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1,169 results for “Infrared”
Data and Software for: Resolved Near-infrared Stellar Photometry from the Magellan Telescope for 13 Nearby Galaxies: JAGB Method Distances
<p><strong>12/17/24 Update: I was made aware that the MRT files had the J-band and H-band column header labels incorrectly switched (this occured when converting the CSV files to MRT). I have fixed this mistake. The CSV files were correct the entire time. No data have been changed. Apologies for any inconvinence this may have caused. </strong></p> <p> </p> <p>Data for Resolved Near-infrared Stellar Photometry from the Magellan Telescope for 13 Nearby Galaxies: JAGB Method Distances</p> <p>The files are provided in two formats. A CSV format with a single line header, and a MRT files (zipped) written in the machine-readable format used by AAS & CDS: <a href="https://journals.aas.org/mrt-overview/" target="_blank" rel="noopener noreferrer">https://journals.aas.org/mrt-overview/</a></p> <p>Column descriptions in photometry catalogs:</p> <p>X/Y: Image coordinates from Fourstar camera</p> <p>J, J_err: J-band magnitudes and photometric errors returned from DAOPHOT/ALLFRAME</p> <p>H, H_er: H-band magnitudes and photometric errors returned from DAOPHOT/ALLFRAME</p> <p>K, K_err: K-band magnitudes and photometric errors returned from DAOPHOT/ALLFRAME</p> <p>chi: chi value returned from DAOPHOT/ALLFRAME</p> <p>sharp: sharpness value returned from DAOPHOT/ALLFRAME</p> <p>ra/dec: RA/Decl </p> <p>RGC: semi-major axis distance</p> <p> </p> <p>Notes on versions: all catalogs from all versions contain the same information, style was just adjusted to adhere to AAS standards. </p> <p> </p>
Long-term Continuous Red and Near-infrared Channel Reflectance from MODIS, 2001-2023 (LCREF-MODIS)
<p><strong>Usage Notes</strong>:<br>This is the updated LCREF-MODIS dataset (v3.2) consists of BRDF-normalized MODIS red and near-infrared surface reflectance. The LCREF-MODIS product was used to calibrate and benchmark the AVHRR surface reflectance to produce a temporally consistent record of surface reflectance prior to the MODIS era. It was also used to generate LCSPP-MODIS (previously known as LCSIF-MODIS) as a benchmark.</p> <p><strong>Key updates in version 3.2 include:</strong></p> <ul> <li><strong>Quality Flags</strong>: New quality flag layer enables users to identify whether a pixel is derived from observed surface reflectance (QA=0), high-quality gap-filled values (QA=1), lower-quality gap-filled based on the mean seasonal cycle (QA=2), or missing entirely (QA=3). We advice the user to rely only on observed and high-quality gap-filled values for their analyses.</li> <li><strong>Extension:</strong> to include observations from the year of 2023.</li> <li><strong>Snow mask: </strong>we note that all pixels marked with percent_snow >0 in the original MCD43C1.v061 have been removed. This conservative approach was applied to reduce bias during cross-calibration, since unlike MODIS, AVHRR does not have a reliable snow detection algorithm. Therefore, surface reflectance values in high latitude regions are almost entirely gap-filled and should never be used for analysis for both LCREF-AVHRR and LCREF-MODIS. We encourage users to use only QA=0 and QA=1 pixels for their analysis. Alternatively, users can use LCREF-MODIS from the previous version for high latitude regions (v3.1), which did not mask out snow-covered pixles. </li> </ul> <p>The user can choose between LCREF-AVHRR and LCREF-MODIS for the overlapping period from 2001-2023. The two datasets are generally consistent during this overlapping period, although LCREF-MODIS shows a stronger greening trend between 2001-2023. For studies exploring the long-term vegetation dynamics, the user can either use only LCREF-AVHRR or use a blend dataset of LCREF-AVHRR and LCREF-MODIS as a sensitivity test. </p> <ul> <li>The LCREF-AVHRR v3.2 (1982-2023) is available at <a