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65 results for “Imaging spectroscopy”
Imaging spectroscopy and elemental mapping of Haughton impact melt rock: Datasets
<p>Description of archived data for manuscript Greenberger et al. (accepted, JGR Planets)<br> </p> <p>This archive contains the data underlying the results reported in the following paper:<br> Greenberger, R. N., Ehlmann, B. L., Osinski, G. R., Tornabene, L. L., & Green, R. O. Compositional Heterogeneity of Impact Melt Rocks at the Haughton Impact Structure, Canada: Implications for Planetary Processes and Remote Sensing. Journal of Geophysical Research: Planets, accepted.<br> 1. ImageList.txt: Contains information required to connect sample names from paper with images, which often contain multiple samples.<br> 2. FieldImages.tar.gz: Imaging spectroscopy files from images of outcrops in the field<br> 3. LabImages.tar.gz: Imaging spectroscopy files from samples imaged in the laboratory<br> 4. XRF_Data.tar.gz: Elemental mapping of cut samples via mapping x-ray fluorescence</p> <p>Imaging spectroscopy files (for all below, * is image name from ImageList.txt):<br> 1. *_SWIRcalib.img files: Laboratory images of samples processed to reflectance, including a dark current subtraction, line-by-line ratio to an image of Spectralon acquired with identical lighting, and correction for the reflectance properties of Spectralon. These files are stored with BIL interleave.<br> 2. *_SWIRcalib.hdr files: Header files for (1).<br> 3. *_SWIRcalib_atmcorr.img: Images of outcrops acquired in the field processed to reflectance, including instrument level corrections (dark current subtraction and flat field correction) and atmospheric correction (dark object subtraction and correction to in-scene Spectralon calibration target).<br> 4. *_SWIRcalib_atmcorr.hdr: Header files for (3).<br> 5. mask# and mask#.hdr: Masks and associated header files where the sample or region of interest has a value of 1 and outside of the sample or region of interest has a value of 0. Imaging spectroscopy measurements of multiple samples were sometimes acquired within the same image, and each sample has its own mask. ImageList.txt shows conversions from image and mask names to sample numbers. For files with no # after mask, only one sample or region of interest is present within the image.<br> 6. *_SWIRcalibmask#_MAP, *_SWIRcalib_atmcorrmask_MAP, and corresponding .hdr files: These are image files with 12 bands, one for each lithologic classification in the paper, and associated header files. Values of 1 indicate that the lithology is present, and values of 0 indicate that it is absent. The mask files (5) were used to ignore areas outside of the sample or outcrop. The band named "Mixed 2.2 and 2.3 micron features" is Mixed Carbonate + Si-OH, and "Illite-y" is Illite-like. These files are stored with BSQ interleave.</p> <p>X-ray fluorescence (XRF) data:<br> Each folder corresponds with measurements of a single sample. These are measurements of the same surfaces of some cut samples analyzed by imaging spectroscopy. ImageList.txt gives the corresponding image cubes. All exported elements are .tsv files and are in quantized relative counts as exported by the instrument software. Data for Mg are unreliable due to its low atomic number, as is typical for XRF.</p>
The UltracoolSheet: Photometry, Astrometry, Spectroscopy, and Multiplicity for 4000+ Ultracool Dwarfs and Imaged Exoplanets
