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74 results for “Hyperspectral Data”
Data from: Reflections of stress: Ozone damage in broadleaf saplings can be identified from hyperspectral leaf reflectance
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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>
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>
Data from: Detection of tephra layers in Antarctic sediment cores with hyperspectral imaging
Tephrochronology uses recognizable volcanic ash layers (from airborne pyroclastic deposits, or tephras) in geological strata to set unique time references for paleoenvironmental events across wide geographic areas. This involves the detection of tephra layers which sometimes are not evident to the naked eye, including the so-called cryptotephras. Tests that are expensive, time-consuming, and/or destructive are often required. Destructive testing for tephra layers of cores from difficult regions, such as Antarctica, which are useful sources of other kinds of information beyond tephras, is always undesirable. Here we propose hyperspectral imaging of cores, Self-Organizing Map (SOM) clustering of the preprocessed spectral signatures, and spatial analysis of the classified images as a convenient, fast, non-destructive method for tephra detection. We test the method in five sediment cores from three Antarctic lakes, and show its potential for detection of tephras and cryptotephras.
Data from: Tree-centric mapping of forest carbon density from airborne laser scanning and hyperspectral data
Forests are a major component of the global carbon cycle, and accurate estimation of forest carbon stocks and fluxes is important in the context of anthropogenic global change. Airborne laser scanning (ALS) data sets are increasingly recognized as outstanding data sources for high-fidelity mapping of carbon stocks at regional scales. We develop a tree-centric approach to carbon mapping, based on identifying individual tree crowns (ITCs) and species from airborne remote sensing data, from which individual tree carbon stocks are calculated. We identify ITCs from the laser scanning point cloud using a region-growing algorithm and identifying species from airborne hyperspectral data by machine learning. For each detected tree, we predict stem diameter from its height and crown-width estimate. From that point on, we use well-established approaches developed for field-based inventories: above-ground biomasses of trees are estimated using published allometries and summed within plots to estimate carbon density. We show this approach is highly reliable: tests in the Italian Alps demonstrated a close relationship between field- and ALS-based estimates of carbon stocks (r2 = 0·98). Small trees are invisible from the air, and a correction factor is required to accommodate this effect. An advantage of the tree-centric approach over existing area-based methods is that it can produce maps at any scale and is fundamentally based on field-based inventory methods, making it intuitive and transparent. Airborne laser scanning, hyperspectral sensing and computational power are all advancing rapidly, making it increasingly feasible to use ITC approaches for effective mapping of forest carbon density also inside wider carbon mapping programs like REDD++.
Hyperspectral and LiDAR data of the Botanical Garden of Rio de Janeiro
<p>In this repository you can find a hyperspectral image, a LiDAR point cloud and a shapefile of polygons of individual tree crowns from the the Botanical Garden of Rio de Janeiro. For more details refer to <a href="https://doi.org/10.1016/j.ufug.2024.128362">Ferreira et al. (2024)</a>.</p>
Data for: Classification of benign-malignant thyroid nodules based on hyperspectral technology
