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9,302 results for “DETECT”
Epimutation detection in the clinical context: guidelines and a use case from a new Bioconductor package
<p>This project contain the simulation data used to evaluate the epimutacions methods in the manuscript: Epimutation detection in the clinical context: guidelines and a use case from a new Bioconductor package</p>
Data for: An experimental assessment of detection dog ability to locate great crested newts (Triturus cristatus) at a channeled distance and through soil
<p>Detection dogs are increasingly used to locate cryptic wildlife species, but their use for amphibians is still rather underexplored. In the present paper we focus on the great crested newts (<em>Triturus cristatus</em>), a European species which is experiencing high conservation concerns across its range, and assess the ability of a trained detection dog to locate individuals during their terrestrial phase. More specifically, we used a series of randomised, double-blinded experiments to document whether a range of distances between target newts and the detection dog affects the ability of localisation, and to assess the ability and efficiency of target newt detection in simulated subterranean refugia through 20 cm of two common soil types (clay and sandy soil, both with and without air vents to mimic mammal burrows, a common refuge used by <em>T. cristatus</em>). The detection dog accurately located all individual <em>T. cristatus</em> across the entire range of tested distances (25 cm - 2 m). The substrate trials revealed that the detection dog could locate individuals also through soil. As expected, the detection time during the soil discrimination trials was significantly reduced for treatments with vents. Contrary to existing studies with detection dogs in human forensic contexts, however, detection was generally faster for <em>T. cristatus</em> under clay soil compared to sandy soil. Our study provides a general baseline for the use of detection dogs in locating <em>T. cristatus</em> and similar amphibian species during their terrestrial phase. </p>
Dataset for "A 5.3-minute-period pulsing white dwarf in a binary detected from radio to X-rays"
<p>Photometric and spectroscopic data obtained for the manuscript "A 5.3-minute-period pulsing white dwarf in a binary detected from radio to X-rays", to be published in Nature Astronomy.</p> <p>This repository includes ultracam log files with photometry for all runs and ESO spectra.</p>
Iraqi Kurd or Arab Male Authenticity Detection Based on Facial Features
<p>This collection comprises images of Iraqi males from the two biggest ethnic groupings in Iraq, Arabs, and Kurds. 260 picture samples were used to depict male Iraqi Kurds and Arabs. A range of preprocessing and data improvement approaches were used for the dataset.</p>
Data for Change Detection Demo
<p>Data accompanying Github notebook demonstrating how to conduct change detection for protein localization changes in high-content image screens: <a href="https://github.com/alexxijielu/change_detection_book_chapter/blob/master/change_detection.ipynb">change_detection_book_chapter/change_detection.ipynb at master · alexxijielu/change_detection_book_chapter (github.com)</a></p> <ul> <li>data.zip contains all of the data required to run the Python notebook.</li> <li>single_cell_yeast.zip contains all of the single-cell image crops required to reproduce the analysis.</li> </ul>
Dataset for the Paper: "Security Defect Detection via Code Review: A Study of the OpenStack and Qt Communities"
<p>This is the dataset for the paper: "Security Defect Detection via Code Review: A Study of the OpenStack and Qt Communities ", including the extracted data and results.</p> <p>The dataset contains the following three folders:</p> <p><strong>1. RQ1</strong>: </p> <ul> <li><strong>Security defect in Nova.xlsx</strong></li> <li><strong>Security defect in Neutron.xlsx</strong></li> <li><strong>Security defect in Qt Base.xlsx</strong></li> <li><strong>Security defect in Qt Creator.xlsx;</strong></li> </ul> <p>The RQ1 folder contains four files corresponding to the four projects (i.e., Nova and Neutron from OpenStack, Qt Base and Qt Creator from Qt), including 539 security-related review comments, in which security defects were identified by the reviewers. These instances were obtained from manual labelling after keyword-based search. The security defect type of these instances are presented to answer RQ1.