href="https://doi.org/10.5281/zenodo.11905959" target="_blank" rel="noopener">10.5281/zenodo.11905959</a></li> </ul> <p>The LCREF-AVHRR dataset was used as the input to generate LCSPP-AVHRR (previously known as LCSIF-AVHRR), and it can also be used to derive temporally consistant records of NDVI, NIRv, kNDVI, and other vegetation indices based on red and NIR surface reflectance variables. The user can access LCSPP products at:</p> <ul> <li>LCSPP-AVHRR v3.2 (1982-2000): <a href="https://doi.org/10.5281/zenodo.7916850" target="_blank" rel="noopener">10.5281/zenodo.7916850</a></li> <li>LCSPP-AVHRR v3.2 (2001-2023): <a href="https://doi.org/10.5281/zenodo.11906675" target="_blank" rel="noopener">10.5281/zenodo.11906675</a></li> <li>LCSPP-MODIS v3.2(2001-2023): <a href="https://doi.org/10.5281/zenodo.11657458" target="_blank" rel="noopener">10.5281/zenodo.11657458</a></li> </ul> <p>A manuscript describing the technical details is available at <a href="https://arxiv.org/abs/2311.14987" target="_blank" rel="noopener">https://arxiv.org/abs/2311.14987</a>, which detailed the uses and limitations of the dataset. All data outputs from this study are available at 0.05° spatial resolution and biweekly temporal resolution in NetCDF format. Each month is divided into two files, with the first file “a” representative of the 1<sup>st</sup> day to the 15<sup>th</sup> day of a month, and the second file “b” representative of the 16<sup>th</sup> day to the last day of a month.</p> <p><strong>Abstract:</strong></p> <p>Satellite-observed solar-induced chlorophyll fluorescence (SIF) is a powerful proxy for the photosynthetic characteristics of terrestrial ecosystems. Direct SIF observations are primarily limited to the recent decade, impeding their application in detecting long-term dynamics of ecosystem function. In this study, we leverage two surface reflectance bands available both from Advanced Very High-Resolution Radiometer (AVHRR, 1982-2023) and MODerate-resolution Imaging Spectroradiometer (MODIS, 2001-2023). Importantly, we calibrate and orbit-correct the AVHRR bands against their MODIS counterparts during their overlapping period. Using the long-term bias-corrected reflectance data from AVHRR and MODIS, a neural network is trained to produce a Long-term Continuous SIF-informed Photosynthesis Proxy (LCSPP) by emulating Orbiting Carbon Observatory-2 SIF, mapping it globally over the 1982-2023 period. Compared with previous SIF-informed photosynthesis proxies, LCSPP has similar skill but can be advantageously extended to the AVHRR period. Further comparison with three widely used vegetation indices (NDVI, kNDVI, NIRv) shows a higher or comparable correlation of LCSPP with satellite SIF and site-level GPP estimates across vegetation types, ensuring a greater capacity for representing long-term photosynthetic activity.</p> <p> </p>
Long-term Continuous Red and Near-infrared Channel Reflectance from AVHRR, 1982-2023 (LCREF-AVHRR)
<p><strong>Usage Notes</strong>:<br>This is the updated LCREF dataset (v3.2) consists of calibrated AVHRR surface reflectance record for the red and near-infrared channel. </p> <p><strong>Key updates in version 3.2 include:</strong></p> <ul> <li><strong>Improved Calibration</strong>: Enhanced consistency in calibration methods, addressing technical limitations in version 3.1 including applying more stringent quality filtering and snow masks.</li> <li><strong>Quality Flags</strong>: New quality flag layer enables users to identify whether a pixel is derived from observed surface reflectance (QA=0), high-quality gap-filled values (QA=1), lower-quality gap-filled based on the mean seasonal cycle (QA=2), or missing entirely (QA=3). We advice the user to rely only on observed and high-quality gap-filled values for their analyses.</li> <li><strong>Extension</strong> to include observations from the year of 2023.