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1,214 results for “Detection data”
Supporting data for the manuscript "Severus: accurate detection and characterization of somatic structural variation in tumor genomes using long reads"
<p>Supporting data for the manuscript "Severus: accurate detection and characterization of somatic structural variation in tumor genomes using long reads".</p> <p>The archive contains files that are necessary to reproduce the cell line benchmarks from the paper, including:</p> <ul> <li>Scripts and command lines</li> <li>Original VCF outpurs of all tools used in benchmarking</li> <li>Minda evaluations and truthset VCF files</li> <li>Full Severus outputs + visualizations</li> <li>truvari calls</li> </ul>
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>
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>
Data for a CML blackout gap detection example
<p><em>blackout_example_cmls.nc</em> consists of three years of data from two commercial microwave links. It includes transmitted and recieved signal levels with a temporal resolution of one minute.</p> <p><em>blackout_example_radar_reference.nc</em> consists of three years of rainfall data from RADKLIM-YW, a radar product from the German Weather Service, path-averaged along the two example CMLs.</p>
Data from: eDNA metabarcoding of log hollow sediments and soils highlights the importance of substrate type, frequency of sampling and animal size, for vertebrate species detection
<p>Fauna monitoring often relies on visual monitoring techniques such as camera trappings, which have biases leading to underestimates of vertebrate species diversity. Environmental DNA (eDNA) has emerged as a new source of biodiversity data that may improve biomonitoring; however, eDNA based assessments of species richness remain relatively untested in terrestrial environments. We investigated the suitability of fallen log hollow sediment as a source of vertebrate eDNA, across two sites in south-western Australia - one with a Mediterranean climate and the other semi-arid. We compared two different approaches (camera trapping and eDNA metabarcoding) for monitoring of vertebrate species, and investigated the effect of other factors (frequency of species, timing of visits, frequency of sampling, body size) on vertebrate species detectability. Metabarcoding of hollow sediments resulted in the detection of higher species richness in comparison Hollow sediment detected higher species richness (29 taxa: six birds, three reptiles and 20 mammals) to metabarcoding of soil at the entrance of the hollow (13 taxa: three birds, two reptiles and eight mammals). We detected 31 taxa in total with eDNA metabarcoding and 47 with camera traps, with 14 taxa detected by both (12 mammals and two birds). By comparing camera trap data with eDNA read abundance, we were able to detect vertebrates through eDNA metabarcoding that had visited the area up to two months prior to sample collection. Larger animals were more likely to be detected, and so were vertebrates that were identified multiple times in the camera traps. These findings demonstrate the importance of substrate selection, frequency of sampling, and animal size, on eDNA based monitoring. Future eDNA experimental design should consider all these factors as they affect detection of target taxa. </p>
Phage RBP detection data
<p>These datasets contain the filtered <em>RBP </em>and <em>Other</em> sequences utilized in our manuscript in submission for the special issue 'Virus Bioinformatics 2022' in <em>Viruses</em>: "Identification of phage receptor-binding protein sequences with hidden Markov models and an extreme gradient boosting classifier". These datasets enable to fully reproduce our analyses.</p>
Detection of early seeding of Richter transformation in chronic lymphocytic leukemia: scRNA-seq data
