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Dataset results
326 results for “NIR”
Uro-NIRS Clinical Study
ClinicalTrials.gov study NCT00706407. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Use of NIRS in Preterm Population Born at Altitude
ClinicalTrials.gov study NCT04639583. IPD Sharing: NO. Countries: 1. Publications: 0.
Evaluation of rSO2 Between Frontal Lesion Area and Normal Area of Brain by NIRSITX Using NIRS in Acute Ischemic Stroke Patients.
ClinicalTrials.gov study NCT06250608. IPD Sharing: NO. Countries: 1. Publications: 0.
Evaluation of a Combined Near Infrared Spectroscopy (NIRS) and Intravascular Ultrasound (IVUS) Catheter for Detection of Lipid Rich Plaque
ClinicalTrials.gov study NCT01506960. IPD Sharing: Not stated. Countries: 1. Publications: 0.
NIRS Monitoring in the NICU and AKI
ClinicalTrials.gov study NCT07222722. IPD Sharing: YES. Countries: 1. Publications: 0.
Near-infrared Light-emitting Diode (NIR-LED) Therapy for Leber's Hereditary Optic Neuropathy (LHON)
ClinicalTrials.gov study NCT01389817. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Cerebral Tissue Oxygenation Values in Newborns Measured Using Laser and LED NIRS Oximeters
ClinicalTrials.gov study NCT01496027. IPD Sharing: Not stated. Countries: 1. Publications: 0.
NIR Fluorescence Cholangiography With Low Dose of ICG
ClinicalTrials.gov study NCT04005898. IPD Sharing: Not stated. Countries: 0. Publications: 1.
Sentinel Lymph Node Mapping in Esophageal Cancer Using ICG Dye and NIR Imaging
ClinicalTrials.gov study NCT04400292. IPD Sharing: YES. Countries: 1. Publications: 0.
The Role of Cerebral NIRS in Preventing Brain Injury of Very Low Birth Weight Preterm Infants
ClinicalTrials.gov study NCT06729398. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
NIRS Directed Optimal Cerebral Perfusion Pressure in Septic Shock Patients: A Feasibility Study
ClinicalTrials.gov study NCT03879317. IPD Sharing: UNDECIDED. Countries: 0. Publications: 1.
NIRS and DO2i Correlation
ClinicalTrials.gov study NCT03281707. IPD Sharing: UNDECIDED. Countries: 0. Publications: 4.
NIR Arthroscopic Fluorescence Angiography of Menisci
ClinicalTrials.gov study NCT05420974. IPD Sharing: UNDECIDED. Countries: 0. Publications: 3.
fMRI and NIRS Imaging for Traumatic Brain Injury
ClinicalTrials.gov study NCT01668758. IPD Sharing: Not stated. Countries: 0. Publications: 3.
AIRS/Aqua L1B Visible/Near Infrared (VIS/NIR) geolocated and calibrated radiances V005 (AIRVBRAD) at GES DISC
The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The VIS/NIR level 1B data set contains visible and near-infrared calibrated and geolocated radiances in W/m^2/micron/steradian. This data set includes 4 channels in the 0.4 to 1.0 um region of the spectrum. Each day of AIRS data are divided into 240 granules each of 6 minute duration. However, the VIS/NIR granules are only produced in the daytime so there will always be fewer VIS/NIR granules. The primary purpose of the VIS/NIR channels is the detection and flagging of significant inhomogeneities in the infrared field-of-view,which may adversely impact the quality of the temperature and moisture soundings. Therefore the VIS/NIR radiance product has a higher spatial resolution than the Infrared radiance product. Each VIS/NIR scan has 9 alongtrack footprints and 720 across track footprints. For ease in comparing with the infrared product which has 135 along track footprints and 90 across track footprints, the VIS/NIR product has additional dimensions to account for the 9 additional alongtrack and 8 additional across track footprints.
HSP90 specific nIR probe identifies aggressive prostate cancers: a translational study from preclinical models to a human pilot study
GEO Series GSE164865. Homo sapiens. 2 samples. Type: Expression profiling by high throughput sequencing.