</li> </ul> <p>The user can choose between LCREF-AVHRR and LCREF-MODIS for the overlapping period from 2001-2023. The two datasets are generally consistent during this overlapping period, although LCREF-MODIS shows a stronger greening trend between 2001-2023. For studies exploring the long-term vegetation dynamics, the user can either use only LCREF-AVHRR or use a blend dataset of LCREF-AVHRR and LCREF-MODIS as a sensitivity test. </p> <ul> <li>The LCREF-MODIS v3.2 (2001-2023) is available at <a href="https://doi.org/10.5281/zenodo.11657458" target="_blank" rel="noopener">10.5281/zenodo.11657458</a>.</li> </ul> <p>The LCREF-AVHRR dataset was used as the input to generate LCSPP-AVHRR (previously known as LCSIF-AVHRR), and it can also be used to derive temporally consistant records of NDVI, NIRv, kNDVI, and other vegetation indices based on red and NIR surface reflectance variables. The user can access LCSPP products at:</p> <ul> <li>LCSPP-AVHRR v3.2 (1982-2000): <a href="https://doi.org/10.5281/zenodo.7916850" target="_blank" rel="noopener">10.5281/zenodo.7916850</a></li> <li>LCSPP-AVHRR v3.2 (2001-2023): <a href="https://doi.org/10.5281/zenodo.11906675" target="_blank" rel="noopener">10.5281/zenodo.11906675</a></li> <li>LCSPP-MODIS v3.2(2001-2023): <a href="https://doi.org/10.5281/zenodo.11658088" target="_blank" rel="noopener">10.5281/zenodo.11658088</a></li> </ul> <p>A manuscript describing the technical details is available at <a href="https://arxiv.org/abs/2311.14987" target="_blank" rel="noopener">https://arxiv.org/abs/2311.14987</a>, which detailed the uses and limitations of the dataset. All data outputs from this study are available at 0.05° spatial resolution and biweekly temporal resolution in NetCDF format. Each month is divided into two files, with the first file “a” representative of the 1<sup>st</sup> day to the 15<sup>th</sup> day of a month, and the second file “b” representative of the 16<sup>th</sup> day to the last day of a month.</p> <p><strong>Abstract:</strong></p> <p>Satellite-observed solar-induced chlorophyll fluorescence (SIF) is a powerful proxy for the photosynthetic characteristics of terrestrial ecosystems. Direct SIF observations are primarily limited to the recent decade, impeding their application in detecting long-term dynamics of ecosystem function. In this study, we leverage two surface reflectance bands available both from Advanced Very High-Resolution Radiometer (AVHRR, 1982-2023) and MODerate-resolution Imaging Spectroradiometer (MODIS, 2001-2023). Importantly, we calibrate and orbit-correct the AVHRR bands against their MODIS counterparts during their overlapping period. Using the long-term bias-corrected reflectance data from AVHRR and MODIS, a neural network is trained to produce a Long-term Continuous SIF-informed Photosynthesis Proxy (LCSPP) by emulating Orbiting Carbon Observatory-2 SIF, mapping it globally over the 1982-2023 period. Compared with previous SIF-informed photosynthesis proxies, LCSPP has similar skill but can be advantageously extended to the AVHRR period. Further comparison with three widely used vegetation indices (NDVI, kNDVI, NIRv) shows a higher or comparable correlation of LCSPP with satellite SIF and site-level GPP estimates across vegetation types, ensuring a greater capacity for representing long-term photosynthetic activity.</p>
X-ray diffraction data, crystallographic information file, infrared spectra, and LA-ICP-MS depthprofiles of davemaoite
<p>Calcium silicate perovskite, CaSiO3, is arguably the most geochemically important phase in the lower mantle, because it concentrates elements that are incompatible in the upper mantle, including the heat-generating elements thorium and uranium, which have half-lives longer than the geologic history of Earth. We report CaSiO3-perovskite as an approved mineral (IMA2020-12a) with the name davemaoite. The natural specimen of davemaoite proves the existence of compositional heterogeneity within the lower mantle. Our observations indicate that davemaoite also hosts potassium in addition to uranium and thorium in its structure. Hence, the regional and global abundances of davemaoite influence the heat budget of the deep mantle, where the mineral is thermodynamically stable.</p>