</p> <p><strong>How to Read the MS Excel files in RQ1:</strong></p> <p>Each of the four MS Excel files in this folder contains 6 sheets for six years from 2017 to 2022. Each sheet has 10 columns for recoding 10 data items, among which the last four data items are used in our study to answer the RQs. We list the data items in the following table.</p> <table> <tbody> <tr> <td><strong>Data Item</strong></td> <td><strong>Description</strong></td> <td><strong>Source</strong></td> </tr> <tr> <td>Keyword</td> <td>The corresponding keyword of the comment.</td> <td>Keyword-based Search</td> </tr> <tr> <td>Code_change_id</td> <td>The code_change_id of the comment.</td> <td>Gerrit</td> </tr> <tr> <td>File</td> <td>The file in which the comment is added.</td> <td>Gerrit</td> </tr> <tr> <td>Patchset</td> <td>The patchset of the comment within the code change.</td> <td>Gerrit</td> </tr> <tr> <td>Line</td> <td>The line number in the file at which the comment is added.</td> <td>Gerrit</td> </tr> <tr> <td>Message</td> <td>The text of the review comment.</td> <td>Gerrit</td> </tr> <tr> <td>Security-related</td> <td>Whether the review comment is security-related (i.e., Yes or No).</td> <td>Labelling</td> </tr> <tr> <td>Security defect type</td> <td>The type of the security defect identified in the comment.</td> <td>Labelling</td> </tr> <tr> <td>Consequence</td> <td>The Consequence of the security defect.</td> <td>Extraction</td> </tr> <tr> <td>Resolution Evidence</td> <td>The information about where the identified security defect was resolved in the code</td> <td>Extraction</td> </tr> </tbody> </table> <p><strong>2. RQ2</strong>: </p> <ul> <li><strong>Extracted data for RQ2.mx22</strong></li> </ul> <p>The RQ2 folder contains the extracted data of 539 security-related review comments in <strong>Extracted data for RQ2.mx22</strong>, which was encoded and analyzed by the MAXQDA tool, investigating the treatment of security defects by developers and reviewers to answer RQ2.</p> <p><strong>3. RQ3</strong>: </p> <ul> <li><strong>Extracted data for RQ3.mx22</strong></li> </ul> <p>The RQ3 folder contains the extracted data of 161 review comments in which identified security defects were not resolved by developers in <strong>Extracted data for RQ3.mx22</strong>. which was also encoded and analyzed by the MAXQDA tool, exploring the causes of not resolving security defects to answer RQ3.</p> <p><strong>Note</strong>: The mx22 can be opened by MAXQDA 22, which are available at <a href="https://www.maxqda.com/">https://www.maxqda.com/</a> for download. You may also use the free trial version of MAXQDA 2022, which is available at <a href="https://www.maxqda.com/trial">https://www.maxqda.com/trial</a> for download.</p>
5GC PFCP Intrusion Detection Dataset
<p>The 5GC PFCP Intrusion Detection Dataset was implemented following relevant methodological frameworks, including eleven features: (a) Complete Network Configuration, (b) Complete Traffic, (c) Labelled Dataset, (d) Complete Interaction, (e) Complete Capture, (f) Available Protocols, (g) Attack Diversity, (h) Heterogeneity, (i) Feature Set and (j) Metadata. A 5GC architecture was emulated, including the Network Slice Selection Function (NSSF), the Network Exposure Function (NEF), the Network Repository Function (NRF), the Policy Control Function (PCF), the User Data Management (UDM), the Access and Mobility Management Function (AF), the Authentication Server Function (AUSF), the Access Management Function (AMF), SMF, and UPF, in addition to a virtualised UE device, a virtualised gNodeB (gNB), and a cyberattacker impersonating a maliciously instantiated SMF. In particular, the following cyberattacks were performed:</p> <ul> <li>On Wednesday, October 05, 2022, the PFCP Session Establishment DoS Attack was implemented for 4 hours.</li> <li>On Thursday, October 13, 2022, the PFCP Session Deletion DoS Attack was implemented for four hours.</li> <li>On Tuesday, November 01, 2022, the PFCP Session Modification DoS Attack (DROP Apply Action Field Flags) was implemented for 4 hours.</li> <li>On Tuesday, November 22, 2022, the PFCP Session Modification DoS Attack (DUPL Apply Action Field Flag) was implemented for 4 hours.