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9,302 results for “DETECT”
Investigating cooccurrence patterns and dynamics for many imperfectly detected species, using a log-linear modelling parameterisation
<p>1. Patterns in, and the underlying dynamics of, species cooccurrence is of interest in many ecological applications. Unaccounted for, imperfect detection of the species can lead to misleading inferences about the nature and magnitude of any interaction. A range of different parameterisations have been published that could be used with the same fundamental modelling framework that accounts for imperfect detection, although each parameterisation has different advantages and disadvantages.</p> <p>2. We propose a parameterisation based on log-linear modelling that does not require a species hierarchy to be defined (in terms of dominance), and enables a numerically robust approach for estimating covariate effects.</p> <p>3. Conceptually the parameterisation is equivalent to using the presence of species in the current, or a previous, time period as predictor variables for the current occurrence of other species. This leads to natural, 'symmetric', interpretations of parameter estimates.</p> <p>4. The parameterisation can be applied to many species, in either a maximum-likelihood or Bayesian estimation framework. We illustrate the method using camera trapping data collected on three mesocarnivore species in South Texas.</p>
Towards a reproducible interactome: semantic-based detection of redundancies to unify protein-protein interaction databases
<p>Protein-protein interactions (PPIs) play an ubiquitous and fundamental role in all biological processes. Information on PPIs described in the literature is annotated and made available by several protein-interaction databases. Because most databases have their own curation rules and priorities, they often annotate overlapping sets of publications, which leads to redundancies. We developed a semantic-based approach which enables to accurately detect redundancies within PPI datasets from multiple databases. We applied this approach to assemble a "reproducible interactome", with PPIs supported by at least two methods or publications.</p>
Supplementary material for the paper "Detection and Characterization of Multiple Discontinuities in Cables with Time Domain Reflectometry and Convolutional Neural Networks"
<p>This archive contains supplementary material for the paper "Detection and Characterization of Multiple Discontinuities in Cables with Time Domain Reflectometry and Convolutional Neural Networks".</p> <p>In particular, the following data is provided:</p> <ul> <li>dataset of labeled simulated TDR signals (1 million samples)</li> <li>dataset of labeled experimental signals (16 samples)</li> <li>trained convolutional neural network model</li> </ul>
Supplementary Information for "UV-Spectroscopic Detection of (Pyro-)Phosphate with the PUB module"
<p>This is the external Supplementary Information for our publication "UV-Spectroscopic Detection of (Pyro-)Phosphate with the PUB module".</p> <p>The .pdf file contains the Supplementary Information: author contributions, accessibility statement, experimental procedures, additional discussions and supplementary items, among others.</p> <p>The .zip file contains the raw data and metadata for all items (supplementary and main text) as well as the calculation results.</p> <p>To some extent, this work builds on and borrows from our previous publications on spectral unmixing (https://doi.org/10.3390/mps2030060, https://doi.org/10.1002/cbic.202000204) and thermodynamic reaction control (https://doi.org/10.1002/adsc.201901230, https://doi.org/10.1002/cphc.202000901, https://doi.org/10.1021/acscatal.1c02589).</p>
evaluation data for "Capturing the songs of mice with an improved detection and classification method for ultrasonic vocalizations (BootSnap)"
<p>Data contains sound files of mouse vocalization needed to reproduce the evaluation results for "Capturing the songs of mice with an improved detection and classification method for ultrasonic vocalizations (BootSnap)"</p> <p>If you use any of this data, cite the original source: <a href="https://doi.org/10.1016/j.anbehav.2020.09.006">https://doi.org/10.1016/j.anbehav.2020.09.006</a></p>
Data for PAN at SemEval 2019 Task 4: Hyperpartisan News Detection
