Find research datasets worth reusing

openCC (other)Feb 2021View details →

Hubbard Brook Experimental Forest: Hyperspectral Foliar N map and associated field data, 2012

A canopy nitrogen map was created for the Hubbard Brook Experimental Forest and watersheds using airborne imaging spectrometer data collected by SpecTIR LLC (Reno, NV) on August 7, 2012, and associated field data. Leaf samples collected in the field were analyzed for nitrogen concentration, scaled to plot (whole canopy) level, and related to airborne imaging spectrometer reflectance data using partial least squares regression modeling to derive spatially explicit estimates of canopy nitrogen concentration (mass-based) for the spatial extent of the airborne imagery.

openCC (other)Oct 2023View details →

Hyperspectral reflectance values and biophysicochemical properties of biocrusts and soils in the Fryxell Basin, McMurdo Dry Valleys, Antarctica (2019)

This data package includes ecological parameters of biocrust and soil from samples collected in-situ within the Lake Fryxell Basin of the McMurdo Dry Valleys, Antarctica during December of 2019. Parameters include biological (ash-free dry mass, pigment concentration, and counts of soil invertebrates), physical (water content, electrical conductivity, and pH), and chemical properties (inorganic nitrogen, inorganic phosphorous, total nitrogen, and total organic carbon) of the surface soil, biocrust, and underlying soil. This data package also contains reflectance measurements of biocrust, soil, and granite samples acquired in a laboratory using a hyperspectral spectrometer. Included are hyperspectral reflectance measurements of a laboratory study using a variety of mixtures of soil and biocrust (0 – 100% biocrust), as well as reflectance measurements of individual grab samples collected from each of the field plots. These data aid our understanding of the ecological structure and functioning of biocrust microhabitats as well as the location of these communities throughout the Lake Fryxell Basin. This work also aims to assist in understanding the carbon budget of the basin and highlight the importance of these snowpack-fed biocrust communities, which are understudied but likely an important piece of the overall carbon budget in this region.

openCC (other)May 2024View details →

Biophysicochemical properties and hyperspectral reflectance values of biocrust and soil samples, Fryxell Basin, McMurdo Dry Valleys, Antarctica (2022)

This data package includes biophysicochemical properties of biocrust and soil samples collected in-situ within the Lake Fryxell basin of the McMurdo Dry Valleys, Antarctica during December 2022 at 64 different terrestrial locations. These parameters include biological (ash-free dry mass, pigment concentration, soil invertebrate counts), physical (gravimetric water content, electrical conductivity, pH), and chemical (inorganic nitrogen, inorganic phosphorous, total nitrogen, soil organic carbon) properties of the surface soil, biocrust, and underlying soil. This package also contains reflectance measurements of individual grab samples of biocrust, soil, and rock collected from each of the field sites, acquired in a laboratory using a hyperspectral spectrometer. Additionally, this package contains parameters derived solely from geospatial data for each of the field sites, including aspect, slope, elevation, gravimetric water content, and snow frequency. These data were used to model habitat suitability of biocrusts in the Fryxell Basin using both in-situ field survey data and geospatial parameters. They aid in our understanding of the ecology of biocrust communities, where they are located throughout the Fryxell basin, and the structure and functioning of these microhabitats as well as improving understanding of the regional carbon budget. These data also highlight the importance of snow patch-associated biocrust communities, which are understudied but likely an important piece of the overall carbon budget in this region.

openCC (other)Aug 2021View details →

Kelp canopy chlorophyll to carbon ratio derived from aerial hyperspectral imagery

This dataset represents a time series of giant kelp canopy chlorophyll to carbon ratio (Chl:C) derived from aerial hyperspectral imagery, along with associated environmental, canopy age determinations, and validation datasets. Additional data from a frond cohort experiment are also presented, representing empirical observations of the decline in chlorophyll pigment concentration with blade age. This dataset contains eight geotiff rasters showing canopy Chl:C at a 30-meter pixel resolution for giant kelp forests in the Santa Barbara Channel during the months of April, June, and August from 2013 – 2015.

opencc-by-4.0Jun 2020View details →

Close range hyperspectral camera dataset with high temporal resolution of strawberry with eco-physiological data of one leaf