</li> </ul> <p>The previous PFCP-related cyberattacks were executed, utilising penetration testing tools, such as Scapy. For each attack, a relevant folder is provided, including the network traffic and the network flow statistics for each entity. In particular, for each cyberattack, a folder is given, providing (a) the pcap files for each entity, (b) the Transmission Control Protocol (TCP)/ Internet Protocol (IP) network flow statistics for 120 seconds in a Comma-Separated Values (CSV) format and (c) the PFCP flow statistics for each entity (using different timeout values in terms of second (such as 45, 60, 75, 90, 120 and 240 seconds)). The TCP/IP network flow statistics were produced by using the CICFlowMeter, while the PFCP flow statistics were generated based on a Custom PFCP Flow Generator, taking full advantage of Scapy.</p> <p><strong>The users of this dataset are kindly asked to cite the following paper(s).</strong></p> <p><em>G. Amponis, P. Radoglou-Grammatikis, T. Lagkas, W. Mallouli, A. Cavalli, D. Klonidis, E. Markakis, and P. Sarigiannidis, “Threatening the 5G core via PFCP DOS attacks: The case of blocking UAV Communications”, EURASIP Journal on Wireless Communications and Networking, vol. 2022, no. 1, 2022, doi: 10.1186/s13638-022-02204-5.</em></p>
A Systematic Evaluation of Automated Tools for Side-Channel Vulnerabilities Detection in Cryptographic Libraries
<p>Artifact for CCS'23 submission</p>
Dataset from: A lightweight framework for chromatin loop detection at the single-cell level
<p>This dataset includes the following components:</p> <ul> <li>Loops identified by scGSLoop at the single-cell level on the mES dataset and the hPFC dataset</li> <li>Consensus loops of each cell type. These consensus loops are derived from the single-cell results and represent the common loops shared across the cells of a particular cell type.</li> </ul> <p> </p>
The detection of relativistic corrections in cosmological N-body simulations
<p>This directory contains all the necessary data, codes, and notebooks to reproduce the results of the paper titled "The detection of relativistic corrections in cosmological N-body simulations" (<a href="https://arxiv.org/abs/1909.04652">https://arxiv.org/abs/1909.04652</a>).<br> <br> This paper has also been published in Celestial Mechanics and Dynamical Astronomy and can be found in volume 132, Article number: 2 (2020) which can be accessed at this link: <a href="https://link.springer.com/article/10.1007/s10569-019-9943-z">https://link.springer.com/article/10.1007/s10569-019-9943-z </a>. </p> <p>Directories</p> <ul> <li><strong>codes</strong>: This directory contains different codes used to generate and post-process the simulation data.</li> <li><strong>data</strong>: This directory contains the data generated as part of the project.</li> <li><strong>notebooks</strong>: This directory includes Jupyter notebooks and perl files to reproduce the figures presented in the paper.</li> <li><strong>supplementary</strong>: This directory contains the supplementary materials associated with the project.</li> </ul> <p>How to Use</p> <ol> <li>Download the files to your local machine.</li> <li>Navigate to the directory where the files are saved.</li> <li>Install the necessary packages</li> <li>Navigate to the "<strong>notebooks</strong>" directory and open the Jupyter notebooks in your preferred environment.</li> <li>Run the cells in the notebooks to reproduce the figures.</li> <li>Navigate to the "<strong>codes</strong>" directory and use the appropriate code to generate and post-process the simulation data.</li> <li>Navigate to the "<strong>data</strong>" directory to access the data.</li> </ol> <p><br> If you have any feedback or request feel free to email farbod.hassani@gmail.com or Jean-Pierre.Eckmann@unige.ch</p>
Supplementary material 2 from: Colombari F, Battisti A (2023) Citizen science at school increases awareness of biological invasions and contributes to the detection of exotic ambrosia beetles. In: Jactel H, Orazio C, Robinet C, Douma JC, Santini A, Battisti A, Branco M, Seehausen L, Kenis M (Eds) Conceptual and technical innovations to better manage invasions of alien pests and pathogens in forests. NeoBiota 84: 211-229. https://doi.org/10.3897/neobiota.84.95177
Slides of the lecture on 'Monitoring of insect species harmful to trees and forests' and link to educational videos
