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990 results for “quantification”
Nissl_6, Raw images for Machine learning for histological annotation and quantification of cortical layers.
<p>This dataset contains images (TIFF image data) of <strong>brain juvenile rats Wistar Han (P14)</strong> scanned immunostained slides by using an automated slide scanner (Olympus, VS120-L100, GER) equipped with a UPLSAPO 20x/0.75 air objective (Olympus, GER) and a Pike F505 Color camera leading to a pixel size of 0.346 μm/pixel.</p> <p>These raw images are part of another Zenodo dataset <span><a href="https://doi.org/10.5281/zenodo.11544829">https://doi.org/10.5281/zenodo.11544829</a></span>, that contains the QuPath projects that uses this dataset and 5 others (from Nissl_1 to Nissl_6).</p> <p> </p> <p> </p>
Nissl_5, Raw images for Machine learning for histological annotation and quantification of cortical layers
<p>This dataset contains images (TIFF image data) of <strong>brain juvenile rats Wistar Han (P14)</strong> scanned immunostained slides by using an automated slide scanner (Olympus, VS120-L100, GER) equipped with a UPLSAPO 20x/0.75 air objective (Olympus, GER) and a Pike F505 Color camera leading to a pixel size of 0.346 μm/pixel.</p> <p>These raw images are part of another Zenodo dataset <span><a href="https://doi.org/10.5281/zenodo.11544829" target="_blank" rel="noopener noreferrer">https://doi.org/10.5281/zenodo.11544829</a></span>, that contains the QuPath projects that uses this dataset and 5 others (from Nissl_1 to Nissl_6).</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>
Data for "A replicable and modular benchmark for long-read transcript quantification methods"
<p>This archive contains the input necessary to run the inital (TranSigner-protocol and IsoQuant-protocol) benchmarks associated with the <a href="https://github.com/COMBINE-lab/lr_quant_benchmarks" target="_blank" rel="noopener"><code>lr_quant_benchmarks repository</code></a>. The archive can be decompressed with <code>tar</code> and <code>zstd</code> using the command <code>tar --use-compress-program=zstd -xf input.tar.zstd</code>.</p>
Research data supporting "Raman spectroscopic imaging for quantification of depth-dependent and local heterogeneities in native and engineered cartilage"
<p>Research data supporting the publication: Albro M. et al., 2018, npj Regenerative Medicine, DOI: https://doi.org/10.1038/s41536-018-0042-7.</p>
Reference data for "Limitations of alignment-free tools in total RNA-seq quantification"
<p>This repository contains reference data for "Limitations of alignment-free tools in total RNA-seq quantification"</p>
Validation of a standardized MRI method for liver fat and T2* quantification
<p><strong>Dataset description:</strong> These data have been uploaded and shared as part of the manuscript <em>“Validation of a standardized MRI method for liver fat and T2* quantification, Chloe Hutton, Michael L. Gyngell, Matteo Milanesi, Alexandre Bagur, and Michael Brady, Perspectum Diagnostics, Oxford, United Kingdom", which was submitted for publication to PLOS ONE on August 27th 2018.</em></p> <p><strong>Details:</strong> The LMSIDEAL_Results.zip file extracts into 28 MATLAB files (MATLAB R2017b) corresponding to LMS IDEAL PDFF results calculated as described in the above manuscript for 28 sets of publicly-available phantom data available from another repository. The original phantom data can be accessed from (<a href="http://dx.doi.org/10.5281/zenodo.48266)">http://dx.doi.org/10.5281/zenodo.48266)</a> and are described in detail in [Hernando et al., Magn Reson Med. 2017;77:1516-1524. doi: 10.1002/mrm.26228. Epub 2016 Apr 15.].</p> <p>To summarise, the original phantom data were acquired using one phantom at six sites, covering: 3 vendors (GE Healthcare, Siemens and Philips); 2 field strengths (1.5T and 3T); and 2 protocols. One of the six sites had two sets of data (one at the beginning of the phantom study and one at the end), to give (6+1)x2x2=28 sets of data in total. The phantom consisted of 11 vials with oil/water concentrations: 0%, 2.6%, 5.3%, 7.9%, 10.5%, 15.7%, 20.9%, 31.2%, 41.3%, 51.4%, 100%. The data from each system, and for each protocol, involved 6 echoes of complex-valued multi-echo gradient echo MR images.</p> <p>Each of the 28 LMSIDEAL_Results_* MATLAB files contains 3 MAT files:</p> <p>LMSIDEAL_PDFF - contains PDFF maps (sized X x Y x 3 slices)</p> <p>ROI - contains x,y coordinates for each ROI (sized 2 x 11) (circular ROI with diameter approximately = 19.5mm)</p> <p>MEAN - contains mean for each slice and each ROI (sized 3 x 11)</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Stitched Image files - adenoma quantification using Featurecounter
<p>Hugin-merged & stitched image files from Apc<sup>min</sup> mice</p>
Fig. 4 in Detection and quantification of house mouse Eimeria at the species level - Challenges and solutions for the assessment of coccidia in wildlife
Fig. 4. qPCR detection of intracellular stages of Eimeria in cecum and ileum from Mus musculus. -Delta Ct value (CtEimeria - CtMouse) from each tissue for 164 mice are plotted on the graph. The dotted line indicate the threshold of −5, values above the line are considered positive for the corresponding tissue. Circles represent negative samples, triangles indicate samples with Eimeria species identification and colors correspond to the Eimeria species identified in those samples. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 1 in Detection and quantification of house mouse Eimeria at the species level - Challenges and solutions for the assessment of coccidia in wildlife
Fig. 1. Geographical localization of house mice (Mus musculus) collected for this study and comparison of diagnostic methods for Eimeria. A) Localization from the 378 mice included in the present study, colors indicate the Eimeria species identified for each. B) Venn diagram showing the overlap between detection methods and successful genotyping identification of the isolates. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 3 in Detection and quantification of house mouse Eimeria at the species level - Challenges and solutions for the assessment of coccidia in wildlife
Fig. 3. Morphological and morphometrical characteristics of Eimeria oocyst isolated from Mus musculus. a) Photomicrographs at 1000x amplification of Eimeria oocyst from the three species isolated from Mus musculus (red = E. falciformis; green = E. ferrisi and yellow = E. vermiformis). Length/Width ratio from b) oocyst and c) sporocysts corresponding to each species (E. falciformis n = 31; E. ferrisi n = 127 and E. vermiformis n = 35). Mean ± 95% Confidence Interval is plotted. * Represent significant difference (Tukey HSD, p <0.05). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Figure 4. Land use quantification around 1200 meters from a in Odonata (Insecta) in high-altitude fields and associated ecosystems in the Poços de Caldas Plateau, Brazil
Figure 4. Land use quantification around 1200 meters from a central point at the collection sites. JB: Jardim Botânico (Botanical Garden); MO: Moinhos (Mills); MF: Morro do Ferro (Iron Hill). / Cuantificación del uso del suelo alrededor de 1200 metros desde un punto central en los sitios de recolección. JB: Jardim Botânico (JardÍn Botánico); MO: Moinhos (Molinos); MF: Morro do Ferro (Colina de Hierro).
