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1,169 results for “Infrared”
UAV-based colour-infrared orthomosaics and digital elevation models of basalts and rock glaciers on Disko Island, West Greenland
<p><span>This data set contains multispectral surveys conducted with an unoccupied aerial vehicle over rock glaciers and steep mafic outcrops (intrusive and flood volcanics) near the coastline of Disko Island.</span></p> <ul> <li><span>Acquisition date: 07.08.2019 – 10.08.2019</span></li> <li><span>Location: Illukunnguaq, Disko Island, Greenland</span></li> <li><span>UAV: SenseFly eBee Plus</span></li> <li><span>Flight altitude above ground level: >100m</span></li> <li><span>Image Overlap forward/side: various</span></li> <li><span>Camera: Parrot Sequoia multispectral</span></li> <li><span>EPSG: 32622</span></li> <li><span>Center coordinates: 69.885277°N, -52.577724°E</span></li> <li><span>Flight mode: automatic flight plan</span></li> </ul> <p><span>Data products: </span></p> <ul> <li><span>Orthomosaic colour-infrared, 10-16 cm pixel resolution</span></li> <li><span>Colour-infrared spectral bands: 790nm, 660nm, 550nm</span></li> <li><span>DEM, 20-30cm pixel resolution</span></li> <li><span>Data coverage: approx. 5500 x 2500 m</span></li> <li><span>Elevation profile: 20-680m </span></li> <li><span>Processing in Agisoft Metashape</span></li> </ul> <p><span>Additional data supplement for article:<br>Barnes, E. (2020). Assessment of Drone-Borne Multispectral Mapping in the Exploration of Magmatic Ni-Cu Sulphides–an Example from Disko Island, West Greenland. <br><em>URN: urn:nbn:se:uu:diva-418858</em></span></p> <p>MULSEDRO field campaign was conducted under scientific survey licence (VU-00158-2019) within mineral exploration licence MEL 2018-16 by Blue Jay Mining PLC. This research has been supported by the project MULSEDRO, funded by HZDR-HIF & EITRawMaterials (project ID 16193) and the European Union.</p>
Imaging Data from Sub-Seasonal Variation in Neptune's Mid-Infrared Emission by Roman et al.
<p>Images of Neptune as used in <a href="https://arxiv.org/abs/2112.00033"><strong>Imaging Data from Sub-Seasonal Variation in Neptune's Mid-Infrared Emission </strong></a>by Michael T. Roman, Leigh N. Fletcher, Glenn S. Orton, Thomas K. Greathouse, Julianne I. Moses, Naomi Rowe-Gurney, Patrick G. J. Irwin, Arrate Antunano, James Sinclair, Yasumasa Kasaba, Takuya Fujiyoshi, Imke de Pater, Heidi B. Hammel.</p> <p>Data are from various observatories/telescope instruments, including:</p> <ul> <li>The European Southern Observatory's Very Large Telescope, VISIR</li> <li>Keck Observatory, LWS</li> <li>Subaru Observatory, COMICS</li> <li>Gemini North, Michelle and TEXES</li> <li>Gemini South, T-ReCS</li> </ul> <p>Data were acquired from observatory archives and flux calibrated, when possible, by comparison to standard stars, with spectral radiances expressed in units of W/m<sup>2</sup>/sr/micron. North is up in the images. The first extension (ext=0) is the image in native spatial resolution. The second extension (ext=1) features the disk normalized in size to that of the finest data (i.e., to a disk with an equatorial width of 51.8 pixels, as imaged by VLT-VISIR on August 13, 2018).</p> <p>Image file names and times correspond to approximate mid-time of combined image sequences, and will differ from original file headers.</p> <p>Questions concerning these data should be directed towards Michael Roman, m.t.roman@le.ac.uk or michael.thomas.roman@gmail.com</p> <p> </p> <p> </p>
Exploring AdaBoost and Random Forests machine learning approaches for infrared pathology on unbalanced data sets
<p>The use of infrared spectroscopy to augment decision-making in histopathology is a promising direction for the diagnosis of many disease types. Hyperspectral images of healthy and diseased tissue, generated by infrared spectroscopy, are used to build chemometric models that can provide objective metrics of disease state. It is important to build robust and stable models to provide confidence to the end user. The data used to develop such models can have a variety of characteristics which can pose problems to many model-building approaches. Here we have compared the performance of two machine learning algorithms – AdaBoost and Random Forests – on a