science platforms using 7 species of Anacardiaceae in Florida as a case study. We explored two methods of incorporating information about collection effort to inform our uncertainty around species presence: (1) filtering the data to exclude collectors unlikely to collect the focal species and (2) incorporating collection covariates (collection type, time of collection, and history of previous detections) into a model of collection probability. We found that the best models incorporated both the background data filtration step as well as collector covariates. Month, method of collection and whether a collector had previously collected the focal species were important predictors of collection probability. Efforts to standardize meta-data associated with data collection will improve efforts for modeling the spatial distribution of a variety of species.
Data and code for: Terrestrial eDNA survey outperforms conventional approach for detecting an invasive pest insect within an agricultural ecosystem
<p>Recent methodological advances permit surveys for terrestrial insects from the direct collection of environmental DNA (eDNA) deposited on vegetation or other surfaces. However, in contrast to well-studied aquatic applications, little is known about how detection rates for such terrestrial eDNA-based surveys compare with conventional survey methods. <i>Lycorma delicatula</i>, the spotted lanternfly, is an emerging invasive insect in eastern North America, and a significant ecological and economic pest of forested and agricultural systems, especially grapes. During fall 2019 we conducted two rounds of paired eDNA and visual surveys for spotted lanternflies within 48 plots at 12 vineyards in New Jersey, USA. We compared detection probabilities within a multimethod occupancy modeling framework and used the results to extrapolate and inform survey design. The probability of detecting spotted lanternflies given presence in a plot was over two times higher for eDNA (84%) versus visual surveys (36%). In mid-September, lanternfly eDNA was detected at five plots in three vineyards while visual surveys revealed only a single individual in one plot. In early October, after dispersal of lanternflies into vineyards, lanternfly eDNA was detected in 12 plots within six vineyards compared with visual detections in six plots in two vineyards. Extrapolations based on detection and local-scale occupancy rates indicate that only five and 12 plots would have been needed to positively detect lanternfly presence with 95% confidence using eDNA in contrast to 14 and 29 plots with visual surveys alone, respective to survey rounds. Log-linear models revealed that visual counts of lanternflies were positively related to eDNA concentrations (R<sup>2</sup> = 71%). We provide some of the first quantitative evidence to support the enhanced sensitivity of terrestrial eDNA approaches compared with conventional methods. Such methods can augment efforts to combat invasive species through improved ability to delimit invasion fronts, identify satellite populations, and confirm local eradications.</p>
The Mountain Habitats Segmentation and Change Detection Dataset
<p>This is the dataset presented in the paper <em>The Mountain Habitats Segmentation and Change Detection Dataset</em> accepted for publication in the <em>IEEE Winter Conference on Applications of Computer Vision </em>(WACV), Waikoloa Beach, HI, USA, January 6-9, 2015. The full-sized images and masks along with the accompanying files and results can be downloaded here. The size of the dataset is about 2.1 GB.</p> <p>The dataset is released under the <em>Creative Commons Attribution-Non Commercial 4.0 International License </em>(http://creativecommons.org/licenses/by-nc/4.0/legalcode).</p> <p>The dataset documentation is hosted on GitHub at the following address: http://github.com/fjean/mhscd-dataset-doc. Direct download links to the latest revision of the documentation are provided below:</p> <ul> <li><strong>PDF format</strong>: http://github.com/fjean/mhscd-dataset-doc/raw/master/mhscd-dataset-doc.pdf</li> <li><strong>Text format</strong>: http://github.com/fjean/mhscd-dataset-doc/raw/master/mhscd-dataset-doc.rst</li> </ul> <p> </p>
A Bayesian Approach to Detect Pedestrian Destination-Sequences from WiFi Signatures: Data (tech. report 2013)