<p>We propose a rapid diagnostic method for benign and malignant thyroid nodules based on hyperspectral technology to address the issue of insufficient diagnostic efficiency in thyroid cancer during surgery. Firstly, through the self-developed thyroid nodule hyperspectral collection system, a large number of diverse thyroid nodule samples were obtained. These thyroid nodule samples were collected through the hyperspectral collection system during thyroidectomy surgery, providing a foundation for subsequent diagnosis. We propose a benign and malignant classification method based on hyperspectral data blocks of thyroid nodules to better meet clinical needs. Meanwhile, using 3D CNN and VGG networks, we designed a neural network algorithm for classifying three-dimensional hyperspectral cubes. The classification accuracy of benign and malignant samples reached 84.63%. Overall, we have effectively classified the benign and malignant thyroid nodules using a collection system and data.</p>
A 2D hyperspectral library of mineral reflectance, from 900 to 2500nm - Raw data
<p>Each <strong>zip</strong> file contains the text description file: <strong>description.txt</strong>. It also contains the data of one or two measurement, <strong>A</strong> and possibly B. The files are named as follows (<strong>####</strong> is the sample ID, <strong>@</strong> the letter of the measurement):</p> <ul> <li><strong>####-@.ply</strong> and <strong>####-@.png</strong>: 3D reconstruction in Stanford PLY format, and associated texture file.</li> <li><strong>@-im-000.jpg</strong>, <strong>@-im-010.jpg</strong>, ..., <strong>@-im-360.jpg</strong>: JPEG images of the sample, the angle is in degrees. 0 degree correspond to the position used during scanning.</li> <li><strong>@.scan.h5</strong>: HDF5 file containing the raw scan data.</li> </ul> <p>The zip files correspond to the sample IDs, according to the following table:</p> <pre><code>Mineral Sample IDs Datapoints ---------------------------------------------------------------- Actinolite 0020, 0021, 0064 56840 Albite 0107 22521 Almandine 0025, 0026, 0073, 0074 17425 Andalusite 0014 18862 Anhydrite 0004 30832 Apatite 0089(2), 0090(2) 69062 Aragonite 0061 40111 Arsenopyrite 0087(2) 66333 Augite 0038 8503 Barite 0006 37130 Beryl 0075(2) 37743 Biotite 0049(2), 0050 45400 Blende 0086(2) 15596 Bronzite 0112 50452 Bytownite 0103 14666 Calcite 0010, 0011, 0052, 0078, 0079 112501 Cassiterite 0119, 0120 38535 Celestite 0000, 0001, 0002 56075 Chalcedony 0108(2), 0109(2) 96431 Chalcopyrite 0106 19375 Chlorite 0013 75802 Clinochlore 0126 45301 Coal 0081, 0082 44664 Copper 0101 14556 Diopside 0069 58959 Dolomite 0091(2), 0092(2) 76810 Enstatite 0047 31072 Epidote 0023, 0024 47306 Fluorite 0003(2), 0012(2) 98638 Galena 0053, 0054 17931 Garnet 0115 27331 Glaucophane 0016, 0017, 0076, 0077 200830 Goethite 0114 10717 Graphite 0083, 0084 20047 Grossular 0030, 0031 46592 Gypsum 0005(2), 0007, 0063 159829 Halite 0008, 0056 66201 Halloysite 0121, 0122 102028 Hematite 0039(2), 0085(2), 0095, 0096 165228 Hornblende 0046 9175 Hypersthene 0111 62932 Ilmenite 0116 16256 Kaolinite 0113 20764 Kyanite 0029 12320 Labradorite 0088(2), 0104(2) 62762 Limonite 0128, 0129 64550 Magnetite 0055, 0072 7103 Microcline 0071 60699 Montmorillonite 0123, 0124, 0125 87986 Muscovite 0034 62481 Nepheline 0097(2) 51930 Olivine 0065 7965 Omphacite 0019, 0067 108032 Opal 0102 34404 Orthoclase 0057 49824 Phlogopite 0045, 0070 105119 Pyrite 0042, 0048 31598 Pyrolusite 0117, 0118 45534 Pyrrhotite 0051 21572 Quartz 0009(2), 0035 126760 Rutile 0093 4959 Sanidine 0099(2) 49260 Serpentine 0018, 0068 78937 Siderite 0080 11651 Silicified wood 0127(2) 92935 Sillimanite 0032, 0033 70546 Sodalite 0043, 0060 61852 Sphalerite 0105 27485 Staurolite 0027, 0028, 0066 30628 Sulfur 0036, 0037 34751 Talc 0022, 0040, 0041, 0058, 0059 138317 Titanite 0094 4121 Tourmaline 0044, 0062 68419 Tremolite 0098(2), 0100 70913 Zircon 0110(2) 5110 </code></pre> <p> </p>
