Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
326
datasets available to search
ShareScore release 0.9.0
Dataset results
326 results for “NIRS”
SpeX NIR spectra and extinction curves
<p><strong>SpeX near-infrared spectra and measured extinction curves for a sample of Milky Way sightlines.</strong></p> <ul> <li><em>SpeX_spectra.zip</em> contains the SpeX near-infrared spectra of 15 comparison and 25 reddened O/B type Milky Way stars, that were used to measure extinction curves.</li> <li><em>Data_files.zip</em> contains the data files of the stars in the format required by the code.</li> <li><em>Ext_curves.zip</em> contains 15 measured extinction curves and the measured average diffuse Milky Way extinction curve.</li> </ul> <p>See our paper Decleir et al. (ApJ, submitted) for a detailed discussion of our method and results.</p> <p>See the associated <a href="https://github.com/mdecleir/spex_nir_extinction/tree/v1.0.0">spex_nir_extinction GitHub repository</a> (<a href="https://zenodo.org/record/5806703#.YdYhrS-cY2I">Decleir et al. 2021</a>) for the code, Tables and Figures.</p> <p>This version (2.0) is an update from the first version (1.0), and is associated with the revised version of the paper. Changes include:</p> <ul> <li>updated uncertainties for R(V) in the headers of the extinction curve fits files.</li> <li>addition of 1/R(V) in the headers of the extinction curve fits files.</li> <li>addition of comments in the data files about the references and quality of the photometry </li> </ul>
(SEN12MS) deepNIR: Dataset for generating synthetic NIR images
<p>This dataset contains <strong>SEN12MS </strong>NIR+RGB dataset used in our paper; deepNIR: Dataset for generating synthetic NIR images and improved fruit detection system using deep learning techniques.</p> <p>Please refer to <a href="http://tiny.one/deepNIR">http://tiny.one/deepNIR</a> for more detail.</p>
(capsicum) deepNIR: Dataset for generating synthetic NIR images
<p>This dataset contains <strong>capsicum</strong> NIR+RGB dataset used in our paper; deepNIR: Dataset for generating synthetic NIR images and improved fruit detection system using deep learning techniques.</p> <p>Please refer to <a href="http://tiny.one/deepNIR">http://tiny.one/deepNIR</a> for more detail.</p>
(nirscene) deepNIR: Dataset for generating synthetic NIR images
<p>This dataset contains <strong>nirscene</strong> NIR+RGB dataset used in our paper; deepNIR: Dataset for generating synthetic NIR images and improved fruit detection system using deep learning techniques.</p> <p>Please refer to <a href="http://tiny.one/deepNIR">http://tiny.one/deepNIR</a> for more detail.</p> <p> </p>
Aprendizaje automático para la clasificación de café tostado a partir de reflectancia espectral en el rango Visible-Nir
<p>El café es uno de los pilares de la economía colombiana, y Colombia es un productor importante de café de calidad del mundo. El departamento del Cauca tiene potencial para producir cafés de alta calidad, gracias a sus condiciones ambientales. La creciente demanda mundial de café especial aumenta la necesidad de mejorar los métodos de evaluación de la calidad del café, y el grado de tostión de los granos es importante para esta evaluación, pues determina las características de sabor del café. La presente investigación se llevó a cabo en el Parque Tecnológico del Café – Tecnicafé km 13 Corregimiento de La venta Cajibío –Cauca. En el proyecto que aquí se presenta, se desarrolló una herramienta tecnológica, que permite clasificar el café tostado en cinco grados de tostión (ligero, medio-ligero, medio, medio-alto y oscuro) usando técnicas ópticas espectrales.</p>
NIR spectra of Hawaii avocados
<p>Avocados are an important economic crop of Hawaii, contributing to approximately 3% of all avocados grown in the United States. To export Hawaii-grown avocados, growers must follow strict United States Department of Agriculture Animal and Plant Health Inspection Service (USDA-APHIS) regulations. Currently, only the Sharwil variety can be exported relying on a systems approach, which allows fruit to be exported without quarantine treatment; treatments that can negatively impact the quality of avocados. However, for the systems approach to be applied, Hawaii avocado growers must positively identify the avocados variety as Sharwil with APHIS prior to export. Currently, variety identification relies on physical characteristics, which can be erroneous and subjective, and has been disputed by growers. Once the fruit is harvested, variety identification is difficult. While molecular markers can be used through DNA extraction from the skin, the process leaves the fruit unmarketable. This study evaluated the feasibility of using near-infrared spectroscopy to non-destructively discriminate between different Hawaii-grown avocado varieties, such as Sharwil, Beshore, and Yamagata, Nishikawa, and Greengold, and to positively identify Sharwil from the other varieties mentioned above. The classifiers built using a bench-top system achieved 95% total classification rates for both discriminating the varieties from one another and positively identifying Sharwil while the classifier built using a handheld spectrometer achieved 96% and 96.7% total classification rates for discriminating the varieties from one another and positively identifying Sharwil, respectively. Results from chemometric methods and chemical analysis suggested that water and lipid were key contributors to the performance of classifiers. The positive results demonstrate the feasibility of NIR spectroscopy for discriminating different avocado varieties as well as authenticating Sharwil. To develop robust and stable models for the growers, distributors, and regulators in Hawaii, more varieties and additional seasons should continue to be added.</p>