<p>Richter transformation (RT) is a paradigmatic evolution of chronic lymphocytic leukemia (CLL) into a very aggressive large B cell lymphoma conferring a dismal prognosis. The mechanisms driving RT remain largely unknown. We characterized the whole genome, epigenome and transcriptome, combined with single-cell DNA/RNA-sequencing analyses and functional experiments, of 19 cases of CLL developing RT. Studying 54 longitudinal samples covering up to 19 years of disease course, we uncovered minute subclones carrying genomic, immunogenetic and transcriptomic features of RT cells already at CLL diagnosis, which were dormant for up to 19 years before transformation. We also identified new driver alterations, discovered a new mutational signature (SBS-RT), recognized an oxidative phosphorylation (OXPHOS)high–B cell receptor (BCR)low-signaling transcriptional axis in RT and showed that OXPHOS inhibition reduces the proliferation of RT cells. These findings demonstrate the early seed- ing of subclones driving advanced stages of cancer evolution and uncover potential therapeutic targets for RT.</p> <p>This repository contains the processed scRNA-seq data (expression matrices, Seurat objects, metadata) related with this publication.</p>
The segregation of recycled basaltic material within mantle plumes explains the detection of the X-Discontinuity beneath hotspots: 2D geodynamic simulations: Data
<pre>This repository accompanies the paper ``` The segregation of recycled basaltic material within mantle plumes explains the detection of the X-Discontinuity beneath hotspots: 2D geodynamic simulations by Martina Monaco, Juliane Dannberg, Rene Gassmoeller, Stephen Pugh ``` The global models presented in the manuscript were run using the following dependencies: ``` ----------------------------------------------------------------------------- -- This is ASPECT, the Advanced Solver for Problems in Earth's ConvecTion. -- . version 2.3.0-pre (master, 74e48be) -- . using deal.II 9.3.0 -- . with 32 bit indices and vectorization level 2 (256 bits) -- . using Trilinos 12.10.1 -- . using p4est 2.2.0 ----------------------------------------------------------------------------- ``` This repository contains: - The 'all_model_series' folder with the files used to analyze the depth averages. Each series (100, Aoki, Hefesto) has its own subfolder; - The 'plugins' folder, with the required plugin to run the models. To compile the plugin, navigate into this directory and follow the steps: 1. `cmake -D Aspect_DIR=PATH_TO_ASPECT` (replace `PATH_TO_ASPECT` with the directory where you compiled ASPECT). 2. `make` - The 'run_series' bash script, with the command to run multiple models at once. The user should modify: ° The input file name: INPUT_FILE=heterogeneity-several-blobs-INSERT-SERIES.prm ° The directory where ASPECT is located: srun --mpi=pmix_v2 $HOME/aspect/aspect-build/aspect --" echo -e $COMMAND | sbatch --job-name gs_${viscosity}_spacing_${blob_spacing} -p hpg2-compute -N 1 -n 32 -t 3-23:59:00 -o output_gs_${viscosity}_spacing_${blob_spacing}.%j -e error_gs_${viscosity}_spacing_${blob_spacing}.%j --constraint 'haswell|skylake' --mem-per-cpu '3gb' --distribution block; - The three parameter files (.prm), one per series; - Two .py files containing the scripts necessary to plot all the figures in the paper</pre>
Light camera position detection - experiment data
<p>Experiment data measure the accuracy of automatic light fixtures position detection using a single monocular web camera with different resolutions.</p>
Data on the detection of clinically significant prostate cancer by magnetic resonance imaging (MRI)-guided targeted and systematic biopsy
<p>This is a dataset from the original publication “Reasons for missing clinically significant prostate cancer by targeted magnetic resonance imaging/ultrasound fusion-guided biopsy”. From 01/2014 to 04/2019 a sample collective of 785 patients with 3T multiparametric magnetic resonance imaging (mp-MRI) of the prostate and subsequent combined systematic biopsy (SB) and magnetic resonance imaging/ultrasound (US) fusion-guided biopsy (TB) was retrospectively analyzed. Prostate carcinoma (PCa) detection by TB and/or additional SB was analyzed.</p>
Data: Guiding the design of SARS-CoV-2 genomic surveillance by estimating the resolution of outbreak detection
