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1,169 results for “Infrared”
Dataset: Infrared-radiofluorescence: dose saturation and long-term signal stability of a K-feldspar sample
<p>Original measurement and processed data of the study <em>Infrared-radiofluorescence: dose saturation and long-term signal stability of a K-feldspar sample </em>submitted for review to Radiation Measurements. The data are structured as follows:</p> <ol> <li><strong>Measurement data </strong></li> <li><strong>Processed data</strong></li> </ol> <p>Experiments were carried out at the Archéosciences Bordeaux (UMR 6034, CNRS - Université Bordeaux Montaigne; former IRAMAT-CRP2A) in Bordeaux (France) and at the Département des sciences de la Terre of the Université du Québec à Montréal (Canada). The subfolders are organised by the laboratory where the experiments were carried out: spectrometer measurements in Montréal (00_Montreal_Spectrometer) and spatially resolved measurements (camera) in Bordeaux (10_Bordeaux_Camera). </p> <p><strong>Measurement data </strong>contains sequence files used to run the experiments (so-called *.lseq files) as well as the raw, unaltered measurement output in the form of files with the ending *.xsyg and *.tiff. For the camera measurements in Bordeaux, the system returned a couple of single TIFF files. We merged those files in two files, one for <em>RF<sub>nat</sub></em> and <em>RF<sub>reg</sub></em>, for convenience reasons. The data are, however, unprocessed. </p> <p><strong>Processed data</strong> is organized like the measurement data folder containing all kinds of semi-automated processed data (PDF files, images). All data were processed with the R (R Core Team, 2021) package 'Luminescence' (Kreutzer et al., 2012; 2021) and an <em>ImageJ </em>macro detailed in Mittelstraß and Kreutzer (2021)</p> <p> </p> <p><strong>References</strong></p> <p>Kreutzer, S., Schmidt, C., Fuchs, M.C., Dietze, M., Fischer, M., Fuchs, M., 2012. Introducing an R package for luminescence dating analysis. Ancient TL 30, 1–8.</p> <p>Kreutzer, S., Burow, C., Dietze, M., Fuchs, M.C., Schmidt, C., Fischer, M., Friedrich, J., Mercier, N., Smedley, R.K., Christophe, C., Zink, A., Durcan, J., King, G.E., Philippe, A., Guérin, G., Riedesel, S., Autzen, M., Guibert, P., Mittelstrass, D., Gray, H.J., 2021. Luminescence: Comprehensive luminescence dating data analysis. CRAN. https://doi.org/10.5281/zenodo.4729933</p> <p>Mittelstraß, D., Kreutzer, S., 2021. Spatially resolved infrared radiofluorescence: single-grain K-feldspar dating using CCD imaging. Geochronology 3, 299–319. https://doi.org/10.5194/gchron-3-299-2021</p> <p>R Core Team, 2021. R: A language and environment for statistical computing. https://www.r-project.org</p> <p> </p> <p> </p>
Dataset: A New Infrared Criterion for Selecting Active Galactic Nuclei to Lower Luminosities
<p>The dataset accompanying the paper "A New Infrared Criterion for Selecting Active Galactic Nuclei to Lower Luminosities" (Hviding et al. in prep)</p>
Infrared spectroscopy of the benzylium-like (and tropylium-like) isomers formed in the --H dissociative ionization of methylated PAHs
<p>Dataset for article "Infrared spectroscopy of the benzylium-like (and tropylium-like) isomers formed in the --H dissociative ionization of methylated PAHs". DOI: 10.1016/j.jms.2022.111620</p> <p> - folder Experimental contains<br> - folder Fig2_IRPDspectra containing<br> - with IRPD spectra data of all three species (Fig. 2)<br> - folder Fig3_Depletion containing<br> - the saturation depletion measurements on six bands (Fig. 3)<br> - folder FigS1_MassSpectra containing<br> - the mass spectra of all three species in Trap ON/OFF modes (Fig. S1)</p> <p> - folder Theoretical contains<br> - .log files for each considered species<br> - e.g.: folder C11H9+ contains folders for the<br> - NapC7+<br> - NapC7+Ne<br> - NapCH2+<br> - NapCH2+Ne<br> These contain all .log files needed to reproduce Figs. 4, 5, 6 of the main<br> mansucript and Figs. S3, S4, S5, S6, S7, S8, S9 of the supplementary material<br> - folder Fig7_EnergyProfile containing<br> - all minima and transition states for the computed energy profile (doublet<br> spin state surface) for the H loss from NapCH3+ leading to NapCH2+ and NapC7+<br> (Fig. 7)</p> <p> </p>
