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4,612 results for “Labels”
Sex affects immunolabeling for histone 3 K27me3 in the trophectoderm of the bovine blastocyst but not labeling for histone 3 K18ac
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Data from: Dynamics of the CD9 interactome during bacterial infection of epithelial cells by proximity labelling proteomics
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Data from: Label-free imaging of intracellular structures in living mammalian cells via external apodization phase-contrast microscopy
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Machine learning feature data from EHR, labels, and estimates for next generation sequencing-based assay
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Codes and source data files for: Proximity labeling identifies LOTUS domain proteins that promote the formation of perinuclear germ granules in C. elegans
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Data from: Long-lived metabolic enzymes in the crystalline lens identified by pulse-labeling of mice and mass spectrometry
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Fluorescent images of actin and DAPI-labelled MCF10A, MCF7 and MDA-MB-231 cell lines
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Five-years of ocrelizumab in relapsing multiple sclerosis: OPERA studies open-label extension
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Supplementary methods and data for: Dogs with a vocabulary of object label remember labels for at least two years
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Data from: Proximity-labeling proteomics reveals remodeled interactomes and altered localization of pathogenic SHP2 variants
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MiRoR2 - P2 - Use of test accuracy study design labels in NICE's diagnostic guidance
<p>Data set for the study “Use of test accuracy study design labels in NICE's diagnostic guidance”, including list of the included NICE guidance, data extraction form, extracted data with summary of results, and extraction guidance.</p> <p>Reference to associated article: Olsen M, Zhelev Z, Hunt H, Peters JL, Bossuyt PM, Hyde C. Use of test accuracy study design labels in NICE’s diagnostic guidance. Diagnostic and Prognostic Research. 2019;3:17</p>
Mars novelty detection Mastcam labeled dataset
<p>These datasets were used for experiments in the paper:<br> Kerner, H. R., Wagstaff, K. L., Bue, B. D., Wellington, Jacob, S., D. F., Bell III, J. F., Kwan, C., Ben Amor, H. Comparison of novelty detection methods for multispectral images in rover-based planetary exploration missions. Under review, 2020.</p> <p>All source images are publicly released Experiment Data Records (EDRs) archived by the Planetary Data System (PDS).</p>
Metabolic pathway inference using multi-label classification with rich pathway features
<p>We include samples of various data types used in the work "Metabolic pathway inference using multi-label classification with rich pathway features"</p> <p>More information about the software package and instructions are provided in <a href="https://github.com/hallamlab/mlLGPR">hallamlab/mlLGPR</a></p>
Multispectral and augmented Landsat data with land cover labels
<p>Benchmark set at 77.1% O.A at: https://doi.org/10.1117/1.JRS.14.048503</p> <p>The dataset consists of 60,000 images, corresponding to Landsat patches of 33x33 pixels with 102 bands. Randomly selected from Mexico (country). Each patch is labeled with one of 12 Land Use and Vegetation classes according to the classification described at https://doi.org/10.3390/rs6053923.</p> <p>The zip file contains 12 folders numbered 1-12 and each contains 5,000 .npy python files (can be loaded with the NumPy library).</p> <p>The labeled classes correspond to the following identifier.</p> <p>1, Temperate Coniferous forest<br> 2, Temperate Decidius Forest<br> 3, Temperate Mixed Forest<br> 4, Tropical Evergreen Forest<br> 5, Tropical Deciduous Forest<br> 6, Scrubland<br> 7, Wetland Vegetation<br> 8, Agriculture<br> 9, Grassland<br> 10, Water body<br> 11, Barren Land<br> 12, Urban Area</p> <p>To build that dataset, we take the information of the National Continuum of Land Use and Vegetation series number 5 generated by the National Institute of Statistics and Geography from Mexico (INEGI) from The National Commission for the Knowledge and Use of Biodiversity (CONABIO) web page (http://geoportal.conabio.gob.mx/metadatos/doc/html/usv250s5ugw.html).</p> <p>The file used for this dataset construction is the shape format file with geographic coordinates located in http://www.conabio.gob.mx/informacion/gis/maps/geo/usv250s5ugw.zip.<br> Later, a transformation to Albers equal-area conic projection was done with the followings parameters:</p> <p>Fake east: 2500000.0<br> Fake North: 0.0<br> Origin longitude: -102.0º<br> Origin latitude: 12.0º<br> First standard parallel: 17.5º<br> Second standard parallel: 29.5º<br> Linear unit: Meter (1.0)<br> Reference ellipsoid: GRS80</p> <p><br> Once the data was projected, using the classes identified in the National Continuum of Land Use and Vegetation, correspondence was applied to the classes identified in https://doi.org/10.3390/rs6053923, these classes being: Agriculture, Barren land, Grassland, Scrubland, Temperate coniferous forest, Temperate deciduous forest, Temperate mixed forest, Tropical deciduous forest, Tropical evergreen forest, Urban area, Waterbody and Wetland vegetation.