<p>Contains data necessary to reproduce the quantitative results related to a SARS-CoV-2 outbreak in NSW, Australia in the associated paper.</p> <p><strong>icpmr_delta_gisaid.csv</strong><br> Tabular data containing the GISAID accession numbers, dates of collection and submission for all sequences used in the NSW outbreak analysis. The epi set is available on GISAID as EPI_SET_220919ef. The <code>wgs_cluster</code> column contains identifiers of genomic clusters defined at ICPMR, NSW Health Pathology. A value of "Other" means that the sequence either did not belong to a cluster or was part of a cluster that contained fewer than 30 sequences in the study period, and sequences with this value should not be considered to form a single cluster.</p> <p><strong>icpmr_delta_gisaid.dists.tsv.gz</strong><br> Compressed pairwise SNP distance matrix in the format output by snp-dists. The script that creates this file from sequence data is available in the linked code archive.</p>
DATA: Comparison between optical tissue clearing methods for detecting administered mesenchymal stromal cells in mouse lungs
<p>This data set includes all the raw data collected for the following article: "Comparison between optical tissue clearing methods for detecting administered mesenchymal stromal cells in mouse lungs".</p>
Tables and data for "Downward Trend in Methane Detected in a Northern Colorado Oil and Gas Production Region Using AIRS Satellite Data"
<p>These are tables and data files for the paper "Downward Trend in Methane Detected in a Northern Colorado Oil and Gas Production Region Using AIRS Satellite Data" submitted to the Journal of Atmospheric Research: Atmospheres</p>
Common guillemots in the Baltic Sea studied with video surveillance and object detection: raw data, annotations, model, and model outputs
<p>The data comes from common guillemots studied at Stora Karlsö, Sweden between 2019 and 2021. The common guillemots breed at an artificial cliff, and has been filmed continusly from above over three breeding seasons. Using the video material, a YOLOv5 model has been trained to detect adult birds, chicks and eggs. The dataset contains annotations (bounding boxes) used for training the model, the model itself, and outputs from the model (object detections).</p> <p>The data can be used and shared freely.</p>
Raw and processed cell lines (melanomaC818/melanomaMUM-2B/SK-MEL-28) data for detecting DMKN's mutations in melanoma cancer
<p>Raw and processed cell lines (melanomaC818/melanomaMUM-2B/SK-MEL-28) data for detecting DMKN's mutations in melanoma cancer. This research was concluded that DMKN is a trigger of epithelial-mesenchymal transition-driven melanoma.</p>
An Objective Detection of Separation Scenario in Tropical Cyclone Trajectories Based on Ensemble Weather Forecast Data
<p>This repository contains the data used in "An Objective Detection of Separation Scenario in Tropical Cyclone Trajectories Based on Ensemble Weather Forecast Data" by Oettli and Kotsuki (submitted to Journal of Geophysical Research: Atmospheres).</p>
Detecting anomalies in melt-extruded 3D printed parts using in situ data
<p>The data in this repository was gathered from a study to collect real-time, in situ data from polymer melt extrusion (ME) 3D printing, using a set of sensors to non-destructively identfy printed parts that contain defects. The data underwent variance analysis to determine an "acceptable" range of filament diameters and non-destructivley identify spatial regions of printed cylinders in multi-part builds that contain defects.</p> <p>The data consists of two folders and a log meant to track procedural adherence for each cylinder printed, the introduced defects, or lack thereof, and the pressurization of the part. The "Final Build Logs" spreadsheet contains information regarding the two locations of the deformations along the 56 meters of filament needed to have no more than three anomalous cylinders out of the six printed cylinders, the date of the applied deformations to the filament, the initials of the researcher applying the deformations, the date that the build was printed along with the initials of the researcher who printed it, the part number, researcher initials, and date of the pressurization test for each cylinder within the build, and a comment describing any deviations from the procedure that play into the random error of the statistical analysis for each cylinder. </p> <p>The "Pressure Test Data" folder contains a folder for each build. Within these folders are .tdms files containing metadata on the measurement system in the header and tab-delimeted values for the columns. The columns of interest to the study are X_Value, representing time elapsed, and pressure, which we evaluated on the values' exponential decay rate. The files also contain supplemental information such as a column for temperature (celsius), and the flow rate (SLPM Normalized). The "Build Data" folder contains in situ data from the sensor-equipped printer in a .csv file, the STL file for the build, the gcode file from the applied slicer settings, the AMRP file stores printer settings, and a .pdf file for the setup specifications.</p>