Visible and NIR Upconverting Er3+–Yb3+ Luminescent Nanorattles and Other Hybrid PMO‐Inorganic Structures for In Vivo Nanothermometry
<p>Dataset accompanying figures published in the publication DOI: 10.5281/zenodo.4001573</p>
Data from: Near infrared spectroscopy (NIRS) predicts non-structural carbohydrate concentrations in different tissue types of a broad range of tree species
1. The allocation of non-structural carbohydrates (NSCs) to reserves constitutes an important physiological mechanism associated with tree growth and survival. However, procedures for measuring NSC in plant tissue are expensive and time-consuming. Near-infrared spectroscopy (NIRS) is a high-throughput technology that has the potential to infer the concentration of organic constituents for a large number of samples in a rapid and inexpensive way based on empirical calibrations with chemical analysis. 2. The main objectives of this study were (i) to develop a general NSC concentration calibration that integrates various forms of variation such as tree species and tissue types and (ii) to identify characteristic spectral regions associated with NSC molecules. In total, 180 samples from different tree organs (root, stem, branch, leaf) belonging to 73 tree species from tropical and temperate biomes were analysed. Statistical relationships between NSC concentration and NIRS spectra were assessed using partial least squares regression (PLSR) and a variable selection procedure (competitive adaptive reweighted sampling, CARS), in order to identify key wavelengths. 3. Parsimonious and accurate calibration models were obtained for total NSC (r2 of 0·91, RMSE of 1·34% in external validation), followed by starch (r2 = 0·85 and RMSE = 1·20%) and sugars (r2 = 0·82 and RMSE = 1·10%). Key wavelengths coincided among these models and were mainly located in the 1740–1800, 2100–2300 and 2410–2490 nm spectral regions. 4. This study demonstrates the ability of general calibration model to infer NSC concentrations across species and tissue types in a rapid and cost-effective way. The estimation of NSC in plants using NIRS therefore serves as a tool for functional biodiversity research, in particular for the study of the growth–survival trade-off and its implications in response to changing environmental conditions, including growth limitation and mortality.
SD4EO: Sentinel-2 Northern France Patch Dataset (RGB+NIR)
<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>.</p> <p>It consists of orthogonal patches from real satellite images of Sentinel-2, covering the visible and near-infrared bands, taken over large areas in northern France. The following tiles are included:</p> <div> <table> <tbody> <tr> <td> <p><strong>Tile ID</strong></p> </td> <td> <p><strong>Sampling Date</strong></p> </td> <td> <p><strong>Number of patches</strong></p> </td> </tr> </tbody> <tbody> <tr> <td> <p>T31TCJ</p> </td> <td> <p>2021/04/15</p> </td> <td> <p>3306</p> </td> </tr> <tr> <td> <p>T31TCN</p> </td> <td> <p>2021/04/25</p> </td> <td> <p>3120</p> </td> </tr> <tr> <td> <p>T31TDN</p> </td> <td> <p>2021/04/25</p> </td> <td> <p>3481</p> </td> </tr> <tr> <td> <p>T31UDP</p> </td> <td> <p>2021/04/25</p> </td> <td> <p>3540</p> </td> </tr> <tr> <td> <p>T31UEP</p> </td> <td> <p>2021/04/25</p> </td> <td> <p>3599</p> </td> </tr> <tr> <td> <p>T30UWU</p> </td> <td> <p>2023/11/07</p> </td> <td> <p>2928</p> </td> </tr> <tr> <td> <p>T31TCL</p> </td> <td> <p>2024/05/09</p> </td> <td> <p>3422</p> </td> </tr> <tr> <td> <p>T31TDL</p> </td> <td> <p>2024/05/09</p> </td> <td> <p>3306</p> </td> </tr> <tr> <td> <p>T31TDM</p> </td> <td> <p>2021/03/31</p> </td> <td> <p>2891</p> </td> </tr> <tr> <td> <p>T31TEM</p> </td> <td> <p>2021/04/27</p> </td> <td> <p>3480</p> </td> </tr> <tr> <td> <p>T31TEN</p> </td> <td> <p>2021/04/27</p> </td> <td> <p>3038</p> </td> </tr> <tr> <td> <p>T31TFM</p> </td> <td> <p>2021/04/27</p> </td> <td> <p>2576</p> </td> </tr> <tr> <td> <p>T31UDQ</p> </td> <td> <p>2023/10/07</p> </td> <td> <p>3599</p> </td> </tr> <tr> <td> <p>T31UEQ</p> </td> <td> <p>2023/10/07</p> </td> <td> <p>3540</p> </td> </tr> <tr> <td> <p>T30TXT</p> </td> <td> <p>2023/10/10</p> </td> <td> <p>3420</p> </td> </tr> <tr> <td> <p>T30TYS</p> </td> <td> <p>2023/10