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270 results for “near-infrared”
Cloud-free Chinese Gaofen-1 WFV near-infrared surface reflectance over Huailai remote sensing test site throughout 2020
<p>Land surface reflectance product form the starting point for many application regions such as land cover mapping and the generation of biophysical essential climate variables (ECV). Therefore, ensuring the quality of surface reflectance products is necessary to maintain the integrity of the research outcoming of these application areas. However, ground validation of surface reflectance satellite products is challenging, because ground “truth” on a coarse grid scale based on sparse ground measurements is subject to uncertainty due to spatial heterogeneity. In order to quantify the influence of spatial heterogeneity on the uncertainty of surface reflectance ground “truth” in different sampling cases, we generated the high-resolution (16 m) near-infrared surface reflectance over Huailai remote sensing test site based on Chinese Gaofen-1 WFV Band4 data.</p> <p> </p> <p>All cloud-free GF-1 WFV images throughout the year 2020 were extracted. And there are 25 images in total, with at least one image for each month. The WFV Band4 data covering the whole Huailai test station have been processed into Analysis Ready Data (ARD) system, which aims to simplify and reduce the users’ burden by providing pre-processing such as geometric alignment, radiometric recalibration, and atmospheric correction (Zhong et al., 2021). The geometric normalization of the GF-1 WFV data was realized with the procedure developed by Shan et al. (2014). And the radiometric normalization was finished through cross-calibrating with the Landsat TM/OLI with the method proposed by Yang et al. (2015). The 25 images have been layer stacked into one file according to their acquisition time.</p> <p> </p> <p> </p> <p>Reference:</p> <p>Shan, X. J., P. Tang, and C. M. Hu (2014), An automatic geometric precision correction system based on hierarchical registration for HJ-1 A/B CCD images, Int J Remote Sens, 35(20), 7154-7178.</p> <p>Yang, A., B. Zhong et al. (2015), Cross-calibration of GF-1/WFV over a desert site using Landsat-8/OLI imagery and ZY-3/TLC data, Remote Sens., 7, 10763–10787.</p> <p>Zhong, B., A. Yang, Q. Liu, S. Wu, X. Shan, and X Mu (2021), Analysis ready data of the chinese gaofen satellite data, Remote Sens., 13, 9, 1709.</p>
Fig.1 in Application Of Near-Infrared Reflectance Spectroscopy For Detection Of Acrylamide Content In Potato Crisps And Bread
Fig.1. Predicted acrylamide vs reference acrylamide in bread samples.
Fig.2 in Application Of Near-Infrared Reflectance Spectroscopy For Detection Of Acrylamide Content In Potato Crisps And Bread
Fig.2. Predicted acrylamide vs reference acrylamide in potato crisp samples.
DATASET FOR: A multimodal spectroscopic approach combining mid-infrared and near-infrared for discriminating Gram-positive and Gram-negative bacteria
<h4>Description:</h4> <p>This dataset comprises a comprehensive set of files designed for the analysis and 2D correlation of spectral data, specifically focusing on ATR and NIR spectra. It includes MATLAB scripts and supporting functions necessary to replicate the analysis, as well as the raw datasets used in the study. Below is a detailed description of the included files:</p> <ol> <li> <p><strong>Data Analysis</strong>:</p> <ul> <li><strong>File Name</strong>: <code>Data_Analysis.mlx</code></li> <li><strong>Description</strong>: This MATLAB Live Script file contains the main script used for the classification analysis of the spectral data. It includes steps for preprocessing, analysis, and visualization of the ATR and NIR spectra.</li> </ul> </li> <li> <p><strong>2D Correlation Data Analysis</strong>:</p> <ul> <li><strong>File Name</strong>: <code>Data_Analysis_2Dcorr.mlx</code></li> <li><strong>Description</strong>: This MATLAB Live Script file is similar to the primary analysis script but is specifically tailored for performing 2D correlation analysis on the spectral data. It includes detailed steps and code for executing the 2D correlation.</li> </ul> </li> <li> <p><strong>Functions</strong>:</p> <ul> <li><strong>Folder Name</strong>: <code>Functions</code></li> <li><strong>Description</strong>: This folder contains all the necessary MATLAB function files required to replicate the analyses presented in the scripts. These functions handle various preprocessing steps, calculations, and visualizations.