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9,302 results for “DETECT”
Adult sex-ratio bias does not lead to detectable adaptive offspring sex allocation via nest-site choice in a turtle with temperature-dependent sex determination
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Data for: Optimizing a metabarcoding marker portfolio for species detection from complex mixtures of globally diverse fishes
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Data from: When can we detect lianas from space? Towards a mechanistic understanding of liana-infested forest optics
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Common guillemots in the Baltic Sea studied with video surveillance and object detection: raw data, annotations, model, and model outputs
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Detection and analysis of complex structural variation in human genomes across populations and in brains of donors with psychiatric disorders
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Detecting frequency-dependent selection through the effects of genotype similarity on fitness components
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Nationwide real-world implementation of AI for cancer detection in population-based mammography screening (PRAIM)
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Synthetic urban gamma-ray spectra for training spectral detection and identification models
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Transcranial detection of amyloid-beta at single plaque resolution in vivo with large-field multifocal illumination fluorescence microscopy
<p>The abnormal deposition of beta-amyloid proteins in the brain is one of the major histopathological hallmarks of Alzheimer’s disease. Currently available intravital microscopy techniques can visualize plaques with high resolution, but are limited to a small field-of-view and with depth limitation. Here, we report the transcranial detection of amyloid-beta deposits at the whole brain scale with 20 mm resolution in APP/PS1 and arcAb mouse models of Alzheimer’s disease amyloidosis using a novel large-field multifocal illumination (LMI) fluorescence microscopy technique. High sensitive and specific detection of amyloid-beta deposits at single plaque level in APP/PS1 and arcAb mice was facilitated using luminescent conjugated oligothiophene HS-169. Immunohistochemical staining with HS-169, anti-Ab antibody 6E10, conformation antibodies OC (fibrillar) on brain tissue sections further showed that HS-169 resolved compact parenchymal and vessel-associated amyloid deposits. In conclusion, we demonstrate a new <em>in vivo </em>imaging platform for detection of amyloid-beta deposits at single plaque resolution in murine models of Alzheimer’s disease amyloidosis.</p>
Benign samples used in article "DeepDetectNet vs RLAttackNet: An Adversarial Method to Improve Deep Learning-based Static Malware Detection Model"
<p>This repository contains all benign samples used in article "DeepDetectNet vs RLAttackNet: An Adversarial Method to Improve Deep Learning-based Static Malware Detection Model". It is safe to download these samples.</p>
Hypernym-LIBre: A free Web-based Corpus for Hypernym Detection
<p>The task of finding hypernyms from large text corpora is a fundamental problem in NLP. It provides a basis for the main-stream natural language problems in AI. In our paper, we introduce a free new web-based corpus for hypernym detection and we show that using this corpus we achieve similar results to the state-of-the-art pattern-based methods achieved by a well known corpus that is not freely available. The dataset provided here is the one we use in our paper and we provide it with an open license so others can apply different methods and techniques for hypernym detection.</p> <p> </p> <p>The dataset is a combination of UMBC corpus and the Wikipedia corpus. Its dependency parsed and POS-tagged versions are available at this DOI: 10.5281/zenodo.3689303</p> <p>Contents:</p> <p>Hypernym-LIBre.zip 11.3GB compresssed, 32GB uncompressed raw text</p> <p>288 files of ~110 MB each</p> <p> </p> <p>10.5281/zenodo.3689303</p> <p>PoS and dep annotated</p> <p>~15GB compressed, 80GB uncompressed, 442 files of ~180MB each</p> <p> </p> <pre>10.5281/zenodo.3695237</pre> <p>hyponym-hypernym pairs extracted from Hypernym-LIBre using Hearst patterns</p>
Specific monitoring results of ESBL-/AmpC-/carbapenemase-producing bacteria and specific monitoring of carbapenemase-producing bacteria, in the absence of isolates detected complementing the European Union Overview Summary Report on Antimicrobial Resistance in zoonotic and indicator bacteria from humans, animals and food in 2017/2018
<p>This dataset derives from the specific monitoring of E. coli producers of ESBLs/AmpC/carbapenemases, as well as the specific monitoring of carbapenemase-producers (voluntary reporting), in the absence of any isolates detected. This dataset contains only data when Total units positive equals zero '0'.</p>