variety of non-uniform data sets. Using samples of breast cancer tissue, we devised a range of training data capable of describing the problem space. Models were constructed from these training sets and their characteristics compared. In terms of separating infrared spectra of cancerous epithelium tissue from normal-associated tissue on the tissue microarray, both AdaBoost and Random Forests algorithms were shown to give excellent classification performance (over 95% accuracy) in this study. AdaBoost models were more robust when datasets with large imbalance were provided. The outcomes of this work are a measure of classification accuracy as a function of training data available, and a clear recommendation for choice of machine learning approach.</p>
Thermal infrared emissivity spectral library of silicates measured under the Mercury simulated environment
<p>This is the thermal emissivity spectral library of silicates measured as a function of temperature under Mercury simulated environment. Data is measured at the Planetary Spectroscopy Laboratory (PSL), Institute of Planetary Research, German Aerospace Center (DLR), Berlin. The spectral library will be used for mineral identification of Mercury surface using MERTIS datasets. The manuscript related to this work is submitted to Icarus on the title "<strong>Thermal Infrared Spectroscopy (7-14 µm) of Silicates under Simulated Mercury Daytime Surface Conditions and their Detection: Supporting MERTIS onboard the BepiColombo Mission".</strong></p>
Thermally switchable, bifunctional, scalable, mid-infrared metasurfaces with VO2 grids capable of versatile polarization manipulation and asymmetric transmission
<p>The data generated by CST Studio Suite that are used to plot a part of the figures, and sample CST scripts. </p> <p>Research supported by Narodowe Centrum Nauki, project no UMO-2020/39/I/ST3/02413. </p>
Sublimation and infrared spectral properties of ammonium cyanide
<p>Data from</p> <p>Perry A. Gerakines, Yukiko Y. Yarnall, Reggie L. Hudson,<br>Sublimation and infrared spectral properties of ammonium cyanide,<br>Icarus,<br>Volume 413,<br>2024,<br>116007,<br>ISSN 0019-1035,<br>https://doi.org/10.1016/j.icarus.2024.116007.<br>(https://www.sciencedirect.com/science/article/pii/S0019103524000654)<br>Abstract: The ammonium ion (NH4+) has been suggested to be present in interstellar ices and has been observed on the surfaces of planetary bodies using infrared (IR) spectroscopy as the primary means of identification. Evidence for several ammonium salts has also been found in the dust and surface ices of comet 67P/Churyumov-Gerasimenko. Here we present a laboratory study of ammonium cyanide (NH4CN) and report on several properties of this compound, measured with higher accuracy than in previous reports, including its IR band strengths and optical constants for use in quantifying its abundance in interstellar and planetary ices. We also report the first measurements since 1882 of NH4CN vapor pressures, sublimation fluxes, and sublimation enthalpy measured at temperatures relevant to subliming cometary ices (134–155 K). The density and refractive index of NH4CN at 125 K and the sublimation enthalpy and vapor pressures of NH3 at ~100 K are also reported.</p> <p><br>Keywords: Ices; IR spectroscopy; Comets; Infrared observations</p> <p>This work was funded by the NASA Astrophysics Research and Analysis (APRA) and Planetary Data Archiving, Restoration, and Tools (PDART) Programs, as well as NASA's Planetary Science Division Internal Scientist Funding Program through the Fundamental Laboratory Research (FLaRe) work package at the NASA Goddard Space Flight Center.</p>
The data for "Accurate Infrared Line Lists for 20 Isotopologues of Carbon Disulfide (CS2) at Room Temperature"