Fourier-transform Infrared (FT-IR) spectroscopy fingerprints subpopulations of extracellular vesicles of different sizes and cellular origin
<p>Atomic Force Microscopy images of Large (LEV), Medium (MEV) and Small (SEV) Extrzcellular vesicles (EVs) from murine cell line B16 (B16-F10, ATCC CRL-647; Mus musculus, mouse; tissue: melanoma skin). Image size 8.3 x 8.3 um. Analysis mode: Tapping mode in air as described in Paolini et al. https://doi.org/10.1080/20013078.2020.1741174</p>
CRIRES high-resolution near-infrared spectroscopy of diffuse interstellar band profiles
<p>This archive contains data used for the paper:</p> <p>CRIRES high-resolution near-infrared spectroscopy of diffuse interstellar band profiles. Detection of 12 new DIBs in the YJ band and the introduction of a combined ISM sight line and stellar analysis approach</p> <p>Paper-DOI: 10.1051/0004-6361/202142990</p> <p>It contains reduced oCRIRES spectra. For more details on the reduction see the paper.</p>
Time seqUential theRmal inFrared - Turbulence campaign 1 - TURF-T1 Experiment data
<p>This dataset is a thermal infrared dataset collected alongside with sonic anemometer and thermocouple data. The experiment took place on the 12/01/2019 starting at 16:21:11 NZDT and ending at 16:31:11 NZDT. The collection of the infrared data was done via uncrewed aerial vehicle with an Optris PI 450 camera flying above a TURF Sportsground located at N -43.29186830137823, E 172.6008992376682.</p>
Functional Near-Infrared Spectroscopy Reveals Delayed Hemodynamic Changes in the Primary Motor Cortex During Fine Motor Tasks and Decreased Interhemispheric Connectivity in Parkinson's Disease Patients
<p>This dataset contains functional near-infrared spectroscopy (fNIRS) data from 20 patients with Parkinson’s disease and 20 age- and sex-matched healthy subjects without movement disorders. There are 3 folders, each corresponding to a different task: a 10-second finger-tapping task, a 2-minute walking task, and a 6-minute resting-state. When using this dataset, please cite our work:</p> <div> <div>Guevara, E., Rivas-Ruvalcaba, F. J., Kolosovas-Machuca, E. S., Ramírez-Elías, M., Zapata, R. D. de L., Ramirez-GarciaLuna, J. L., & Rodríguez-Leyva, I. (2024). Parkinson’s disease patients show delayed hemodynamic changes in primary motor cortex in fine motor tasks and decreased resting-state interhemispheric functional connectivity: A functional near-infrared spectroscopy study. <em>Neurophotonics</em>, <em>11</em>(2), 025004. <a href="https://doi.org/10.1117/1.NPh.11.2.025004">https://doi.org/10.1117/1.NPh.11.2.025004</a></div> <div> <div> <div>Guevara, E., Solana-Lavalle, G., & Rosas-Romero, R. (2024). Integrating fNIRS and machine learning: Shedding light on Parkinson’s disease detection. <em>EXCLI Journal</em>, <em>23</em>, 763–771. <a href="https://doi.org/10.17179/excli2024-7151">https://doi.org/10.17179/excli2024-7151</a></div> <div> <div> <div> <div> <div>Guevara, E., Kolosovas-Machuca, E. S., & Rodríguez-Leyva, I. (2024). Exploring motor cortex functional connectivity in Parkinson’s disease using fNIRS. <em>Brain Organoid and Systems Neuroscience Journal</em>, <em>2</em>, 23–30. <a href="https://doi.org/10.1016/j.bosn.2024.04.001">https://doi.org/10.1016/j.bosn.2024.04.001</a></div> </div> </div> </div> </div> </div> </div> </div>
Data set: UAS-based optical- and thermal infrared remote sensing of the fumarole field of La Fossa cone, Vulcano Island (Italy), reveals the degassing and hydrothermal alteration structure
<p>This is the data set supporting the paper "Anatomy of a fumarole field; drone remote sensing and petrological approaches reveal the degassing and alteration structure at La Fossa cone, Vulcano Island, Italy" (DOI: <a href="https://doi.org/10.5194/egusphere-2023-1692" target="_blank" rel="noopener noreferrer">10.5194/egusphere-2023-1692</a>).