</li> </ul> </li> <li> <p><strong>Datasets</strong>:</p> <ul> <li><strong>File Names</strong>: <code>ATR_dataset.xlsx</code>, <code>NIR_dataset.xlsx</code>, <code>Reference_data.csv</code></li> <li><strong>Description</strong>: These Excel files contain the raw spectral data for ATR and NIR analyses, as well as reference datasets. Each file includes multiple sheets with detailed measurements and metadata.</li> </ul> </li> </ol> <h4>Usage Notes:</h4> <ul> <li><strong>Software Requirements</strong>: <ul> <li>MATLAB is required to run the .mlx files and utilize the functions.</li> <li><strong>PLS_Toolbox</strong>: Necessary for certain preprocessing and analysis steps.</li> <li><strong>MIDAS 2010</strong>: Available at <a href="https://www.mathworks.com/matlabcentral/fileexchange/32384-midas-2010" target="_new" rel="noreferrer">MIDAS 2010</a>, required for the 2D correlation analysis.</li> </ul> </li> <li><strong>Replication</strong>: Users can replicate the analyses by running the <code>Data_Analysis.mlx</code> and <code>Data_Analysis_2Dcorr.mlx</code> scripts in MATLAB, ensuring that the <code>Functions</code> folder is in the MATLAB path.</li> <li><strong>Data Handling</strong>: The datasets are provided in .xlsx format, which can be easily imported into MATLAB or other data analysis software.</li> </ul>
Atmospheric observations of the water vapour continuum in the near-infrared windows - Data
<p>Dataset for our AMT paper (Atmospheric observations of the water vapour continuum in the near-infrared windows).</p> <p>Included is the best estimate of the continuum optical depth (18 September 2008). the ratio of this best estimate to the corresponding MT_CKD 3.2 optical depth from 18 September 2008, and the CAVIAR-field estimated continuum absorption coefficients using both MT_CKD and CAVIAR-lab. Other data are available from the corresponding author upon request.</p>
Fig. 4 in Near-infrared spectroscopy and microstructure of the scales of Sabethes (Sabethes) albiprivus (Diptera: Culicidae)
Fig. 4. Localities were the Sabethes albiprivus specimens were collected.
Reduced atomic models for large-scale computations: Fe XIII near-infrared lines
<p>The tar file contains two directories. One contains the CHIANTI v.8-11 format main files<br>produced relative to a 33 merged states Fe XIII model.</p> <p>The other contains CHIANTI and P-CORONA format files for a 55-states Fe XIII model.</p> <p>In both cases the models were built to obtain accurate emissivities of the Fe XIII near-infrared lines withtin the ground configuration. </p> <p><br>The models are described in Del Zanna and Hebbur Dayananda, 2024, submitted to MNRAS.</p> <p>Information on the CHIANTI programs can be found in chianti-vip.com</p> <p>Information on P-CORONA can be found on https://research.iac.es/proyecto/polmag/pages/codes/p-corona.php</p>
Functional near-infrared spectroscopy visually evoked measurements and Autism Questionnaire score in adults and children
<p><strong>Functional near-infrared spectroscopy (fNIRS) visually-evoked data collected from healthy adults and children. We recruited a total of 40 adult participants (20 women, age: 31.05 ± 3.94 (SD) years) and 19 children (5 girls, age: 7.20 ± 3.01 (SD) years). Adult participants filled in the Autistic-traits Quotient (AQ) questionnaire, a 50-items self-administered report validated for the Italian version. The items consist of descriptive statements assessing personal preferences and typical behavior. Since child self-report might be affected by reading and comprehension difficulties, the children's version of Autism Spectrum Quotient (Italian version of AQ-child) was completed by parents. To measure changes in total Hb (THb) concentration and relative oxygenation levels (OHb and DHb) in the occipital cortex during the task, we used a continuous-wave NIRS system with 8 red light-sources operating at 760 nm and 850 nm, and 7 detectors, forming an array of 22 multi-distant channels. </strong></p> <p> </p> <p><strong>The dataset includes 2 '.zip' files containing artifact-free visually-evoked transients HDF data, exported from Homer 3 (https://github.com/BUNPC/Homer3) in '.txt' format. Subfolders correspond to different stimulation conditions, for details refer to this paper [preprint coming soon].