<p>[<strong>Updates on 2025-03-02</strong>: energy levels and line lists of CS2 323 and 333 isotopologues are corrected; energy levels of 224, 223, and 232 isotopologues are extended to 0.1 au (ZPE included); partition function (Q) of first 4 isotopologues by direct summation up to 4000 K; number of 323 and 333 iso lines in natural line list are updated; natural line lists are updated]</p> <p>The paper was published online at <a href="https://iopscience.iop.org/article/10.3847/1538-4365/ad3809">ApJS</a> with open access to public, DOI: 10.3847/1538-4365/ad3809</p> <p>First-generation data product and IR line lists for Carbon Disulfide (CS2), including an isotopologue-independent <em>ab initio</em> PES of Carbon Disulfide refined with selected HITRAN energy levels below 7000 cm-1, an <em>ab initio </em>DMS fitted with CCSD(T)/aug-cc-pV(T/Q/5+d)Z dipoles computed up to 20,000 cm-1 above potential minimum and extrapolated to one-electron basis set limit, room temperature IR line lists for 20 individual isotopologues of 12/13C and 32/33/34/36S, denoted Ames-296K, and a "natural" CS2 list with intensities scaled by their terrestrial abundances. This project is funded by NASA Grant 18-2XRP18_2-0046 through NASA/SETI Institute Co-operative Agreement 80NSSC19M0121. See https://huang.seti.org/CS2/cs2.html for data format and abundance information. Resources supporting this work were provided by the NASA High-End Computing (HEC) Program through the NASA Advanced Supercomputing (NAS) Division at Ames Research Center. The line profile parameters and room temperature simulations are supported through 80NSSC20K1596.</p> <p><strong>List of Files</strong>, supplement to article " Accurate IR Line Lists for CS2 and Isotopologues at Room Temperature"</p> <ol> <li>Ames-1.PES.zip: Ames-0 and Ames-1 PES subroutine & coefficient files;<br>PES.refinement.files.zip: PES refinement related files including reference energy level list and refinement output.</li> <li><em>J</em>=0-200 energy level lists of 12C32S2 and 19 minor isotopologues, computed on the Ames-1 PES. The .zip file contains 20 compressed .tgz (or .xz) files, and partition function of 222, 224, 223, and 232 isotopologues.</li> <li>Ames-1.DMS.zip: Ames-1 DMS subroutine & coefficient files, and <em>ab initio</em> data;</li> <li>cs2.xxx.Ames-1.296K.1E-31.dat.tgz (or .xz) : 20 files, "xxx" is the S-C-S isotope mass unit number. These are the Ames-296K IR line lists for 12C32S2 and 19 minor isotopologues, each with 100% abundance. Computed using Ames-1 DMS and rovibrational wavefunctions for those energy levels acquired on Ames-1 PES;</li> <li>cs2.20iso.Ames.natural.296K.1E-31.10Kcm-1.dat.updated.tgz: A "natural" Ames-296K IR line list for CS2, including 10,018,977 transitions from all 20 isotopologues with their 296K intensities scaled by terrestrial abundances, covering the range of 0 - 10,000 cm-1. Computed on the Ames-1 PES and DMS.</li> <li>cs2.222.A+I.296K.ames+heff.natural.tgz: A(mes)+I(AO).296K line list for the main isotopologue 222, with terrestrial abundance. Ames-296K intensity prediction is combined with the more accurate energy levels (and line positions) from Effective Hamiltonian model.</li> <li>cs2.iso2-20.Ames-1.natural.1E-31.dat.iso2-4_use_HITRAN2020_purified.v5.xz: the Ames "natural" line list for minor isotopologues #2 - #20, in which the energy levels of 224, 223 and 232 are replaced with reliable values in HITRAN2020. Therefore, the final composite line list = 6) + 7)</li> <li>Heff.and.HITRAN.energy.level.matches.and.line.list.update.zip: the short FORTRAN programs for energy level matches between Ames-1 PES levels and Heff model levels, and the subroutines to use Heff and HITRAN energy levels and line positions. Lists of matched Ames vs HITRAN/H_eff levels are also included.</li> <li>ORIGIN project file for related analysis and figures. Use Origin Viewer to open on PC and MAC, <a href="https://www.originlab.com/viewer/dl.aspx">https://www.originlab.com/viewer/dl.aspx</a></li> <li>a Python program to generate line-broadening parameters for rovibrational CS2 molecule</li> <li>CS2 cross-section data of PNNL, HITRAN and Ames line lists. </li> </ol> <p><strong> # Iso #Lines #in"natural" abundance </strong><br> 1 222 1,903,882 1,856,648 0.892811 <br> 2 224 3,983,009 2,159,579 0.0792103 <br> 3 223 3,745,299 1,328,631 0.0140944 <br> 4 232 1,925,377 658,645 0.100306 <br> 5 424 1,940,490 439,254 1.207E-3 <br> 6 234 4,211,645 698,654 8.151E-4 <br> 7 324 3,671,708 589,323 6.510E-4 <br> 8 226 4,155,423 558,540 3.566E-4 <br> 9 233 3,918,753 410,542 1.439E-4 <br>10 323 1,937,950 290,799 5.142E-5 <br>11 434 2,080,912 138,209 1.692E-5 <br>12 426 3,852,415 223,268 1.976E-5 <br>13 334 4,062,765 172,858 6.773E-6 <br>14 236 4,742,198 158,941 4.630E-6 <br>15 326 4,113,292 137,128 3.075E-6 <br>16 333 