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326 results for “NIR”
Fig.1 in Nir Raman Scattering For The Study Of Biochemical Features Of The Human Skin Epidermis And A Skin Surface Micro-Mapping In Vitro
Fig.1. The estimates of pure Raman spectra: A. - the man's skin right–hand index fingertips epidermis, B. - the women's skin right–hand index fingertips epidermis, measured directly from the sample surface (in vitro).
ASASSN2018gq_NIR
<p>Near IR observations of the transient ASAS SN2018gq</p> <p>The fits file include the data i.e. spectrum and errors, and information on the reduction steps and telluric calibration in the header.</p> <p> </p> <p> </p>
NIR-MFCO dataset: Near-infrared-based false-color images of post-consumer plastics at different material flow compositions and material flow presentations
<p>Determining mass-based material flow compositions (MFCOs) is crucial for assessing and optimizing the recycling of post-consumer plastics. Currently, MFCOs in plastic recycling are mostly determined through manual sorting analysis, but the use of inline near-infrared (NIR) sensors holds potential to automate the characterization process, paving the way for novel sensor-based material flow characterization (SBMC) applications. The NIR-MFCO dataset aims to expedite SBMC research by providing NIR-based false-color images of plastic material flows with their corresponding MFCOs. The false-color images were created through the pixel-based classification of binary material mixtures using a hyperspectral imaging camera (EVK HELIOS NIR G2-320; 990 nm – 1678 nm wavelength range) and the on-chip classification algorithm (CLASS 32). The resulting NIR-MFCO dataset includes <em>n</em> = 880 false-color images from three test series: (T1) high-density polyethylene (HDPE) and polyethylene terephthalate (PET) flakes, (T2a) post-consumer HDPE packaging and PET bottles, and (T2b) post-consumer HDPE packaging and beverage cartons for <em>n</em> = 11 different HDPE shares (0% - 50%) at four different material flow presentations (singled, monolayer, bulk height H1, bulk height H2). The dataset can be used, e.g., to train machine learning algorithms, evaluate the accuracy of inline SBMC applications, and deepen the understanding of segregation effects of anthropogenic material flows, thus further advancing SBMC research and enhancing post-consumer plastic recycling.</p>
NIRS data during sandplay and interview
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Data from: Non-invasive estimation of absorbed ionizing radiation dose in mice using Near-Infrared Spectroscopy (NIRS) and aquaphotomics
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Accurate In Vivo Nanothermometry through NIR-II Lanthanide Luminescence Lifetime
<p>Dataset of https://zenodo.org/record/5805844#.YcmOK2jMJPY</p>
The influence of the Er3+ dopant concentration in LaPO4:Nd3+, Er3+ on thermometric properties of ratiometric and kinetic-based luminescent thermometers operating in NIR II and NIR III optical windows
<p>In this work, properties of the near infrared absorbing and near infrared emitting Er<sup>3+</sup> and Nd<sup>3+</sup> co-doped LaPO<sub>4</sub> nanocrystals luminescent thermometer were investigated by exploiting luminescence spectra ratiometric and luminescence lifetime. The unique configuration of the energy levels of Nd<sup>3+</sup> and Er<sup>3+</sup> ions and the energy transfer between them requires temperature dependent phonon assistance. Since, the probability of this process is dependent on the distance between interacting ions, the thermometric parameters of luminescent thermometers were investigated as a function of Er<sup>3+</sup> dopant concentration. Strong susceptibility of its probability to temperature changes enables to quantify temperature with relative sensitivity as high as S<sub>R</sub> = 1.15%/K for LaPO<sub>4</sub>:1%Nd<sup>3+</sup>, 20%Er<sup>3+</sup> nanocrystals in the ratiometric approach and S<sub>R</sub> = 2.3%/K at 600 K for LaPO<sub>4</sub>:1%Nd<sup>3+</sup>, 5%Er<sup>3+</sup> in the luminescence lifetime based mode. The obtained results confirm the high applicative potential of this phosphor for remote temperature measurements.</p> <ul> </ul>
Portable near infrared spectroscopy (NIRS)
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SD4EO: AI-based synthetic satellite Sentinel-2 images of cities and building coverture (RGB+NIR bands)