/10</p> </td> <td> <p>3534</p> </td> </tr> <tr> <td> <p>T30UXU</p> </td> <td> <p>2023/10/10</p> </td> <td> <p>3599</p> </td> </tr> <tr> <td> <p>T30UYU</p> </td> <td> <p>2023/10/10</p> </td> <td> <p>3654</p> </td> </tr> <tr> <td> <p>T31TFN</p> </td> <td> <p>2024/05/11</p> </td> <td> <p>3363</p> </td> </tr> <tr> <td> <p>T31UFP</p> </td> <td> <p>2024/05/11</p> </td> <td> <p>3658</p> </td> </tr> <tr> <td> <p>T31UFQ</p> </td> <td> <p>2024/05/11</p> </td> <td> <p>3660</p> </td> </tr> <tr> <td> <p>T30TVK</p> </td> <td> <p>2024/05/11</p> </td> <td> <p>2970</p> </td> </tr> <tr> <td> <p><strong>TOTAL</strong></p> </td> <td> <p><strong>73,684</strong></p> </td> </tr> </tbody> </table> </div> <p> </p> <p>The tiles were selected to minimize cloud cover, which is why the sampling dates are spread across a wide range. Additionally, care was taken to capture images close to winter and late spring, ensuring that lighting conditions were either at dawn or dusk, which helps reduce saturation effects on highly reflective surfaces such as flat industrial rooftops.</p> <p>Each patch covers an area of approximately 1700x1700 meters (though the exact size may vary slightly with latitude). Patches located at the edges of tiles were excluded to avoid potential cutting or distortion issues. Likewise, patches near large bodies of water, including coastlines, were also discarded.</p> <p>It's important to note that, unlike the synthetic image dataset [<a href="https://zenodo.org/records/13208361">link</a>], in this case, the patches are always disjoint.</p> <p>To facilitate the use of these images for training generative AI models (such as GANs and Diffusion models), the patch size has been standardized to 512x512 pixels, providing an effective resolution of 3.3 meters per pixel. The color channels were scaled according to the accumulated histograms in order to embrace most of the energy (>90%): Thus, the values from 0 to 256 in the red channel correspond to the range of 460.0 to 6700.0 in the irradiance captured by Sentinel-2 for the patches in this dataset. The values in the green channel correspond to a range of 740.0 to 5800.0, and the blue channel values span from 400.0 to 4840.0 as recorded by Sentinel-2 sensors.</p> <p>To simplify their usage, the images are stored in lossless PNG format.</p> <p>Each patch is also accompanied by pixel-level labels based on the color-coded annotations from OpenStreetMap data in the dataset <a href="https://zenodo.org/records/13958096">doi:10.5281/zenodo.13958096</a> Both this dataset and the OpenStreetMap-labeled dataset have been used to train a conditional diffusion model, which generated a synthetic dataset of 46.5 GB that can be accessed at this [<a href="https://zenodo.org/records/13208361">link</a>].</p> <p>The file naming convention is straightforward:</p> <p> Patch_<Tile Name>_<Column Number>_<Row Number>_S2.png</p> <p>Columns and rows are always referenced within the same tile.</p> <p> </p> <p>The images from Sentinel satellites, part of the European Union's Copernicus program, are available under an open access policy. Specifically, they are distributed under the <strong>Creative Commons Attribution-ShareAlike 3.0 IGO (CC BY-SA 3.0 IGO)</strong> license. This means that you are free to share, use, and adapt the images, even for commercial purposes, as long as appropriate credit is given, and any derivative work is shared under the same license: <a href="https://open.esa.int/copernicus-sentinel-satellite-imagery-under-open-licence/">Open Access at ESA</a>. This open access policy allows wide usage for research, education, and even commercial applications, with the goal of supporting environmental monitoring and other societal needs. So, <strong>we have selected the closest avaliable licence in Zenodo, as this dataset is a derivative work.</strong></p> <p>The SD4EO Project is funded by ESA’s FutureEO programme under contract no. 4000142334/23/I-DT and is supervised by ESA Φ-lab.</p>
Measurements of Anemia and Physiologic Tissue Response to Blood Transfusions in VLBW Infants Using Quantitative NIRS
ClinicalTrials.gov study NCT00544375. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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.