<p>This dataset contains the data used in:</p> <p>Danalet, A., Farooq, B. and Bierlaire, M. (2013). A Bayesian Approach to Detect Pedestrian Destination-Sequences from WiFi Signatures, Technical report, Transport and Mobility Laboratory, ENAC, Ecole Polytechnique Fédérale de Lausanne, Lausanne. URL: http://infoscience.epfl.ch/record/189759 (full text available)</p> <p>It contains data and a technical report describing</p> <ul> <li>WiFi traces</li> <li>Pedestrian Semantically-Enriched Routing Graph (SERG), and</li> <li>Potential Attractivity measure (PAM).</li> </ul>
Detecting social interactions via mobile sensing - Dataset
<p>The goal of Markus' master thesis was to establish whether real-life social interactions could be detected using smartphone internal sensors.</p> <p>The present dataset is a labelled set of sensor data. It contains sensing data of six persons, who each used the sensing application for three days in a row. During these days, the test persons in addition created labels for their real-life sensing data.</p> <p>Participants in the first group in addition worked in the same office, and used the sensing at the same time. Their social interactions therefore overlap.</p>
BRAMS Radio Spectrograms and Spectrogram Samples for Automatic Detection of Meteor Echoes
<p>The files in this dataset are based of radio recordings taped by BRAMS (Belgian RAdio Meteor Stations), the Belgian meteor detection network.</p> <p>Included in the dataset are the original BRAMS radio recordings (stored as .wav audio files), the spectrogram data for each radio recording (stored as .csv files) and the meteor and non-meteor samples extracted from the radio spectrograms (stored as .csv files).</p> <p>It should be noted that the the spectrogram data was sampled using a sliding window of size 30x20 pixels and the samples extracted in this manner were further processed by calculating the vertical average of each column in the 30x20 matrixes. The result of this sampling procedure is a set of data vectors containing the average power of the signal found in the original 30x20 spectrogram sample.</p>
Tapir detection histories (Tapirus bairdii) from Rio Indio, Nicaragua
<p>Camera trap detection histories from June-August 2014.</p>
Dataset for Anomaly Detection Using Inter-Arrival Curves for Real-time Systems
<p>The dataset shows the input files and detailed results for the experiments discussed in the paper. A README file provide more details on the data.</p>
Data from "Spontaneous alpha power lateralization predicts detection performance in an un-cued signal detection task", PlosOne2016
<p>32-channel(+6) data (Biosemi Active two, 10-20, 2048Hz) unfiltered from the 2016 Plos ONE paper "Spontaneous alpha power lateralization predicts detection performance in an un-cued signal detection task"</p>
Max&Lux Photonics Detective - The stolen Cup
<p>The photonics animation video is a small cartoon for children aiming to present photonics and how this technology is used today. It addresses people from all age groups, also grown-ups and grand-parents. The story is about heroine ‘Max’ and her friend ‘Lux’ chasing after the stolen cup!</p>
EIRSAT-1 Test Campaign and Flight Dataset for Anomaly Detection
<p><span>We have curated a unique dataset derived from EIRSAT-1, Ireland's inaugural domestically produced satellite, as a testing and validation resource for these ML models and the future development cycle of AI-enabled small satellites. This dataset consists of a training set developed during ground testing and containing artificial anomalies induced to train satellite operators, a validation dataset containing real anomalies encountered during the qualification campaign, and an early flight test dataset collected since the satellite was launched on December 1<sup>st</sup>, 2023. This paper presents an in-depth analysis of the efficacy of these ML techniques when applied to the EIRSAT-1 dataset, offering insights into their potential to revolutionize the domain of satellite operations through enhanced autonomy and responsiveness. This study not only showcases the capabilities of these ML techniques in an operational environment but also sets the stage for future research and development in autonomous satellite systems.</span></p>
Explainable few-shot learning workflow for detecting invasive and exotic tree species
