Find research datasets worth reusing

opencc-by-4.0Nov 2021View details →

Dataset: Planetary-scale waves seen in thermal infrared images of Venusian cloud top

<p>The data archive contains data used in the paper "Planetary-Scale Waves Seen in Thermal Infrared Images of Venusian Cloud Top" by Kajiwara et al.</p> <p>The contents of the directories are as follows.</p> <p>"data_used_in_figures" : The table data used in the figures are given in Excel and CSV. The table format is described in the data files. An image in NetCDF format is also included.</p> <p>"time_series_of_brightness_temperature_gradient" : The files contain the time series of the longitudinal gradient of Venusian cloud's brightness temperature measured by LIR onboard JAXA's Venus orbiter Akatsuki. The data were derived and analyzed in the paper "Planetary-Scale Waves Seen in Thermal Infrared Images of Venusian Cloud Top" by Kajiwara et al. The filename represents the latitude for each time series (For example, "10N" means 10 degrees north, and "EQ" means the equator). In all files, the first column gives the approximate elapsed time in days from 18 May 2017: the exact dates are given in the paper (Table S1 in the Supporting Information). The second column gives the longitudinal gradient of the brightness temperature in unit of K/degree.</p>

opencc-by-4.0Jun 2022View details →

Carbon Nanotube Uptake in Cyanobacteria for Near-infrared Imaging and Enhancing Bioelectricity Generation in Living Photovoltaics

<p>Dataset of the work entitled "Carbon Nanotube Uptake in Cyanobacteria for Near-infrared Imaging and Enhancing Bioelectricity Generation in Living Photovoltaics".</p>

openapache2.0Aug 2024View details →

High-throughput Computational Screening of Hydrocarbon Molecules for Long-wavelength Infrared Imaging

<p>This repository contains datasets associated with the paper titled "High-throughput Computational Screening of Hydrocarbon Molecules for Long-wavelength Infrared Imaging," accepted at ACS Materials Letters Journal.</p> <p><strong>Contents:</strong></p> <ol> <li> <p><strong>Optimized XYZ Coordinates:</strong> The hydrocarbon molecules' XYZ coordinates, obtained using the B3LYP functional and the 6-31g(d,p) basis set in Gaussian 16 software, used to simulate the IR spectra (including transition energies and absorption intensities) of the molecules.</p> </li> <li> <p><strong>Broadened Molar Absorptivity IR Spectra:</strong> The dataset's IR spectra, broadened using a Lorentzian band shape with a gamma (half-width at half-height) value of 5 cm⁻¹. Molecules with imaginary frequencies have been excluded.</p> </li> <li> <p><strong>Related SMILES Strings:</strong> Contains SMILES strings for these hydrocarbons.</p> </li> <li> <p><strong>NUMBERS_SMILES.csv:</strong> Provides the associated SMILES string for each numerated XYZ coordinate.</p> </li> </ol> <p>For any inquiries, please contact Dr. Maliheh Shaban Tameh at malihe.shaban<a rel="noreferrer">@gmail.com</a></p>

opencc-by-4.0Oct 2024View details →

Exploring the potential of Near Infrared Hyperspectral Imaging and chemometrics to discriminate soil seed bank of two timber species central African : Erythrophleum suaveolens (Guill. & Perr.) Brenan, and Erythrophleum ivorense A. Chev.

<p>The data of this study are accessible by sending a request to the corresponding author at the email address: douhch382@gmail.com. <a href="https://doi.org/10.5281/zenodo.13908452" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13908452</a></p>

opencc-by-4.0Feb 2023View details →

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>

dryad36/100

Data from: Bio-inspired imager improves sensitivity in near-infrared fluorescence image-guided surgery

Image-guided surgery can enhance cancer treatment by decreasing, and ideally eliminating, positive tumor margins and iatrogenic damage to healthy tissue. Current state-of-the-art near-infrared fluorescence imaging systems are bulky and costly, lack sensitivity under surgical illumination, and lack co-registration accuracy between multimodal images. As a result, an overwhelming majority of physicians still rely on their unaided eyes and palpation as the primary sensing modalities for distinguishing cancerous from healthy tissue. Here we introduce an innovative design, comprising an artificial multispectral sensor inspired by the Morpho butterfly's compound eye, which can significantly improve image-guided surgery. By monolithically integrating spectral tapetal filters with photodetectors, we have realized a single-chip multispectral imager with 1000× higher sensitivity and 7× better spatial co-registration accuracy compared to clinical imaging systems in current use. Preclinical and clinical data demonstrate that this technology seamlessly integrates into the surgical workflow while providing surgeons with real-time information on the location of cancerous tissue and sentinel lymph nodes. Due to its low manufacturing cost, our bio-inspired sensor will provide resource-limited hospitals with much-needed technology to enable more accurate value-based health care.

opencc-zeroDec 2017View details →

opencc-by-4.0Jan 2024View details →

Surface plasmons-phonons for mid-infrared hyperspectral imaging

<p>Dataset for the hyperspectral imaging of spike proteins of the severe acute respiratory syndrome coronavirus (SARS-CoV) using the synergistic plasmon-phonon hyperspectral bioimaging system.</p>

opencc-by-4.0Dec 2023View details →

Raw data for "Deep mouse brain two-photon near-infrared fluorescence imaging using a superconducting nanowire single-photon detector array"