1,988,991 80,862 6.803E-7 <br>17 436 4,434,008 58,478 1.361E-7 <br>18 626 1,986,823 21,692 3.572E-8 <br>19 336 4,613,173 32,681 3.528E-8 <br>20 636 2,194,332 4,245 4.28E-10 </p> <p><strong>Line List Data Format: </strong>(CS2 is the 53rd molecule in HITRAN, we use iso# from table below, e.g., 1 - 222; 2 - 224; ...; 10 - 323; ....; 20 - 636)</p> <ol> <li>in the original line list files: cs2.xxx.Ames-1.296K.1E-31.dat<br>iso wavenumber S(Ames) A21(Ames) E"(cm-1) <em>v1v2l2v3' v1v2l2v3" JPS' #root' JPS" #root" J' J" e/f</em>_symmetry<br> 2 6.165375 1.492856E-30 4.851961E-12 876.91172 0 2 2 0 0 2 2 0 29 1 2 4 28 2 2 4 29 28 e e</li> <li>in cs2.iso2-20.Ames-1.natural.1E-31.dat.iso2-4_use_HITRAN2020_purified.v5, original Ames-1 line position and the difference = Heff - Ames are appended to the end of each line of iso #2 (224), iso #3 (223), and iso #4 (232). </li> <li>in cs2.222.AI-296K.ames+heff.natural.dat.v2, two integers are added to each line to keep the record for the number of cycles after which a match (or no match) was made for upper and lower levels, "0-41" for "matched", '99' for "not matched", "-1" for out of range, i.e. > 9000 cm-1. The differences between the original Ames and corrected/replaced transition wavenumber, E', and E" are also appended at the end. The relation is wv/E'/E" (Heff) + diff = wv/E'/E" (Ames). For example, in the transition below, E''(Ames) = 3445.4018+0.7865 = 3446.1883 cm-1. <br><em> 1 36.666580 1.538246E-31 2.306988E-07 3445.40177 0 4 2 1 1 6 2 0 57 1 2 31 58 2 2 34 57 58 e e 3 3 -0.7207 0.0658 0.7865</em></li> </ol> <p> </p>
DATA SET: Performance Assessment of a Commercial Continuous-Wave Near-Infrared Spectroscopy Tissue Oximeter for Suitability for Use in an International, Multi-Center Clinical Trial
<p>This repository contains the data sets related to the publication:</p> <p>Cortese, L.; Zanoletti, M.; Karadeniz, U.; Pagliazzi, M.; Yaqub, M.A.; Busch, D.R.; Mesquida, J.; Durduran, T. Performance Assessment of a Commercial Continuous-Wave Near-Infrared Spectroscopy Tissue Oximeter for Suitability for Use in an International, Multi-Center Clinical Trial. <em>Sensors</em> <strong>2021</strong>, <em>21</em>, 6957. https://doi.org/10.3390/s21216957</p>
Soil visible–near infrared (vis–NIR) spectra for the Biomes of Australian Soil Environments (BASE) soil microbial diversity database
<p>Visible–near infrared spectra of 695 soil samples collected in the Biomes of Australian Soil Environments (BASE) soil microbial diversity project (Bissett et al., 2016). The spectra represent reflectance values from 2151 wavelengths that range from 350 nm to 2500 nm with a 1 nm interval. The dataset has unique sample identification numbers and the date of sampling, which can be related to the BASE (Australian Microbiome) database (https://data.bioplatforms.com/organization/australian-microbiome)</p>
Near infrared imaging data from brown rot decayed wood
<p>This dataset contains near infrared imaging data from the following publication: Belt, T.; Awais, M.; Mäkelä, M. (2022) Chemical characterization and visualization of progressive brown rot decay of wood by near infrared imaging and multivariate analysis. Frontiers in Plant Science 13:940745. DOI: 10.3389/fpls.2022.940745. Details on the samples, the decay test, and the image collection parameters can be found in the publication.</p> <p>The “Sample IDs and mass losses.cvs” file contains the sample ID and mass loss due to decay of each sample in the dataset. The “C puteana.mat” and “R. placenta.mat” files contain the near infrared imaging data of samples exposed to the fungus <em>Coniophora puteana</em> and the fungus <em>Rhodonia placent</em>a, respectively, organised into cell arrays of sample IDs and corresponding image files. To generate the image files, a region of interest of 551 x 384 pixels was selected from the raw image files to produce an image that contains the sample surrounded by background. The spectral data were then converted to reflectance and corrected using the calibration reflectance target values.</p>
Near-infrared (NIR) soil spectral library using the NeoSpectra Handheld NIR Analyzer by Si-Ware