<p>Training, validation, and test data for the <a href="https://webis.de/events/semeval-19/">PAN @ SemEval 2019 Task 4: Hyperpartisan News Detection</a>.</p> <p>See the README for details.</p>
Emergence and function of cortical offset responses in sound termination detection
<p>Offset responses in auditory processing appear after a sound terminates. They arise in neuronal circuits within the peripheral auditory system, but their role in the central auditory system remains unknown. Here we ask what the behavioral relevance of cortical offset responses is and what circuit mechanisms drive them. At the perceptual level, our results reveal that experimentally minimizing auditory cortical offset responses decreases the mouse performance to detect sound termination, assigning a behavioral role to offset responses. By combining <i>in vivo</i> electrophysiology in the auditory cortex and thalamus of awake mice, we also demonstrate that cortical offset responses are not only inherited from the periphery but also amplified and generated <i>de novo</i>. Finally, we show that offset responses code more than silence, including relevant changes in sound trajectories. Together, our results reveal the importance of cortical offset responses in encoding sound termination and detecting changes within temporally discontinuous sounds crucial for speech and vocalization.</p>
Mask Detection Application Performance Models
<p>The repository includes the source code and the datasets used to build performance models for OSCAR Mask Detection application. The results are included in the AI-SPRINT project deliverable "D2.1 - First release and evaluation of the AI-SPRINT design tools".</p>
ABR raw data and results from automated hearing threshold detection
<p><strong>This repository contains: </strong></p> <ol> <li><strong>data.tar.gz</strong>: raw data from Auditory Brainstem Response (ABR) measurements performed at over 4,000 mice at the German Mouse Clinic. It also contains suitably pre-processed ABR raw data of over 8,000 mice from another public data repository (10.5061/DRYAD.CV803RV) of the Wellcome Sanger Institute. All data is intended to be used by and compatible with code published on GitHub (https://github.com/ExperimentalGenetics/ABR_thresholder). <br> </li> <li><strong>results.tar.gz</strong>: results and visualisations - comparison of two new, independent automatic hearing threshold finding methods and comparison with the manual gold standard method.</li> </ol>
Time-Resolved Photoionization Detection of a Single Er3+ Ion in Silicon
<p>The data used for generating the plots in the manuscript and supporting information.</p>
PlastOPol: A Dataset for Litter Detection
<p>PlastOPol dataset aiming of giving to the computer science and environmental science communities a new set of images with the presence of litter in several types of environments. We hope that PlastOPol serves as a basis for the proposal of automatic detection methods which can support the furthering Sensors 2022, 1, 0 6 of 20 of research on litter in the environment. The images were collected by the Marine Debris Tracker available under an open access Creative Commons Attribution license. Building the dataset involved the meticulous task of labeling each litter instance in each image. PlastOPol is a one-class labeled dataset, where all the data corresponds to the “litter” class as its super-category. This dataset has 2418 images collected by the Marine Debris Tracker with a total of 5300 instances of litter. Each instance is wrapped within a rectangular bounding box represented by four values (x1, y1, width, and height), where (x1, y1) corresponds to the upper left corner of the bounding box.</p> <p>If you use this dataset, please cite our paper:</p> <p>@Article{Cordova2022Sensors,<br> AUTHOR = {Córdova, Manuel and Pinto, Allan and Hellevik, Christina Carrozzo and Alaliyat, Saleh Abdel-Afou and Hameed, Ibrahim A. and Pedrini, Helio and Torres, Ricardo da S.},<br> TITLE = {Litter Detection with Deep Learning: A Comparative Study},<br> JOURNAL = {Sensors},<br> VOLUME = {22},<br> YEAR = {2022},<br> NUMBER = {2},<br> ARTICLE-NUMBER = {548},<br> URL = {https://www.mdpi.com/1424-8220/22/2/548},<br> ISSN = {1424-8220},<br> DOI = {10.3390/s22020548}<br> }</p> <p> </p>
Detection of neurofilament light chain with label-free electrolyte-gated organic field-effect transistors