<p>This is the supporting dataset of research work: <a href="Link"><strong>Explainable few-shot learning workflow for detecting</strong></a> <a href="Link"><strong>invasive and exotic tree species</strong></a>. (Link to be added after the publication) In this research, we presents a workflow that tackles both challenges by proposing an explainable few-shot learning workflow for detecting invasive and exotic tree species in the Atlantic Forest of Brazil using Unmanned Aerial Vehicle (UAV) images. By integrating a Siamese network with explainable AI (XAI), the workflow enables the classification of tree species with minimal labeled data while providing visual, case-based explanations for the predictions.</p> <p>The workflow is accessible in <a href="Link">this GitHub repository</a> (Link to be added after the publication). The required dataset of this workflow in provided in this Zenodo repository.</p> <p>This dataset repository has the following contents</p> <ul> <li> <p>uav_img.zip: the UAV orthomosaic image (.tif) of the study area used in this research, with related metadata</p> </li> <li>tree_labels.zip: the labels of trees created by expert, available in .shp and .gpkg</li> <li> <p>cutouts.zip: tree cutouts used in this study. They are two sub-directories:</p> <ul> <li>all_cutouts: all the candidated cutouts from three sources. See the README.md file insisde this folder for more information</li> <li>selected cutout: the manually selected cutouts from all cutouts used for training.</li> </ul> </li> <li> <p>training_pairs_20000.zarr.zip: training data created for base network traning. It is created by pairing the selected cutouts.</p> </li> <li>netflora.zip: Netflora workflow prediction results</li> <li>optimized_models.zip: Optimized base models (shallow and deep) and refined models with different shots/fold setup.</li> <li>n_fold_x_validation.zip: data pairs for refinement traing, with n fold and x valiation setup.</li> </ul>
Datasets associated with the manuscript "Differential detection workflows for multi-sample single-cell RNA-seq data"
<p>In this Zenodo repository, we share the data that is required to reproduce all the analyses from our publication "Differential detection workflows for multi-sample single-cell RNA-seq data".</p> <p>This repository includes all* input data, intermediate results and final outputs that are represented in our manuscript. For a more elaborate description of the data, we refer to the companion GitHub. https://github.com/statOmics/DD_benchmarks for the benchmarks and https://github.com/statOmics/DD_cases for the case studies, respectively.</p>
ComplexVAD Video Anomaly Detection Dataset
<p><strong>Introduction</strong></p> <p>The ComplexVAD dataset consists of 104 training and 113 testing video sequences taken from a static camera looking at a scene of a two-lane street with sidewalks on either side of the street and another sidewalk going across the street at a crosswalk. The videos were collected over a period of a few months on the campus of the University of South Florida using a camcorder with 1920 x 1080 pixel resolution. Videos were collected at various times during the day and on each day of the week. Videos vary in duration with most being about 12 minutes long. The total duration of all training and testing videos is a little over 34 hours. The scene includes cars, buses and golf carts driving in two directions on the street, pedestrians walking and jogging on the sidewalks and crossing the street, people on scooters, skateboards and bicycles on the street and sidewalks, and cars moving in the parking lot in the background. Branches of a tree also move at the top of many frames.</p> <p>The 113 testing videos have a total of 118 anomalous events consisting of 40 different anomaly types.</p> <p>Ground truth annotations are provided for each testing video in the form of bounding boxes around each anomalous event in each frame. Each bounding box is also labeled with a track number, meaning each anomalous event is labeled as a track of bounding boxes. A single frame can have more than one anomaly labeled.</p> <p><strong>At a Glance</strong></p> <ul> <li>The size of the unzipped dataset is ~39GB</li> <li>The dataset consists of Train sequences (containing only videos with normal activity), Test sequences (containing some anomalous activity), a ground truth annotation file for each Test sequence, and a README.md file describing the data organization and ground truth annotation format.</li> <li>The zip files contain a Train directory, a Test directory, an annotations directory, and a README.md file.</li> </ul> <p><strong>License</strong></p> <p>The ComplexVAD dataset is released under <a href="https://creativecommons.org/licenses/by-sa/4.0/">CC-BY-SA-4.0 license</a>.</p> <p>All data:</p> <pre><code>Created by Mitsubishi Electric Research Laboratories (MERL), 2024 SPDX-License-Identifier: CC-BY-SA-4.0</code></pre>
Photophysical lock-in detection enables background-free upconversion emission imaging