NIR Spectroscopy and Cobalt Electrochemistry Data set for Estimating Phosphate Concentration in Hydroponic Solution
<p>These data are raw-data used in the development of phosphate sensors for quantitative detection of phosphate ion concentrations in hydroponics nutrient solution. A total of 80 samples were used, of which 56 were used for model development and 24 were used for validation. See below for more details.</p> <p><br> 1. EMF response data from three cobalt electrodes<br> 2. The intensity value of the sample obtained from the NIR spectrometer (904 nm to 1600 nm)</p>
Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, other)
<p><em><strong>Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, other)</strong></em></p> <p>Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat 5-band (R+G+B+NIR+SWIR) satellite images of coasts (water, other)</p> <p><strong>Description</strong></p> <p>3649 images and 3649 associated labels for semantic segmentation of Sentinel-2 and Landsat 5-band (R+G+B+NIR+SWIR) satellite images of coasts. The 2 classes are 1=water, 0=other. Imagery are a mixture of 10-m Sentinel-2 and 15-m pansharpened Landsat 7, 8, and 9 visible-band imagery of various sizes. Red, Green, Blue, near-infrared, and short-wave infrared bands only</p> <p>These images and labels could be used within numerous Machine Learning frameworks for image segmentation, but have specifically been made for use with the Doodleverse software package, Segmentation Gym**.</p> <p>Two data sources have been combined</p> <p><strong>Dataset 1</strong></p> <p>* 579 image-label pairs from the following data release**** https://doi.org/10.5281/zenodo.7344571<br> * Labels have been reclassified from 4 classes to 2 classes.<br> * Some (422) of these images and labels were originally included in the Coast Train*** data release, and have been modified from their original by reclassifying from the original classes to the present 2 classes.<br> * These images and labels have been made using the Doodleverse software package, Doodler*.</p> <p><strong>Dataset 2</strong></p> <ul> <li>3070 image-label pairs from the Sentinel-2 Water Edges Dataset (SWED)***** dataset, https://openmldata.ukho.gov.uk/, described by Seale et al. (2022)******</li> <li>A subset of the original SWED imagery (256 x 256 x 12) and labels (256 x 256 x 1) have been chosen, based on the criteria of more than 2.5% of the pixels represent water</li> </ul> <p><strong>File descriptions</strong></p> <ul> <li> classes.txt, a file containing the class names</li> <li> images.zip, a zipped folder containing the 3-band RGB images of varying sizes and extents</li> <li> labels.zip, a zipped folder containing the 1-band label images</li> <li> nir.zip, a zipped folder containing the 1-band near-infrared (NIR) images</li> <li> swir.zip, a zipped folder containing the 1-band shorttwave infrared (SWIR) images</li> <li> overlays.zip, a zipped folder containing a semi-transparent overlay of the color-coded label on the image (red=1=water, blue=0=other)</li> <li> resized_images.zip, RGB images resized to 512x512x3 pixels</li> <li> resized_labels.zip, label images resized to 512x512x1 pixels</li> <li> resized_nir.zip, NIR images resized to 512x512x1 pixels</li> <li> resized_swir.zip, SWIR images resized to 512x512x1 pixels</li> </ul> <p>References</p> <p>*Doodler: Buscombe, D., Goldstein, E.B., Sherwood, C.R., Bodine, C., Brown, J.A., Favela, J., Fitzpatrick, S., Kranenburg, C.J., Over, J.R., Ritchie, A.C. and Warrick, J.A., 2021. Human‐in‐the‐Loop Segmentation of Earth Surface Imagery. Earth and Space Science, p.e2021EA002085https://doi.org/10.1029/2021EA002085. See https://github.com/Doodleverse/dash_doodler.</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>***Coast Train data release: Wernette, P.A., Buscombe, D.D., Favela, J., Fitzpatrick, S., and Goldstein E., 2022, Coast Train--Labeled imagery for training and evaluation of data-driven models for image segmentation: U.S. Geological Survey data release, https://doi.org/10.5066/P91NP87I. See https://coasttrain.github.io/CoastTrain/ for more information</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>*****Seale, C., Redfern, T., Chatfield, P. 2022. Sentinel-2 Water Edges Dataset (SWED) https://openmldata.ukho.gov.uk/</p> <p>******Seale, C., Redfern, T., Chatfield, P., Luo, C. and Dempsey, K., 2022. Coastline detection in satellite imagery: A deep learning approach on new benchmark data. Remote Sensing of Environment, 278, p.113044.</p>
Doodleverse/Segmentation Zoo Res-UNet models for 2-class (water, other) segmentation of Sentinel-2 and Landsat-7/8 5-band (RGB+NIR+SWIR) images of coasts.
