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.
108
datasets available to search
ShareScore release 0.7.1
Dataset results
108 results for “Thermal images”
Data from: Radar wind profilers and avian migration - a qualitative and quantitative assessment verified by thermal imaging and moon watching
1. Radars of various types have been used in ornithological research for about 70 years. However, the potential of radar wind profiler (RWP) as a tool for biological purposes remains poorly understood. The aim of this study is to assess the suitability of RWP for ornithological research questions. 2. A 1290 MHz RWP at the southeastern coast of the Bay of Biscay has been known to exhibit seasonally occurring nocturnal signals attributed to migrating birds. As a first step to verify the origin of these seasonal patterns, historical radar data from 2010-2012 were analysed, and both bird patterns and temporal occurrence were identified in RWP data at different levels of the signal processing. A thermal-imaging (TI) camera in conjunction with moon watching was used as verification systems at the radar site to confirm the ornithological origin of the radar echoes. The simultaneous data on spring migration served as a basis for the identification of biological signatures (qualitative parameters) on time series level (raw data) and to derive quantitative migration parameters (flight altitude, migration traffic rates) thereof. Finally the quantitative measurements of the TI camera and the radar were compared considering meteorological conditions. 3. The approach allowed identifying reproducible criteria based on time series to calculate migration traffic rates and altitudinal flight distribution. General flight directions were only available in the final wind data. In clear weather conditions the calibration methods coincided well with the wind profiler data. 4. Findings show that wind profiler raw data offers reliable information on migration intensity, flight altitudes and flight directions in a variety of meteorological conditions. The method presented can be applied as a complement to present efforts to use weather radars for large-scale bird monitoring. Furthermore it is also interesting for the meteorological community to refine signal-processing methods.
Estimating density of mountain hares using distance sampling: a comparison of daylight visual surveys, night-time thermal imaging and camera traps
<p><a name="_Hlk58254629"></a></p> <p><a name="_Hlk58254629">Surveying cryptic, nocturnal animals is logistically challenging. Consequently, density estimates may be imprecise and uncertain. Survey innovations mitigate ecological and observational difficulties contributing to estimation variance. Thus, comparisons of survey techniques are critical to evaluate estimates of abundance. We simultaneously compared three methods for observing mountain hare (<i>Lepus timidus</i>) using Distance sampling to estimate abundance. Daylight visual surveys achieved 41 detections, estimating density at 14.3 hares km<sup>-2</sup> (95%CI 6.3–32.5) resulting in the lowest estimate and widest confidence interval. Night-time thermal imaging achieved 206 detections, estimating density at 12.1 hares km<sup>-2 </sup>(95%CI 7.6–19.4). Thermal imaging captured more observations at furthest distances, and detected larger group sizes. Camera traps achieved 3,705 night-time detections, estimating density at 22.6 hares km<sup>-2 </sup>(95%CI 17.1–29.9). Between the methods, detections were spatially correlated, although the estimates of density varied. Our results suggest that daylight visual surveys tended to underestimate density, failing to reflect nocturnal activity. Thermal imaging captured nocturnal activity, providing a higher detection rate, but required fine weather. Camera traps captured nocturnal activity, and operated 24/7 throughout harsh weather, but needed careful consideration of empirical assumptions. </a>We discuss the merits and limitations of each method with respect to the estimation of population density in the field.</p>
A Dataset of Thermal images of User Interfaces