<p>The uploaded data include</p> <p>i) transfer curves registered at every NF-L concentration;</p> <p>ii) transfer curves for control experiments;</p> <p>iii) raw AFM data acquired after electrode incubation in 0 M, 1 pM, 100 pM and 10 nM [NF-L].</p> <p>Electrical measurements were acquired in 50 mM PBS, pH 7.4 under static conditions.</p> <p>Anti-NF-L antibodies were immobilized on the gate electrode through cys-Protein G. The gate surface has been subsequently passivated through 11-mercaptoundecyl-triethylene glycol SAM.</p>
Dataset for: An experimental comparison of anomaly detection methods for collaborative robot manipulators
<p>The dataset contains data recordings from a UR5e robot during normal and anomalous operation and is recorded to support the authors Master thesis project and the associated Paper: <em>"An Experimental Comparison of Anomaly Detection Methods for Collaborative Robot Manipulators" </em>(inProceeding).</p> <p>An in-depth description of the dataset can be found in the pdf uploaded with the dataset and an example of a data loader is also provided.</p>
Dataset used by the paper MADE: Learning to Detect and Explain Chaos in Microservice Architectures
<p>Dataset used by the paper MADE: Learning to Detect and Explain Chaos in Microservice Architectures</p> <p>Include the raw dataset of 10 chaos</p>
A Twitter dataset (with labels) for Life-Event detection
<p>The file contains an anonymized version of the dataset collected in the framework of the “Tsundoku” project financed by the Autonomous Province of Trento according to the province law 13 of December 1999, n. 6 (and subsequent modifications), art. 5. - “Aids for promoting research and development”, financing approved with APIAE manager’s provision n. 691. The purpose of this project is training a Deep Learning (DL) model capable of detecting the occurrence of a so-called Life Event - a wedding and/or the birth of a child - in a person’s life on the basis of the contents she shared on social media (Twitter, in this case). </p> <p>More precisely, the dataset consists of the most recent tweets - up to approximately 3200 for each account - of 8 Italian and 27 English-speaking users (all of them randomly picked), totalling 74722 tweets, 20302 written in Italian and 54420 in English.</p> <p>For each user (labelled as ‘user_<em>x</em>’ with <em>x</em> an integer number between 1 and 35), the file includes the language her tweets are written in as well as the list of said tweets. For every one of the latter, the information made available includes the text of the tweet (appropriately modified as explained below), its length, the number of hashtags and of user mentions it featured, the number of retweets and of likes it received, two Boolean flags (“True” or “False”) assessing whether it was a quote or a reply and a label (‘birth’, ‘wedding’ and ‘not Life-Event-related’, depending on whether the tweet refers to a birth/wedding experienced by the user or not). With respect to the label, it is worth stressing that a tweet was labelled as ‘birth’/‘wedding’ only if the event “actively” involved the user (thus, a tweet reading “Today I get married” is labelled as ‘wedding’, while the label ‘not Life-Event-related’ is associated with ‘Today my sister gets married’) and if the tweet is <strong>by itself</strong> unambiguous (in other words, a tweet reading ‘My wife is pregnant’ is labelled as ‘birth’, while ‘Josephine is pregnant’ does not, since the tweet alone does not allow to determine who Josephine exactly is - this could perhaps be inferred from other tweets but this kind of contextualization is very hard to be carried out and was out of the scope of this project.).</p> <p>In order to comply with GDPR, each one of the texts included in the file was obtained from the original text after taking the following anonymization steps:</p> <p> </p> <ul> <li> <p>every web link got replaced by the ‘WEBLINK’ string;</p> </li> <li> <p>every mention to another Twitter user (for instance, “@joedoe”) got replaced by the “OTHERUSER” string;</p> </li> <li> <p>every hashtag got replaced by the “HASHTAG” string;</p> </li> <li> <p>every name and surname detected by the spaCy library (see <a href="https://spacy.io/">https://spacy.io</a> for more info) got replaced by the “NAME/SURNAME” string;</p> </li> <li> <p>10% of the available words got randomly picked and erased. </p> </li> </ul> <p> </p> <p> </p>
Supplementary Information: Investigating the detectability of hydrocarbons in exoplanet atmospheres with JWST