Rock pools as a source of environmental DNA for the detection of the threatened Pilbara olive python (Liasis olivaceus barroni)
<p>Environmental DNA (eDNA) research is transforming biomonitoring at a global scale, but applicability to reptiles has been restricted because of their presumed low shedding rate. Consequently, eDNA may have considerable limitations as a biomonitoring tool in Australia where 40% of the terrestrial vertebrates are reptiles. However, there is a need to evaluate if method improvements such as targeting certain substrates, improves the ability to detect reptile eDNA. The Pilbara olive python (<em>Liasis olivaceus barroni</em>) is an uncommon and elusive Australian top predator with a high conservation priority. Like many other snake species, Pilbara olive pythons are challenging to monitor with traditional survey methods, therefore exploring an eDNA-based approach is highly relevant. The pythons are known to occasionally reside in rock pools. Thus, development of a reliable eDNA-based approach to detect the pythons in water would provide a needed alternative method. Here we use a previously developed metabarcoding assay targeting reptiles, to sequence a total of 228 water samples collected from 40 rock pools from six broad locations across the Pilbara region of Western Australia, and we confirm the presence of Pilbara olive python eDNA in 37 samples from 12 of those pools at three of the six broad sampling locations. Other vertebrate taxa, including other reptiles, amphibians, mammals and birds were also detected. Our documented ability to detect Pilbara olive python eDNA from rock pool water samples represents an important step towards eDNA-based precision monitoring of this species.</p>
Benchmark datasets for "Detecting T-cell expansion and quantifying clone survival from deep profiling of immune repertoires"
<p>T-cell receptor repertoire sequencing datasets describing time courses obtained for vaccination, normal aging and blood transplant cases. Datasets reported here were previously published (except for Tem/Tcm data), this is just a compendium of selected samples that is properly pre-processed and formatted.</p>
Serum miRnome Profiling: A Novel Biomarker Platform for the Early Detection of NSCLC
<p>Table S1: DEmiRNAs between cancerous and non-cancerous patients; Table S2: Shared DE miRNAs between subgroups; Table S3: NSCLC vs control_GO_BP_results; Table S4: NSCLC vs control_GO_CC_results; Table S5: NSCLC vs control_GO_MF_results; Table S6: NSCLC vs control_KEGG_results.</p>
Photonic Radar for Contactless Vital Sign Detection
<p>Data and code used to produce the figures in "Photonic Radar for Contactless Vital Sign Detection". Nature Photonics (2023). DOI:10.1038/s41566-023-01245-6</p> <p>The code has been tested with Matlab R2020b.</p>
2023 IEEE SPS Video and Image Processing (VIP) Cup: Ophthalmic Biomarker Detection
<p>Ophthalmic clinical trials that study treatment efficacy of eye diseases are performed with a specific purpose and a set of procedures that are predetermined before trial initiation. Hence, they result in a controlled data collection process with gradual changes in the state of a diseased eye. In general, these data include 1D clinical measurements and 3D optical coherence tomography (OCT) imagery. Physicians interpret structural biomarkers for every patient using the 3D OCT images and clinical measurements to make personalized decisions for every patient.</p> <p>Two main challenges in medical image processing has been <em>generalization</em> and <em>personalization</em>.</p> <p>Generalization aims to develop algorithms that work well across diverse patients and scenarios, providing standardized and widely applicable solutions. Personalization, in contrast, tailors algorithms to individual patients based on their unique characteristics, optimizing diagnosis and treatment planning. Generalization offers broad applicability but may overlook individual variations. Personalization provides tailored solutions but requires patient-specific data. While deep learning has shown an affinity towards generalization, it is lacking in personalization.</p> <p>The presence and absence of biomarkers is a personalization challenge rather than a generalization challenge. The variation within OCT scans of patients between visits can be minimal while the difference in manifestation of the same disease across patients may be substantial. The domain difference between OCT scans can arise due to pathology manifestation across patients, clinical labels, and the visit along the treatment process when the scan is taken. Morphological, texture, statistical and fuzzy image processing techniques through adaptive thresholds and preprocessing may prove substantial to overcome these fine-grained challenges. This challenge provides the data and application to address personalization.</p>