<p>Recent advancement in sensor technology facilitates having a thermal camera at a lower price. These cameras have many potential applications but can also be used for malicious purposes, such as capturing user interfaces and retrieving user information from heat traces in the thermal images. This dataset is created during an interactive study investigating the threat of thermal attacks on user interfaces. We adapted the following experimental setup during data collection.</p> <ol> <li>2 camera perspectives- FLIR E8-XT camera placed behind the participant and Optris PI 450i camera placed left of the participant. </li> <li>4 types of input devices- i) smartphone, ii) 3 keyboards- a PBT keyboard, an ABS keyboard, and a metal frame keyboard</li> <li>3 types of user input data (text, email address, password)</li> </ol> <p>In summary, we have collected 1152 images from the FLIR camera and another 1152 images from the Optris camera through an interactive study with 32 participants. For each participant, we captured 36 images (9 types of user input, 4 types of input devices). The created dataset can be used to evaluate the deep learning model developed to prevent thermal imaging attacks. Furthermore, the ground truth user input of text, email address, and passwords are structured along with the corresponding image ID so that the advanced data-driven model can be employed to identify user input and investigate the type of user input that can be easily cracked using machine learning techniques.</p>
Low Resolution Thermal Imaging Dataset
<p>The dataset contains low resolution thermal images corresponding to various sign language digits represented by hand and captured using the Omron D6T thermal camera with a resolution of 32x32.</p>
Plain_Background_Thermal_Imaging_Dataset
<p>The dataset contains the images captured from high resolution thermal camera. The thermal images contains ten different hand gestures captured from random people. We also captured images of both color and gray scale under different environment conditions. Further, different hand orientations are considered for the creation of effective dataset.</p>
Raw images, annotations, and vvipr code archive to support 'Evaluating thermal and color sensors for automating detection of penguins and pinnipeds in images collected with an unoccupied aerial system''
<p>Images, annotations, and code archived here were used in the paper "Evaluating a machine learning approach to detect penguins and pinnipeds in thermal and color images collected with an unoccupied aerial system" submitted for publication in Drones. The files contain raw thermal and color images of aggregations of gentoo (<em>Pygoscelis papua</em>) and chinstrap (<em>P. antarcticus</em>) penguins and Antarctic fur seals (<em>Arctocephalus gazella</em>). All images were collected with the Flir DuoPro R camera (Teledyne FLIR LLC, Wilsonville, OR, U.S.A.), carried into flight under an APH-28 hexacopter (Aerial Imaging Solutions, LLC, Old Lyme, CT, U<strong>.</strong>S<strong>.</strong>A<strong>.)</strong> at Cape Shirreff, Livingston Island, Antarctica (60.79 °W, 62.46 °S), during the austral summer of 2019-20. All aerial surveys occurred under the Marine Mammal Protection Act Permit No. 20599 granted by the Office of Protected Resources/National Marine Fisheries Service, the Antarctic Conservation Act Permit No. 2017-012, NMFS-SWFSC Institutional Animal Care and Use Committee Permit No. SWPI 2014-03R, and all domestic and international UAS flight regulations. The annotations of the images were conducted using VIAME desktop software (v 0.16.1 or later;<a href="https://github.com/VIAME/VIAME">https://github.com/VIAME</a>) or the online using the DIVE interface (https://viame.kitware.com/). Model results were assessed with the vvipr code (v.0.3.2), archived here and available online (https://github.com/us-amlr/vvipr/releases/tag/v0.3.2).</p> <p> </p>
Thermal Environment and Illumination of Lunar Pits and Caves Data, Code, and Images
<p>Lunar Reconnaissance Orbiter Diviner data focusing on the Tranquillitatis pit and analysis code for determining the thermal and illumination environments of the collapse pits and caves on the Moon. Also includes figures created using the code.</p>
Characterizing Inter-Annual/Seasonal Dust Deposition and Removal on Mars Using Thermal Emission Imaging System (THEMIS) Infrared Data
<p>Included in the zipped data file are two subfolders that contain the following:</p> <ol> <li>KRC thermal model look up table data used in the work</li> <li>Data for figures within the paper</li> </ol> <p>Each subfolder includes a text file with helpful information for interpreting the data.</p>
Aerial RGB and Thermal Infrared (TIR) Images of Vineyards and Pseudo-coloring RGB Images of the Plant's Stressed Areas.