Data and Code: Spatiotemporal Variability of Fin Whale and Blue Whale Calls Detected by Land Seismometers in the Lower St. Lawrence Seaway
<h3>Code</h3> <p><strong>MATLABWhaleDetectionCodeNWA.zip</strong></p> <p>The code used in the manuscript Spatiotemporal Variability of Fin Whale and Blue Whale Calls Detected by Land Seismometers in the Lower St. Lawrence Seaway<em> </em>(Goblot et al., in review)<em> </em>to detect whale calls in seismic waveforms was written by Alexandre Plourde and uploaded here with permission. This .zip file contains 3 folders, ‘Numerical Methods’, ‘Signal Processing’ and ‘Whale Subroutines’, as well as 3 main scripts (x2 for fin and blue whales). </p> <ul> <li>Pre-processing: <ul> <li>Convert .mseed to SAC files and rename with the format: YYYY.MM.DD.NETWORK.STATION..CHANNEL.SAC. Each SAC file must then be placed in a folder named with the format YYYYMMDD, referred to as events (evs) in the code. Each of these folders must then be placed into a single folder known as the events directory (drE) in the code.</li> </ul> </li> </ul> <ul> <li>Processing: Fin whales <ul> <li>Run ‘RecordWhaleNoiseLSZ.m’ for fin whales. This reads through daily SAC files, and computes fin whale power ratio every 120 s. The folder ‘FinWhalePower’ (drP) should now be created. These contain text files with the power ratios (W) for every window at each station.</li> <li>Run ‘createFinWhaleDectectionList.m' to check which 120s time windows have W > threshold (3.0 in our case). These time windows are stored in the matrix FWD.</li> <li>Run 'RecordFinWhaleCallsLSZ.m' to identify individual whale calls within each of the 120s time segments in the FWD matrix. The labelled call times are stored in the matrix FWC.</li> </ul> </li> </ul> <ul> <li>Processing: Blue whales <ul> <li>Run ‘RecordBlueWhalesLSZ.m’ for fin whales. This reads through daily SAC files, and computes fin whale power ratio every 120 s. The folder ‘BlueWhalePower’ (drP) should now be created. These contain text files with the power ratios (W) for every window at each station.</li> <li>Run ‘createBlueWhaleDectectionList.m' to check which 720s time windows have W > threshold (1.5 in our case). These time windows are stored in the matrix BWD.</li> <li>Run 'RecordFinWhaleCallsLSZ.m' to identify individual whale calls within each of the 720s time segments in the BWD matrix. The labelled call times are stored in the matrix BWC.</li> </ul> </li> </ul> <h3>Data</h3> <p>The whale detection code was applied to seismic waveform data downloaded through the EarthScope Consortium Web Services (<a href="https://service.iris.edu/" target="_blank" rel="noopener">https://service.iris.edu/</a>), including the following seismic network: CN (Natural Resources Canada, 1975). All whale calls were detected using the characteristic reccurence interval method (MATLABWhaleDetectionCodeNWA.zip).</p> <p><strong>Table S2</strong></p> <p><strong>WhaleDetectionsFeb2020Jan2022LSZ.mat</strong></p> <ul> <li>This dataset contains the center time of fin whale and blue whale detections and calls, from land 6 seismometers (CNQ, ICQ, SMQ, SNFQ, PMAQ, RISQ) in the Lower St-Lawrence Seaway between February 2020 and January 2022. <ul> <li>FWD_ <ul> <li>Column 1: list of fin whale detections (2 minute time window with presence)</li> <li>Column 2: corresponding station label</li> </ul> </li> <li>BWD_ <ul> <li>Column 1: list of blue whale detections (12 minute time window with presence)</li> <li>Column 2: corresponding station label</li> </ul> </li> <li>FWC_ <ul> <li>Column 1: list of individual ~1s fin whale calls</li> <li>Column 2: corresponding station label</li> </ul> </li> <li>BWC_ <ul> <li>Column 1: list of individual ~8s blue whale calls</li> <li>Column 2: corresponding station label</li> </ul> </li> <li>stadir: list of stations and labels (1-6)</li> </ul> </li> </ul> <p><strong>Table S3</strong></p> <p>a)<strong> MonthlyBlueWhaleDetectionsOct2015Jan2022.csv</strong> and b) <strong>MonthlyFinWhaleDetectionsOct2015Jan2022.csv</strong></p> <ul> <li> <div> <div> <div> <p>Number of monthly a) fin whale detections and b) blue whale detections from October 2015 to January 2022. Empty cells indicate periods when stations were not operating. Quiet day detections are included.