</p> <p> </p> <p><strong>Short description of the study:</strong> Hydrothermal alteration is common on actively degassing volcanoes and can lead to significant changes in the physical and chemical properties of the volcanic rocks, such as changes in permeability or rock strength. Despite the potentially far-reaching consequences of hydrothermal alteration for volcano stability, less is known about the detailed structures and dynamics of degassing and alteration systems. In this study, we use UAS-derived high-resolution data to analyze the fumarole field at La Fossa cone, Vulcano Island (Italy), aiming to better understand the structures and dynamics of volcanic degassing and alteration systems. By combining Principal Component Analysis, image analysis, and classification applied to high-resolution optical data and analysis of thermal infrared data, we resolve the detailed structure of the surficial degassing and alteration system based on optical and thermal anomalies. We identified characteristic anomaly patterns that indicate local degassing and alteration variability, and larger units of diffuse activity that, next to high-temperature fumaroles, contribute significantly to the total activity. We compared the observed anomaly patterns with the mineralogical and geochemical composition of representative rock samples, and with the surface degassing activity, and are able to provide the anatomy of the La Fossa fumarole field at great resolution. We show local alteration gradients, the presence of larger diffuse active complexes, and evidence for dynamic processes associated with the hydrothermal alteration. For more details, please read on: "<em>Müller, D., Walter, T. R., Troll, V. R., Stammeier, J., Karlsson, A., De Paolo, E., ... & De Jarnatt, B. (2023). Anatomy of a fumarole field; drone remote sensing and petrological approaches reveal the degassing and alteration structure at La Fossa cone, Vulcano Island, Italy. EGUsphere, 2023, 1-45. </em> https://doi.org/10.5194/egusphere-2023-1692".</p> <p> </p> <p> </p> <p><strong>Data set:</strong> We provide a UAS-based high-resolution dataset covering the whole La Fossa cone, including aerial Orthomosaic, Digital Elevation Model, and a Temperature Map derived from an airborne optical- and thermal infrared sensor (acquired in 2018 and 2019). </p> <p>The dataset is organized in 1) photogrammetric data, and 2) relevant processing results and related data. <strong>Filenames</strong> are written in bold letters and are a composite of the file type and the date (YYYYMMDD). </p> <p> </p> <p> </p> <p><strong>1) Photogrammetric data: </strong></p> <ul> <li><strong>Orthomosaic_20191114.tif</strong> is the in Agisoft Metashape processed orthomosaic of a 150 m (above fumarole field) optical overflight (DJI Phantom 4 Pro camera). </li> <li><strong>DigitalElevationModel_20191114.tif</strong> is the in Agisoft Metashape processed Digital Elevation Model (DEM) from the above-mentioned 150 m overflight. </li> <li><strong>Hillshade_20191114.tif</strong> is the 2.5-D representation of the DigitalElevationModel_20191114. Note, for viewing use a stretched (black to white) color scale.</li> <li><strong>TemperatureMap_20181115.tif</strong> is showing the apparent surface temperature for the La Fossa cone, acquired by a Flir Tau 2 thermal infrared camera at ~150 m (above fumarole field) flight altitude in the early morning hours (before sunrise) of 15 November 2018. Note that apparent temperatures shown may underestimate real in situ fumarole temperatures due to pixel-to-vent size ratios and atmospheric- or gas-plume distortion effects. Note further that the data has some processing artifacts, due to blind pixels of our IR camera system. For more detailed information or an updated data set please contact dmueller@gfz-potsdam.de.</li> <li><strong>T_20to40C.tif</strong> shows the diffuse thermally active surface at the fumarole field of the La Fossa cone (units a-g, see Fig. 4 in "Anatomy of a fumarole field...", https://doi.org/10.5194/egusphere-2023-1692). This raster shows the extracted pixels from TemperatureMap_20181115 in the range of 22 - 40 °C.</li> <li><strong>T_higher40C.tif</strong> outlines the high-temperature fumarole locations of the La Fossa fumarole field (HTF, see Fig. 4 in "Anatomy of a fumarole field...", https://doi.org/10.5194/egusphere-2023-1692), based on the extracted pixels with temperatures > 40 °C from TemperatureMap_20181115.