This high temporal resolution dataset of a strawberry plant was captured in two experiments, each lasting 100h. On leaf was inserted into a leaf chamber of the LI-6400XT gas exchange system, capturing information on transpiration, photosynthesis and stomatal conductance. Environmental characteristics are also captured at canopy height. These experiments were conducted in a growth chamber at ILVO (Melle, Belgium) and only covered conditions that did not result in stress in the plant. As such, this dataset attempts to capture subtle dynamic variation in the plant.

opencc-by-4.0Nov 2023View details →

Hyperspectral imager acquisitions from SMART Soils Test Bed, 2022-04-18

Hyperpsectral imager data retrieved over the Lawrence Berkeley National Lab SMART Soils Test Bed on 2022-04-18 at three times (11:20, 12:34, 13:44). Radiance data (mW cm−2 μm−1 sr−1) subset to bands of interest over the Headwall Hyperspec Imager's 680-800nm range (680-682, 757-652, 769-772, and 778-780 nm) to retrieve RED, NIR, NDVI and SIF while minimizing file size. Data associated with Ruehr et al. 2023, 'Quantifying seasonal and diurnal cycles of solar-induced fluorescence with a novel hyperspectral imager,' submitted to Geophysical Research Letters in November 2023. Code for processing these data and descriptions of the files are available at https://github.com/sruehr/SIFretrieval.

opencc-by-4.0Dec 2023View details →

Hyperspectral photoluminescence and reflectance microscopy of 2D materials

<h2>Description of Uploaded Raw Data and Programs for Recreating Figures</h2><h3>Raw Data</h3>The raw data in this dataset is primarily in  ".sif" binary format, which is used in the creation of Figures 2, 3, 4, and Supplementary Information (SI) Figure 2 in the paper. The ".sif" files contain spectrum data. The data for Figure 3 also includes focal data provided as .png and intensity line-cuts in .csv files.A Python program,  "load_sif.py", is included in the dataset to read and process these ".sif" files.<h3>Software and Programs</h3>The figures in the paper were generated using Python programs, which are included in the dataset. These programs are:for Figure 2: RClf_calibration.py  for Figure 3: knife_edge_measurement.py and  plot_intensity_profile_imageJ.py for Figure 4 as well as SI Figure 1: PL_linefocus_2color.py, PL_fit_image.py, PL_line_fit.py, RC_linefocus_2color.py and RC_line.py for SI Figure 2: BG_spectum_PL.py and Ref_spectum_RC.py <h3>Steps to Recreate Figures</h3>Download the zipped folder for each figure. The Python programs are using the ".sif", ".png", and ".csv" files from the downloaded folder.Please ensure you have the appropriate software to run these Python programs and handle the provided file formats.

opencc-by-4.0Dec 2023View details →

Dataset of Hyperspectral Melt Pool Signatures and Thermal Anomalies in DED of 316L steel

Description of the dataset The dataset includes in-situ melt pool signatures (hyperspectral NIR images) during the Directed Energy Deposition of 316L steel for several classes of thermal anomalies. Thermal anomalies were created during the process by varying the scanning speed. Samples were printed on the MiCLAD machine at the Vrije Universiteit Brussel (Belgium). Process and acquisition parameters: <ul> <li>Hardware: <ul> <li>Machine: MiCLAD (Vrije Universiteit Brussel)</li> <li>Laser: High-YAG BIMO 1064nm, 2.55mm fibre, flat-top</li> <li>Nozzle: Harald-Dickler HighNo 4.0</li> </ul> </li> <li>Process parameters: <ul> <li>Laser power: 600 W</li> <li>Scanning speed: 500/700/900/1100/1300 mm/min</li> <li>Powder: 316L 45-105 um</li> <li>Powder flow rate: 3.5 g/m</li> <li>Layer thickness: 0.2 mm</li> </ul> </li> <li>Image characteristics: <ul> <li>Camera: 3D-One Avior AX-M25NIR</li> <li>Hyperspectral filter layout: 5x5 (25 wavelengths per image)</li> </ul> </li> </ul> Description of the files <ul> <li>CSV dataset (hyperspectral_nir_meltpool_dataset.csv): List of filename, sample, label, time (ms), X and Z position (mm) and local scanning speed (mm/min) for all melt pool signatures. Thermal anomalies are labelled accordingly: <ul> <li>0 : baseline</li> <li>1 : edge</li> <li>2 : underheat</li> <li>3 : strong underheat</li> <li>4 : overheat</li> <li>5 : strong overheat</li> </ul> </li> <li>Melt pool signatures (hyperspectral_nir_meltpool_images_*.zip): Raw .tif thermal images of the melt pool taken in-situ. The raw images must debayered to retrieve the spectral information, see the Python function and example script. </li> <li>Python debayer function (debayer.py): Debayering function to retrieve the spectral information from the raw images. </li> </ul>