<p>The UltracoolSheet is a catalog of 4000+ ultracool dwarfs (spectral types M6 and later) and imaged exoplanets, including photometry, absolute astrometry, proper motions, parallaxes, multiplicity, spectroscopic classifications, memberships, and ages.</p> <p>The catalog lives in a Google spreadsheet at</p> <p> <a href="http://bit.ly/UltracoolSheet"><strong>http://bit.ly/UltracoolSheet</strong></a></p> <p>where it will receive future updates. The README tab in the spreadsheet contains a detailed description of the contents of the catalog.</p> <p>UltracoolSheet started as a catalog of all spectroscopically confirmed objects of spectral type L0 or later in the extended solar neighborhood (out to ~100 pc) that were known as of April 15, 2015. It has subsequently been extended to include many late-M dwarfs and also augmented with more recent discoveries spanning all ultracool spectral types.</p> <p>The component spreadsheets of UltracoolSheet are posted here as CSV files, along with summary figures and (.dat) files containing ASCII tables to simplify figure plotting for users. The Summary Plots.pdf file shows the sky map, age distribution, publication dates, and proper motion since publication for all objects in UltracoolSheet, as well as the distribution of projected separations of companions.</p> <p>When using data from the UltracoolSheet, please cite the individual papers from which the data comes. Citations codes are included for all the data in the tables, and the References table translates the citation codes into ADS bibcodes, Papers citekeys, and publication titles.</p> <p>For research that benefits from this compilation, please cite this Zenodo post and include the following acknowledgment:</p> <p>"This work has benefited from The UltracoolSheet at <a href="http://bit.ly/UltracoolSheet">http://bit.ly/UltracoolSheet</a>, maintained by Will Best, Trent Dupuy, Michael Liu, Aniket Sanghi, Rob Siverd, and Zhoujian Zhang, and developed from compilations by <a href="http://adsabs.harvard.edu/abs/2012ApJS..201...19D">Dupuy & Liu (2012)</a>, <a href="http://adsabs.harvard.edu/abs/2013Sci...341.1492D">Dupuy & Kraus (2013)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2014ApJ...792..119D/abstract">Deacon et al. (2014)</a>, <a href="http://adsabs.harvard.edu/abs/2016ApJ...833...96L">Liu et al. (2016)</a>, <a href="http://adsabs.harvard.edu/abs/2018ApJS..234....1B">Best et al. (2018)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2021AJ....161...42B/abstract">Best et al. (2021)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2023ApJ...959...63S">Sanghi et al. (2023)</a>, and <a href="https://ui.adsabs.harvard.edu/abs/2023AJ....166..103S">Schneider et al. (2023)</a>."</p> <p>The UltracoolSheet itself can be cited by this Zenodo DOI: 10.5281/zenodo.4169084 </p> <p>Questions, comments, suggestions, or contributions?<br>Please send them to <a href="mailto:ultracool.sheet@gmail.com">ultracool.sheet@gmail.com</a> (especially contributions!)</p> <p> </p>
Research data supporting "Raman spectroscopy imaging reveals interplay between atherosclerosis and medial calcification in human aorta"
<p>Research data supporting the publication:</p> <p>You, A. Y. F. <em>et al.</em>, 2017, "Raman spectroscopy imaging reveals interplay between atherosclerosis and medial calcification in human aorta", Science Advances, DOI: 10.1126/sciadv.1701156.</p>
Raster Image Correlation Spectroscopy and Brightness Measurements of AtLEA proteins from Arabidopsis thaliana
<p>Temporal sequences of various fluorescent leaves were captured using a confocal scanning microscope (Olympus FV1000 inverted microscope), equipped with a 1.3 NA oil immersion 60X objective and the photon counting detection mode. Utilizing a 488 nm laser at 0.1% power and GFP filters/cubes, each temporal sequence involved the acquisition of 100 frames of 64x64 pixels, with a dwell time of 10 μs (1.76 ms per line, 130.24 ms per frame) and a pixel size of 66 nm (50X digital zoom). The interval between frames was set at 131.6 ms.