<p><strong><span lang="EN-GB">This folder contains all raw data underlying the results presented in a manuscript, submitted to <em>Nano Letters</em>, and entitled:</span></strong></p> <p><strong><span lang="EN-GB">Photophysical lock-in detection enables background-free upconversion emission imaging</span></strong></p> <p><strong><span lang="EN-GB">Authored by:</span></strong></p> <p><span lang="EN-GB">Niusha Bagheri<sup>a</sup>, Chenyi Wang<sup>b</sup>, Du Guo<sup>a</sup>, Anbharasi Lakshmanan<sup>a</sup>, Qi Zhu<sup>a</sup>, Xu Chen<sup>a</sup>, Nahid Ghazyani<sup>c</sup>, Qiuqiang Zhan<sup>b</sup>, Georgios A. Sotiriou<sup>d</sup>, Haichun Liu*<sup>a</sup>, Jerker Widengren*<sup>a</sup> </span></p> <p><em><sup><span lang="EN-GB">a</span></sup></em><em><span lang="EN-GB"> Experimental Biomolecular Physics, Department of Applied Physics, KTH Royal Institute of Technology, SE-106 91, Stockholm, Sweden</span></em></p> <p><em><sup><span lang="EN-GB">b</span></sup></em><em><span lang="EN-GB"> Centre for Optical and Electromagnetic Research, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, P. R. China</span></em></p> <p><em><sup><span lang="EN-GB">c</span></sup></em><em><span lang="EN-GB"> Faculty of Physics, Kharazmi University, Tehran, Iran.</span></em></p> <p><em><sup><span lang="EN-GB">d</span></sup></em><em><span lang="EN-GB"> Department of Microbiology Tumor and Cell Biology Karolinska Institute, SE-171 77, Stockholm, Sweden</span></em></p> <p><span lang="EN-GB">Corresponding authors:</span></p> <p><span lang="EN-GB">*haichun@kth.se, jwideng@kth.se</span></p> <p><strong><span lang="EN-GB">The data files containing raw data and results of the analysis are grouped according to the different figures in the manuscript where the extracted results are presented.</span></strong></p> <p><strong><span lang="EN-GB">ABSTRACT</span></strong></p> <p><span><span lang="EN-GB">Lanthanide-based upconversion nanoparticles (UCNPs) have attracted considerable attention in biomedical applications, due to their anti-Stokes shifted emission enabling autofluorescence-free signal detection. However, residual excitation light can still interfere with their relatively low brightness. While commonly used lock-in detection can distinguish weak signals from substantial random background, concurrently modulated residual excitation light is not eliminated. This remains a challenge, particularly under demanding experimental conditions.</span></span></p> <p><span><span lang="EN-GB">Here, we </span></span><span><span><span lang="EN-GB">pro</span></span></span><span><span><span lang="EN-GB">pose a photophysical lock-in detection (PP-LID) approach based on the discovery that UCNPs can act as frequency mixers in response to intensity-modulated excitation. Particularly, modulated excitation with more than one base modulation frequency can generate additional low-frequency beating-signals. These signals are resolvable by frame-rate-limited cameras, devoid of ambient and residual excitation light, and can be regulated through nanoparticle engineering. Extracting beating-signals by PP-LID thus provides a strategy to significantly enhance signal-to-background conditions in UCNP-based bioimaging and biosensing.</span></span></span></p> <p><strong><span lang="EN-GB">Keywords: </span></strong><span lang="EN-GB">Upconversion nanoparticles (UCNPs), nonlinearity, modulation, lock-in detection, second harmonic, beating frequency, fast Fourier Transform (FFT)</span></p>
Wind Turbine SCADA Data For Early Fault Detection
<p>This dataset is published together with the <a href="https://doi.org/10.3390/data9120138">paper</a> "CARE to Compare: A real-world dataset for anomaly detection in wind turbine data" which explains the dataset in detail and defines the CARE score that can be used to evaluate anomaly detection algorithms on this dataset. When referring to this dataset, please cite the paper mentioned in the related work section. </p> <p>The data consists of 95 datasets, containing 89 years of SCADA time series distributed across 36 different wind turbines<br>from the three wind farms A, B and C. The number of features depends on the wind farm; Wind farm A has 86 features, wind farm B has 257 features and wind farm C has 957 features. </p> <p>The overall dataset is balanced, as 45 out the 95 datasets contain a labeled anomaly event that leads up to a turbine fault and the other 50 datasets represent normal behavior. Additionally, the quality of training data is ensured by turbine-status-based labels for each data point and further information about some of the given turbine faults are included.