opencc-by-4.0Dec 2023View details →

STEM-EELS hyperspectral data: Nanowires, Nanoparticles, Interface

STEM-EELS hyperspectral datasets. These 3D datasets accompany the manuscript titled "Discovering Invariant Spatial Features in Electron Energy Loss Spectroscopy Images on the Mesoscopic and Atomic Levels" by K. Roccapriore et. al. These data are used to demonstrate correlations between pixels in space, not just energy, using what are known as multichannel rVAE models.   See the following url for Jupyter Notebook walkthrough https://github.com/kevinroccapriore/Multichannel-rVAE

opencc-by-4.0Sep 2024View details →

Dataset and software for processing of hyperspectral images of different CDW materials

<h2>Overview</h2> The provided scripts are designed to process hyperspectral images of construction and demolition waste (CDW) materials, extract relevant features, and train a machine-learning model for material classification. The scripts perform the following tasks: <ol> <li>Feature Extraction: Extract spectral features from hyperspectral data.</li> <li>Background Removal and Subset Extraction: Remove backgrounds from images and extract subsets for analysis.</li> <li>Data Visualization: Generate plots to visualize the extracted features and reflectance curves.</li> <li>Machine Learning Model Training: Using the extracted features, train and evaluate a multilayer perceptron (MLP) classifier.</li> </ol> <h2>Prerequisites</h2> Before running the scripts, ensure that you have the following: <ul> <li>Python 3.x installed on your system.</li> <li>Required Python packages: <ul> <li><code>numpy</code></li> <li><code>matplotlib</code></li> <li><code>scipy</code></li> <li><code>pandas</code></li> <li><code>scikit-learn</code></li> <li><code>seaborn</code></li> <li><code>rembg</code> (for background removal)</li> <li><code>Pillow</code> (PIL)</li> </ul> </li> <li>Hyperspectral data files in <code>.mat</code> format containing calibrated hyperspectral cubes and wavelength information.</li> <li>A directory structure to organize input and output files as described in each script.</li> </ul> <h2>Scripts Description</h2> <h3>1. <code>hyperspectral_features_v2.py</code></h3> <h4>Purpose</h4> This script processes individual hyperspectral image files to extract spectral features from a central subset of the image. It generates RGB images from the hyperspectral data, plots the mean reflectance spectra, and outputs a LaTeX-formatted table containing the extracted features. <h4>Functionality</h4> <ul> <li>Loading Data: Reads <code>.mat</code> files containing hyperspectral data from a specified input directory.</li> <li>Feature Calculation: <ul> <li>Calculates mean reflectance within a central window of the image.</li> <li>Extracts spectral features such as peak wavelength and area under the reflectance curve.</li> <li>Records reflectance values at selected wavelengths, including standard RGB channels and additional wavelengths.</li> </ul> </li> <li>RGB Image Generation: Creates RGB images using specific wavelengths corresponding to the red, green, and blue channels.</li> <li>Spectra Plotting: Plots the mean reflectance spectra for each sample.</li> <li>LaTeX Table Generation: Produces a LaTeX-formatted table of the extracted features for inclusion in a report or paper.</li> </ul> <h4>Usage Instructions</h4> <ol> <li> Prepare Input Data: <ul> <li>Place your <code>.mat</code> files containing the hyperspectral data in the appropriate input directory (e.g., <code>input/mortar</code>).</li> </ul> </li> <li> Run the Script: <ul> <li>Modify the <code>materials</code> list at the end of the script to include the materials you want to process (e.g., <code>materials = ['mortar']</code>).</li> <li>Execute the script: <div> <div>bash</div> <div> <div> <div> </div> </div> </div> </div> </li> </ul> </li> </ol>

opencc-by-4.0Nov 2021View details →

OHS data provided by Competition in Hyperspectral Remote Sensing Image Intelligent Processing Application