</p> <p>Five plants were analyzed, each expressing one of four distinct genetic constructs fused to complementary fragments of Yellow Fluorescent Protein: pYFN-4-/5pYFC-4-5 (representing the complete AtLEA4-5 protein), pYFN-4-51-77/pYFC-4-51-77 (associated with the N-terminal region of AtLEA4-5), pYFN-4-578-158/pYFC-4-578-158 (relating to the C-terminal region of AtLEA4-5), and pYFN-pYFC (serving as the control). The raw data (*.oib files) were collected during three imaging sessions within a one-week period:</p> <p>- 220618 raw oib dataset.zip</p> <p>- 220622 raw oib dataset.zip</p> <p>- 220623 raw oib dataset.zip</p> <p>Images were converted to *.tif format using FIJI/ImageJ for further analysis and were archived in "tif dataset RICS NB LEAs.zip," excluding files with excessive movement of biological specimens. These images were then subjected to "Raster Image Correlation Spectroscopy" and "Number and Brightness" techniques for analysis.</p> <p>Notation:</p> <p>- h1, h2, h3, h4, h5: Replicates (plants) expressing one of four specific genetic constructs fused to complementary fragments of Yellow Fluorescent Protein.</p> <p>- 45: Fused to the full-length AtLEA4-5 protein (pYFN-4-/5pYFC-4-5).</p> <p>- 4h: Fused to the N-terminal region of AtLEA4-5 (pYFN-4-51-77/pYFC-4-51-77).</p> <p>- rc: Fused to the C-terminal region of AtLEA4-5 (pYFN-4-578-158/pYFC-4-578-158).</p> <p>- ct: The control condition (pYFN-pYFC).</p>
Supporting data for boundary layer water vapour statistics from high-spatial-resolution spaceborne imaging spectroscopy
<p>This dataset includes the properties necessary to reproduce the analysis of water vapour statistics derived from imaging spectroscopy as in:</p> <p>Richardson et al. (2021a) DOI: 10.5194/amt-14-5555-2021<br> Richardson et al. (2021b) DOI: 10.5194/amt-2021-163 (pre-acceptance DOI, follow links to published version)</p> <p>Files include the retrieval emulator parameters, atmospheric profiles used in the emulator development, column-mean water vapour and cloud water both for the total column water vapour (TCWV) and "effective" TCWV, which accounts for the water vapour integrated along the direct solar path at a range of solar zenith angles, see Richardson 2021b, Eq. (7).</p>
Correlative Raman Imaging and Scanning Electron Microscopy: The Role of Single Ga Islands in Surface-Enhanced Raman Spectroscopy of Graphene_experimental dataset
<p>This dataset contains the raw unprocessed data for Piastek et al., Correlative Raman Imaging and Scanning Electron Microscopy: The Role of Single Ga Islands in Surface-Enhanced Raman Spectroscopy of Graphene, <em>J. Phys. Chem. C</em> 2022, 126, 9, 4508–4514. </p>
tttrlib: modular software for integrating fluorescence spectroscopy and imaging
<p>This repository contains scripts and datasets associated with the manuscript "tttrlib: modular software for integrating fluorescence spectroscopy and imaging". The software is designed to facilitate the analysis of fluorescence spectroscopy and imaging data to enable integration with molecular modeling.</p> <p>The provided scripts encompass various functionalities, including:</p> <ul> <li>Single-molecule FRET (smFRET) analysis for studying conformational dynamics, particularly focusing on human guanylate binding protein 1 (hGBP1).</li> <li>Comprehensive image spectroscopy workflows applicable to murine guanylate binding protein 2 (mGBP2), featuring intensity and time-resolved analyses.</li> <li>Tools for burst variance analysis (BVA), multiparameter fluorescence detection, and correlative analysis of fluorescence lifetime and intensity.</li> </ul> <p>Datasets for both hGBP1 and mGBP2 are included to exemplify the software's capabilities in real-world applications.</p>
Magnetic Resonance Spectroscopy Imaging in Predicting Response to Vorinostat and Temozolomide in Patients With Recurrent or Progressive Glioblastoma
ClinicalTrials.gov study NCT01342757. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Hyperpolarized Xenon-129 Magnetic Resonance Imaging and Spectroscopy of Brown Fat: Healthy Adult Volunteer Pilot Study
ClinicalTrials.gov study NCT02220426. IPD Sharing: NO. Countries: 1. Publications: 1.
Imaging spectroscopy reveals topographic variability effects on grassland functional traits and drought responses
Open the record for dataset details and reuse information.