A 2D hyperspectral library of mineral reflectance, from 900 to 2500nm - High dynamic range data
<p>Each <strong>zip</strong> file contains the text description file: <strong>description.txt</strong>. It also contains the data of one or two measurement, <strong>A</strong> and possibly B. The files are named as follows (<strong>####</strong> is the sample ID, <strong>@</strong> the letter of the measurement):</p> <ul> <li><strong>####-@.ply</strong> and <strong>####-@.png</strong>: 3D reconstruction in Stanford PLY format, and associated texture file.</li> <li><strong>@-im-000.jpg</strong>, <strong>@-im-010.jpg</strong>, ..., <strong>@-im-360.jpg</strong>: JPEG images of the sample, the angle is in degrees. 0 degree correspond to the position used during scanning.</li> <li><strong>@.hdr.h5</strong>: HDF5 file containing the HDR scan data.</li> </ul> <p>The zip files correspond to the sample IDs, according to the following table:</p> <pre><code>Mineral Sample IDs Datapoints ---------------------------------------------------------------- Actinolite 0020, 0021, 0064 56840 Albite 0107 22521 Almandine 0025, 0026, 0073, 0074 17425 Andalusite 0014 18862 Anhydrite 0004 30832 Apatite 0089(2), 0090(2) 69062 Aragonite 0061 40111 Arsenopyrite 0087(2) 66333 Augite 0038 8503 Barite 0006 37130 Beryl 0075(2) 37743 Biotite 0049(2), 0050 45400 Blende 0086(2) 15596 Bronzite 0112 50452 Bytownite 0103 14666 Calcite 0010, 0011, 0052, 0078, 0079 112501 Cassiterite 0119, 0120 38535 Celestite 0000, 0001, 0002 56075 Chalcedony 0108(2), 0109(2) 96431 Chalcopyrite 0106 19375 Chlorite 0013 75802 Clinochlore 0126 45301 Coal 0081, 0082 44664 Copper 0101 14556 Diopside 0069 58959 Dolomite 0091(2), 0092(2) 76810 Enstatite 0047 31072 Epidote 0023, 0024 47306 Fluorite 0003(2), 0012(2) 98638 Galena 0053, 0054 17931 Garnet 0115 27331 Glaucophane 0016, 0017, 0076, 0077 200830 Goethite 0114 10717 Graphite 0083, 0084 20047 Grossular 0030, 0031 46592 Gypsum 0005(2), 0007, 0063 159829 Halite 0008, 0056 66201 Halloysite 0121, 0122 102028 Hematite 0039(2), 0085(2), 0095, 0096 165228 Hornblende 0046 9175 Hypersthene 0111 62932 Ilmenite 0116 16256 Kaolinite 0113 20764 Kyanite 0029 12320 Labradorite 0088(2), 0104(2) 62762 Limonite 0128, 0129 64550 Magnetite 0055, 0072 7103 Microcline 0071 60699 Montmorillonite 0123, 0124, 0125 87986 Muscovite 0034 62481 Nepheline 0097(2) 51930 Olivine 0065 7965 Omphacite 0019, 0067 108032 Opal 0102 34404 Orthoclase 0057 49824 Phlogopite 0045, 0070 105119 Pyrite 0042, 0048 31598 Pyrolusite 0117, 0118 45534 Pyrrhotite 0051 21572 Quartz 0009(2), 0035 126760 Rutile 0093 4959 Sanidine 0099(2) 49260 Serpentine 0018, 0068 78937 Siderite 0080 11651 Silicified wood 0127(2) 92935 Sillimanite 0032, 0033 70546 Sodalite 0043, 0060 61852 Sphalerite 0105 27485 Staurolite 0027, 0028, 0066 30628 Sulfur 0036, 0037 34751 Talc 0022, 0040, 0041, 0058, 0059 138317 Titanite 0094 4121 Tourmaline 0044, 0062 68419 Tremolite 0098(2), 0100 70913 Zircon 0110(2) 5110 </code></pre>
A 2D hyperspectral library of mineral reflectance, from 900 to 2500nm - Masked high dynamic range data
<p>Each <strong>zip</strong> file contains the text description file: <strong>description.txt</strong>. It also contains the data of one or two measurement, <strong>A</strong> and possibly B. The files are named as follows (<strong>####</strong> is the sample ID, <strong>@</strong> the letter of the measurement):</p> <ul> <li><strong>####-@.ply</strong> and <strong>####-@.png</strong>: 3D reconstruction in Stanford PLY format, and associated texture file.</li> <li><strong>@-im-000.jpg</strong>, <strong>@-im-010.jpg</strong>, ..., <strong>@-im-360.jpg</strong>: JPEG images of the sample, the angle is in degrees. 0 degree correspond to the position used during scanning.</li> <li><strong>@.mhdr.h5</strong>: HDF5 file containing masked HDR raw scan data. Masked values are represented by nan.