<p><em><strong>Doodleverse/Segmentation Zoo Res-UNet models for 2-class (water, other) segmentation of Sentinel-2 and Landsat-7/8 5-band (RGB+NIR+SWIR) images of coasts.</strong></em></p> <p>These Residual-UNet model data are based on RGB+NIR+SWIR (red, green, blue, near infrared and shortwave infrared) images of coasts and associated labels.</p> <p>Models have been created using Segmentation Gym* using the following dataset**: <a href="https://doi.org/10.5281/zenodo.7384263">https://doi.org/10.5281/zenodo.7384263</a></p> <p>Classes: {0=other, 1=water}</p> <p><strong>File descriptions</strong></p> <p>For each model, there are 5 files with the same root name:</p> <p>1. <strong>'.json' </strong>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.<strong> '.h5'</strong> 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.<strong> '_modelcard.json'</strong> 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. <strong> '_model_history.npz'</strong> 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. <strong> '.png'</strong> 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> </p> <p><strong>References</strong></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. <a href="https://doi.org/10.1029/2022EA002332">https://doi.org/10.1029/2022EA002332</a> See: <a href="https://github.com/Doodleverse/segmentation_gym">https://github.com/Doodleverse/segmentation_gym</a></p> <p>** Buscombe, Daniel. (2022). Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, other) (v1.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7384263">https://doi.org/10.5281/zenodo.7384263</a></p>
NIRS-MRgFUS_Data
<p>Research data relative to the article: "Changes in cerebral cortex activity during a simple motor task after MRgFUS treatment in patients affected by Essential Tremor and Parkinson's Disease: a pilot study using functional NIRS"</p>
Intracoronary Imaging With NIRS-IVUS to Characterize Arterial Plaques
ClinicalTrials.gov study NCT01694368. IPD Sharing: NO. Countries: 1. Publications: 1.
Influence of a Bolus Administration of Ephedrine and Phenylephrine on the Spinal Oxygen Saturation, Measured With NIRS.
ClinicalTrials.gov study NCT03767296. IPD Sharing: NO. Countries: 1. Publications: 1.
NIRS in Neonatal Cardiac Surgery
ClinicalTrials.gov study NCT00166101. IPD Sharing: Not stated. Countries: 1. Publications: 10.
Evaluation of the Brain and Renal Regional Oxygenation Using NIRS in Preterm Infants
ClinicalTrials.gov study NCT04295395. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Effect of Transcranial NIR Light Upon Memory
ClinicalTrials.gov study NCT04568057. IPD Sharing: NO. Countries: 1. Publications: 5.
Multi-site Near Infrared Spectroscopy (NIRS) Monitoring of Children During Exercise
ClinicalTrials.gov study NCT00556231. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Multi-Site Near Infrared Spectroscopy (NIRS) Monitoring of Children During Tilt Table Testing
ClinicalTrials.gov study NCT00659464. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Cerebral NIRS Profiles During Premedication for Neonatal Intubation
ClinicalTrials.gov study NCT02700893. IPD Sharing: NO. Countries: 1. Publications: 1.
Use of Near Infrared Spectroscopy (NIRS) as a Biomarker of Delirium in Hospitalized Older Adults Doing Physical Exercise
ClinicalTrials.gov study NCT05442892. IPD Sharing: YES. Countries: 1. Publications: 1.
Cerebral Near-infrared Spectroscopy (NIRS) Monitoring Throughout Caesarean Deliveries
ClinicalTrials.gov study NCT02473978. IPD Sharing: Not stated. Countries: 1. Publications: 20.
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.