<p>This dataset has been created as part of the deliverables for ESA’s <a href="https://eo4society.esa.int/projects/sd4eo/">SD4EO project</a>. It consists of synthetic versions of Sentinel-2 images in urban areas. These images were synthetically generated using schematic representations from Open Street Maps as a guide to create AI-based conditioned diffusion model images in the visible and near-infrared spectrum, along with coverage masks for non-residential buildings and the set of residential buildings combined with the former.</p> <p>At least five synthetic variants were generated for each of the eleven cities:</p> <ul> <li>Paris (11 variants)</li> <li>Toulouse (9 variants)</li> <li>Poitiers (8 variants)</li> <li>Bordeaux (6 variants)</li> <li>Limoges (9 variants)</li> <li>Clermont-Ferrand (5 variants)</li> <li>Troyes (6 variants)</li> <li>Le Mans (14 variants)</li> <li>Angers (7 variants)</li> <li>Madrid (15 variants)</li> <li>Niort (6 variants)</li> </ul> <p>The file names within the ZIP archives follow a very simple schema:</p> <p>`assembled_` + city name + usage or band indicator + variant + PNG extension / NC extension</p> <p>Each of the four types of images has a different indicator or band:</p> <ul> <li>`_RGB_` for images encoding visible spectrum signals</li> <li>`_NIR_` for images generated for the near-infrared band</li> <li>`_full_allbuildingmask` for the coverage pixel mask of all building types in floating point</li> <li>`_full_nonresidentialmask` for the coverage pixel mask of non-residential buildings in floating point</li> <li>if we have no indicator, then it is a netCDF file with a labelled xarray that merges RGB+NIR as the original Sentinel-2 spectral bands in full original range</li> </ul> <p>NOTE: This 5th version corrects a minor bug in 3rd version of this dataset. If you want to access to version 4 (with non-already assembled patches), it is also available in the right side control version list.</p> <p>The SD4EO Project is funded by the ESA’s FutureEO programme under contract no. 4000142334/23/I-DT and supervised by ESA Φ-lab.</p>
Doodleverse/Segmentation Zoo Res-UNet models for 4-class (water, whitewater, sediment and other) segmentation of Sentinel-2 and Landsat-7/8 7-band (RGB+NIR+SWIR+NDWI+MNDWI) images of coasts.
<p>Doodleverse/Segmentation Zoo Res-UNet models for 4-class (water, whitewater, sediment and other) segmentation of Sentinel-2 and Landsat-7/8 7-band (RGB+NIR+SWIR+NDWI+MNDWI) images of coasts.</p> <p>Models have been created using Segmentation Gym* using the following dataset**: https://doi.org/10.5281/zenodo.7344571</p> <p>Classes: {0=water, 1=whitewater, 2=sediment, 3=other}</p> <p>File descriptions</p> <p>For each model, there are 5 files with the same root name:</p> <p>1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse.</p> <p>2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function `seg_images_in_folder.py`. Models may be ensembled.</p> <p>3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model</p> <p>4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py`</p> <p>5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py`</p> <p>Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU</p> <p><br> References</p> <p>*Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym</p> <p>** Buscombe, Daniel. (2022). Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7344571</p> <p> </p>
Push-broom NIR-HSI scanning of painting reconstruction, inspired by Sandro Botticelli's "Venus"
<p>The dataset contains the push-broom scanning NIR-HSI of Sandro Botticelli’s Venus painting detail, produced in the Microchemistry and Microscopy Art Diagnostic Laboratory (M2ADL) of the University of Bologna (2018). The painting was executed with egg tempera on a wood panel prepared with a layer of gypsum and glue. Ancient and modern pigments were selected and employed according to their vibrational signals, detectable by NIR spectroscopy. In a more detail, earth-based pigments were used for the hair and for the flesh tone, while zinc white (ZnO) was applied in the white areas of the eyes and in the hair ribbon. Finally, dammar varnish was applied to half of the painted surface, according to ancient practices which involved the use of a terpenic varnish to improve color rendering and protect the underlying painted layer (Figure 8A). The painting was scanned with a SWIR3 hyperspectral push-broom camera working in the 1000–2500 nm spectral range, at 5.6 nm spectral resolution (Specim Ltd, Finland). The scanning system is also characterized by three halogen lamps (35 W, 430 lm, 2900 K, each) used as illumination sources and a horizontal moving stage (40 × 20 cm) on which the painting was scanned. The scanning parameters were set as follows: scan rate equal to 0.7 mm/s, push-broom camera frame rate equal to 50.00 Hz and exposure time equal to 9 ms. The hyperspectral system was controlled with the Lumo Scanner software (Specim Ltd, Finland). Before the scan, dark (with the camera shutter closed) and white (using a Spectralon reference tile) reference images were acquired, to compute reflectance values.