</p> <p>Note the Oct 2015-Feb 2020 catalogue is from Plourde and Nedimović (2022) and includes up to 14 stations throughout this period from the following seismic networks: CN (Natural Resources Canada, 1975) and C8 (Natural Resources Canada, 2002). The Feb 2020-Jan 2022 catalogue is from Goblot et al. (in review) and includes the same stations from Table S1.</p> </div> </div> </div> </li> </ul> <h3>Additional</h3> <p><strong>Movie S1</strong></p> <p><strong>LSLSWhales.mp4</strong></p> <ul> <li>This movie contains an audiovisual representation of a series of fin whale calls and blue whale calls detected by 2 land seismometers in the Lower St-Lawrence Seaway. The fin whale detection was recorded by land seismometer CNQ (Côte-Nord) on Dec 12 2021 from 18:19:40 to 18:21:40. The blue whale detection was recorded by land seismometer SNFQ (Sainte-Félicité) on Aug 22 2021 from 05:36:00 to 05:48:00.</li> <li>The MATLAB soundsc(x) function was applied to seismic waveform data with fin whale and a blue whale calls. The signal with the fin whale detection is bandpassed from 18-21 Hz and the blue whale signal is bandpassed from 16-18 Hz. These signals were sped up 1000x in order to make them audible.</li> <li>The audio file was then uploaded to veed.io to produce a frequency response visualization of the whale calls.</li> <li>More audiovisuals can be viewed <a href="https://seismicsoundscapes.myportfolio.com/" target="_blank" rel="noopener">here</a>.</li> </ul>
Data to accompany the outlier-waveform-detection Github repository (internal globus pallidus, GPi)
<p>This repository contains data based on neuronal recordings from two monkeys (G and I, in the pre- and post-MPTP states) that serve as input to the code provided at <a href="https://github.com/turner-lab-pitt/outlier-waveform-detection">https://github.com/turner-lab-pitt/outlier-waveform-detection</a>. Text files located within that Github repository provide detailed instructions on how these data may be used with that code. As described in those text files, extra data are provided for Monkey G, in the pre-MPTP state.</p> <p>The data-description.txt file provides detailed information regarding the contents of each zipped tar archive. Briefly, the most important components of the files are the "snips" (individual spike waveforms) from the two monkeys and MPTP states, as extracted for each of a series of single sorted units from the internal globus pallidus (GPi). The additional G-Pre data provides examples of the high-pass filtered voltage signals from which these snips were extracted. All data are stored in the Matlab .mat format.</p> <p>All zipped files can be decompressed with 7-zip: <a href="https://www.7-zip.org/" target="_blank" rel="noopener">https://www.7-zip.org/</a></p> <p>These data and the associated Github code were used for analyses reported in an in-preparation manuscript (Kase et al., "Movement-related activity in the internal globus pallidus of the parkinsonian macaque"), and also with a preprint that is currently under review:</p> <div> <div>Detecting rhythmic spiking through the power spectra of point process model residuals</div> </div> <div>Karin M. Cox, Daisuke Kase, Taieb Znati, Robert S. Turner</div> <div>bioRxiv 2023.09.08.556120; doi: <a href="https://doi.org/10.1101/2023.09.08.556120" target="_blank" rel="noopener">https://doi.org/10.1101/2023.09.08.556120</a></div> <div> </div> <p>This research was funded in part by Aligning Science Across Parkinson's [ASAP-020519] through the Michael J. Fox Foundation for Parkinson's Research (MJFF). For the purpose of open access, the authors have applied a Creative Commons Attribution 4.0 International (CC BY) public copyright license to this dataset. </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.