Images from Chinese Orbita Hyperspectral Satellites (OHS) provided by the Competition in Hyperspectral Remote Sensing Image Intelligent Processing Application are shared. All the images have been radiometric calibrated and atmospheric corrected by the author. Paper: J. He, J. Li, Q. Yuan, H. Shen, and L. Zhang, "Spectral Response Function-Guided Deep Optimization-Driven Network for Spectral Super-Resolution," IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS), 2021. More information about the author can be found at https://jianghe96.github.io/ If this dataset is helpful please cite as: <pre>@article{he2021spectral, title={Spectral Response Function-Guided Deep Optimization-Driven Network for Spectral Super-Resolution}, author={He, Jiang and Li, Jie and Yuan, Qiangqiang and Shen, Huanfeng and Zhang, Liangpei}, journal={IEEE Transactions on Neural Networks and Learning Systems}, year={2021}, }</pre>

opencc-by-4.0Sep 2022View details →

Data (corrected spectra per pixels) from "Artificial reefs efficiency changes among types as revealed by underwater hyperspectral imagery"

open access data related to the paper "Artificial reefs efficiency changes among types as revealed by underwater hyperspectral imagery" Riera E., Ungerman M., Pey A., Rigot G., Hubas C., Rossi F. (under revision to Restoration Ecology) <ul> <li>pixels_corrected_spectra.npy: data matrix of the corrected spectra for each pixels</li> <li>wavelenghts.npy: the vector of the wavelengths  </li> <li>pixels_metada.csv: metadata related the corrected spectra for each pixels</li> </ul> To be processed on python for further statistical analyses. script available on github: <a href="https://github.com/ELI-RIERA/HYPER3D">https://github.com/ELI-RIERA/HYPER3D</a>

opencc-by-4.0Jul 2024View details →

Epipremnum aureum VIS-SWIR Hyperspectral Image from HYPERIA

The spectral image show two Epipremnum aureum leaves measured in the visible and short wave infrared (400-1700 nm) using the HERAVISSWIR device developed during the HYPERIA project. The leaf on the right is healty while the leaf on the left has evident stress signatures. The image was recorded from a 1m distance using a combination of a LED and halogen lamps to cover the full spectral range.

HyRANK Hyperspectral Satellite Dataset I

The HyRANK hyperspectral datasets has been developed in the framework of the ISPRS Scientific Initiatives. In particular, the “HyRANK Hyperspectral Satellite Dataset I” contains the openly available Hyperion hyperspectral dataset along with the corresponding reference/ ground truth data. The training set contains two hyperspectral images (i.e., Dioni and Loukia) and the validation set contains three hyperspectral images (i.e., Erato, Nefeli, Kiriki). The <a href="http://www2.isprs.org/commissions/comm3/wg4/HyRANK.html">HyRANK</a> benchmark platform can be reached through the <a href="http://www2.isprs.org/">ISPRS</a> website under the <a href="http://www2.isprs.org/commissions/comm3.html">Commission III</a>, <a href="http://www2.isprs.org/commissions/comm3/wg4.html">Working Group III/4</a> ‘Hyperspectral Image Processing’ webpage.

Underwater hyperspectral scan from India, scene 29

Underwater hyperspectral scan from India, scene 29 Used previously in publication Zimmermann, Nevala, Yoshimatsu et al., 2018, Current Biology, in press.

Underwater hyperspectral scan from India, scene 24

Underwater hyperspectral scan from India, scene 24 Used previously in publication Zimmermann, Nevala, Yoshimatsu et al., 2018, Current Biology, in press.

Underwater hyperspectral scan from India, scene 28

Underwater hyperspectral scan from India, scene 28 Used previously in publication Zimmermann, Nevala, Yoshimatsu et al., 2018, Current Biology, in press.