A Near-Infrared-II Emissive Chromium(III) Complex
<p>Electronic data accompanying the publication in <em>Angew. Chem. Int. Ed.</em> <strong>2021</strong>, <em>60</em>, 23722-23728; https://onlinelibrary.wiley.com/doi/10.1002/anie.202106398</p>
Boosting the Near-Infrared Emission of Ag2S Nanoparticles by a Controllable Surface Treatment for Bioimaging Applications
<p>Dataset of https://pubs.acs.org/doi/10.1021/acsami.1c19344</p>
Long-wave infrared super-resolution wide-field microscopy by sum-frequency generation - experimental data
<p>Experimental Data for "Long-wave infrared super-resolution wide-field microscopy by sum-frequency generation", under consideration at APL, preprint: <a href="https://doi.org/10.48550/arXiv.2112.08112">https://doi.org/10.48550/arXiv.2112.08112</a></p> <p>Files:</p> <p>readme.txt: explanation of the content<br> SFGmicroscope.h5: microscope data<br> APL_test_script.m: matlab test script generating the relevant figures from the data</p> <p>For more information, please contact Richarda Niemann (niemann@fhi-berlin.mpg.de) or Alex Paarmann (alexander.paarmann@fhi-berlin.mpg.de).</p>
Cloud-free Chinese Gaofen-1 WFV near-infrared surface reflectance over Huailai remote sensing test site throughout 2020
<p>Land surface reflectance product form the starting point for many application regions such as land cover mapping and the generation of biophysical essential climate variables (ECV). Therefore, ensuring the quality of surface reflectance products is necessary to maintain the integrity of the research outcoming of these application areas. However, ground validation of surface reflectance satellite products is challenging, because ground “truth” on a coarse grid scale based on sparse ground measurements is subject to uncertainty due to spatial heterogeneity. In order to quantify the influence of spatial heterogeneity on the uncertainty of surface reflectance ground “truth” in different sampling cases, we generated the high-resolution (16 m) near-infrared surface reflectance over Huailai remote sensing test site based on Chinese Gaofen-1 WFV Band4 data.</p> <p> </p> <p>All cloud-free GF-1 WFV images throughout the year 2020 were extracted. And there are 25 images in total, with at least one image for each month. The WFV Band4 data covering the whole Huailai test station have been processed into Analysis Ready Data (ARD) system, which aims to simplify and reduce the users’ burden by providing pre-processing such as geometric alignment, radiometric recalibration, and atmospheric correction (Zhong et al., 2021). The geometric normalization of the GF-1 WFV data was realized with the procedure developed by Shan et al. (2014). And the radiometric normalization was finished through cross-calibrating with the Landsat TM/OLI with the method proposed by Yang et al. (2015). The 25 images have been layer stacked into one file according to their acquisition time.</p> <p> </p> <p> </p> <p>Reference:</p> <p>Shan, X. J., P. Tang, and C. M. Hu (2014), An automatic geometric precision correction system based on hierarchical registration for HJ-1 A/B CCD images, Int J Remote Sens, 35(20), 7154-7178.</p> <p>Yang, A., B. Zhong et al. (2015), Cross-calibration of GF-1/WFV over a desert site using Landsat-8/OLI imagery and ZY-3/TLC data, Remote Sens., 7, 10763–10787.</p> <p>Zhong, B., A. Yang, Q. Liu, S. Wu, X. Shan, and X Mu (2021), Analysis ready data of the chinese gaofen satellite data, Remote Sens., 13, 9, 1709.</p>
Data from: Detecting sub-micron space weathering effects in lunar grains with synchrotron infrared nanospectroscopy
