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100 results for “hyperspectral images”
Spectral data for direct detection and quantification of phycocyanin in sediments by hyperspectral imaging
<p>Accompanying data to publication: <strong>Direct detection and quantification of phycocyanin in sediments by hyperspectral imaging</strong></p> <p>Excel files include hyperspectral indices of Chlorophyll a and Phycocyanin pigments from spiking experiments on sediments.</p> <p>Scripts used for producing plots in the publication and supplementary material are available on Renku; see the Software section.</p> <p>Hyperspectral data are submitted separately due to their size; see the Related works.</p>
Spectral data for direct detection and quantification of phycocyanin in sediments by hyperspectral imaging: Spiking Session 2 - 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>
Spectral data for direct detection and quantification of phycocyanin in sediments by hyperspectral imaging: Spiking Session 2 - Phycocyanin & Chlorophyll a
<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>
Hyperspectral Imaging of cake
<p>Excerpt of a hyperspectral image acquisition of a cake, including noramlization data. Used in the napari-sediment widget as an example dataset.</p>
Data from: Hyperspectral imaging reveals differential carotenoid and chlorophyll temporal dynamics and spatial patterns in Scots pine under water stress
<p>Data and codes associated with the manuscript '<span>Hyperspectral imaging reveals differential carotenoid and chlorophyll temporal dynamics and spatial patterns in Scots pine under water stress</span>'. </p>
Transmittance hyperspectral images of microalgae on well plates
<p>Images are stored in folders whose name indicate the imaging date as yyyy_mm_dd. These folders contain folders with an ID given by the imager (SpecimIQ, Specim, Finland). Inside these are the white and dark references and raw radiance images (folder: capture), transmittance images (REFLECTANCE, in file names, but transmittance in practice due to the imaging in transmission light) calculated by the SpecimIQ (folder: results) and information about the images (metadata.txt). Each rolling ID-folder contain also an RGB image of the target in png format.</p>
Data Set: Hyperspectral image unmixing with LiDAR data-aided spatial regularization
<p>Data set and matlab codes used for the experimental section of "Hyperspectral Image Unmixing With LiDAR Data-Aided Spatial Regularization"</p> <p>T. Uezato, M. Fauvel and N. Dobigeon, "Hyperspectral Image Unmixing With LiDAR Data-Aided Spatial Regularization," in <em>IEEE Transactions on Geoscience and Remote Sensing</em>, vol. 56, no. 7, pp. 4098-4108, July 2018.<br> doi: 10.1109/TGRS.2018.2823419<br> URL: <a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8347066&isnumber=8393475">http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8347066&isnumber=8393475</a><br> </p>
Hyperspectral imaging of exciton confinement within a moiré unit cell with a subnanometer electron probe
<p>Data and processing notebooks for article titled "Hyperspectral imaging of exciton confinement within a moiré unit cell with a subnanometer electron probe" published in Science in Decembner 2022.</p> <p>The `ADF Image.dm4` is the simultaneously acquired annular dark field (ADF-) scanning transmission electron microscopy (STEM) image. This was used to determine the structural reconstruction of the WSe2 / WS2 heterostructure.</p> <p>The `EELS Spectrum Image.dm4` is a spectrum image (one spectra per probe position). This was used to determine the extent of the localization of the exciton peak I.</p> <p>The two `ipynb` jupyter notebooks were used to do the analysis shown in the paper. The output is embedded in the notebooks. Also, each notebook was run and then exported as a static HTML file to retain the code and output together in a generally readable format.</p> <p>Contact Peter Ercius (percius@lbl.gov) regarding the code or data.</p>
Active and low-cost hyperspectral imaging for spectral analysis in low lighting environment
<p>Hyperspectral imaging can capture information beyond conventional RGB cameras; thus, it has many applications, such as material identification and spectral analysis. However, like many camera systems, most of the existing hyperspectral cameras are still passive imaging systems: they require external light sources to illuminate the objects to capture the spectral intensity. As a result, the collected images highly depend on the environment lighting, and the imaging system cannot function in a dark or low-lighting environment. This work develops a prototype system for active hyperspectral imaging, which actively emits different single-wavelength lights at different frequencies when imaging. This concept has several advantages: first, using the controlled lighting, the magnitude of the individual bands is normalized to extract reflectance information; second, the system is capable of collecting information at the desired spectral