<p>This dataset consists of 375 high-resolution visible-spectrum (RGB) and 375 thermal infrared (TIR) images of a vineyard (Vitis vinifera L.) captured by a Unmanned Aerial Vehicle (UAV) carrying TIR and RGB sensors. Also, the dataset contains 375 RGB images with pseudo-coloring where plants' stressed areas exist, aligned, and cropped based on the TIR images' Field of View (FOV).</p>
RT-Trees: Thermal training images
<p>This is the RT-Trees dataset proposed and used in the paper titled, "Shadowsense: Unsupervised Domain Adaptation and Feature Fusion for Shadow-Agnostic Tree Crown Detection From RGB-Thermal Drone Imagery", published at the <a href="https://openaccess.thecvf.com/content/WACV2024/html/Kapil_ShadowSense_Unsupervised_Domain_Adaptation_and_Feature_Fusion_for_Shadow-Agnostic_Tree_WACV_2024_paper.html">IEEE/CVF WACV 2024</a> conference. Due to the size of the dataset and Zenodo's 50GB limit, the dataset is partitioned into two separate uploads. This upload contains the unlabelled thermal images used for unsupervised training</p> <p>The <a href="doi.org/10.5281/zenodo.14007908">first upload</a> includes evaluation splits (test & val), along with the labelled subset of RGB training images used for a supervised training experiment, and the much larger set of unlabelled RGB images used for fully-unsupervised training. </p>
Ground thermal image temperatures (Campi Flegrei and Vesuvius) and atmospheric temperature and pressure
<p>The dataset contains the ground thermal image temperatures measured by the thermal infrared camera network of Campi Flegrei and Vesuvius (Italy) and the temperature and pressure acquired by a local meteo station. The data refer to the period Jun 25, 2016-May 29, 2020.</p>
Data from: Thermal failure of diamond tools indicated by diamond degradation: Damage evaluation and property prediction on small image datasets
<p>High temperature induced diamond degradation often leads to the failure of diamond tools. In this work, diamond samples holding different degrees of thermal damage were prepared by heating and sintering. The influence of diamond particle size and processing temperature was investigated through mechanical testing and micromorphology observation, meanwhile, a dataset containing 2870 SEM images showing diamonds with different degrees of degradation was constructed. By modification of VGG16 network, classification models and regression models were developed for thermal damage evaluation and sample property prediction. Training strategies including transfer learning and data augmentation were implemented and verified essential on the small dataset, where drop-out showed no positive effects. Two classification models (3-class and 65-class) were constructed and trained for damage evaluation. Visualized damage feature maps exported from Grad-CAM revealed the influential mechanism of thermal damage on diamonds, which proved the effectiveness of the classification models as well. Under the optimized training strategies, regression models were built for sample property prediction. The models towards toughness index, bending strength loss, relative density and Rockwell hardness were examined. Comparing the output results with real property values in test sets, the first two models matched well, and the latter two showed the opposite. It verified the validity of the regression models for property prediction as they were all established based on diamond damage image datasets. The loss in bending strength loss prediction model was smaller than that of toughness index, indicating bending strength easier to be shorten than impact toughness for diamond/metal composites suffering thermal impacts.</p>
Thermal Images of glass bottles
<p>Set of thermal images from one of the openZDM use cases.</p>
Thermal Imaging in the Diagnosis of Acute Testicular Pain
ClinicalTrials.gov study NCT07324590. IPD Sharing: YES. Countries: 1. Publications: 1.
Thermal Imaging as a Potential Diagnostic Tool of Nasal Airflow
ClinicalTrials.gov study NCT03233373. IPD Sharing: NO. Countries: 1. Publications: 7.
Detecting Lesions in the Oral Cavity With Thermal Imaging
ClinicalTrials.gov study NCT00868725. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Medical Imaging and Thermal Treatment for Breast Tumors Using Harmonic Motion Imaging (HMI)
ClinicalTrials.gov study NCT05219695. IPD Sharing: NO. Countries: 1. Publications: 2.
Clinical Study on Prostate Thermal Vapor Ablation Guided by MRI/TRUS Fusion Imaging
ClinicalTrials.gov study NCT06817733. IPD Sharing: NO. Countries: 1. Publications: 8.
Estimating density of mountain hares using distance sampling: a comparison of daylight visual surveys, night-time thermal imaging and camera traps
Open the record for dataset details and reuse information.
Data from: Radar wind profilers and avian migration - a qualitative and quantitative assessment verified by thermal imaging and moon watching
Open the record for dataset details and reuse information.
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.