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569 results for “spectrometer”
Spectral reflectance data of Mercury's surface collected by the Mercury Atmospheric and Surface Composition Spectrometer (MASCS) instrument during orbital observations of the NASA MESSENGER mission between 2011 and 2015 resampled to a [55399 × 396] tabular data format.
<p>MASCS is a three sensor point spectrometer with a spectral coverage from 200 nm to 1450 nm.<br> Single spectra are resamples in to steps to a format useful for our ML application : a datacube with ~400 spectral channel covering the whole surface of Mercury.<br> The final dataset has dimension [N×M] where N is the number of grid cells (360 × 180 = 64, 800) and M is the number of spectral features (396).<br> Due to the incomplete coverage and data filtering, some grid cells are empty.<br> After removing these empty cells, the size of the dataset is [55399 × 396].</p> <p>This specific product is stored as a gzip compressed json, where each element is a grid cell.<br> We are in the process to publish a complete pipeline to produce this product from RAW data on https://github.com/epn-ml/MESSENGER-Mercury-Surface-Cassification-Unsupervised_DLR/ .</p> <p>Spectral reflectance data of Mercury’s surface collected by the Mercury Atmospheric and Surface Composition Spectrometer (MASCS) instrument during orbital observations of the NASA MESSENGER mission between 2011 and 2015.<br> MASCS is a three sensor point spectrometer with a spectral coverage from 200 nm to 1450 nm.<br> Single spectra are resamples in to steps to a format useful for our ML application : a datacube with ~400 spectral channel covering the whole surface of Mercury.<br> The final dataset has dimension [N×M] where N is the number of grid cells (360 × 180 = 64, 800) and M is the number of spectral features (396).<br> Due to the incomplete coverage and data filtering, some grid cells are empty.<br> After removing these empty cells, the size of the dataset is [55399 × 396].</p> <p>0. Pre-filtering<br> We used the most recent dataset that had large-scale photometric corrections and thus was almost free from observation geometry effects.<br> However, extreme geometry are still present and are typically associated with high noise and some residual instrumental effects.<br> Based on our empirical tests, we filtered out observations with an emission/incidence angle ≥80∘.<br> We also calculated the median value per wavelength and per cell grid when constructing the global hyperspectral data cube and filtered out observations falling under the 2nd percentile and above 99.9th percentile to clean some residual geometry effects.<br> With this approach we create an effective noise filter while retaining enough observations to be able to analyse the entirety of the surface of the planet.</p> <p>1. Spectral resmpling<br> Unprocessed MASCS spectra could have 512 or 256 channes, depending on binning.<br> We resampled the data in the spectral dimension to a common wavelength range from 260 nm to 1052 nm with a 4 nm resolution (2 nm spectral sampling), resulting in 396 spectral channels.<br> This approach slightly oversamples the original 4.77 nm spectral resolution and removes some points from the original 200-1050 nm range.<br> The resulting data matrix is expressed in tabular form, with each row representing a single grid cell or pixel on the surface.<br> The elements of each row are the spectral reflectance values from the VIS instrument at 396 (resampled) wavelengths.</p> <p>2. Spatial resmpling<br> The whole dataset of ∼ 5 million spectra is resampled to a planet-wide rectangular grid of 1×1deg in the latitudinal band between ± 80.<br> The cell longitudinal size varies between ∼ 40 km at the equator to a minimum of ∼ 10 km at ±80∘.<br> Thus, the area spanned by each grid cell depends on the latitude. However, the same is true for the acquisition process, where higher spatial resolution is reached near the equator and lower resolution at the poles.</p>
AIRBORNE SPECTROMETER MEASUREMENTS FMOM BOREAL SNOW-COVERED LANDSCAPE
<p>The dataset contains 10 meter resolution reflectance data from boreal snow-covered landscape. The purpose of the airborne measurements was to investigate the effect of forest canopy on optical remote sensing signals for snow-covered surfaces. The hyperspectral airborne data was acquired with an AisaDUAL imaging spectrometer on March 18 and on March 21, 2010 in Sodankylä, Finland. The image swath was 240 meters and flight lines were several kilometers long. The original spatial resolution of the data is 80 cm x 80 cm, but it was resampled to pixel size of 10 m x 10 m. All measurements were carried out in non-cloudy conditions (0/8 to 2/8 cloud cover). On 18 March, the tree canopy was snow-free and snow on the ground was several days old, while on 21 March, the tree canopy was snow-covered and snow on ground was fresh. The data contains mosaics of the flight lines for the bands 555 nm, 645 nm, 858.5 nm and 1640 nm for both days 18 March 2010 and 21 March 2010.</p>