<p>Supplementary information containing additional figures of the journal article 'Investigating the detectability of hydrocarbons in exoplanet atmospheres with JWST' by D. Gasman, M. Min, and K. L. Chubb, published in Astronomy & Astrophysics (2022).</p>
Replication Package for ROSDiscover: Statically Detecting Run-Time Architecture Misconfigurations in Robotics Systems
<p><strong>Replication Package for ROSDiscover: Statically Detecting Run-Time Architecture Misconfigurations in Robotics Systems</strong></p> <p>This is the replication package for the paper, ROSDiscover: Statically Detecting Run-Time Architecture Misconfigurations in Robotics Systems, which has been accepted at the International Conference on Software Architecture (ICSA), 2021. A preprint of the paper is included in this replication package (paper.pdf).</p> <p>This artifact is archived on Zenodo with the following DOI: <a href="https://doi.org/10.5281/zenodo.5834633">https://doi.org/10.5281/zenodo.5834633</a></p> <p>The study associated with this artifact was carried out by the following investigators:</p> <ul> <li><a href="http://christimperley.co.uk">Christopher S. Timperley</a> (Carnegie Mellon University)</li> <li><a href="https://tobiasduerschmid.github.io">Tobias Dürschmid</a> (Carnegie Mellon University)</li> <li><a href="https://www.cs.cmu.edu/~schmerl">Bradley Schmerl</a> (Carnegie Mellon University)</li> <li><a href="http://www.cs.cmu.edu/~garlan">David Garlan</a> (Carnegie Mellon University)</li> <li><a href="https://clairelegoues.com">Claire Le Goues</a> (Carnegie Mellon University)</li> </ul> <p>If you have any questions regarding the research or the replication package, you should contact Christopher, Tobias, or Bradley.</p> <p><strong>Abstract</strong></p> <p>Robot systems are growing in importance and complexity. Ecosystems for robot software, such as the Robot Operating System (ROS), provide libraries of reusable software components that can be configured and composed into larger systems. To support compositionality, ROS uses late binding and architecture configuration via “launch files” that describe how to initialize the components in a system. However, late binding often leads to systems failing silently due to misconfiguration, for example by misrouting or dropping messages entirely.</p> <p>In this paper we present ROSDiscover, which statically recovers the run-time architecture of ROS systems to find such architecture misconfiguration bugs. First, ROSDiscover constructs component level architectural models (ports, parameters) from source code. Second, architecture configuration files are analyzed to compose the system from these component models and derive the connections in the system. Finally, the reconstructed architecture is checked against architectural rules described in first-order logic to identify potential misconfigurations.</p> <p>We present an evaluation of ROSDiscover on real world, off-the-shelf robotic systems, measuring the accuracy, effectiveness, and practicality of our approach. To that end, we collected the first data set of architecture configuration bugs in ROS from popular open-source systems and measure how effective our approach is for detecting configuration bugs in that set.</p>
Machine Learning based scratches on printed paper detection, in high-speed printing systems [Dataset]
<p>Printing industry rapidly is adopting digital technologies and the requirements in terms of speed and print quality are also becoming more demanding. The is a wide range of possible quality defects in printed paper. This makes it impossible to have humans inspect the printed paper for such a big amount of possible quality defects at the high-speeds the printouts are produced.</p> <p>Printing industry is not taking advantage of the Artificial Intelligence to detect defects in printed paper at speed without human intervention. It is possible to generate millions of images (captures) with printed content from a printing system every day. Most of these images will not have any defect but some other will and can be used to generate a data set to be used in a machine learning system.</p> <p>The intention of this research work is to find ways artificial intelligence can help on automatically detecting defects on printed paper in a printing system and classifying them, without human intervention. Focusing on scratches, I’ve explored what are the actual proposals and solutions, and how machine learning can help improving them by using datasets with different techniques, implementing possible solutions and comparing the obtained results.</p>