Tagged original datasets for 'Genetically Optimized Massively Parallel Binary Neural Networks for Intrusion Detection Systems'
<p>Tagged, non-formatted, original datasets used in 'Genetically Optimized Massively Parallel Binary Neural Networks for Intrusion Detection Systems', T. Murovič, A. Trost.</p> <p>Available from the original authors:</p> <p>1. <a href="https://www.unsw.adfa.edu.au/unsw-canberra-cyber/cybersecurity/ADFA-NB15-Datasets/">https://www.unsw.adfa.edu.au/unsw-canberra-cyber/cybersecurity/ADFA-NB15-Datasets/</a> (UNWS-NB15 dataset)</p> <p>2. <a href="https://www.unb.ca/cic/datasets/nsl.html">https://www.unb.ca/cic/datasets/nsl.html</a> (NSL-KDD dataset)</p>
PredCheck: Detecting Predatory Behaviour in Scholarly World
<p>Dataset used in the paper "PredCheck: Detecting Predatory Behaviour in Scholarly World" accepted at JCDL 2020 as a poster.<br> <br> Abstract: High solicitation for publishing a paper in scientific journals has led to the emergence of a large number of open-access predatory publishers. They fail to provide a rigorous peer-review process, thereby diluting the quality of research work and charge high article processing fees. Identification of such publishers has remained a challenge due to the vast diversity of the scholarly publishing ecosystem. Earlier works utilises only the objective features such as metadata. In this work, we aim to explore the possibility of identifying predatory behaviour through text-based features. We propose PredCheck, a four-step classificaton pipeline. The first classifier identifies the subject of the paper using TF-IDF vectors. Based on the subject of the paper, the Doc2Vec embeddings of the text are found. These embeddings are then fed into a Naive Bayes classifier that identifies the text to be predatory or non-predatory. Our pipeline gives a macro accuracy of 95% and an F1-score of 0.89.</p>
Fluorescence Microscopy Data for Cellular Detection using Object Detection Networks.
<p>This data accompanies work from the paper entitled: </p> <p><strong>Object Detection Networks and Augmented Reality for Cellular Detection in Fluorescence Microscopy Acquisition and Analysis. </strong></p> <p>Waithe D1*,2,, Brown JM3, Reglinski K4,6,7, Diez-Sevilla I<sup>5</sup>, Roberts D<sup>5</sup>, Christian Eggeling1,4,6,8</p> <p>1 Wolfson Imaging Centre Oxford and 2 MRC WIMM Centre for Computational Biology and 3 MRC Molecular Haematology Unit and 4 MRC Human Immunology Unit, Weatherall Institute of Molecular Medicine, University of Oxford, OX3 9DS, Oxford, United Kingdom. 5 Nuffield Division of Clinical Laboratory Sciences, Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Headley Way, Oxford, OX3 9DU.<br> 6 Institute of Applied Optics and Biophysics, Friedrich-Schiller-University Jena, Max-Wien Platz 4, 07743 Jena, Germany.<br> 7 University Hospital Jena (UKJ), Bachstraße 18, 07743 Jena, Germany.<br> 8 Leibniz Institute of Photonic Technology e.V., Albert-Einstein-Straße 9, 07745 Jena, Germany.</p> <p>Further details of these datasets can be found in the methods section of the above paper.</p> <p><strong>Erythroblast DAPI (+glycophorin A):</strong> erythroblast cells were stained with DAPI and for glycophorin A protein (CD235a antibody, JC159 clone, Dako) and with Alexa Fluor 488 secondary antibody (Invitrogen). DAPI staining was performed through using VectaShield Hard Set mounting solution with DAPI (Vector Lab). Num. of images used for training: 80 and testing: 80. Average number of cells per image: 4.5.</p> <p><strong>Neuroblastoma phalloidin (+DAPI): </strong>images of neuroblastoma cells (N1E115) stained with phalloidin and DAPI were acquired from the Cell Image Library [26]. Cell images in the original dataset were acquired with a larger field of view than our system and so we divided each image into four sub-images and also created ROI bounding boxes for each of the cells in the image. The images were stained for FITC-phalloidin and DAPI. Num. of images used for training: 180, testing: 180. Average number of cells per image: 11.7.</p> <p><strong>Fibroblast nucleopore</strong>: fibroblast (GM5756T) cells were stained for a nucleopore protein (anti-Nup153 mouse antibody, Abcam) and detected with anti-mouse Alexa Fluor 488. Num. of images for training: 26 and testing: 20. Average number of cells per image: 4.8.</p> <p><strong>Eukaryote DAPI:</strong> eukaryote cells were stained with DAPI and fixed and mounted in Vectashield (Vector Lab). Num. of images for training: 40 and testing: 40. Average number of cells per image: 8.9.</p> <p><strong>C127 DAPI:</strong> C127 cells were initially treated with a technique called RASER-FISH[27], stained with DAPI and fixed and mounted in Vectashield (Vector Lab). Num. of images for training: 30 and testing: 30. Average number of cells per image: 7.1.