AIRBORNE SPECTROMETER MEASUREMENTS FROM BOREAL AND TUNDRA SITE DURING SPRING SNOW MELT
<p>The dataset contains 10 meter resolution reflectance data from boreal and tundra sites during spring snow melt. The purpose of the airborne measurements was to investigate the effect of forest canopy and snow melting on optical remote sensing signals at the very end of melting period. The hyperspectral airborne data was acquired with an AisaDUAL imaging spectrometer on 5 May 2011 in Sodankylä and in Saariselkä, Finland. Saariselkä is a fell region and partly represents open tundra. The image swath was 240 meters and flight lines were several kilometers long. The original spatial resolution of the data is 80 cm x 80 cm, but it was resampled to pixel size of 10 m x 10 m. Snow depth was between 0 cm and 30 cm at the Sodankylä site and between 0 cm and 60 cm at the Saariselkä site implying that the spring melt was clearly more advanced in Sodankylä. Additionally, more snow-free pixels were found at Sodankylä than Saariselkä. During the measurements the sky was cloudless in Sodankylä (cloud cover 0/8) and cloudy (cloud cover 7/8) in Saariselkä. The data contains mosaics of the flight lines for the bands 555 nm, 645 nm, 858.5 nm and 1640 nm for both study sites.</p>
Mars Odyssey Neutron Spectrometer Time Series of Corrected Cateogry 1 Counting Rates, 2002-2017
<p>Ubinned time series of derived neutron data from the Mars Odyssey Neutron Spectrometer (MONS) Category 1 data, from 200 - 2017.</p>
Spectrometer measurements of snow and bare ground targets and simultaneous measurements of snow conditions
<p>The dataset holds measurements of reflected sunlight spectrum from snow and different snow free targets (moss, lichen, rock surface, ground vegetation). The purpose of the measurements has been to create a dataset to better understand the effect of snow cover characteristics and different bare ground targets on reflected sunlight, and to relate this information to observed at satellite reflectances from snow covered and partially snow covered boreal forests. The instrument in the measurements was Field Spec Pro JR from Analytical Spectral Devices Inc. Additionally to the 237 bands measured between 350-2500 nm, the dataset contains the following parameters from the measurement sites: id, location, time of measurement, target type (snow, bare ground etc.), cloudiness (in octas), snow depth (cm), snow temperature at 5 cm (degree C), snow temperature half way through the snow pack (degree C), soil temperature (degree C), air temperature (degree C), average grain size (mm), grain type, snow water content (subjective), snow patchiness (%) in the surrounding area, visible impurities in snow yes/no and land cover.</p>
Sensor Response Files for the Relativistic Proton Spectrometer aboard NASA's Van Allen Probes
<p>This data set provides the NASA Van Allen Probes Relativistic Proton Spectrometer (RPS) sensor response function files. These files provide the sensor’s response to protons and electrons as a function of energy and angle of incidence.</p>
Suomi 100 satellite's HEARER radio spectrometer's measurements on Dec. 9, 2020
<p>Metadata (included also inside the file):</p> <ul> <li>Spacecraft: Suomi 100</li> <li>Name of the instruments: HEARER</li> <li>Instrument type: Radiospectrometer</li> <li>Measurement day: Dec. 9, 2020</li> <li>HEARER's data file ID: m1_1607509173</li> <li>Data: <ul> <li>the 1st column: The number of the data point (an integer between 280000 - 319999)</li> <li>the 2nd column: Observation (an integer): <ul> <li>Measurement frequency: 7.953 MHz</li> <li>Data rate: 1000 data points per a second</li> </ul> </li> </ul> </li> </ul> <p> </p> <ul> <li>The file prepared by Esa Kallio (esa.kallio@aalto.fi), Aalto University, Finland, on Sep. 8, 2022</li> </ul> <p> </p> <ul> <li>Miscellanous:</li> </ul> <p> (the number of the data point, -observation) plots can be found from the publication</p> <p> Kallio et al., Radar – CubeSat Transionospheric HF Propagation Observations: Suomi 100 - Satellite and EISCAT HF Facility, <em>Radio Science.</em></p>
The dataset for publication "Characterization of scintillating materials in use for brachytherapy fiber based dosimeters" by S. Commeti, et al., Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2022.