<p>Up-to-date information on soil properties and the ability to track changes in soil properties over time are critical for improving multiple decisions on soil security at various scales, ranging from global climate change modeling and policy to national level environmental and development planning, to farm and field level resource management. Diffuse reflectance infrared spectroscopy has become an indispensable laboratory tool for the rapid estimation of numerous soil properties to support various soil mapping, soil monitoring, and soil testing applications. Recent advances in hardware technology have enabled the development of handheld sensors with similar performance specifications as laboratory-grade near-infrared (NIR) spectrometers.</p> <p>Here, we've compiled a hand-held NIR spectral library (1350-2550 nm) using the NeoSpectra Handheld NIR Analyzer developed by <a href="https://www.si-ware.com/">Si-Ware</a>. Each scanner is fitted with Fourier-Transform technology based on the semiconductor Micro Electromechanical Systems (MEMS) manufacturing technique, promising accuracy, and consistency between devices.</p> <p>This library includes 2,106 distinct mineral soil samples scanned across 9 of these portable low-cost NIR spectrometers (indicated by serial no). 2,016 of these soil samples were selected to represent the diversity of mineral soils found in the United States, and 90 samples were selected across Ghana, Kenya, and Nigeria. 519 of the US samples were selected and scanned by <a href="https://www.woodwellclimate.org/">Woodwell Climate Research Center</a>. These samples were queried from the <a href="https://ncsslabdatamart.sc.egov.usda.gov/">USDA NRCS NSSC-KSSL Soil Archives</a> as having a complete set of eight measured properties (TC, OC, TN, CEC, pH, clay, sand, and silt). They were stratified based on the major horizon and taxonomic order, omitting the categories with less than 500 samples. Three percent of each stratum (i.e., a combination of major horizon and taxonomic order) was then randomly selected as the final subset retrieved from KSSL's physical soil archive as 2-mm sieved samples. The remaining 1,604 US samples were queried from the USDA NRCS NSSC-KSSL Soil Archives by the <a href="https://www.unl.edu/">University of Nebraska - Lincoln</a> to meet the following criteria: Lower depth <= 30 cm, pH range 4.0 to 9.5, Organic carbon <10%, Greater than lower detection limits, Actual physical samples available in the archive, Samples collected and analyzed from 2001 onwards, Samples having complete analyses for high-priority properties (Sand, Silt, Clay, CEC, Exchangeable Ca, Exchangeable Mg, Exchangeable K, Exchangeable Na, CaCO3, OC, TN), & MIR scanned.</p> <p>All samples were scanned dry 2mm sieved. ~20g of sample was added to a plastic weighing boat where the NeoSpectra scanner would be placed down to make direct contact with the soil surface. The scanner was gently moved across the surface of the sample as 6 replicate scans were taken. These replicates were then averaged so that there is one spectra per sample per scanner in the resulting database.</p> <p>A subset of 1,976 US topsoil samples was used to create Cubist models for 8 soil properties including bulk density (BD, <2mm fraction, 1/3 Bar, units in grams per cubic centimeter), calcium carbonate (CaCO3, <2mm fraction, units in weight percent), clay content (percent), buffered ammonium-acetate exchangeable potassium (Ex. K, units in centimoles of charge per kilogram of soil), pH, sand content (percent), silt content (percent), and estimated organic carbon (SOC, estimated after inorganic carbon removal, units in weight percent). Two strategies were evaluated for handling scanner-to-scanner variability: averaging scans per sample (avg) versus retaining replicate scans across all scanners (reps) during model building. Cubist avg models and cubist reps models are provided here for the 8 soil properties outlined in “.qs” file format and can be opened and worked with in the R programming language. The subset of 1,976 samples has also been provided here for reproducibility (1976_NSlibrary_withmetadata.csv).</p> <p>The repository contains:</p> <ul> <li><em>Neospectra_database_column_names.csv</em>: describes the variables (columns) of site and soil data, and the range of near-infrared (NIR, 1350-2550 nm) and mid-infrared (MIR, 600-4000 cm-1) spectra. The CSV is composed of the file name, column name, type, example, and description with measurement unit.