<p>Space weathering processes induce changes to the physical, chemical, and optical properties of space-exposed soil grains. For the Moon, space weathering causes reddening, darkening, and diminished contrast in reflectance spectra over visible and near-infrared wavelengths. The physical and chemical changes responsible for these optical effects occur on scales below the diffraction limit of traditional far-field spectroscopic techniques. Recently developed super-resolution spectroscopic techniques provide an opportunity to understand better the optical effects of space weathering on the sub-micrometer length scale. This paper uses synchrotron infrared nanospectroscopy to examine depth-profile samples from two mature lunar soils in the mid-infrared, 1500–700 cm<sup>-1</sup> (6.7–14.3 µm). Our findings are broadly consistent with prior bulk observations and theoretical models of space weathered spectra of lunar materials. These results provide a direct spatial link between the physical/chemical changes in space-exposed grain surfaces and spectral changes of space-weathered bodies.</p>
Design and performance of an ecosystem-scale forest soil warming experiment with infrared heater arrays
<p><span>How forest ecosystems respond to climate warming will determine forest trajectories over the next 100 years. However, the potential effects of elevated temperature on forests remain unclear, </span><span>primarily because of the absence of long-term and large-size field warming experiments in forests, especially in Asia.</span></p> <p><span>Here, we present the design and performance of an ecosystem-scale warming experiment</span><span> using an infrared (IR) heater array in a 60-year-old temperate mixed forest at Qingyuan Forest CERN in northeastern China.</span></p> <p><span>In paired </span><span>108 m<sup>2</sup> plots (n = 3),</span><span> the surface soils were constantly elevated 2 degrees</span><span> above control plots with a feedback control system over four years (2018-2021). Subsoils down to 60 cm depth were warmed 1.2-2 degrees</span><span>. Soil warming did not affect soil moisture either in surface soils or subsoils. Turn-off time due to weather extremes (heavy rains, snow) and power outages only accounted for 2.5% of the total warming period.</span></p> <p><span>In conclusion, we provide a proof-of-principle setup that allows long-term analysis of forest response to warming temperatures in large-size field plots. Importantly, our warming experiment demonstrated the feasibility of IR heater arrays for soil warming in tall-statured forest ecosystems.</span></p>
The dataset for an article - An Evaluation of 3D-Printed Materials' Structural Properties Using Active Infrared Thermography and Deep Neural Networks Trained on the Numerical Data
<p>Dataset used in the research presented in the article:</p> <p>Szymanik, Barbara. 2022. "An Evaluation of 3D-Printed Materials’ Structural Properties Using Active Infrared Thermography and Deep Neural Networks Trained on the Numerical Data" <em>Materials</em> 15, no. 10: 3727. https://doi.org/10.3390/ma15103727</p> <p>The database in the .mat (matlab) format contains arrays of double type related to: A - original thermograms obtained for the plate made with the 3D printing technique Ar - thermograms with ROI included FITorg - approximation of original thermograms ImDiff, ImInt, ImProp - data obtained after subtracting the approximation.</p>
Structural Dynamics of an Excited Donor-Acceptor Complex from Ultrafast Polarized Infrared Spectroscopy, Molecular Dynamics Simulations, and Quantum Chemical Calculations
<p>The files contains all the data that are shown in the figures of the article:</p> <p>Rumble, C.; Vauthey, E. Structural Dynamics of an Excited Donor-Acceptor Complex from Ultrafast Polarized Infrared Spectroscopy, Molecular Dynamics Simulations, and Quantum Chemical Calculations. Phys. Chem. Chem. Phys. 21 (2019). 10.1039/C9CP00795D</p>
Comprehensive database of fluorescence lifetime values for fluorochromes with emission peaks in the visible or near infrared