range by tailoring the light sources; third, an active system is mechanically easier to make, since it does not require complex band filters as used in passive systems; last, such a system may work under low light or dark environments, which greatly facilitate underground/subsurface sensing applications such as borehole based mining exploration. This prototype is achieved by using an array of low-cost and single-wavelength LED (Light Emitting Diode) lights, a remote control module controlling the LED illuminator, and the shutter of a full spectrum camera. We demonstrate that such design is feasible and could yield informative hyperspectral images for spectral analysis and machine learning-based object identification in low light or dark environments, having great potential to benefit both the academic and industry such as in geochemistry, earth science, subsurface energy, and mining.</p>
Hyperspectral Placenta Dataset: Hyperspectral Image Acquisition, Annotations, and Processing of Biological Tissues in Microsurgical Training
<p>The dataset consists of 101 hyperspectral images of four fresh human placentas and six hyperspectral images of contrast dyes (i.e., indocyanine green and red and blue food colorant) that were captured in the range 515-900 nm, step = 5 nm. The hyperspectral images were manually annotated, delineating the key anatomical structures: arteries, veins, stroma, and the umbilical cord. Standard reference materials were used for flat-field correction. The dataset can be used to develop machine learning algorithms for the automated classification of biological structures, particularly the classification of superficial and deep vessels and transparent tissue layers.</p>
Image dataset: Applicability of hyperspectral imaging during salinity stress in rice for tracking Na+ and K+ levels in planta
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SNP genotype and hyperspectral reflectance data from: Ensembles of genomic and hyperspectral imaging-based prediction enable selection for reduced deoxynivalenol content in wheat grains
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Data from: Hyperspectral imaging predicts differences in carbon and nitrogen status among representative biocrust functional groups of the Colorado Plateau
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Data from: Hyperspectral imaging has a limited ability to remotely sense the onset of beech bark disease
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Active and low-cost hyperspectral imaging for spectral analysis in low lighting environment
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Data from: Black-grass monitoring using hyperspectral image data is limited by between-site variability
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Data from: A hyperspectral image can predict tropical tree growth rates in single-species stands
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RGB and VIS/NIR Hyperspectral Imaging Data for 90 Rice Seed Varieties
<p>The dataset contains 90 rice seed species and 96 kernels per species resulting in 8,640 rice seed kernels in total. The dataset was collected in 2017 using the following two imaging systems:</p> <ol> <li>Visible - Near Infrared (VIS/NIR) Hyperspectral Imaging Device System (~385nm - ~1000nm) consisting of a Specim V10E Imaging Spectrograph and Hamamatsu ORCA-05G CCD camera.</li> <li>RGB - Fujifilm X-M1 with a 35mm/F2.0, ISO 400.</li> </ol> <p>For each species, 96 kernels have been captured in two imaging bundles with 48 kernels in each bundle. For each imaging bundle, the 48 kernels were carefully positioned on a sheet of white paper and arranged in an <code>8x6</code> matrix. This rice seed matrix was then positioned on a translational stage and imaged using the HSI and RGB cameras described above.</p> <p>The following three files result from a single acquisition:</p> <ul> <li><code>.hdr</code>: The HSI ENVI header file (More information on the ENVI format can be found at the <a href="https://www.harrisgeospatial.com/docs/ENVIHeaderFiles.html">Harris Geospatial Solutions</a> documentation.</li> <li><code>.raw</code>: The HSI datacube data.</li> <li><code>.jpg</code>: The RGB image.</li> </ul> <p>The filename convention used is the (short) species name followed by a dash, followed by the bundle number (i.e. 1 or 2), followed by the filename suffix. For instance, the data for the <code>BC15</code> rice seed variety are contained in the following 6 files:</p> <ul> <li><code>BC15-01.hdr</code></li> <li><code>BC15-01.raw</code></li> <li><code>BC15-01.jpg</code></li> <li><code>BC15-02.hdr</code></li> <li><code>BC15-02.raw</code></li> <li><code>BC15-02.jpg</code></li> </ul> <p>The data were captured in 9 batches across multiple days. All the data from the same batch are contained in a dedicated folder. For instance the folder <code>Data-VIS-20170111-2-room-light-off</code>indicates that the data are in the VIS/NIR range, captured on the 11th of January 2017 and this was the second batch for that day with the room lights off. Two halogen bulbs were used for illumination and these were accurately positioned to provide balanced lighting across the scene. To ensure stability, the halogen bulbs were switched on and allowed to reach constant operating temperature before the data were acquired in a dark room to minimise any other sources of illumination variance.