</li> <li><em>Neospectra_WoodwellKSSL_MIR.csv</em>: the equivalent MIR spectra of neospectra samples fetched from the KSSL database and formatted to the OSSL specifications.</li> <li><em>Neospectra_WoodwellKSSL_soil+site+NIR.csv</em>: soil, site, and Neospectra's NIR. Each row contains one replicated spectra of a given scanner (6 repeats per scanner per soil sample). Soil and site info is filled within the same soil sample.</li> <li>1976_NSlibrary_withmetadata.csv: data matrix for reproducible model calibration.</li> <li>Models: <ul> <li>log..bd_model_nir.neospectra_cubist_AVG_ossl_na_v1.2.qs: Cubist average NIR model for log(1+BD).</li> <li> <p>log..caco3_model_nir.neospectra_cubist_AVG_ossl_na_v1.2.qs: Cubist average NIR model for log(1+CaCO3).</p> </li> <li> <p>clay_model_nir.neospectra_cubist_AVG_ossl_na_v1.2.qs: Cubist average NIR model for clay.</p> </li> <li> <p>log..k.ex_model_nir.neospectra_cubist_AVG_ossl_na_v1.2.qs: Cubist average NIR model for log(1+Ex. K).</p> </li> <li> <p>ph.h2o_model_nir.neospectra_cubist_AVG_ossl_na_v1.2.qs: Cubist average NIR model for pH.</p> </li> <li> <p>sand_model_nir.neospectra_cubist_AVG_ossl_na_v1.2.qs: Cubist average NIR model for sand.</p> </li> <li> <p>silt_model_nir.neospectra_cubist_AVG_ossl_na_v1.2.qs: Cubist average NIR model for silt.</p> </li> <li> <p>log..soc_model_nir.neospectra_cubist_AVG_ossl_na_v1.2.qs: Cubist average NIR model for log(1+SOC).</p> </li> <li> <p>log..bd_model_nir.neospectra_cubist_REPS_ossl_na_v1.2.qs: Cubist replicates NIR model for log(1+BD).</p> </li> <li> <p>log..caco3_model_nir.neospectra_cubist_REPS_ossl_na_v1.2.qs: Cubist replicates NIR model for log(1+CaCO3).</p> </li> <li> <p>clay_model_nir.neospectra_cubist_REPS_ossl_na_v1.2.qs: Cubist replicates NIR model for clay.</p> </li> <li> <p>log..k.ex_model_nir.neospectra_cubist_REPS_ossl_na_v1.2.qs: Cubist replicates NIR model for log(1+Ex. K).</p> </li> <li> <p>ph.h2o_model_nir.neospectra_cubist_REPS_ossl_na_v1.2.qs: Cubist replicates NIR model for pH.</p> </li> <li> <p>sand_model_nir.neospectra_cubist_REPS_ossl_na_v1.2.qs: Cubist replicates NIR model for sand.</p> </li> <li> <p>silt_model_nir.neospectra_cubist_REPS_ossl_na_v1.2.qs: Cubist replicates NIR model for silt.</p> </li> <li> <p>log..soc_model_nir.neospectra_cubist_REPS_ossl_na_v1.2.qs: Cubist replicates NIR model for log(1+SOC).</p> </li> </ul> </li> </ul>
Infrared Chemical Image of a Breast Cancer Tissue Microarray
<p>This data set relates to an open access paper published in Analyst <em>Exploring AdaBoost and Random Forests machine learning approaches for infrared pathology on unbalanced data sets</em> by Jiayi Tang, Alex Henderson* and Peter Gardner. <a href="https://doi.org/10.1039/D0AN02155E"> https://doi.org/10.1039/D0AN02155E</a></p> <p>The files in this archive are mid-infrared spectroscopy chemical images of a breast cancer tissue microarray. The tissue microarray is BR20832 from Biomax. <a href="http://www.biomax.us/tissue-arrays/Breast/BR20832">http://www.biomax.us/tissue-arrays/Breast/BR20832</a></p> <p>Processed versions of these data in MATLAB file format can be found in another Zenodo archive at <a href="https://doi.org/10.5281/zenodo.4730312">https://doi.org/10.5281/zenodo.4730312</a></p> <p>This processed data refers to a paper published in Analyst</p>
Dataset for manuscript "Thermal infrared dust optical depth and coarse-mode effective diameter over oceans retrieved from collocated MODIS and CALIOP observations"
<p>This is the long-term satellite retrieval dataset of dust aerosol optical depth at 10 μm (DAOD<sub>10μm</sub>) and dust coarse mode effective diameter (D<sub>eff</sub>) based on collocated MODIS and CALIOP observations from July 2006 to August 2018. The full description is in the manuscript "<strong>Thermal infrared dust optical depth and coarse-mode effective diameter over oceans retrieved from collocated MODIS and CALIOP observations" </strong>by Zheng, Jianyu, et al. The readme file for the data is in "readme_dust_aod_size_product.txt". The variable list of Level-2 data is in "variable_list_L2.txt". The variable list of Level-3 data is in "variable_list_L3.txt".</p>
Surface temperature mapped from thermal infrared survey from UAV campaign at Niwot Ridge, 2017.