<p>This dataset is related to paper journal paper with DOI: <a href="http://dx.doi.org/10.1016/j.nima.2022.167083">10.1016/j.nima.2022.167083</a>.</p> <p>The dataset contains raw txt file and matlab files on the transmittance and the attenuation of Gadox and YVO specimens. </p> <p>Data files were prepared by agnieszka.gierej@vub.be</p>
Datasets for Wohlfarth et al. (2023) An advanced thermal roughness model for airless planetary bodies - Implications for global variations of lunar hydration and mineralogical mapping of Mercury with the MERTIS spectrometer
<p>This document describes the datasets and modeling results presented and discussed in our full research article.<br> <br> Wohlfarth, K., Wöhler, C., Hiesinger, H., Helbert, J. 2023, An advanced thermal roughness model for airless planetary bodies - Implications for global variations of lunar hydration and mineralogical mapping of Mercury with the MERTIS spectrometer, Astronomy and Astrophysics, 672<br> <br> <a href="https://doi.org/10.1051/0004-6361/202245343">https://doi.org/10.1051/0004-6361/202245343</a><br> <br> We provide several visualization scripts that read and display the results for convenience. Access to the original MATLAB® code for the thermal model implementation is available upon request (<a href="mailto:kay.wohlfarth@tu-dortmund.de">kay.wohlfarth@tu-dortmund.de</a>).<br> <br> More info in Dataproducts.pdf</p>
Raw Data for: Fast rate dual-comb spectrometer in the water-transparent 7.5-11.5 μm region
<p>This repository shares the data acquired and used to produce the figures in the paper "Fast rate dual-comb spectrometer in the water-transparent 7.5-11.5 μm region" published in Optics Letters (DOI: <a href="https://doi.org/10.1364/OL.515199">https://doi.org/10.1364/OL.515199</a>)</p> <p><br>The "Figure_Data" zip file contains a ".txt" file for each Figure present in the Main Paper and Supplemental Document. The raw data acquired by the FT spectrometer and uploaded here for Figure 2 are afterward smoothed and normalized properly in the Main Paper to better show the spectra. Data shown in Figure 3 are calculated from the corresponding raw data. Data used for Figure 4 are calculated from the corresponding raw blank DCS data. Data for Figure 5, 6 and S1 are retrieved from the corresponding raw Gas DCS data upon Cepstral removal of the spectral baseline and spectral fit exploiting the Hitran Database.</p> <p><br>The "Raw_Data" zip file contains the postprocessed Data of the DCS measurements and the RIN data as acquired. The Raw data of the DCS measurements as acquired are too large to be uploaded here, but may be obtained from the authors upon request.<br>"NH3_1o6kHz_IGMmean", "N2O_5o1kHz_IGMmean", "N2O_26kHz_IGMmean" contain the DCS interferogram averaged over 15s of acquisition for Gas measurements, performed with different DeltaFrep as described in the main text of the paper.<br>"Blank_1o6kHz_IGMs", "Blank_5o1kHz_IGMs", "Blank_26kHz_IGMs" contain all the phase corrected DCS interferograms acquired over 100ms, used to calculate the average SNR shown in Figure 4.<br>"RIN_DFG" contain the raw data acquired from the detector for RIN calculations, with an average voltage values of 25mV.</p> <p><br>The scripts for processing the Raw Data and retrieve the data as shown in the paper are home made.</p> <p> </p> <p> </p>
Radiation hardness of ultrabroadband spintronic terahertz emitters: En-route to a space-qualified terahertz time-domain gas spectrometer
<p>Data for the publication "Radiation hardness of ultrabroadband spintronic terahertz emitters: En-route to a space-qualified terahertz time-domain gas spectrometer" accepted for publication in Applied Physics Letters. The following datasets are provided: time-domain raw data (dry vs ambient conditions) and corresponding Fourier amplitude spectra as well as the impedance of the spintronic THz emitter vs frequency, THz transmission Fourier amplitude spectra from a spintronic THz emitter propagating through a gas cell with CO2 and N2, CO2 absorption coefficient vs frequency from THz transmission measurements through the gas cell, electro-optic THz signals and corresponding Fourier amplitude spectra for gamma and proton irradiated spintronic THz emitters as well as THz amplitudes vs irradiation dose/proton fluence.</p>