FIGURE 4 in Methods for isolation and quantification of microfossil fish teeth and elasmobranch dermal denticles (ichthyoliths) from marine sediments
FIGURE 4. Examples of select taxonomically identifiable fossil ichthyoliths and modern counterparts. All modern ichthyoliths were isolated from specimens in the Scripps Marine Vertebrate Collection. The fossil Myctophidae and Triakidae specimens are from ODP Site 1262, and are 62 million years old. The Scaridae modern teeth are from Smithsonian National Museum of Natural History's Fish Collection and subfossil teeth are from coral reef sediment cores taken off of the coast of Bocas del Toro, Panama, and are approximately 1200 years old.
FIGURE 3. Paleocene-aged ichthyoliths from ODP Site 1262, stained with Alizarin Red S in Methods for isolation and quantification of microfossil fish teeth and elasmobranch dermal denticles (ichthyoliths) from marine sediments
FIGURE 3. Paleocene-aged ichthyoliths from ODP Site 1262, stained with Alizarin Red S. The scale bar is 500 μm, with teeth>106 μm in the upper row and teeth <106 μm in the lower. Note that in the coloring effect is present in all teeth, however, the degree of staining varies.
FIGURE 2. A flowchart showing the steps for sediment processing for efficient and effective ichthyolith isolation from a in Methods for isolation and quantification of microfossil fish teeth and elasmobranch dermal denticles (ichthyoliths) from marine sediments
FIGURE 2. A flowchart showing the steps for sediment processing for efficient and effective ichthyolith isolation from a variety of sediment types. Sediment types are in boxes, while processing steps are shown in ovals.
FIGURE 1 in Methods for isolation and quantification of microfossil fish teeth and elasmobranch dermal denticles (ichthyoliths) from marine sediments
FIGURE 1. An assortment of large (>106 μm fraction) denticles (elasmobranch scales; left) and fish teeth (right) from DSDP Site 596, a red clay core in the South Pacific. These ichthyoliths are approximately 52 million years old. Image was taken on the Hull Lab Imaging System, Yale University. Scale bar is 500 μm.
Spectral data for direct detection and quantification of phycocyanin in sediments by hyperspectral imaging: Spiking Session 1 - Phycocyanin
<p>Accompanying data to publication: <strong>Direct detection and quantification of phycocyanin in sediments by hyperspectral imaging</strong></p> <p>Hyperspectral data with all their processing steps - normalization, ROI, subsets, masking - resulting in RABD indices of Chlorophyll a and Phycocyanin pigments from spiking experiments on sediments.</p>
Data From: Clinical evaluation of patterned dried plasma spot cards to support quantification of HIV viral load and reflexive genotyping
<p>This is the data set from all figures and tables from the manuscript "Clinical evaluation of patterned dried plasma spot cards to support quantification of HIV viral load and reflexive genotyping", which is posted to the ChemRxiv preprint server (10.26434/chemrxiv-2024-5bqm7) and currently in consideration for peer-reviewed publication elsewhere.</p>
Fig. 1 in Quantification of prey consumption by the predators Chauliognathus flavipes (Coleoptera: Cantharidae), Cycloneda sanguinea (Coleoptera: Coccinellidae), and Orius insidiosus (Heteroptera: Anthocoridae)
Fig. 1. Mean number of prey (± SE) consumed by (A) Orius insidiosus, (B) Cycloneda sanguinea, and (C) Chauliognathus flavipes predators. Columns with the same letter (within predator species) are not different (Tukey's multiple comparison test, P <0.05).
Quantification of Fatty Acids in Hemp Seeds (Cannabis sativa L.) and Yield Prediction Using Machine Learning for Soxhlet and Ultrasound Extraction Methods
<p>This study focuses on the quantification of fatty acids present in hemp seeds (Cannabis sativa L.) cultivated in the Ecuadorian Andes using Soxhlet and ultrasound extraction methods. The aim is to evaluate and compare the extraction efficiency of these two techniques. Furthermore, machine learning models are applied to predict extraction yields based on experimental conditions. Using locally cultivated seeds provides valuable insights into the influence of regional agro-climatic conditions on the chemical composition. The integration of predictive algorithms offers a novel approach to optimizing the extraction process, enhancing both precision and efficiency. The findings could contribute to developing sustainable extraction methods for high-value bioactive compounds in the food and pharmaceutical industries.</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.