</strong></p> <p><strong>Each txt file is named with the corresponding subject code and contains two events [tagged as 1 (baseline) and 2 (stimulus)]. </strong></p> <p><strong>The file 'annotations.csv' contains the following fields:</strong></p> <p><strong>Code [Subjects id name, that corresponds to the filename in the recording folders]</strong></p> <p><strong>Sex [M or F]</strong></p> <p><strong>Valid [1 or 0, 0 means excluded subject]</strong></p> <p><strong>Age [int]</strong></p> <p><strong>CH_exclude [list of excluded channels]</strong></p> <p><strong>CH_Red [ list of channels that did not passed the calibration step]</strong></p> <p><strong>Category [adult or child] </strong></p> <p><strong>Cartoon_fixed [cartoon name]</strong></p> <p><strong>Cartoon_chosen [cartoon name]</strong></p> <p><strong>AQ [global AQ score] </strong></p> <p><strong>AQ_S [ AQ subscale social ] </strong></p> <p><strong>AQ_C [ AQ subscale communication ] </strong></p> <p><strong>AQ_A [ AQ subscale attention] </strong></p> <p><strong>AQ_D [ AQ subscale detail] </strong></p> <p><strong>AQ_I [ AQ subscale imagination] </strong><br> </p> <p> </p> <p> </p> <p> </p>
Data from: Heating rates are more strongly influenced by near-infrared than visible reflectance in beetles
<p>Adaptations to control heat transfer through the integument are a key component of temperature regulation in animals. However, there remain significant gaps in our understanding of how different optical and morphological properties of the integument affect heating rates. To address these gaps, we examined the effect of reflectivity in both ultraviolet-visible and near-infrared wavelengths, surface micro-sculpturing, effective area (area subjected to illumination) and cuticle thickness on radiative heat gain in jewel beetles (Buprestidae). We measured heating rate using a solar simulator to mimic natural sunlight, a thermal chamber to control the effects of conduction and convection, and optical filters to isolate different wavelengths. We found that effective area and reflectivity predicted heating rate. The thermal effect of reflectivity was driven by variation in near-infrared rather than ultraviolet-visible reflectivity. By contrast, cuticle thickness and surface rugosity had no detectable effect. Our results provide empirical evidence that near-infrared reflectivity has an important effect on radiative heat gain. Modulating reflectance of near-infrared wavelengths of light may be a more widespread adaptation to control heat gain than previously appreciated.</p>
Visible and Near-Infrared Reflectance Spectra of Igneous Rocks and Their Powders
<p>Spectral reflectance data for "Visible and Near-Infrared Reflectance Spectra of Igneous Rocks and Their Powders".</p>
Laser Beam Characterization Data Related to Testing Different Near-Infrared Laser Therapy Devices Used in Sports Medicine
<p>This dataset contains laser beam characterization data collected for different near-infrared laser therapy devices that are used in sports medicine. Data were collected between September 2021 and December 2022 at the Department of Anatomy II (Neuroanatomy) of the Ludwig-Maximilians University of Munich, Germany. Laser therapy devices were characterized using a thermal power sensor, a photodiode, and a beam profiling camera (for details, see [1]). Two different laser therapy devices were investigated (Dolorclast High Power Laser, Electro Medical Systems; Cube 4 Med, Eltech K-laser s.r.l.). The measurements were repeated for three units of each laser therapy device (EMS-1, EMS-2, EMS3; K-1, K-2, K-3). The dataset contains raw data files from each sensor that include text (TXT) files, comma-separated-values (CSV) formatted files, and raw camera recordings in binary format (BINARY.BGDATA). The data was used to create figures that were submitted to the journal MDPI Biomedicines [1]. The figures in [1] were created with Python in a Jupyter Notebook, which is also included in this dataset.</p> <p><strong>2023-02-17 Update: The manuscirpt has been published in MDPI Biomedicines [2]. If you make any use of this dataset, please cite [2].</strong></p> <p>[1] Kaub, L.; Schmitz, C. Spread of the Optical Power Emission of Several Units of the Same Laser Therapy Devices Used in Sports Medicine, Which Cannot Be Assessed by the Users, Shown by Means of High-Fidelity Laser Measurement Technology. <em>Preprints</em> <strong>2023</strong>, <em>2023010251</em>, https://doi.org/10.20944/preprints202301.0251.v1.</p> <p>[2] Kaub, L.; Schmitz, C. Spread of the Optical Power Emission of Three Units Each of Two Different Laser Therapy Devices Used in Sports Medicine, Which Cannot Be Assessed by the Users, Shown by Means of High-Fidelity Laser Measurement Technology. <em>Biomedicines</em> <strong>2023</strong>, <em>11</em>, 585. https://doi.org/10.3390/biomedicines11020585</p>
RT Dataset -- Updated radiative transfer model for Titan in the near-infrared wavelength range: Validation against Huygens atmospheric and surface measurements and application to the Cassini/VIMS observations of the Dragonfly landing area