<p>Multiplexing techniques rely on fluorescent probes to simultaneously detect and visualise multiple mRNA or protein molecules in a single cell. Although more than 1200 fluorochromes are available in the visible and near-infrared spectral range, it is difficult to separate the different fluorochromes spectrally into orthogonal channels as their excitation and emission spectra often overlap. Fluorescence lifetimes can be used as an effective method to segregate fluorochromes for multiplex imaging. However, information on fluorescence lifetimes is not always easy to find as it is often only mentioned in passing on websites or in publications. In an effort to overcome this challenge, we performed a systematic literature review to make it easier to access the information required to attempt unmixing fluorochromes by fluorescence lifetime for multiplexed imaging. We found that at least 88 fluorochromes can be used, in principle, to attempt unmixing fluorochromes by lifetime and thus multiplexing. Our data are summarised in a table as well as in a graph in which we plotted the lifetime (tau) against the emission peak (Em). For all fluorochromes, we found that are available as NHS derivatives for easy coupling to DNA oligonucleotides or antibodies. Some additional, potentially very useful fluorophores, for which no lifetime data are available, are shown within a dashed line in a relatively “sparse” region of the spectrum above 800nm. A key is displayed to describe the colour code used to represent different fluorochrome classes, such as Alexa Fluor.</p>
Molecular factors determining brightness in fluorescence-encoded infrared vibrational spectroscopy
<p>Uploaded to this link are the datasets used for calculating the FEIR activities of the normal modes of ten coumarins studied in this work. We recommend going through the readme file (readme_file.txt), which should guide the reader through the data files.</p>
Data from: Three-dimensional infrared scanning: An enhanced approach for spatial registration of probes for neuroimaging
<p>Significance: Accurate spatial registration of probes (e.g., optodes and electrodes) for measurement of brain activity is a crucial aspect in many neuroimaging modalities. It may increase measurement precision and enable the transition from channel-based calculations to volumetric representations.</p> <p>Aim: This technical note evaluates the efficacy of a commercially available infrared three-dimensional (3D) scanner under actual experimental (or clinical) conditions and provides guidelines for its use.</p> <p>Method: We registered probe positions using an infrared 3D scanner and validated them against magnetic resonance imaging (MRI) scans on five volunteer participants.</p> <p>Results: Our analysis showed that with standard cap fixation, the average Euclidean distance of probe position among subjects could reach up to 43 mm, with an average distance of 15.25 mm [standard deviation (SD) = 8.0]. By contrast, the average distance between the infrared 3D scanner and the MRI-acquired positions was 5.69 mm (SD = 1.73), while the average difference between consecutive infrared 3D scans was 3.43 mm (SD = 1.62). The inter-optode distance, which was fixed at 30 mm, was measured as 29.28 mm (SD = 1.12) on the MRI and 29.43 mm (SD = 1.96) on infrared 3D scans. Our results demonstrate the high accuracy and reproducibility of the proposed spatial registration method, making it suitable for both functional near-infrared spectroscopy and electroencephalogram studies.</p> <p>Conclusions: The 3D infrared scanning technique for spatial registration of probes provides economic efficiency, simplicity, practicality, repeatability, and high accuracy, with potential benefits for a range of neuroimaging applications. We provide practical guidance on anonymization, labeling, and post-processing of acquired scans.</p>
Dataset for: Asphalt pavement fatigue crack severity classification by infrared thermography and deep learning