</li> </ul> <p>Shapefiles for temperatures > 40 °C representing the high-temperature fumarole locations (HTF) and for temperatures of 20 - 40 °C representing diffuse active units, are attached at the end of the upload list and named <strong>T_higher40C_polygon</strong> and <strong>T_20_40C_polygon</strong> and consist of multiple files per shapefile with the file extensions .CPG, .dbf, .prj, .sbn, .sbx, .shp, .shp.xml, .shx. </p> <p>The coordinate system of the data sets is WGS84 EPSG:4326. For nadir projection use WGS 84 / UTM zone 33N - EPSG:32633. Note that the data might have horizontal and vertical offsets in the typical range of SfM-derived products with single-band GPS accuracy.</p> <p> </p> <p> </p> <p><strong>2) Relevant processing steps and related data:</strong></p> <ul> <li>Step 1) Principal Component Analysis applied to Orthomosaic_20191114 results in the following 3 Principal Components (decorrelated variance representations of the initial RGB bands): <ul> <li><strong>1_PCA_PC1.tif </strong>1st principal component </li> <li><strong>1_PCA_PC2.tif</strong> 2nd principal component</li> <li><strong>1_PCA_PC3.tif</strong> 3rd principal component - highlights well the effects of concentrated and diffuse degassing, resulting in different alteration effects from a simple shift from reddish oxidized surface to gray, up to strong silicic alteration effects. This can be used to extract the data of interest, the hydrothermally altered surface, and to create a new alteration sub-dataset. </li> </ul> </li> <li>Step 2) Extraction of hydrothermally altered surface / alteration sub-dataset <ul> <li><strong>2_alteration_subdata_RGB.tif</strong> The alteration sub-data set was extracted from the original Orthomosaic_20191114 based on a mask obtained from Principal Component 3 (1_PCA_PC3) for values > 85. The resulting raster data set is an extract of the original RGB data.</li> </ul> </li> <li>Step 3) PCA applied to 2_alteration_subdata_RGB will adjust to the reduced spectral range of the alteration sub-data set, provide a more sensitive variance representation, and highlight variability within the hydrothermally altered surface. <ul> <li><strong>3_PCA_PC1.tif</strong> 1st principal component of 2_alteration_subdata_RGB</li> <li><strong>3_PCA_PC2.tif</strong> 2nd principal component of 2_alteration_subdata_RGB</li> <li><strong>3_PCA_PC3.tif</strong> 3rd principal component of 2_alteration_subdata_RGB</li> </ul> </li> <li>Step 4) Unsupervised classification <ul> <li><strong>4_classification.tif</strong> is the unsupervised classification result of 3_PCA (all Principal Components), classified into 32 classes to achieve a high class resolution. When combining different classes, they form larger spatial units / surface types with similar spectral characteristics. This way, we divide the alteration surface into 3 surface types (see Fig. 4B in "Anatomy of a fumarole field..." DOI: 10.5194/egusphere-2023-1692) representing different alteration gradients and important structural units. To achieve the same results, combine classes 1 -19 (surface type 3), 20 - 25 (surface type 2), 26 - 30 (surface type 1), and 31 - 32 for sulfur/fumarole plume. See Image <strong>optical_structure.jpg</strong> for comparison. </li> </ul> </li> </ul> <p>Note that Principal Components and Classification of Principal Components highlight data variability along the axes of highest data variance. Results have to be evaluated carefully and may be valid only locally. They are efficient for identifying variability in degassing and alteration areas, but at the same time may also highlight certain fractions of vegetation or settlements for instance. We evaluated the structure defined by our classification results by analyzing the thermal structure (<strong>thermal_structure.jpg</strong>) of the fumarole field and additional geochemical- and mineralogical investigations (XRD and XRF) of rock samples and by measuring the diffuse degassing from surface (see "Anatomy of a fumarole field..." DOI: 10.5194/egusphere-2023-1692) to prove that the observed degassing/alteration units are true.</p> <p>To highlight alteration effects throughout the entire La Fossa cone, including the southern inner and outer crater rim, the alteration zones of La Forgia, or alteration on the outer flanks of La Fossa e.g. the 1988 Landslide, we provide the raster <strong>La_Fossa_alteration.tif </strong>and image <strong>La_Fossa_alteration.jpg (</strong>Note that the color scale for strong alteration (classes 31 - 32) was changed from white to purple for highlighting purpose).</p> <p> </p> <p>In case of further questions about the dataset, please contact dmueller@gfz-potsdam.de.</p> <p> </p> <p> </p> <p> </p> <p> </p>