</p> <p><strong>HEK peroxisome All</strong>: HEK-293 cells expressing peroxisome-localized GFP-SCP2 protein. Cells were transfected with GFP-SCP2 protein, which contains the PTS-1 localization signal, which redirects the fluorescently tagged protein to the actively importing peroxisomes[28]. Cells were fixed and mounted. Num. of images for training: 55 and testing: 55. Additionally we sub-categorised the cells as ‘punctuate’ and ‘non-punctuate’, where ‘punctuate’ would represent cells that have staining where the peroxisomes are discretely visible and ‘non-punctuate’ would be diffuse staining within the cell. The ‘HEK peroxisome All’ dataset contains ROI for all the cells: average number of cells per image: 7.9. The ‘HEK peroxisome’ dataset contains only those cells with punctuate fluorescence: average number of punctuate cells per image: 3.9.</p> <p><strong>Erythroid DAPI All: </strong>Murine embryoid body-derived erythroid cells, differentiated from mES cells. Stained with DAPI and fixed and mounted in Vectashield (Vector Lab). Num. of images for training: 51 and testing: 50. Multinucleate cells are seen with this differentiation procedure. There is a variation in size of the nuclei (nuclei become smaller as differentiation proceeds). The smaller, 'late erythroid' nuclei contain heavily condensed DNA and often have heavy ‘blobs’ of heterochromatin visible. Apoptopic cells are also present, with apoptotic bodies clearly present. The ‘Erythroid DAPI All’ dataset contains ROI for all the cells in the image. Average number of cells per image: 21.5. The subset ‘Erythroid DAPI’ contains non-apoptotic cells only: average number of cells per image: 11.9</p> <p><strong>COS-7 nucleopore. </strong>Slides were acquired from GATTAquant. GATTA-Cells 1C are single color COS-7 cells stained for Nuclear pore complexes (Anti-Nup) and with Alexa Fluor 555 Fab(ab’)2 secondary stain. GATTA-Cells are embedded in ProLong Diamond. Num. of images for training: 50 and testing: 50. Average number of cells per image: 13.2</p> <p><strong>COS-7 nucleopore 40x</strong>. Same GATTA-Cells 1C slides (GATTAquant) as above but imaged on Nikon microscope, with 40x NA 0.6 objective. Num. of images for testing: 11. Average number of cells per image: 31.6.</p> <p><strong>COS-7 nucleopore 10x.</strong> Same GATTA-Cells 1C slides (GATTAquant) as above but imaged on Nikon microscope, with 10x NA 0.25 objective. Num. of images for testing: 20. Average number of cells per image: 24.6</p> <p><strong>Dataset Annotation</strong></p> <p>Datasets were annotated by a skilled user. These annotations represent the ground-truth of each image with bounding boxes (regions) drawn around each cell present within the staining. Annotations were produced using Fiji/ImageJ [29] ROI Manager and also through using the OMERO [30] ROI drawing interface (<a href="https://www.openmicroscopy.org/omero/">https://www.openmicroscopy.org/omero/</a>). The dataset labels were then converted into a format compatible with Faster-RCNN (Pascal), YOLOv2, YOLOv3 and also RetinaNet. The scripts used to perform this conversion are documented in the repository (<a href="https://github.com/dwaithe/amca">https://github.com/dwaithe/amca</a>/scripts/).</p>
Paper-Based Detection of Campylobacter spp
<p>On April 10, 2020, 2nd IPANEMA Scientific Online Lecture was held. The lecture was about the Paper-Based Detection of <em>Campylobacter</em> spp. and it was prepared by our consortium members from <a href="https://www.inrae.fr/en">INRAE</a>: dr Jasmina Vidic, Priya Vizzini, and Marco Marin. Through this lecture, our researchers became more aware of possible experimental issues, but also discussed potential solutions to these problems for our project.</p>
SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection
<p><strong>Authors</strong></p> <p>Dominik Schlechtweg, Barbara McGillivray, Simon Hengchen, Haim Dubossarsky, and Nina Tahmasebi</p> <p><strong>Description</strong></p> <p>This data collection contains the <strong>post-evaluation</strong> data for <a href="https://languagechange.org/semeval">SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection</a>:</p> <ul> <li>the starting kit to download data, and examples for competing in the CodaLab challenge including baselines</li> <li>the true binary change scores of the targets for Subtask 1, and their true graded change scores for Subtask 2 (<code>test_data_truth/</code>),</li> <li>the scoring program used to score submissions against the true test data in the evaluation and post-evaluation phase (<code>scoring_program/</code>),</li> <li>the results of the evaluation phase including <ul> <li>the final rankings of the participating teams by their best submission (<code>results/rankings_teams.csv</code>),</li> <li>the submitted files of each team (<code>results/submissions/</code>),</li> <li>an overview of the results for each submission ordered by team (<code>results/submissions_results.csv</code>),</li> <li>analysis plots (<code>plots/</code>) displaying the results: <ul> <li>under <code>per_target/</code> we provide the gold change scores and the normalized prediction error of target words plotted against their frequency and polysemy statistics,</li> <li>under <code>per_team/</code> we provide the model predictions from the best submission per team (per subtask) plotted against frequency/polysemy statistics and performance on gold data (gray lines give the correlation with the respective variable in the gold data); we also provide plots of visualizing the teams' prediction similarities.