</p> <p>The data for Wind farm A is based on data from the EDP open data platform (https://www.edp.com/en/innovation/open-data/data), <br>and consists of 5 wind turbines of an onshore wind farm in Portugal. <br>It contains SCADA data and information derived by a given fault logbook which defines start timestamps for specified faults. <br>From this data 22 datasets were selected to be included in this data collection. <br>The other two wind farms are offshore wind farms located in Germany. All three datasets were anonymized due to confidentiality reasons for the wind farms B and C.<br>Each dataset is provided in form of a csv-file with columns defining the features and rows representing the data points of the time series. Files</p> <p>More detailed information can be found in the included README-file.</p> <p><strong>Notes</strong></p> <p>In wind farm A status_type_id labels can be ignored while evaluating prediction time frames of error events with metrics like the CARE-score since the status_type_id is of wind farm A is based on the EDP failure logbook and it is intended to be used for filtering of the training data.</p> <p><strong>Version Changes:</strong></p> <p><em>Version 5 -> 6:</em></p> <ul> <li>Changed unit of sensor_40 and sensor_61 for wind farm C to hPa instead of bar. This unit error became obvious when looking at the data and comparing it to the standard air pressure.</li> <li>Edited event_description of events 34, 7 and 19 to high temperature in transformer cell.</li> <li>Changed date in event description of event 44 since it was not affected by the change in the date anonymization procedure from version 2.</li> <li>Changed date in event description of event 47 since it was not affected by the change in the date anonymization procedure from version 2 and edited the description text</li> <li> Changed date format in event_info files to match the date format in the dataset files.</li> <li>Fixed typo in Readme</li> <li>Re-added Readme files</li> </ul> <p><em>Version</em> 4->5:</p> <p>Corrections to labels were made:</p> <ul> <li>Previously missing status_type_id 4 labels were added to datasets in Wind Farm A. </li> <li>Event 51 from Wind Farm A was wrongly labeled as a normal event. With the newly added status_type_id 4 occurences, it is to be considered an anomaly event due to a gearbox bearing damage within the prediction data.</li> <li>Wind Farm A no longer contains status_type_id 5. All occurences of status_type_id 5 have been changed to 0 and are considered normal time stamps. This change is done, because status_type_id 5 was set as a result of a wind speed and power analysis, flagging potential anomalous data. This is not based on a fixed ground truth, so status_type_id 5 was removed. For Wind Farms B and C status_type_id 5 is still valid since it is based on real SCADA-status codes.</li> <li>The event_info.csv files now contain an additional column 'asset_id'.</li> </ul> <p><em>Version 3->4:<br></em></p> <ul> <li>The change of the timestamp anonymization lead to duplicate timestamps when transitioning from a leap year to 2022. This is now fixed in Version 4.</li> </ul> <p><em>Version 2->3:</em></p> <ul> <li>In version 2 timestamp changes were not consistent with the timestamps in the event-info-files. Version 3 fixes this.</li> </ul> <p><em>Version 1->2:<br></em></p> <ul> <li>Version 2 contains one deviation from version 1 regarding the anonymization procedure. Instead of shifting the timestamps of each sub-dataset by a random number of years, the size of the time shift is now determined to be the number of years so that each sub-dataset starts in 2022. This change is made to make the timestamp anonymization more consistent and to avoid future timestamps being present within the data.</li> </ul>
Raga Ornamentation Detection (ROD)
<p>The Raga Ornamentation Detection (ROD) dataset is composed of 212 audio files of varying lengths recorded by two expert musicians. Singer 1 performed 108 recordings, while singer 2 produced 104. In total, the data set contains 4.08 hours of audio. In addition to the audio files, the dataset also includes accompaniments like drone Tanpura and percussion (Tabla). The audio files are labelled in the strong labelling fashion.</p>
Plastic Litter Project 2022-2023 - Minimum Detection Fraction Dataset
<p>Sentinel-2 and PlanetScope SuperDove data, with corresponding UAS RGB reference images (where available) used in </p>
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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.