Strawberry dataset for object detection
<p>Object detection dataset with annotated ripe and unripe strawberries and their peduncles. Data is annotated in YOLO format. Collected for: ”Real-Time CNN-based Computer Vision System for Open-Field Strawberry Harvesting Robot” <a href="https://doi.org/10.1016/j.ifacol.2022.11.109">https://doi.org/10.1016/j.ifacol.2022.11.109</a> .</p>
Dataset for Anomaly Detection in a Production Wireless Mesh Community Network
<p>CSV dataset generated gathering data from a production wireless mesh community network. Data is gathered every 5 minutes during the interval 2021-04-13 00:00:00 to 2021-04-16 00:00:00. During the interval 2021-04-14 02:00:00 2021-04-14 17:50:00 (both included) there is the failure of a gateway in the mesh (nodeid 24). </p> <p>Live mesh network monitoring link: <a href="http://dsg.ac.upc.edu/qmpsu">http://dsg.ac.upc.edu/qmpsu</a></p> <p>The dataset consists of single gzip compressed CSV file. The first line of the file is a header describing the features. The first column is a GMT timestamp of the sample in the format as "2021-03-16 00:00:00". The rest of the columns provide the comma-separated values of the features collected from each node in the corresponding capture.</p> <p>A suffix with the nodeid is added to each feature. For instance, the feature having the number of processes of node with nodeid 24 is named as "processes-24". In total, 63 different nodes showed up during the samples, each being assigned a different nodeid.</p> <p><br> Features are of two types: (i) absolute values, for instance, the CPU 1-minute load average, and (ii) counters that are monotonically increased, for instance the number of transmitted packets. We have converted counter-type kernel variables to rates, by dividing the difference between two consecutive samples, over the difference of the corresponding timestamps in seconds, as shown in the following pseudo-code:<br> feature.rate are columns computed from feature as<br> feature.rate <- (feature[2:n]-feature[1:(n-1)])/(epoch[2:n]-epoch[1:(n-1)])<br> feature.rate <- feature.rate[feature.rate >= 0] # discard samples where the counter is restarted<br> where n is the number of samples</p> <p><strong>features</strong><br> - processes number of processes<br> - loadavg.m1 1 minute load average<br> - softirq.rate servicing softirqs<br> - iowait.rate waiting for I/O to complete<br> - intr.rate <br> - system.rate processes executing in kernel mode<br> - idle.rate twiddling thumbs<br> - user.rate normal processes executing in user mode<br> - irq.rate servicing interrupts<br> - ctxt.rate total number of context switches across all CPUs<br> - nice.rate niced processes executing in user mode<br> - nr_slab_unreclaimable The part of the Slab that can't be reclaimed under memory pressure<br> - nr_anon_pages anonymous memory pages<br> - swap_cache Memory that once was swapped out, is swapped back in but still also is in the swapfile<br> - page_tables Memory used to map between virtual and physical memory addresses<br> - swap <br> - eth.txe.rate tx errors over all ethernet interfaces<br> - eth.rxe.rate rx errors over all ethernet interfaces<br> - eth.txb.rate tx bytes over all ethernet interfaces<br> - eth.rxb.rate rx bytes over all ethernet interfaces<br> - eth.txp.rate tx packets over all ethernet interfaces<br> - eth.rxp.rate rx packets over all ethernet interfaces<br> - wifi.txe.rate tx errors over all wireless interfaces<br> - wifi.rxe.rate rx errors over all wireless interfaces<br> - wifi.txb.rate tx bytes over all wireless interfaces<br> - wifi.rxb.rate rx bytes over all wireless interfaces<br> - wifi.txp.rate tx packets over all wireless interfaces<br> - wifi.rxp.rate rx packets over all wireless interfaces<br> - txb.rate tx bytes over all ethernet and wifi interfaces<br> - txp.rate tx packets over all ethernet and wifi interfaces<br> - rxb.rate rx bytes over all ethernet and wifi interfaces<br> - rxp.rate rx packets over all ethernet and wifi interfaces<br> - sum.xb.rate tx+rx bytes over all ethernet and wifi interfaces<br> - sum.xp.rate tx+rx packets over all ethernet and wifi interfaces<br> - diff.xb.rate tx-rx bytes over all ethernet and wifi interfaces<br> - diff.xp.rate tx-rx packets over all ethernet and wifi interfaces</p>
ScienceDex guides
Understand access before you commit
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.