Data collected as part of unmanned aerial vehicle (UAV)/drone campaign during Summer 2017. Investigating snow depth variability and spatiotemporal variations and controls on vegetation productivity within the Niwot Ridge LTER Saddle Catchment. Surface temperature of Niwot Ridge saddle was mapped from thermal infrared survey on June 21, July 11, 18, 25, and August 14, 2017.
Calibrated Red/Near Infrared orthomosaic imagery from UAV campaign at Niwot Ridge, 2017.
Red/Near Infrared data were collected as part of unmanned aerial vehicle (UAV)/drone campaign during Summer 2017. The purpose of the project was to investigate snow depth variability and spatiotemporal variations and controls on vegetation productivity within the Niwot Ridge LTER Saddle Catchment.
Far-infrared to millimeter data of protoplanetary disks: dust growth in the Taurus, Ophiuchus, and Chamaeleon I star-forming regions
<p>This repository contains the data set presented in the manuscript "Far-infrared to millimeter data of protoplanetary disks: dust growth in the Taurus, Ophiuchus, and Chamaeleon I star-forming regions" (Ribas et al. 2017), and includes a table with several sample properties (e.g. stellar properties, Herschel photometry, different spectral indices), spectral energy distributions, Spitzer/IRS and Herschel/SPIRE spectra, the median SEDs of Taurus, Ophiuchus and Chamaeleon I, and the Herschel maps used.</p> <p>ERRATUM: three Chamaeleon I sources (Hn 11, T45a, and WY Cha) were mislabeled in the original version of the manuscript, which resulted in their names, stellar parameters, extinction values, infrared slopes, and silicate feature properties being assigned to incorrect coordinates. Because the photometry and spectroscopy presented in the original article is coordinate- based, the provided SEDs and spectra were also missmatched: the data files labeled Hn 11 in the original manuscript correspond to T45a, those labeled T45a correspond to WY Cha, and those labeled WY Cha correspond to Hn 11. Additionally, due to a mislabeling issue in Manoj et al. 2011, the source formerly labeled UX Cha is actually CHSM 8284. Therefore, stellar parameters and photometry labeled UX Cha in our original manuscript correspond to CHSM 8284 The updated version of the repository fixes the issue both in the sample.csv file and in the individual SED and Spitzer/IRS spectra files. The published erratum is available here: <a href="https://iopscience.iop.org/article/10.3847/1538-4357/abb66e">https://iopscience.iop.org/article/10.3847/1538-4357/abb66e</a>.</p>
Spatially Resolved Infrared Radiofluorescence (SR IR-RF) Image Data
<p>This dataset contains measurement sequences and data output <br> of spatially resolved infrared radiofluorescence (SR IR-RF) measurements<br> on K-feldspar samples carried out at the IRAMAT-CRP2A, UMR 5060, CNRS-Université Bordeaux Montaigne (France)<br> in 2019. The data analysis was performed in 2020. </p> <p>The data may serve as reference data and allow detailed inspection by others to <br> verify or advance the used analysis procedures. </p> <p>Along with the raw image data (TIF-files), the datasets also contain documented R scripts used for data processing and partly treated data as an example. To reproduce the full data analysis, additional software is needed; not part of this repository. </p> <p>Further details can be found in the README.md (README.html), which is part of the dataset.</p>
RCSED - A Value-Added Reference Catalog of Spectral Energy Distributions of 800,299 Galaxies in 11 Ultraviolet, Optical, and Near-Infrared Bands: Morphologies, Colors, Ionized Gas and Stellar Populations Properties
<p>We present RCSED, the value-added Reference Catalog of Spectral Energy Distributions of galaxies, which contains homogenized spectrophotometric data for 800,299 low and intermediate redshift galaxies (0.007 < z < 0.6) selected from the Sloan Digital Sky Survey spectroscopic sample. Accessible from the Virtual Observatory (VO) and complemented with detailed information on galaxy properties obtained with the state-of-the-art data analysis, RCSED enables direct studies of galaxy formation and evolution during the last 5 Gyr. We provide tabulated color transformations for galaxies of different morphologies and luminosities and analytic expressions for the red sequence shape in different colors. RCSED comprises integrated k-corrected photometry in up-to 11 ultraviolet, optical, and near-infrared bands published by the GALEX, SDSS, and UKIDSS wide-field imaging surveys; results of the stellar population fitting of SDSS spectra including best-fitting templates, velocity dispersions, parameterized star formation histories, and stellar metallicities computed for instantaneous starburst and exponentially declining star formation models; parametric and non-parametric emission line fluxes and profiles; and gas phase metallicities. We link RCSED to the Galaxy Zoo morphological classification and galaxy bulge+disk decomposition results by Simard et al. We construct the color-magnitude, Faber-Jackson, mass-metallicity relations, compare them with the literature and discuss systematic errors of galaxy properties presented in our catalog. RCSED is accessible from the project web-site and via VO simple spectrum access and table access services using VO compliant applications. We describe several SQL query examples against the database. Finally, we briefly discuss existing and future scientific applications of RCSED and prospectives for the catalog extension to higher redshifts and different wavelengths.</p>
Dose and administration time of indocyanine green in near-infrared fluorescence cholangiography during laparoscopic cholecystectomy (DOTIG) Dataset.