Data supporting publication: Nanoscale Mechanical Manipulation of Ultrathin SiN Membranes Enabling Infrared Near-Field Microscopy of Liquid-Immersed samples
<p>This repository includes the data corresponding to the figures shown in the journal article entitled Nanoscale Mechanical Manipulation of Ultrathin SiN Membranes Enabling Infrared Near-Field Microscopy of Liquid-Immersed samples, published in small. </p>
Infrared Video of Bone Drilling - Supplementary material for article "Thermal Evaluation of Bone Drilling: Assessing Drill Bits and Sequential Drilling"
<p>Video 1 shows sequential bone drilling with 5 drill bits (⌀2.0 mm, ⌀2.5 mm, ⌀3.2 mm, ⌀3.7 mm, and ⌀4.1 mm) used in series following the manufacturer's recommended spindle speeds.</p> <p>Video 2 shows bone drilling with a single drill bit (⌀2.0 mm) with a spindle speed of 1500 rpm.</p> <p>These videos are supplementary to the article, "Thermal Evaluation of Bone Drilling: Assessing Drill Bits and Sequential Drilling" published in the journal <em>Bioengineering. </em></p>
Supplementary data for "Are elevated moist layers a blind spot for hyperspectral infrared sounders? - A model study"
<p>This is the base data for the retrieval of water vapor, temperature and surface temperature based on forward simulated IASI measurements in the spectral bands between 1190-1400 and 645-800 cm-1, as well as 5 channels in the atmospheric window between 901.5 and 1115.75 cm-1.</p> <p>The data includes 1599 atmospheric states over tropical ocean regions, which is a subset of the ECMWF IFS diverse profile database with focus on a broad sampling of humidity states, published by Eresmaa et al. (2014). The full dataset is also available as part of the ARTS (Atmospheric Radiative Transfer Simulator) XML database (https://radiativetransfer.org/tools/). The data also includes the forward modelled spectra in units of brightness temperatures and the associated spectral frequency grid. ARTS is used as the forward model (https://radiativetransfer.org).</p> <p>This dataset is supplementary to the article "Are elevated moist layers a blind spot for hyperspectral infrared sounders? - A model study" that has been submitted to Atmospheric Measurement Techniques (AMT).</p>
NOAA PSL thermodynamic profiles retrieved from ASSIST infrared radiances with the optimal estimation physical retrieval TROPoe during SPLASH
<p>This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe (TROPoe, Turner and Löhnert 2014; Turner and Blumberg 2019; Turner and Löhnert 2021). The profiles are retrieved every 10 min from instantaneous radiances observed with an Atmospheric Sounder Spectrometer by Infrared Spectral Technology (ASSIST, Rochette et al. 2009).</p> <p>The ASSIST was deployed at Roaring Judy in the East River Watershed in Colorado (38.7169321 N, 106.853031 W, 2494 m above mean sea level) from 21 October 2021 to 28 January 2022 as part of the National Oceanic and Atmospheric Administration (NOAA) Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH) campaign. </p> <p>The spectral bands used in the retrieval are in the wavenumber range from 612 - 905.4 cm<sup>-1</sup> and are specified in Turner and Löhnert (2021). Additional input data in TROPoe are cloud base height from a collocated ceilometer, temperature, water vapor mixing ratio, and pressure from colocated near-surface measurements and from hourly analysis profiles from the operational Rapid Refresh (RAP, Benjamin et al. 2021) weather prediction model at the closest grid point. The latter are used only outside the atmospheric boundary layer (ABL) above 4 km above ground level (AGL) and provide information in the middle and upper troposphere where little to no information content is available from the infrared radiances.</p> <p>In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) which provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see e.g. Djalalova et al. 