Pure Electronic Noise of an Orbitrap Mass Spectrometer (36 replicates)
<p>The provided data was produced by performing measurements in an HPLC-ESI-Orbitrap instrument setup without the ESI being connected to an eluent flow, and consequently not producing a spray cone. We provide this data to allow researchers developing algorithms for data analysis in mass spectrometry to estimate the behaviour of their tools when confronted with real, non-chemical noise. </p> <p>As a consequence of the experimental setup, chromatographic information is included in the provided files. This does not reflect any condition of the system, as the HPLC was not connected to the MS. When using a (chromatographc) peak finding algorithm, consequently no peaks should be found. This property of the data was confirmed using the qAlgorithms program, with no peaks and at most single-digit numbers of EICs being detected.</p> <p>All data is provided in profile mode, both as the .raw file and converted to .mzML using msconvert. </p>
Data to reproduce the results presented in Sehgal et al. 2022. Water Resources Research, https://doi.org/10.1029/2021WR030624 ("Inferring suspended sediment carbon content and particle size at high-frequency from the optical response of a submerged spectrometer")
<p>This repository consists data to reproduce results as presented in: "Inferring suspended sediment carbon content and particle size at high-frequency from the optical response of a submerged spectrometer", Water Resorces Research. Kindly refer to the readme.text file to navigate through the dataset.</p> <p> </p> <p> </p>
Data Archive for "Speckle Noise Reduction via Linewidth Broadening for Planetary Laser Reflectance Spectrometers"
<p>This archive contains the raw speckle images and experimental notes for the data contained in the journal article: "Speckle Noise Reduction via Linewidth Broadening for Planetary Laser Reflectance Spectrometers".</p> <p>The data available are:</p> <p>Raw speckle images for each of 5 illumination sources:</p> <ol> <li>Single-Frequence diode (files are named "IPS")</li> <li>Dual Mode pump diode (files are named "II-IV")</li> <li>Fabry Perot didoe (files are names "FP")</li> <li>Superluminescent Diode (files are named "SLD")</li> <li>Whilte light halogen source (files are named "WL")</li> </ol> <p>For each of theses source there are 5 images for each reflectance target, four speckle patterns and one background image with the laser source off. The naming convention is:</p> <p>LaserName_TargetReflectance_Target Rotation State or BKG.tiff</p> <p>For example, IPS_50_2 is the single-frequency laser using the 50% reflectance target and second rotation state of the target.</p> <p>The integration time for each image is give in the file "Experiment Paramters.csv" and was manually change to keep the maximum intensity at roughly 75% saturation.</p> <p>The spectra of the three laser sources presented in the manuscript are also included as .csv files</p>
1H NMR spectra of commercial honey from 400 and 700 MHz spectrometers and tables with data after processing and binning
<p>Datasets contain the 1H NMR original raw spectral data (Bruker format) of commercial honey from 400 MHz and 700 MHz NMR spectrometers and Tables (.xlsx) with data after processing and binning.</p> <p> </p> <p> </p>
The Far INfrarEd Spectrometer for Surface Emissivity (FINESSE) Part I: Instrument description and level 1 radiances (data set)