</p> <p>Once the information layer was generated with the 12 classes indicated above, the reference layer was rasterized.<br> Thus, a national grid of 1,975,940 regions of 1 x 1 kilometers was generated and the percentage of pixels of the dominant class in each corresponding 1 km region was associated.</p> <p>A total of cells with 70% or more pixels from one dominant class corresponds to 1,640,827 which represents a total of 83% of the Mexican territory. That means, only 17% of cells have less than 70% of their pixels from one dominant class.<br> Then, 5000 regions were randomly selected from each land cover class at the national level. For this random selection only were selected the regions in which cells have 70% or more of their pixels from one dominant class. The above, for looking to have consistent and reliable data for the automatic classification task. This random selection generates a total of 60,000 regions selected.</p> <p>Image patches were extracted from the selected regions in the sample.</p> <p>The image used is the result of the application of multiple time series analysis algorithms on a cube of image data with mainly Tier 1 (T1) quality and a few Tier 2 (T2) as described in https: // www. usgs.gov/land-resources/nli/landsat/landsat-collection-1. An Open Data Cube (ODC, https://www.opendatacube.org/) was constructed from 3,515 Landsat 5 and 7 images corresponding to the year 2011, which is the same reference year of the National Continuum of Land Use and Vegetation Series 5.</p> <p>From the analysis of the ODC images, the Geomedian (https://doi.org/10.1109/TGRS.2017.2723896) was calculated, which generated a national cloud-free mosaic from 2011, pixels at 30 meters resolution and 6 spectral bands (blue, green, red, nir, swir 1, swir 2). Finally, 15 spectral indices were calculated for each pixel in the image. This resulted in 15 national mosaics from the analysis of the time series of each pixel available for the year 2011 using all the combinations of normalized difference indices, which were possible with the 6 bands that were incorporated into the data cube, with which resulted in 102 information channels. Since Landsat images have a resolution of 30 meters, we have images of 33 pixels x 33 pixels for each region of 1 km x 1 km.</p> <p>The 102 channels in the patches correspond to:</p> <p>Geomedian Bands (6): blue, green, red, nir, swir 1, swir 2<br> Geomedian Based Indexes (15): evi, bu, sr, arvi, ui, ndbi, ibi, ndvi, ndwi, mndwi, nbi, brba, nbai, baei, bi<br> Geomedian Based Tasseled cap transformation (6): brightness, greenness, wetness, fourth, fifth, sixth</p> <p>2011 Landsat Time Analysis Series by Pixel</p> <p>(red-swir 1)/(red+swir 1); (5): min, mean, max, std, median<br> (red-nir)/( red+nir); (5): min, mean, max, std, median<br> (swir 1-swir 2)/( swir 1+swir 2); (5): min, mean, max, std, median<br> (nir-swir 2)/(nir+swir 2); (5): min, mean, max, std, median<br> (nir-swir 1)/( nir+swir 1); (5): min, mean, max, std, median<br> (red-swir 2)/( red+swir 2); (5): min, mean, max, std, median<br> (green-swir 2)/(green+swir 2); (5): min, mean, max, std, median<br> (green-swir 1)/(green+swir 1); (5): min, mean, max, std, median<br> (green-red)/(green+red); (5): min, mean, max, std, median<br> (green-nir)/(green+nir); (5): min, mean, max, std, median<br> (blue-swir 2)/(blue+swir 2); (5): min, mean, max, std, median<br> (blue-swir 1)/(blue+swir 1); (5): min, mean, max, std, median<br> (blue-red)/(blue+red); (5): min, mean, max, std, median<br> (blue-nir)/(blue+nir); (5): min, mean, max, std, median<br> (blue-green)/( blue+green); (5): min, mean, max, std, median</p>
Pulse labelling of deep soil layers in forest with 13CH4: testing a new method for tracing methane in the upper horizons, understorey vegetation and tree stems using laserbased spectrometry
<p>Methane is one the main greenhouse gases and upland forests are one of its main biological sinks. However, vegetation could reduce this sink by allowing a direct path for the methane produced in deep soils to escape into the atmosphere. By tracing emissions with <sup>13</sup>CH<sub>4</sub>, we show for the first time that methane emitted by trees in upland forest could have a deep soil origin even though the soil is an overall methane sink. However, we found that the trees and understorey vegetation at our site had only a small impact on the methane budget.</p>
FIGURES 316–323. Habitus and type labels. 316–319 in Revision of the genus Bioramix Bates, 1879 (Coleoptera: Tenebrionidae: Platyscelidini) from China
FIGURES 316–323. Habitus and type labels. 316–319. Habitus: 316–317. Bioramix yushuana (316. male, holotype; 317. female, paratype); 318. B. pamirensis (male); 319. B. lucidicollis (male). 320–323. Type labels: 320. B. oblongula; 321. B. ovalis (photograph by Keita Matsumoto); 322. B. rotundicollis; 323. Botiras punctatellus (= B. asidioides).