Data from: Modelling plant species distribution in alpine grasslands using airborne imaging spectroscopy
Remote sensing using airborne imaging spectroscopy (AIS) is known to retrieve fundamental optical properties of ecosystems. However, the value of these properties for predicting plant species distribution remains unclear. Here, we assess whether such data can add value to topographic variables for predicting plant distributions in French and Swiss alpine grasslands. We fitted statistical models with high spectral and spatial resolution reflectance data and tested four optical indices sensitive to leaf chlorophyll content, leaf water content and leaf area index. We found moderate added-value of AIS data for predicting alpine plant species distribution. Contrary to expectations, differences between species distribution models (SDMs) were not linked to their local abundance or phylogenetic/functional similarity. Moreover, spectral signatures of species were found to be partly site-specific. We discuss current limits of AIS-based SDMs, highlighting issues of scale and informational content of AIS data.
Plasmonic Au@Ag@mSiO2 Nanorattles for In Situ Imaging of Bacterial Metabolism by Surface-Enhanced Raman Scattering Spectroscopy
<p>Related publication: De Marchi, S; García-Lojo, D; Bodelón, G; Pérez-Juste, J; Pastoriza-Santos, I. Plasmonic Au@Ag@mSiO<sub>2</sub> Nanorattles for In Situ Imaging of Bacterial Metabolism by Surface-Enhanced Raman Scattering Spectroscopy. <em>ACS Applied Materials & Interfaces</em> 2021. <a href="http://doi.org/10.1021/acsami.1c21812">DOI: 10.1021/acsami.1c21812</a>.</p> <p> </p> <p>Abstract:</p> <p>It is well known that microbial populations and their interactions are largely influenced by their secreted metabolites. Noninvasive and spatiotemporal monitoring and imaging of such extracellular metabolic byproducts can be correlated with biological phenotypes of interest and provide new insights into the structure and development of microbial communities. Herein, we report a surface-enhanced Raman scattering (SERS) hybrid substrate consisting of plasmonic Au@Ag@mSiO<sub>2</sub> nanorattles for optophysiological monitoring of extracellular metabolism in microbial populations. A key element of the SERS substrate is the mesoporous silica shell encapsulating single plasmonic nanoparticles, which furnishes colloidal stability and molecular sieving capabilities to the engineered nanostructures, thereby realizing robust, sensitive, and reliable measurements. The reported SERS-based approach may be used as a powerful tool for deciphering the role of extracellular metabolites and physicochemical factors in microbial community dynamics and interactions.</p>
Stellar reddening map from DESI imaging and spectroscopy
<p>Data for reproducing figures in "Stellar reddening map from DESI imaging and spectroscopy" by Rongpu Zhou et al. 2024.</p>
JWST imaging and spectroscopy of a z=9.51 galaxy
<p>HST and JWST Imaging and JWST spectroscopy of a z=9.51 galaxy. </p>
Quantitative Imaging and Proton Spectroscopy in Multiple Sclerosis
ClinicalTrials.gov study NCT01226654. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Multi-elemental Imaging of Lung Tissues With LIBS (Laser-induced Breakdown Spectroscopy)
ClinicalTrials.gov study NCT03901196. IPD Sharing: NO. Countries: 1. Publications: 1.
Development and Validation of Multi-nuclear Magnetic Resonance Imaging and Spectroscopy for Whole Body Human Applications
ClinicalTrials.gov study NCT02663362. IPD Sharing: NO. Countries: 1. Publications: 18.
Evaluation of Kidney Function by Multi-modal Magnetic Resonance Imaging and Spectroscopy
ClinicalTrials.gov study NCT00575432. IPD Sharing: UNDECIDED. Countries: 1. Publications: 12.
Magnetic Resonance Imaging and Spectroscopy Biomarkers for Facioscapulohumeral Muscular Dystrophy
ClinicalTrials.gov study NCT01671865. IPD Sharing: NO. Countries: 1. Publications: 3.
New Magnetic Resonance Imaging and Spectroscopy Software in the Improvement of Image Quality
ClinicalTrials.gov study NCT03980535. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.