</li> </ul> <p>The zip files correspond to the sample IDs, according to the following table:</p> <pre><code>Mineral Sample IDs Datapoints ---------------------------------------------------------------- Actinolite 0020, 0021, 0064 56840 Albite 0107 22521 Almandine 0025, 0026, 0073, 0074 17425 Andalusite 0014 18862 Anhydrite 0004 30832 Apatite 0089(2), 0090(2) 69062 Aragonite 0061 40111 Arsenopyrite 0087(2) 66333 Augite 0038 8503 Barite 0006 37130 Beryl 0075(2) 37743 Biotite 0049(2), 0050 45400 Blende 0086(2) 15596 Bronzite 0112 50452 Bytownite 0103 14666 Calcite 0010, 0011, 0052, 0078, 0079 112501 Cassiterite 0119, 0120 38535 Celestite 0000, 0001, 0002 56075 Chalcedony 0108(2), 0109(2) 96431 Chalcopyrite 0106 19375 Chlorite 0013 75802 Clinochlore 0126 45301 Coal 0081, 0082 44664 Copper 0101 14556 Diopside 0069 58959 Dolomite 0091(2), 0092(2) 76810 Enstatite 0047 31072 Epidote 0023, 0024 47306 Fluorite 0003(2), 0012(2) 98638 Galena 0053, 0054 17931 Garnet 0115 27331 Glaucophane 0016, 0017, 0076, 0077 200830 Goethite 0114 10717 Graphite 0083, 0084 20047 Grossular 0030, 0031 46592 Gypsum 0005(2), 0007, 0063 159829 Halite 0008, 0056 66201 Halloysite 0121, 0122 102028 Hematite 0039(2), 0085(2), 0095, 0096 165228 Hornblende 0046 9175 Hypersthene 0111 62932 Ilmenite 0116 16256 Kaolinite 0113 20764 Kyanite 0029 12320 Labradorite 0088(2), 0104(2) 62762 Limonite 0128, 0129 64550 Magnetite 0055, 0072 7103 Microcline 0071 60699 Montmorillonite 0123, 0124, 0125 87986 Muscovite 0034 62481 Nepheline 0097(2) 51930 Olivine 0065 7965 Omphacite 0019, 0067 108032 Opal 0102 34404 Orthoclase 0057 49824 Phlogopite 0045, 0070 105119 Pyrite 0042, 0048 31598 Pyrolusite 0117, 0118 45534 Pyrrhotite 0051 21572 Quartz 0009(2), 0035 126760 Rutile 0093 4959 Sanidine 0099(2) 49260 Serpentine 0018, 0068 78937 Siderite 0080 11651 Silicified wood 0127(2) 92935 Sillimanite 0032, 0033 70546 Sodalite 0043, 0060 61852 Sphalerite 0105 27485 Staurolite 0027, 0028, 0066 30628 Sulfur 0036, 0037 34751 Talc 0022, 0040, 0041, 0058, 0059 138317 Titanite 0094 4121 Tourmaline 0044, 0062 68419 Tremolite 0098(2), 0100 70913 Zircon 0110(2) 5110 </code></pre> <p> </p>
Paracou dataset / Hyperspectral data for spectra simulation and taxonomic diversity estimation
Open the record for dataset details and reuse information.
Detection of Ore-forming Elements Migrated from Rock and Soil to Vegetation Leaves using Hyperspectral Data
<p>In this paper we present the detection of ore-forming elements migrated from soil and rock to vegetation leaves using hyperspectral data. The rock, soil (C, B, A layer) and vegetation (root, stem, leaf) samples with the measurements of spectra and element concentrations in the vertical section of the abandoned pits were collected. The wavelet approach is applied to analyze the correlations between spectral features and element concentrations. The results show that the significant correlations are found at a lower order mother wavelets such as Haar. Moreover, the significant correlations are negative between Cu and Mo elements and the wavelet energy vector of the soil and rock to vegetation leaves spectrum. The results also show that ore-forming elements migrated from rock and soil to vegetation leaves can be detected using hyperspectral data in the study area.</p>
Cross-Scene Hyperspectral Remote Sensing Wetland image data
<p>Two representative study areas in China, i.e., Yancheng and Huanghekou (i.e, Yellow River Estuary) wetlands, are selected.<br> For Yancheng wetland, there are two HSIs acquired by the Advanced Hyperspectral Imager (AHSI) aboard on China's Ziyuan1-02D (ZY1-02D) and GaoFen-5 (GF-5) satellites, respectively. For Huanghekou wetland, there are also two HSIs acquired by the AHSI aboard on China's ZY1-02D satellite in June 28, 2020 and September 29, 2021, respectively.</p>
Greenhouse gas forcing and climate feedback signatures identified in hyperspectral infrared satellite observations (Data)