FIGURES 1–13. Lectotype labels. 1 in The type material of Mordellidae described by Achille Costa (Coleoptera Tenebrionoidea)
FIGURES 1–13. Lectotype labels. 1, Tomoxia bucephala Costa, 1854. 2, Mordella coronata Costa, 1854. 3, Mordella inter- rupta Costa, 1854. 4, Mordella basalis Costa, 1854. 5, Mordella brevicauda Costa, 1854. 6, Mordella perspicillata Costa, 1854. 7, Mordellistena stricta Costa, 1854. 8, Mordellistena purpurascens Costa, 1854. 9, Mordellistena picipes Costa, 1854. 10, Mordellistena confinis Costa, 1854. 11, Mordellistena minima Costa, 1854. 12, Natirrica meridionalis Costa, 1854. 13, Mordella viridescens Costa, 1859.
Designing and Comparing Optimized Pseudo-Continuous Arterial Spin Labeling Protocols for Measurement of Cerebral Blood Flow
<p>This repository contains the the simulation data, preprocessed in vivo data, and analysis code used in the NeuroImage article titled 'Designing and Comparing Optimized Pseudo-Continuous Arterial Spin Labeling Protocols for Measurement of Cerebral Blood Flow' (<a href="https://doi.org/10.1016/j.neuroimage.2020.117246">https://doi.org/10.1016/j.neuroimage.2020.117246</a>). Please cite this article if you use this code or data in your work.</p>
Multi-label Pathway Prediction based on Active Dataset Subsampling
<p>We include samples of various data types used in the work "Multi-label Pathway Prediction based on Active Dataset Subsampling" (under-review)</p> <p>More information about the software package and instructions are provided in <a href="https://github.com/hallamlab/leADS">hallamlab/leADS</a></p>
Mars orbital image (HiRISE) labeled data set version 3.2
<p> </p> <p><strong>Please note that the file hirise-map-proj-v3_2.zip below contains the latest images and labels associated with this data set. </strong></p> <p> </p> <p>This dataset contains a total of 64,947 landmark images that were detected and extracted from HiRISE browse images, spanning 232 separate source images.</p> <p>This set was formed from 10,815 original landmarks. Each original landmark was cropped to a square bounding box that included the full extent of the landmark plus a 30-pixel margin to the left, right, top, and bottom. Each landmark was then resized to 227x227 pixels. 9,022 of these images were then augmented to generate 6 additional landmarks using the following methods:</p> <p>1. 90 degrees clockwise rotation<br> 2. 180 degrees clockwise rotation<br> 3. 270 degrees clockwise rotation<br> 4. Horizontal flip<br> 5. Vertical flip<br> 6. Random brightness adjustment</p> <p>The remaining 1,793 images were not augmented. Combining these with the 7*9,022 images, gives a total of 64,947 separate images.</p> <p><br> <strong>Contents:</strong><br> - map-proj-v3_2/: Directory containing individual cropped landmark images<br> - labels-map-proj-v3_2.txt: Class labels (ids) for each landmark image. File includes two columns separated by a space: filename, class_id</p> <p>- labels-map-proj-v3_2_train_val_test.txt: Includes train/test/val labels and upsampling used for trained model. File includes three columns separated by a space: filename, class_id, set<br> - landmarks_map-proj-v3_2_classmap.csv: Dictionary that maps class ids to semantic names</p> <p><strong>Class Discussion:</strong></p> <p>We give a discussion of the various landmarks that make up our classes.<strong> </strong></p> <p>Bright dune and dark dune are two sand dune classes found on Mars. Dark dunes are completely defrosted, whereas bright dunes are not. Bright dunes are generally bright due to overlying frost and can exhibit black spots where parts of the dune are defrosting.</p> <p>The crater class consists of crater images in which the diameter of the crater is greater than or equal to 1/5 the width of the image and the circular rim is visible for at least half the crater's circumference.</p> <p>The slope streak class consists of images of dark flow-like features on slopes. These features are believed to be formed by a dry process in which overlying (bright) dust slides down a slope and reveals a darker sub-surface.</p> <p>Impact ejecta refers to material that is blasted out from the impact of a meteorite or the eruption of a volcano. We also include cases in which the impact cleared away overlying dust, exposing the underlying surface. In some cases, the associated crater may be too small to see. Impact ejecta can also include lava that spilled out from the impact (blobby ("lobate") instead of blast-like), more like an eruption (triggered by the impact). Impact ejecta can be isolated, or they can form in clusters when the impactor breaks up into multiple fragments.</p> <p>Spiders and Swiss cheese are phenomena that occur in the south polar region of Mars. Spiders have a central pit with radial troughs, and they are believed to form as a result of sublimation of carbon dioxide ice. This process can produce mineral deposits on top, which look like dark or light dust that highlights cracks in the CO2 ice. Spiders can resemble impact ejecta due to their radial troughs, but impact ejecta tends to have straight radial jets that fade as they get farther from the center. The spider class also includes fan-like features that form when a geyser erupts through the CO2 layer and the material is blown by the wind away from the cracks. Fans are typically unidirectional (following the wind direction), whereas impact ejecta often extends in multiple directions. Swiss cheese is a terrain type that consists of pits that are formed when the sun heats the ice making it sublimate (change solid to gas).</p> <p>Other is a catch-all class that contains images that fit none of the defined classes of interest. This class makes up the majority of our data set.</p>
ScienceDex guides
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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.