</li> </ul> </li> </ul> </li> </ul> <p>Some remarks:</p> <ul> <li>the paper referenced below remains the only source for the rankings between teams,</li> <li>some teams were disqualified, and are thus removed from the analyses and the rankings present in the paper,</li> <li>some teams have changed names, resulting in a discrepancy between team names under <code>results/</code> and team names in the paper. The paper contains a key to match old names with new names.</li> </ul> <p><strong>Test Data </strong>for SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection can be found using the links below:</p> <ul> <li><a href="https://www.ims.uni-stuttgart.de/en/research/resources/corpora/sem-eval-ulscd-eng/">English</a></li> <li><a href="https://www.ims.uni-stuttgart.de/en/research/resources/corpora/sem-eval-ulscd-ger/">German</a></li> <li><a href="https://zenodo.org/record/3734089">Latin</a></li> <li><a href="https://zenodo.org/record/3730550">Swedish</a></li> </ul> <p>Please find more information on the provided data in the paper referenced below.</p> <p><strong>Reference</strong></p> <p>Dominik Schlechtweg, Barbara McGillivray, Simon Hengchen, Haim Dubossarsky and Nina Tahmasebi. 2020. <a href="https://languagechange.org/semeval">SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection</a>. SemEval@COLING2020.</p> <p>The resources are freely available for education, research and other non-commercial purposes.</p> <pre><code>@inproceedings{schlechtweg2020semeval, title = "{S}em{E}val-2020 {T}ask 1: {U}nsupervised {L}exical {S}emantic {C}hange {D}etection", author = "Schlechtweg, Dominik and McGillivray, Barbara and Hengchen, Simon and Dubossarsky, Haim and Tahmasebi, Nina", booktitle = "To appear in Proceedings of the 14th International Workshop on Semantic Evaluation", year = "2020", address = "Barcelona, Spain", publisher = "Association for Computational Linguistics"}</code></pre> <p> </p>
SemEval-2020 Task 5: Modelling Causal Reasoning in Language: Detecting Counterfactuals
<p><strong>SemEval-2020 Task 5</strong></p> <p> </p> <p><strong>Subtask-1:</strong> Recognizing Counterfactual Statements (RCS) -- Determine whether a given sentence is counterfactual or not.</p> <p><strong>Subtask-2: </strong>Detecting Antecedent and Consequent (DAC) -- Extract the antecedent and consequent part in a given counterfactual sentence.</p> <p> </p> <p>The released dataset consists of train/test data of both subtask-1 and subtask-2. In our competition, participants could only use the corresponding dataset in each subtask.</p> <p> </p> <p><strong>Task 5 Codalab Website:</strong> <a href="https://competitions.codalab.org/competitions/21691">https://competitions.codalab.org/competitions/21691</a></p>
SARS-CoV-2 detection dogs - a pilot study
<p>The outstanding olfactory acuity of canines led us to consider whether dogs are able to reliably detect the odour of respiratory diseases associated with a SARS-CoV-2 infection in saliva or tracheobronchial secretion of hospitalized COVID-19 patients. Furthermore, we examined if SARS-CoV-2 detection dogs could provide an appropriate screening method for the human virus.The aim of this data publication is to provide the data acquired in the controlled, randomized and double-blinded pilot study `Scent dog identification of SARS-CoV-2 infection’ (submitted to BMC Infectious Diseases).</p>
Data set for "Projection-specific activity of layer 2/3 neurons imaged in mouse primary somatosensory barrel cortex during a whisker detection task"
<p>Data set for: Vavladeli A, Daigle T, Zeng H, Crochet S, Petersen CCH (2020) Projection-specific activity of layer 2/3 neurons imaged in mouse primary somatosensory barrel cortex during a whisker detection task. FUNCTION 1: zqaa008. doi: 10.1093/function/zqaa008</p> <p>There are 2 files in this upload:</p> <p>1. The file named "2020_Vavladeli_FUNCTION.pdf" is the Open Access pdf file of the manuscript published in FUNCTION.</p> <p>2. The file named "Vavladeli_data_code.zip" (~2 GB) is a zipped version of a folder named "Vavladeli_data_code" (~2 GB), which contains the data analysed in the study along with the Matlab code used to generate the published figures. When unzipped, the folder contains 8 Matlab '.m' files with analysis code and two '.mat' data files. In order to run the analysis of the data set, you need to execute the '.m' file with the corresponding figure name.</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.