<p>README file for LBL-ERA5, LBL-GCM, and band datasets used in:<br> Raghuraman et al., 2023, Geophysical Research Letters,<br> "Greenhouse gas forcing and climate feedback signatures identified in hyperspectral infrared satellite observations "</p> <p>Point of Contact: Shiv Priyam Raghuraman, shivr@alumni.princeton.edu</p> <p> </p> <p>LBL-ERA5</p> <p>6 files (3 experiments with olr and olr_clr output separately):</p> <p>2003-2021.GFDL.all-2010-o3_*.nc - varying WMGHG,Ts,T,q,clouds, fixed o3</p> <p>2003-2021.GFDL.fo3_*.nc - varying WMGHG and fixed Ts,T,q,clouds,surface albedo,o3</p> <p>2003-2021.GFDL.ff_*.nc - fixed Ts,T,q,clouds,surface albedo, varying o3</p> <p> </p> <p>LBL-GCM (clear-sky only)</p> <p>4 AM4 files:</p> <p>AM4piclim-control.nc (for ERF)</p> <p>AM4piclim-4xCO2_1xCO2IRF.nc (for ERF)</p> <p>AM4piclim-control_STRAT.nc (for SARF)</p> <p>AM4piclim-4xCO2_1xCO2IRF_STRAT.nc (for SARF)</p> <p>4 CM3/AM3 files:</p> <p>CTLAM3CM3_P1_APR_allctm_E1_RFM_1CM_A2_WN3_1xCO2N.nc (for \lambda)</p> <p>EXPAM3CM3_P1_APR_allctm_E1_RFM_1CM_A2_WN3_1xCO2N.nc (for \lambda)</p> <p>CTLAM3CM3_P1_2003C.nc (for IRF)</p> <p>CTLAM3CM3_P1_2021C.nc (for IRF)</p> <p> </p> <p>Band-AM4/MERRA-Total: GFDL AM4 with prescribed SSTs and sea-ice (AMIP) and nudged with MERRA winds</p> <p>2 files:</p> <p>atmos.200001-202112.olr.nc - all-sky OLR</p> <p>atmos.200001-202112.olr_clr.nc - clear-sky OLR</p> <p> </p> <p>Observational and reanalysis files used in paper (AIRS, CERES EBAF and SSF, GISTEMP, ERA5 input data) can be downloaded from their respective websites (see paper's "Open Research" section).</p> <p> </p>
Maximizing Societal Benefit Across Multiple Hyperspectral Earth Observation Missions: A User Needs Approach - DATA and CODES
<p>In this repository you will find data elicited from Italian and the NASA Surface Biology and Geology (SBG) Designated Observable users. the first page "read me first" provides a description of the first sheet "User requirements merged" where you will find the reqirements codified for both community of users.</p> <p>Other files include the codes used throug the R software to develop several useful figures.</p>
Fig. 5 in An integrated analysis of hyperspectral and morphological data of cicada ovipositors revealed unexplored links to specific oviposition hosts
Fig. 5 Hierarchical cluster cladogram based on 25 morphological characters for each of the 11 cicada species. Ca, Cryptotympana atrata; Hm, Hyalessa maculaticollis; Kc, Karenia caelatata; Kn, Katoa neokanagana; Mm, Meimuna mongolica; Mo, Meimuna opalifera; Ph, Platypleura hilpa; Pk, Platypleura kaempferi; Pl, Pomponia linearis; Sy, Subpsaltria yangi; Tj, Tanna japonensis. Height: the value of the criterion associated with the clustering method for the particular agglomeration
Fig. 4 in An integrated analysis of hyperspectral and morphological data of cicada ovipositors revealed unexplored links to specific oviposition hosts
Fig. 4 Comparison of morphological characters of ovipositors among different species. a Ovipositor length; b ovipositor height; c basal width of ovipositor; d apical width of ovipositor; e subapical width of ovipositor; f curvature. Data are presented as mean ± SE. Ca, Cryptotympana atrata; Hm, Hyalessa maculaticollis; Kc, Karenia caelatata; Kn, Katoa neokanagana; Mm, Meimuna mongolica; Mo, Meimuna opalifera; Ph, Platypleura hilpa; Pk, Platypleura kaempferi; Pl, Pomponia linearis; Sy, Subpsaltria yangi; Tj, Tanna japonensis
Fig. 2 in An integrated analysis of hyperspectral and morphological data of cicada ovipositors revealed unexplored links to specific oviposition hosts
Fig. 2 Four major types of sensilla. a Campaniform sensilla; b Basiconica sensilla; c Trichoid sensilla; d Coeloconic sensilla [from Zhong et al. (2017), with kind permission from Springer publishers]. Scale bars: a, b, d 5 µm; c 20 µm
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.