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. For this study, we computed the prior from operational radiosondes launched near Denver, CO, and re-centered the mean profiles of water vapor and temperature to account for the elevation difference between the East River Valley and the launch site near Denver to get a more representative prior.</p> <p>The file format is netcdf and the file naming conventions are</p> <p>NOAA_PSL_ASSIST_RoaringJudy_yyyymmdd.cdf</p> <p>with</p> <p>yyyy: Year</p> <p>mm: Month</p> <p>dd: Day</p> <p> </p> <p>The time stamp of all data is in UTC.</p> <p>Selected basic variables are (many more provided):</p> <p> </p> <table> <tbody> <tr> <td> <p>Name</p> </td> <td> <p>Dimension</p> </td> <td> <p>Unit</p> </td> </tr> <tr> <td> <p>base_time</p> </td> <td> <p>Single value</p> </td> <td> <p>Seconds (since 00 UTC 1 Jan 1970)</p> </td> </tr> <tr> <td> <p>time_offset</p> </td> <td> <p>Time</p> </td> <td> <p>Second (since base_time)</p> </td> </tr> <tr> <td> <p>hour</p> </td> <td> <p>Time</p> </td> <td> <p>Hours since 00UTC this day</p> </td> </tr> <tr> <td> <p>height</p> </td> <td> <p>Height</p> </td> <td> <p>km AGL</p> </td> </tr> <tr> <td> <p><strong>temperature </strong></p> </td> <td> <p>Time, Height</p> </td> <td> <p>C, temperature</p> </td> </tr> <tr> <td> <p><strong>waterVapor </strong></p> </td> <td> <p>Time, Height</p> </td> <td> <p>g/kg, water vapor mixing ratio</p> </td> </tr> <tr> <td> <p>theta</p> </td> <td> <p>Time, Height</p> </td> <td> <p>K, potential temperature</p> </td> </tr> <tr> <td> <p>pressure</p> </td> <td> <p>Time, Height</p> </td> <td> <p>hPa, pressure</p> </td> </tr> <tr> <td> <p>rh</p> </td> <td> <p>Time, Height</p> </td> <td> <p>%, relative humidity</p> </td> </tr> <tr> <td> <p>dewpt</p> </td> <td> <p>Time, Height</p> </td> <td> <p>C, dew point temperature</p> </td> </tr> <tr> <td> <p>thetae</p> </td> <td> <p>Time, Height</p> </td> <td> <p>K, equivalent potential temperature</p> </td> </tr> <tr> <td> <p>sigma_temperature</p> </td> <td> <p>Time, Height</p> </td> <td> <p>C, 1-sigma uncertainty temperature</p> </td> </tr> <tr> <td> <p>sigma_waterVapor</p> </td> <td> <p>Time, Height</p> </td> <td> <p>g/kg, 1-sigma uncertainty water vapor</p> </td> </tr> <tr> <td> <p>cdfs_temperature</p> </td> <td> <p>Time, Height</p> </td> <td> <p>cumulative degrees of freedom for temperature</p> </td> </tr> <tr> <td> <p>cdfs_waterVapor</p> </td> <td> <p>Time, Height</p> </td> <td> <p>cumulative degrees of freedom for water vapor</p> </td> </tr> </tbody> </table> <p>Bold variables are the main retrieved profiles, from which the other variables are derived.</p> <p>Note that the vertical resolution of the retrieved profiles decreases with height, because of the broadening of the weighting function as a function of height. Thus, there are relatively few independent pieces of information in the profiles, this is reflected in the cumulative degree of freedom variables. The majority of the information from the ASSIST is in the lowest 2-3 km, above that most information comes from the RAP model.</p> <p>Because of strong emission in the infrared from clouds, clouds strongly impact the ability to retrieve profiles from the ASSIST and care should be taken when analyzing the retrievals in the presence of clouds. </p> <p><strong>References: </strong></p> <p>Rochette, L., W. L. Smith, M. Howard, and T. Bratcher, 2009: ASSIST, atmospheric sounder spectrometer for infrared spectral technology: Latest development and improvement in the atmospheric sounding technology. Imaging spectrometry XIV, Vol. 7457 of, SPIE, 9–17.</p> <p>Turner, D. D., and U. Löhnert, 2014: Information content and uncertainties in thermodynamic profiles and liquid cloud properties retrieved from the ground-based atmospheric emitted radiance interferometer (AERI). J. Appl. Meteor. Climatol., 53, 752–771, https://doi.org/10.1175/JAMC-D-13-0126.1.</p> <p>Turner, D. D., and W. G. Blumberg, 2019: Improvements to the AERIoe thermodynamic profile retrieval algorithm. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12, 1339–1354, https://doi.org/10.1109/JSTARS.2018.2874968.</p> <p>Turner, D. D., and U. Löhnert, 2021: Ground-based temperature and humidity profiling: Combining active and passive remote sensors. Atmos. Meas. Tech., 14, 3033–3048, https://doi.org/10.5194/amt-14-3033-2021.</p>