</p> <p>For the purposes of calibration each HSI image contains in the scene a 100% reflective spectralon tile which is a highly reflective Lambertian scatter. For the dark reference, each folder contains an HSI image with the lens-cap covering the camera. The dark reference can be founds in each folder under the filename <code>black.hdr</code>/<code>black.raw</code>.</p> <p>A full index of the data for each species is provided in the <code>index.csv</code> file. The file contains the following columns:</p> <ul> <li>Species Full Name: The full species name (as used in filenames).</li> <li>Species Short Name: A shorthand of the species name.</li> <li>Bundle Number: Imaging Bundle Number (each bundle contains 48 kernels) every species has 2 bundles.</li> <li>Folder: The name of the folder containing the data (as described above where each folder contains a batch of images captured in a single imaging session).</li> <li>File Name: The stem of the filename. Note: that there are 3 suffixes for each stem (<code>.hdr</code>, <code>.raw</code>, <code>.jpg</code>)</li> </ul> <p>The HSI system was used to capture 256 wavelengths in this experiment and the exact wavelengths corresponding to the data provided are included in the file <code>wavelengths.csv</code>.</p> <p>Both camera systems were fixed on a rigid frame for the duration of the experiments. To permit possible registration between the two cameras, a chessboard pattern has been imaged and the acquired files are also contained in the folder <code>chessboard</code>.</p> <p><strong>Note:</strong> The bundle <code>01</code> for the species <code>NDC1</code> was originally acquired during the batch <code>Data-VIS-20170111-2-room-light-off</code>. However, the file was corrupted and hence, the acquisition was repeated during the batch <code>Data-VIS-20170203-1-room-light-off</code>. As a result, the <code>NDC1-01</code> files are in the <code>Data-VIS-20170203-1-room-light-off</code> folder.</p>
A Dataset for Evaluating Blood Detection in Hyperspectral Images
<p>The sensitivity of hyperspectral imaging (imaging spectroscopy) to haemoglobin derivatives makes it a promising tool for detection and classification of blood. However, due to complexity and high dimensionality of hyperspectral images, the development of hyperspectral blood detection algorithms is challenging. To facilitate their development, we present a new hyperspectral blood detection dataset. This dataset consists of 14 hyperspectral images (ENVI format) of a mock-up scene containing blood and visually similar substances (e.g. artificial blood or tomato concentrate). Images were taken over a period of three weeks and differ in terms of background composition and lighting intensity. To facilitate the use of data, the dataset includes an annotation of classes: pixels where blood and similar substances are visible have been marked by the authors. The main intention behind the dataset is to serve as testing data for Machine Learning methods for hyperspectral target detection and classification.</p>
Data from: Biomimicry of iridescent, patterned insect cuticles: comparison of biological and synthetic, cholesteric microcells using hyperspectral imaging
<p>Biological systems inspire the design of multifunctional materials and devices. However, current syynthetic replicas rarelyy capture the range of structural complexityy observed in natural materials. Prior to the definition of a biomimetic design, a dual investigation with a common set of criteria for comparing the biological material and the replica is required. Here, we deal with this issue by addressing the non-trivial case of insect cuticles tessellated with polygonal microcells with iridescent colors due to the twisted cholesteric organization of chitin fibers. By using hyperspectral imaging within a common methodology, we compare, at several length scales, the textural, structural and spectral properties of the microcells found in the two-band cuticle of the scarab beetle <i>Chrysina gloriosa</i> with those of the polygonal texture formed in flat films of cholesteric liquid crystal oligomers. The hyperspectral imaging technique offers a unique opportunity to reveal the common features and differences in the spectral-spatial signatures of biological and synthetic samples at a 6-nm spectral resolution over 400 nm-1000 nm and a spatial resolution of 150 nm. The biomimetic design of chiral tessellations is relevant to the field of non-specular properties such as deflection and lensing in geometric phase planar optics.</p>
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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.