<p><strong><span>Introduction </span></strong></p> <p><span>Different techniques have been described to reduce the incidence of the intraoperative bile duct injury during laparoscopic cholecystectomy (LC), and Near-Infrared Fluorescence Cholangiography (NIFC) with Indocyanine Green (ICG) is one of the latest additions. Currently, there are great disparities in the usage or administration protocols of ICG.</span></p> <p><strong><span>Methods</span></strong></p> <p><span>The aim of this randomised multicenter clinical trial (RCT) is to analyse whether there are differences between the dose and administration ICG intervals to obtain good-quality NIFC during LC. In addition, different factors were analysed that may have an influence on the results of this technique. </span><span>This trial was approved by the local institutional Ethics Committee</span><span>.</span></p> <p><strong><span>Results </span></strong></p> <p><span>From June 2022 to June 2023, 200 patients were randomised in the four arms (G1: </span><span>2.5 mg ICG >3 hours prior to surgery, G2: 2.5 mg ICG 15-30 minutes prior to surgery, G3: 0.05 mg/kg ICG >3 hours prior to surgery and G4: 0.05 mg/kg ICG 15-30 minutes prior to surgery)</span><span>. We found differences in the DISTURBED score between the groups (<em>p</em><0.001), suggesting that ICG administration 15-30 minutes before surgery was worse than administration >3 hours after LC (<em>p</em>=0.02). We also observed that body mass index, gender, ASA Classification System, previous liver and biliary disease and the type of surgery had influence on NIFC. Finally, the NIFC had impact in intraoperative and postoperative complications, operative time and hospital length of stay. </span></p> <p><strong><span>Conclusion </span></strong></p> <p><span>The time of ICG administration was related to NIFC results, as well as different preoperative predictors. NIFC may also influence in surgical outcomes of LC.</span></p> <p><strong><span>Documentation in ZENODO</span></strong></p> <p>Files stored in this repository correspond to the data extracted from the CRDe RedCAP used in this study. The file 'DOTIG_DATA_NoAA_LABELS_2023-10-31' corresponds to the data collected during the study, and the file 'DOTIG_DATA_AA_LABELS_2023-10-31' corresponds to the adverse events recorded during the study. Additionally, there is one more file which is the recoding of adverse events to "MEDDRA Terms (PT) vs 25.1" and "SOC".</p> <p>IBSAL uses the REDCap system for database creation. A detailed description of the information to be captured in the database is documented in the "DOTID_PGD_Data Dictionary Codebook" document. The structure of this data in the CRDe is described in the "DOTIG_PGD_Annotated CRDe document". The Annotated CRDe details the names of input objects in the CRDe with the name, format, and type of variables that will be used during the data entry process. The database must capture all the elements included in the "DOTIG_PGD_Variable List" document.</p> <p>The data management throughout the study is documented in the document DOTIG_PGD_Data Management Report.</p>
The Water-ice Feature in Near-infrared Disk-scattered Light around HD 142527: Micron-sized Icy Grains Lifted up to the Disk Surface?
<p>This is a reproduction package for the paper "The Water-ice Feature in Near-infrared Disk-scattered Light around HD 142527: Micron-sized Icy Grains Lifted up to the Disk Surface?" by Tazaki et al. (2021). In this repository, you will find the data files used to make figures in the paper. Source codes and scripts are included as well.</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.