List of SHARAD type-III burst candidate based on cross-detections with STEREO and Wind
<p>Complete list of SHARAD type-III burst candidate based on cross-detections with STEREO and Wind, including products in which a burst was not seen.</p>
Real-time Welding Sparks Detection on Construction Sites us-ing Contour Detection and Deep Learning
<p>One of the primary causes of fires at construction sites is welding sparks. Fire detection systems utilizing computer vision technology offer a unique opportunity to monitor fires in construction sites. However, little effort has been made to date in regards to real-time tracking of small sparks that can lead to major fires at construction sites. In this study, a novel method is proposed to detect welding sparks in real time contour detection with deep learning parameter tuning. An automatic parameter tuning algorithm employing a convolutional neural network was developed to identify the optimum hue-saturation-value. Additional filtering methods regarding non-welding zone and contour area-based filter, were also newly developed to enhance prediction accuracy of welding sparks. The method was evaluated using 230 welding sparks images and 104 videos. The results obtained from the welding images indicate that the suggested model for detecting welding sparks achieves a precision of 74.45% and a recall of 63.50% when noise images, such as flashing and reflection light, were removed from the dataset. Furthermore, our findings demonstrate that the proposed model is effective in capturing the number of welding sparks in the video dataset, with a 95.2% accuracy in detecting the moment when the number of welding sparks reaches its peak. These results highlight the potential of automated welding sparks detection for enhancing fire surveillance at construction sites.</p>
Dataset: Automation of tree-ring detection and measurements using deep learning
<p>Datasets used to train and evaluate neural network-based implementation that automates detection and measurement of tree-ring boundaries from coniferous species.</p> <p>SplitTrainValDatasets files contain hand-annotated squared images of <em>Picea abies</em> core samples with over 8000 ring boundaries.</p> <p>RealWorldEvaluation.zip contains real-world core samples of 4 conifer species (<em>Picea abies</em>, <em>Abies alba</em>, <em>Pinus sylvestris</em> and <em>Larix decidua</em>) used to evaluate our application and tables with the results of these evaluations.</p> <p>Full code of the application is available at https://github.com/Gregor-Mendel-Institute/TRG-ImageProcessing/tree/master.</p> <p>More details about the datasets and the application can be found in our publication: Poláček, M., Arizpe, A., Hüther, P., Weidlich, L., Steindl, S., & Swarts, K. (2022). Automation of tree-ring detection and measurements using deep learning (p. 2022.01.10.475709). bioRxiv. <a href="https://doi.org/10.1101/2022.01.10.475709">https://doi.org/10.1101/2022.01.10.475709</a>.</p> <p> </p>
Datasets, scripts and Jupyter Notebook for "Two distinct magma storage regions at Ambrym volcano detected by satellite geodesy", Geophysical Research Letters
<p>This repository includes scripts and files necessary to create Figure 1 (<strong>S1.zip </strong>and <strong>plot_TS_Ambrym_2019_2022.py</strong>) in "Two distinct magma storage regions at Ambrym volcano detected by satellite geodesy", <em>Geophysical Research Letters</em>. We also include the Jupyter Notebook used to run the EnKF data assimilation (<strong>enkf_notebook.zip) </strong>and produce Figures 2 and 3. The Jupyter Notebook and files used to produce Figure 4b,c can be found on <a href="http://github.com/tshreve/jupyterNBs/">GitHub</a>.</p> <p>This version corrects a bug in the code used to plot the cross-sections in Figure 3 with <strong>enkf_notebook.zip</strong>.</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.