Spectral data associated to the publication: "Near-infrared reflectance spectroscopy of sublimating salty ice analogues. Implications for icy moons" by R. Cerubini et al. (Planetary and Space Science 211, 2022)
<p>This is the complete set of experimental NIR reflectance data collected by R. Cerubini and co-authors for the article "Near-infrared reflectance spectroscopy of sublimating salty ice analogues. Implications for icy moons" published in Planetary and Space Science 211 (2022). doi: https://doi.org/10.1016/j.pss.2021.105391.</p> <p>The article itself is published in open-access and provides the methodology for the spectral aquisitions, discussion of the errors and uncertainties, analysis of the spectra and implications for the composition of Solar System surfaces.</p> <p>The data are contained in ASCII files (columns separated by comma). The first column is the wavelength (in micrometers) and the other columns contain the reflectance data (in unit of reflectance factor). The different compositions are indicated in the filenames and correspond directly to the figures in the published paper.</p> <p> </p> <p> </p>
Data for: Two-dimensional infrared-Raman spectroscopy as a probe of water's tetrahedrality
<p>Data for publication: T. Begusic and G. A. Blake, Two-dimensional infrared-Raman spectroscopy as a probe of water’s tetrahedrality (2022).</p> <p>Contains raw data, processed data, and processing and plotting scripts for the results presented in the manuscript. See README files enclosed in the dataset for details about the files and directories. Main results were produced with codes available at https://github.com/tbegusic/i-pi and https://github.com/tbegusic/encorr.</p>
Furious transfer infrared spectrum of Ranolazine bulk drug, soluble in ethanol, isopropanol under vacuum, at different temperature 0, 40, 70 degree Celsius
<p>Ranolazine bulk drug soluble in ethanol observed additional group and in isopropanol also furior transferred infrared spectroscopy of ranolazine bulk drug </p>
First time-resolved measurement of infrared scintillation light in gaseous xenon
<p>Repository with supplemental data to:<br> <strong>First time-resolved measurement of infrared scintillation light in gaseous xenon</strong>. Piotter, M., Cichon, D., <em>Hammann, R.</em>, Jörg, F., Hötzsch, L.,<em> Marrodán Undagoitia, T. Eur. Phys. J. C</em> <strong>83</strong>, 482 (2023).<br> A pre-print of the article is available <em>on arXiv: </em><a href="https://arxiv.org/abs/2303.09344">2303.09344</a></p> <p><strong>Note: </strong>When re-using the data, please make sure to cite the article (and not only the dataset)</p> <p> </p> <p>The files contain all data related to the observed IR scintillation in gaseous xenon presented in the paper. This comprises the IR time profiles obtained via single photon counting and the measured pressure dependence of the IR light yield for the three extrapolation methods:</p> <ul> <li><strong>waveform_before.csv, waveform_during.csv, waveform_after.csv</strong>: These files contain the IR time profiles before, during, and after the purification of the gas (presented in figure 8 in the publication). The column <em>dt</em> is given in nanoseconds relative to the UV pulse and <em>counts </em>corresponds to counts per nanosecond per 100 UV events.</li> <li><strong>light_yield_ir.csv: </strong>This file contains the IR light yield as a function of pressure obtained with the three extrapolation models together with the respective statistical and systematic uncertainties. The data is presented in figure 9 in the publication and all values are given in units of photons per MeV.</li> <li><strong>waveform_495.csv, waveform_742.csv, waveform_1047.csv:</strong> These files contain the IR time profiles for xenon gas pressures of 495.0 mbar, 742.5 mbar, and 1047.0 mbar, respectively (presented in figure 10 in the publication). The column <em>dt</em> is given in nanoseconds relative to the UV pulse and <em>counts </em>corresponds to counts per nanosecond per 100 UV events.