<p>This is the dataset for the following paper: </p> <p>Fangyu Liu, Jian Liu, and Linbing Wang. "Asphalt pavement fatigue crack severity classification by infrared thermography and deep learning." Automation in Construction 143 (2022): 104575. https://doi.org/10.1016/j.autcon.2022.104575.</p> <p>Data component:</p> <ul> <li>01-Visible images: this folder includes fully visible images</li> <li>02-Infrared images: this folder includes fully infrared images</li> <li>03-Fusion(50IRT) images: this folder includes fusion images (50% infrared + 50% visible)</li> <li>04-Ground truth: this folder includes ground truth (txt files): <ul> <li>00-Label_meaning.txt: the meaning of label number</li> <li>01-All_label.txt: Image, label (severity level)</li> <li>02-Train_label.txt: the training set: Image, Label (severity level)</li> <li>02-Test_label.txt: the test set: Image, Label (severity level)</li> </ul> </li> </ul>
Dataset for: Asphalt pavement crack detection based on convolutional neural network and infrared thermography
<p>This is the dataset for the following paper: </p> <p>Fangyu Liu, Jian Liu, and Linbing Wang. "Asphalt pavement crack detection based on convolutional neural network and infrared thermography." IEEE Transactions on Intelligent Transportation Systems 23, no. 11 (2022): 22145-22155. https://doi.org/10.1109/TITS.2022.3142393. </p> <p>Data component:</p> <ul> <li>01-Visible images: this folder includes fully visible images</li> <li>02-Infrared images: this folder includes fully infrared images</li> <li>03-Fusion(50IRT) images: this folder includes fusion images (50% infrared + 50% visible)</li> <li>04-Ground truth: this folder includes ground truth (binary images)</li> </ul>
Dataset for: Deep learning and infrared thermography for asphalt pavement crack severity classification
<p>This is the dataset for the following paper: </p> <p>Fangyu Liu, Jian Liu, and Linbing Wang. "Deep learning and infrared thermography for asphalt pavement crack severity classification." Automation in Construction 140 (2022): 104383. https://doi.org/10.1016/j.autcon.2022.104383. </p> <p>Data component:</p> <ul> <li>01-Visible images: this folder includes fully visible images</li> <li>02-Infrared images: this folder includes fully infrared images</li> <li>03-Fusion(50IRT) images: this folder includes fusion images (50% infrared + 50% visible)</li> <li>04-Ground truth: this folder includes ground truth (txt files): <ul> <li>00-Label_meaning.txt: the meaning of label number</li> <li>01-All_label.txt: Image, label (severity level)</li> <li>02-Train_label.txt: the training set: Image, Label (severity level)</li> <li>02-Test_label.txt: the test set: Image, Label (severity level)</li> </ul> </li> </ul>
Dataset for: Multiple-type distress detection in asphalt concrete pavement using infrared thermography and deep learning
<p>This is the dataset for the following paper: </p> <p>Fangyu Liu, Jian Liu, Linbing Wang, and Imad L. Al-Qadi. "Multiple-type distress detection in asphalt concrete pavement using infrared thermography and deep learning." Automation in Construction 161 (2024): 105355. https://doi.org/10.1016/j.autcon.2024.105355.</p> <p>Data component:</p> <ul> <li>01-Visible images: this folder includes fully visible images</li> <li>02-Infrared images: this folder includes fully infrared images</li> <li>03-Fusion(25IRT) images: this folder includes fusion images (25% infrared + 75% visible)</li> <li>04-Fusion(50IRT) images: this folder includes fusion images (50% infrared + 50% visible)</li> <li>05-Fusion(75IRT) images: this folder includes fusion images (75% infrared + 25% visible)</li> <li>06-Annotations: this folder includes annotations (xml files) based on PASCAL VOC (PASCAL Visual Object Classes Challenge) styles.</li> </ul>
Infrared-Microwave-Sounding methanol
<p>Monthly daytime methanol (ppbv) for 2008-2018 produced using the Rutherford Appleton Laboratory Infrared-Microwave-Sounding scheme. Further data description can be found in Pope et al. (2021) and the associated supplementary materials. </p> <p>This data has been used in Sands et al. (2024), currently available as a preprint: https://doi.org/10.5194/egusphere-2024-503. </p>
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.