</p> <p>ENVI hyperspectral files (.raw and .hdr) related to the scanned sample, white reference, and dark reference are included. Please, note that ALL the files must be downloaded and included in the same folder.</p> <p>Please, cite as:</p> <p>R. Rocha de Oliveira, C. Malegori, G. Sciutto, P. Oliveri<br><strong>PoliBrush – A user-friendly software to aid multivariate image analysis dissemination</strong><br><em>Chemometrics and Intelligent Laboratory Systems</em>, 240 (2023) 104918<br><a href="https://doi.org/10.1016/j.chemolab.2023.104918">https://doi.org/10.1016/j.chemolab.2023.104918</a></p> <p> </p> <p>You may also be interested in:</p> <p> <strong>XRF-HSI Vermeer dataset</strong><br> <a href="https://www.doi.org/10.5281/zenodo.8143464">10.5281/zenodo.8143464</a></p> <p> <strong>PoliBrush</strong><br> <em>A freely distributed software for exploratory multivariate analysis in RGB and spectral imaging</em><br> <a title="PoliBrush" href="https://doi.org/10.5281/zenodo.8143341" target="_blank" rel="noopener">10.5281/zenodo.8143341</a></p> <p> </p>
Noncentrosymmetric Lanthanide-Based MOF Materials Exhibiting Strong SHG Activity and NIR Luminescence of Er3+: Application in Nonlinear Optical Thermometry
<p>Optically active luminescent materials based on lanthanide ions attract significant attention due to their unique spectroscopic properties, nonlinear optical activity, and the possibility of application as contactless sensors. Lanthanide metal-organic frameworks (Ln-MOFs) that exhibit strong second-harmonic generation (SHG) and are optically active in the NIR region are unexpectedly underrepresented. Moreover, such Ln-MOFs require ligands that are chiral and/or need multistep synthetic procedures. Here, we show that the NIR pulsed laser irradiation of the noncentrosymmetric, isostructural Ln-MOF materials (MOF-Er<sup>3+</sup> (1) and codoped MOF-Yb<sup>3+</sup>/Er<sup>3+</sup> (2)) that are constructed from simple, achiral organic substrates in a one-step procedure results in strong and tunable SHG activity. The SHG signals could be easily collected, exciting the materials in a broad NIR spectral range, from ≈800 to 1500 nm, resulting in the intense color of emission, observed in the entire visible spectral region. Moreover, upon excitation in the range of ≈900 to 1025 nm, the materials also exhibit the NIR luminescence of Er<sup>3+</sup> ions, centered at ≈1550 nm. The use of a 975 nm pulse excitation allows simultaneous observations of the conventional NIR emission of Er<sup>3+</sup> and the SHG signal, altogether tuned by the composition of the Ln-MOF materials. Taking the benefits of different thermal responses of the mentioned effects, we have developed a nonlinear optical thermometer based on lanthanide-MOF materials. In this system, the SHG signal decreases with temperature, whereas the NIR emission band of Er<sup>3+</sup> slightly broadens, allowing ratiometric (Er<sup>3+</sup> NIR 1550 nm/SHG 488 nm) temperature monitoring. Our study provides a groundwork for the rational design of readily available and self-monitoring NLO-active Ln-MOFs with the desired optical and electronic properties.</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.
NIRS Monitoring to Detect AKI in Preterm Infants
ClinicalTrials.gov study NCT03384173. IPD Sharing: NO. Countries: 1. Publications: 5.
Near Infrared Spectroscopy (NIRS) as Transfusion Indicator in Neurocritical Patients
ClinicalTrials.gov study NCT00566709. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Tetra-NIRS Clinical Study
ClinicalTrials.gov study NCT00871975. IPD Sharing: NO. Countries: 3. Publications: 1.
NIR Fluorescence Imaging of Lymphatic Transport Using ICG
ClinicalTrials.gov study NCT02680067. IPD Sharing: YES. Countries: 1. Publications: 1.
Data from: Photoacoustic imaging of rat kidney tissue oxygenation using NIR-II wavelengths
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NIRS Mosquito Aging Dataset and Scripts
<p>The datasets zip contains the spreadsheets with the spectral data for each of the 6 sample groups and the three test sets.</p> <p>The R and Matlab scripts zip contains the code necessary to replicate the analysis.</p>
Dual-Responsive Nanoparticles for Smart Drug Delivery: a NIR light Sensitive and Redox- Reactive PEG-PCL based System
<p>This folder contains raw data for the paper titled "Dual-Responsive Nanoparticles for Smart Drug Delivery: A NIR Light-Sensitive and Redox-Reactive PEG–PCL-Based System" authored by Nancy Ferrentino, Taha Behroozi Kohlan, Shokoufeh Mehrtashfar, Anna Finne-Wistrand, and Daniela Pappalardo, published in Biomacromolecules journal. <a title="DOI URL" href="https://doi.org/10.1021/acs.biomac.4c00889">https://doi.org/10.1021/acs.biomac.4c00889</a></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.