</li> </ul> <p> </p> <p><strong>Code examples for plotting the data:</strong></p> <p>The following Python code reproduces figure 9 in the publication:</p> <pre><code class="language-python">import pandas as pd import matplotlib.pyplot as plt if __name__ == '__main__': df = pd.read_csv("light_yield_ir.csv") color_pairs = [("#365898", "#B7D0FF"), ("#AB123B", "#F0B5C5"), ("#E1992E", "#F1DAB9")] fig, ax = plt.subplots(1, figsize=(4, 3)) for fit_func_str, cs in zip(["Recombination model fit", "Exponential fit", "Linear fit"], color_pairs): # Plot systematic error ax.errorbar(df["Pressure"], df[f"IR Light Yield ({fit_func_str} fit)"], yerr=df[f"Syst. uncertainty ({fit_func_str} fit)"], ls="", elinewidth=3, capsize=0, ecolor=cs[1] ) # Plot estimator with statistical error ax.errorbar(df["Pressure"], df[f"IR Light Yield ({fit_func_str} fit)"], yerr=df[f"Stat. uncertainty ({fit_func_str} fit)"], ls="", c=cs[0], ecolor=cs[0], elinewidth=1, capsize=1, marker=".", label=fit_func_str) # Cosmetics ax.set_xlabel("Pressure [mbar]") ax.set_ylabel("IR light yield [ph / MeV]") ax.set_ylim(1200, 12_500) ax.legend(frameon=False, loc="upper left") plt.show()</code></pre> <p> </p> <p>The IR time response of figure 8 can be redrawn as follows:</p> <pre><code class="language-python">import pandas as pd import matplotlib.pyplot as plt if __name__ == '__main__': fig, ax = plt.subplots(1, figsize=(4, 3)) for label in ["before", "during", "after"]: df = pd.read_csv(f"waveform_{label}.csv") ax.step(df["dt"], df["counts"], label=label) # Cosmetics ax.set_xlabel("$\Delta t$ between IR and UV signal [ns]") ax.set_ylabel("Counts per 1 ns per 100 UV events") ax.legend(frameon=False, loc="upper right") plt.show()</code></pre> <p> </p>
Dataset for "Blue-shift photoconversion of near-infrared fluorescent proteins for labeling and tracking in living cells and organisms"
<p>Dataset that supports the observation, characterization and application of the blue-shift photoconversion of the near infrared proteins, miRFPs, reported in the manuscript: "Blue-shift photoconversion of near-infrared fluorescent proteins for labeling and tracking in living cells and organisms". The data references to the specific figures and graphs in the manuscript.</p>
DATASET: Near-Infrared Photothermal Ablation of Biofilms using Protein-Functionalized Gold Nanospheres with a Tunable Temperature Response
<p>This dataset contains the DLS, TEM, temperature data, and other experimental data to accompany the manuscript.</p>
RIFIR – A Far Infrared Dataset
<p>This dataset consists of sequences acquired in an urban environment with two cameras (one Far Infrared and two color cameras in stereovision) mounted on the exterior of a vehicle.</p> <p>Training dataset: 14788 frames (containing aprox. 19000 pedestrain bounding boxes in visible spectrum and aprox. 14000 in infrared spectrum) of 138 unique pedestrians</p> <p>Testing dataset: 9373 frames ( containing aprox. 7000 pedestrain bounding boxes in visible spectrum and aprox. 6000 in infrared spectrum) of 33 unique pedestrians</p> <p>You can cite this dataset by:</p> <p>Miron, Alina Dana. "Multi-modal, Multi-Domain Pedestrian Detection and Classification: Proposals and Explorations in Visible over StereoVision, FIR and SWIR." PhD diss., 2014.</p>
The near‐infrared autofluorescence fingerprint of the brain
<p>The brain is a vital organ involved in most of the central nervous system disorders. Their diagnosis and treatment require fast, cost‐effective, high‐resolution and high‐sensitivity imaging. The combination of a new generation of luminescent nanoparticles and imaging systems working in the second biological window (near‐infrared II [NIR‐II]) is emerging as a reliable alternative. For NIR‐II imaging to become a robust technique at the preclinical level, full knowledge of the NIR‐II brain autofluorescence, responsible for the loss of image resolution and contrast, is required. This work demonstrates that the brain shows a peculiar infrared autofluorescence spectrum that can be correlated with specific molecular components. The existence of particular structures within the brain with well‐defined NIR autofluorescence fingerprints is also evidenced, opening the door to in vivo anatomical imaging. Finally, we propose a rational selection of NIR luminescent probes suitable for low‐noise brain imaging based on their spectral overlap with brain autofluorescence.</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.