<p>Two-photon microscopy (2PM) has become an important tool in biology to study the structure and function of intact tissues in-vivo. However, adult mammalian tissues such as the mouse brain are highly scattering, thereby putting fundamental limits on the achievable imaging depth, which typically resides around 600-800um. In principle, shifting both the excitation as well as (fluorescence) emission light to the shortwave near-infrared (SWIR, 1000-1700 nm) region promises substantially deeper imaging in 2PM, yet has proven challenging in the past due to the limited availability of detectors and probes in this wavelength region. To overcome these limitations and fully capitalize on the SWIR region, in this work we introduce a novel array of superconducting nanowire single-photon detectors (SNSPDs) and associated custom detection electronics for the use in near-infrared 2PM. The SNSPD array exhibits high efficiency and dynamic range, as well as low dark-count rates over a wide wavelength range. Additionally, the electronics and software permit seamless integration into typical 2PM systems. Together with a fluorescent dye emitting at 1105 nm, we report imaging depth of > 1.1mm in the in-vivo mouse brain, limited only by available labeling density and laser power. Our work further establishes SWIR 2PM approaches and SNSPDs as promising technologies for deep tissue biological imaging. </p>

opencc-by-4.0Jun 2024View details →

"WLRI-HRC" - A Dataset of Infrared Images for Human-Robot Collaboration in Manufacturing Environment

<p>This repository contains all needed data sets for the contribution in  Journal of Sensors and Sensor Systems  "Enhancing human–robot collaboration with thermal images and deep neural networks: the unique thermal industrial dataset WLRI-HRC and evaluation of convolutional neural networks". You may use this data for scientific, non-commercial purposes, provided that you give credit to the owners when publishing any work based on this data.</p> <p><strong>DOI: 10.5194/jsss-14-37-2025</strong></p> <p> </p> <p><strong>or as BibTex:</strong></p> <div> <div>@article{sume_enhancing_2025,</div> <div>    title = {Enhancing human–robot collaboration with thermal images and deep neural networks: the unique thermal industrial dataset {WLRI}-{HRC} and evaluation of convolutional neural networks},</div> <div>    volume = {14},</div> <div>    issn = {2194-8771},</div> <div>    shorttitle = {Enhancing human–robot collaboration with thermal images and deep neural networks},</div> <div>    url = {https://jsss.copernicus.org/articles/14/37/2025/},</div> <div>    doi = {10.5194/jsss-14-37-2025},</div> <div>    abstract = {This contribution introduces the use of convolutional neural networks to detect humans and collaborative robots (cobots) in human–robot collaboration (HRC) workspaces based on their thermal radiation fingerprint. The unique data acquisition includes an infrared camera, two cobots, and up to two persons walking and interacting with the cobots in real industrial settings. The dataset also includes different thermal distortions from other heat sources. In contrast to data from the public environment, this data collection addresses the challenges of indoor manufacturing, such as heat distortions from the environment, and allows for it to be applicable in indoor manufacturing. The Work-Life Robotics Institute HRC (WLRI-HRC) dataset contains 6485 images with over 20 000 instances to detect. In this research, the dataset is evaluated for implementation by different convolutional neural networks: first, one-stage methods, i.e., You Only Look Once (YOLO v5, v8, v9 and v10) in different model sizes and, secondly, two-stage methods with Faster R-CNN with three variants of backbone structures (ResNet18, ResNet50 and VGG16). The results indicate promising results with the best mean average precision at an intersection over union (IoU) of 50 (mAP50) value achieved by YOLOv9s (99.4 \%), the best mAP50-95 value achieved by YOLOv9s and YOLOv8m (90.2 \%), and the fastest prediction time of 2.2 ms achieved by the YOLOv10n model. Further differences in detection precision and time between the one-stage and multi-stage methods are discussed. Finally, this paper examines the possibility of the Clever Hans phenomenon to verify the validity of the training data and the models’ prediction capabilities.},</div> <div>    language = {English},</div> <div>    number = {1},</div> <div>    journal = {Journal of Sensors and Sensor Systems},</div> <div>    author = {Süme, Sinan and Ponomarjova, Katrin-Misel and Wendt, Thomas M. and Rupitsch, Stefan J.},</div> <div>    month = feb,</div> <div>    year = {2025},</div> <div>    note = {Publisher: Copernicus GmbH},</div> <div>    pages = {37--46},</div> <div>}</div> </div>

opencc-by-4.0Jun 2023View details →

Spectral data used in "Stratospheric-trace-gas-profile retrievals from balloon-borne limb imaging of mid-infrared emission spectra"

<p>The calibrated spectral data used in the trace gas retrievals by the Limb Imaging Fourier Transform Spectrometer Experiment (LIFE).</p>

controlledIPD-YESFeb 2026View details →

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.

restrictedIPD-UNDECIDEDFeb 2026View details →

A Study Assessing Perfusion Outcomes With PINPOINT® Near Infrared Fluorescence Imaging in Low Anterior Resection

ClinicalTrials.gov study NCT02205307. IPD Sharing: Not stated. Countries: 1. Publications: 1.

closedIPD-NOFeb 2026View details →

Efficacy of Near-Infrared Vein Imaging for Difficult IV Placement

ClinicalTrials.gov study NCT04262947. IPD Sharing: NO. Countries: 1. Publications: 8.

closedIPD-NOFeb 2026View details →

Ultrasound and Near Infrared Imaging for Predicting and Monitoring Neoadjuvant Treatment

ClinicalTrials.gov study NCT02891681. IPD Sharing: NO. Countries: 1. Publications: 1.