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13 results for “Annotated training data”
Training data for 'Genome annotation with Maker' tutorial (Galaxy Training Material)
<p>The data provided here are part of a Galaxy Training Network tutorial for genome annotation with Maker.</p> <p>It is based on data used in <a href="http://weatherby.genetics.utah.edu/MAKER/wiki/index.php/MAKER_Tutorial_for_WGS_Assembly_and_Annotation_Winter_School_2018">another Maker tutorial</a>.</p> <p>The full genome was <a href="https://www.ncbi.nlm.nih.gov/genome/?term=Schizosaccharomyces%20pombe[Organism]&cmd=DetailsSearch">downloaded from NCBI</a>, and mitochondria sequence removed from it for simplicity.</p>
Training data for 'Functional annotation of protein sequences' tutorial (Galaxy Training Material)
<p>The data provided here are part of a Galaxy Training Network tutorial for functional annotation of protein sequences.</p>
Training data for 'Refining Manual Genome Annotations with Apollo (eukaryotes)' tutorial (Galaxy Training Material)
<p>The data provided here are part of a Galaxy Training Network tutorial for manual curation of eukaryotic genome annotation using Apollo.</p>
Training data for 'Long non-coding RNAs (lncRNAs) annotation with FEELnc' tutorial (Galaxy Training Material)
<p>Data needed for the 'Long non-coding RNAs (lncRNAs) annotation with FEELnc' tutorial (Galaxy Training Material).<br>The assembly was generated following the 'Genome assembly using PacBio data' tutorial.<br>The annotation was generated following the 'Genome annotation with Funannotate ' tutorial.</p> <p>The bam file is RNASeq SRR8534859_1.fastq.gz and SRR8534859_2.fastq.gz mapping on the genome assembly.</p>
Cellpose training data and scripts from "Machine learning for histological annotation and quantification of cortical layers"
<p>This Workflow contains all the material necessary to reproduce the cells detection, thanks to the QuPath performed in the paper</p> <p> "<strong>Machine learning for histological annotation and quantification of cortical layers</strong>"</p> <p>Inside this workflow and dataset, you will find the following folders</p> <ol> <li><strong>QuPath Training Project</strong>: A QuPath 0.5.0 project containing all the manual annotations (ground truths) used to train the cellpose model, as well as the script to start the training</li> <li><strong>Training Images</strong> and <strong>Demo Images</strong>: The raw whole slide scanner images needed by the above QuPath project</li> <li><strong>Model</strong>: The fodler containing the trained cellpose model</li> <li><strong>cellpose-training Folder</strong>: The exported raw and ground truth images that the above cellpose model was trained on</li> <li><strong>Scripts</strong>: The QuPath scripts, also located in their respective QuPath projects, that were created for this whole workflow</li> <li><strong>QC</strong>: A Jupyter notebook, based on ZeroCostDL4Mic that computes quality metrics in order to assess the performance of the trained cellpose model. The folder also contains the resulting metrics.</li> </ol> <p>Installation and Use</p> <p>If you are going to use the QuPath projects, you need a local QuPath Installation https://qupath.github.io/ that is configured to run the QuPath Cellpose Extension https://github.com/BIOP/qupath-extension-cellpose as well as a working Cellpose installation https://github.com/MouseLand/cellpose</p> <p>Instructions for installation are available from the links above.</p> <p>After that, you should be able to open the QuPath project, navigate to the "Automate > Project scripts" menu and locate the script you wish to run.</p> <p><br>1. train a cell segmentation algorithm in the context of the rat brain Layer <br>Boundaries project </p> <p>2. trigger cell segmentation from a QuPath project in a semi-automated pipeline</p>
Training data for 'Genome annotation with Apollo' tutorial (Galaxy Training Material)
<p>Published scaffolds from the Apis mellifera assembly Amel_4.5 and Official Gene Set 3.2.</p> <p>Source: <a href="http://hymenopteragenome.org/beebase/?q=download_sequences">http://hymenopteragenome.org/beebase/?q=download_sequences</a></p>
Accurate annotation of protein coding sequences with IDTAXA - Training Data
<p>Training data used to test IDTAXA, HMMER, and BLAST performance of classification of amino acid and nucleotide sequences.</p>
Training data for 'Genome annotation with Funannotate' tutorial (Galaxy Training Material)
<p>The data provided here are part of a Galaxy Training Network tutorial for genome annotation with funannotate.</p> <p>Genome was assembled following the GTN Flye assembly tutorial, then masked with RepeatMasker.</p> <p>RNASeq data: SRR8534859 reads were mapped to the genome using STAR (toolshed.g2.bx.psu.edu/repos/iuc/rgrnastar/rna_star/2.7.8a+galaxy0), then the bam was downsampled (10% with toolshed.g2.bx.psu.edu/repos/devteam/picard/picard_DownsampleSam/2.18.2.1) to reduce the size of the dataset. Fastq files were then extracted from the resulting bam file (toolshed.g2.bx.psu.edu/repos/devteam/picard/picard_SamToFastq/2.18.2.1).</p> <p>SwissProt_subset.fasta is a subset of SwissProt proteins that are known to have some similarity with the genome (found using Diamond against the genome, then extracting sequences matching with e-value < 0.0001).</p>
Annotated spiral ganglion neuron training data for object detection
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Annotated data of simultaneous broadband radio and optical emission of meteor trains imaged by LOFAR / AARTFAAC and CAMS
<p>This data set contains simultaneous 30 - 60 MHz LOFAR / AARTFAAC12 radio observations and CAMS low-light video observations of +4 to -10 magnitude meteors at the peak of the Perseid meteor shower on August 12/13, 2020. 204 meteor trains were imaged in both the radio and optical domain.</p>
A live screen capture of the AVA360VR prototype being used to annotate camerawork training video data
<p>In this 2D video clip, we see a live screen capture of the AVA360VR prototype being used to annotate camerawork training data. This clip was recorded in January 2018 with an alpha version of the prototype. The functions shown do not necessarily reflect those in the final software tool.</p> <p><em>AVA360VR </em>(Annotate, Visualise, Analyse 360° video in VR) is a VR software tool developed by the BigSoftVideo team at Aalborg University. The aim is to support ‘inhabiting’ 360-degree video data – that is, to explore complex spatial video and audio recordings of a scene in which social interaction took place through a tangible interface in virtual reality.</p> <p> </p> <p> </p> <p> </p>
Data from: An integrated iterative annotation technique for easing neural network training in medical image analysis
Neural networks promise to bring robust, quantitative analysis to medical fields. However, their adoption is limited by the technicalities of training these networks and the required volume and quality of human-generated annotations. To address this gap in the field of pathology, we have created an intuitive interface for data annotation and the display of neural network predictions within a commonly used digital pathology whole-slide viewer. This strategy used a 'human-in-the-loop' to reduce the annotation burden. We demonstrate that segmentation of human and mouse renal micro compartments is repeatedly improved when humans interact with automatically generated annotations throughout the training process. Finally, to show the adaptability of this technique to other medical imaging fields, we demonstrate its ability to iteratively segment human prostate glands from radiology imaging data.
Data from: An integrated iterative annotation technique for easing neural network training in medical image analysis
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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.