<p>This dataset contains all Radiative Transfer (RT) results made for the paper.</p> <p>The data are stored in 5 zipped-folders names with the Cassini/VIMS cube flyby and id, or explicitly for Huygens/ULIS calibrated observations:</p> <ul> <li>TB_C1481624349_1</li> <li>T40_C1578266417_1</li> <li>T38_C1575509158_1</li> <li>T40_C1578263500_1</li> <li>T40_C1578263152_1</li> <li>ULIS_observations</li> </ul> <p>The TB_C1481624349_1 folder contains the Cassini/VIMS cube over HLS, the HLS end-member (End_member.txt), the surface albedo retrieved by Karkoschka et al. (2016) corrected for the photometry (HLS_Karkoschka_2016_spectrum.txt), and the inverted surface albedo (Surface_albedo.txt).</p> <p>In these folders, each VIMS pixel is stored in a .txt file with the following pattern:</p> <p><CUBE_ID>_<PIXEL_SAMPLE>_<PIXEL_LINE> .txt</p> <p>It starts with a header describing the observation: </p> <ul> <li>CUBE_ID: the VIMS cube id (`C1234567890_1` format)</li> <li>SAMPLE: the pixel sample number.</li> <li>LINE: the pixel line number.</li> <li>LONG: the pixel longitude (in degree).</li> <li>LAT: the pixel latitude (in degree).</li> <li>INC: the surface incident angle (in degree).</li> <li>EMI: the surface emergent angle (in degree).</li> <li>PHASE: the surface phase angle (in degree).</li> </ul> <p>For the Selk crater cubes (T40_C1578266417_1, T38_C1575509158_1, T40_C1578263500_1, T40_C1578263152_1), the header also contains the spatial sampling and the radiative transfer model outputs: </p> <ul> <li>Spatial sampling (km/pix).</li> <li>Fh: the haze scaling factor.</li> <li>Fm: the mist scaling factor.</li> <li>1-sigma (Fh): the 1-sigma uncertainty on Fh.</li> <li>1-sigma (Fm): the 1-sigma uncertainty on Fm.</li> <li>Reduced chi2: the reduced chi2. </li> </ul> <p>Then contains the observed spectra:</p> <ul> <li>Column 1: the VIMS channel central wavelength (in micrometers).</li> <li>Column 2: the VIMS pixel I/F.</li> <li>Column 3: the VIMS pixel I/F 1-sigma uncertainty. </li> </ul> <p>For the Selk crater cubes (T40_C1578266417_1, T38_C1575509158_1, T40_C1578263500_1, T40_C1578263152_1), 3 columns are added for: </p> <ul> <li>Column 4: the surface albedo.</li> <li>Column 5: the upper 1-sigma uncertainty on the surface albedo.</li> <li>Column 6 : the lower 1-sigma uncertainty on the surface albedo.</li> </ul> <p>The ULIS folder contains the Huygens/ULIS calibrated observations (in I/F) and the simulations with 1-sigma uncertainties as a function of the altitude (in km):</p> <ul> <li>Column 1: the VIMS channel central wavelength (in micrometers), stopped at the end of the Huygens/ULIS wavelength range.</li> <li>Column 2: the ULIS I/F.</li> <li>Column 3 : the simulated I/F.</li> <li>Column 4: the lower 1-sigma uncertainty on the simulation.</li> <li>Column 5 : the upper 1-sigma uncertainty on the simulation.</li> </ul>
Near-infrared spectra dataset of milk composition in transmittance mode
<p><strong><em>Near-infrared spectra dataset of milk composition in transmittance mode</em></strong><strong> </strong></p>
DATA SET: In vivo characterization of the optical and hemodynamic properties of the human sternocleidomastoid muscle through ultrasound-guided hybrid near-infrared spectroscopies.
<p>This repository contains the data sets of the article:</p><p>L. Cortese et al., "In vivo characterization of the optical and hemodynamic properties of the human sternocleidomastoid muscle through ultrasound-guided hybrid near-infrared spectroscopies."</p>
A Multi-Center Study of Near-Infrared Spectroscopy (NIRS) for Hematoma Detection
ClinicalTrials.gov study NCT00576147. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Dual-Channel Near-Infrared Autofluorescence Imaging and AI Analysis to Locate Parathyroid Glands (PTFinder)
ClinicalTrials.gov study NCT07010380. IPD Sharing: YES. Countries: 1. Publications: 1.
Efficacy of Near-Infrared Vein Imaging for Difficult IV Placement
ClinicalTrials.gov study NCT04262947. IPD Sharing: NO. Countries: 1. Publications: 8.
Total Shoulder Arthroplasty Near-infrared Spectroscopy
ClinicalTrials.gov study NCT02198183. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Near-Infrared Laser Stimulation for Various Neurological Conditions
ClinicalTrials.gov study NCT04489082. IPD Sharing: NO. Countries: 1. Publications: 19.
Near-Infrared Spectroscopy for Pediatric Acute Scrotum and Testicular Torsion
ClinicalTrials.gov study NCT01812109. IPD Sharing: Not stated. Countries: 1. Publications: 4.
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.