<p>The data set uploaded to this repository is outlined in a manuscript submitted to the journal Atmospheric Measurement Techniques.</p> <p>A. BB_effective_emissivity: Holds the data set used to characterise instrument calibration target emissivity</p> <p>B. ILS_data: Holds the data set used to model the instrument spectral lineshape</p> <p>C. Time_resolved_spectral_response: Holds the data set establishing the spectral stability of the instrument</p> <p>D. Zenith_radiances_20220323_1100UTC: Holds calibrated radiances acquired by FINESSE to demonstrate it accuracy and precision. Also included in this file is an LBLRTM simulation using coincident ERA5 profile information for the time and location of the observations</p> <p>The Far INfrarEd Spectrometer for Surface Emissivity (FINESSE). Part I: Instrument description and level 1 radiances</p> <p>Jonathan E. Murray1,2, Laura Warwick3, Helen Brindley1,2, Alan Last1, Patrick Quigley1, Andy Rochester1, Alexander. Dewar1, Daniel. Cummins1</p> <p>1 Department of Physics, Imperial College London, SW7 2BX, UK</p> <p>2 National Centre for Earth Observation, UK</p> <p>3 ESA-ESTEC, Noordwijk, Netherlands</p> <p>In the manuscript Part (I) we describe the FINESSE system configuration, outlining the FINESSE spectral characteristics, the data acquisition methodology and the calibration strategy. As part of the process, we evaluate the stability of the system, including the impact of knowledge of blackbody target emissivity and temperature. We also establish a numerical description of the instrument line shape. We demonstrate why it is important to account for these effects by assessing their impact on the overall uncertainty budget on the level 1 radiance products from FINESSE.</p>
VGAM: Compact and Low Power Mass Spectrometer-based Instrumentation for Volcanic Gas Monitoring
<p>Compact mass spectrometers can provide simultaneous multi-species analysis with high sensitivity and precision.<br> For volcanic gas monitoring in situ, instrumental mass spectrometry requires reliability and ruggedness combined<br> with low power usage and portability. The Volcanic Gas Analytical Monitor (VGAM) is capable of quantitative<br> molecular analysis of a variety of atmospheric and volcanic gases in a single sensor by ion trap mass<br> spectrometry. These gases include: H 2 , He, H 2 O, N 2 , O 2 , Ar, NO, N 2 O, CO, CO 2 , H 2 S, SO, SO 2 , and CH 4 . Unlike<br> previous field instruments using magnetic sector and quadrupole mass spectrometers (MS) with vacuums backed<br> by compact turbomolecular pumps, the VGAM uses a low-power autoresonant ion trap MS and NEG-Ion vacuum<br> that operate at only 25 W total power, which is often the power requirement for just the mass spectrometer. Ratio-<br> metric mass spectral response is combined with total pressure measurements to report absolute partial<br> pressures. Data are generated in real time and are recorded to internal flash memory. Relatively low power (&lt;1<br> Watt) data telemetry to remote sites is possible. Analysis of volcanic plumes, fumaroles, and solfatara fields is<br> accomplished by direct inlet of gases, after trapping as much excess water vapor in both the external and internal<br> foreline as possible. We report herein on the VGAM auto-run and post-processing procedures, initial calibration<br> results for CO 2 , and the results of a brief field deployment at Sulphur Banks solfatara field, Kilauea Volcano,<br> Hawaii.</p>
Figure 2. - A in High statistics measurement of the positron fraction in primary cosmic rays of 0.5-500 GeV with the alpha magnetic spectrometer on the international space station
Figure 2. - A: Sea lamprey (Petromyzon marinus); B: shad (either Alosa fallax or A. algeriensis). Scale bars = 10 cm. Photographs: M. JácomeFlores and B. Adrados.
Figure 1 in High statistics measurement of the positron fraction in primary cosmic rays of 0.5-500 GeV with the alpha magnetic spectrometer on the international space station
Figure 1. - Map showing previously known records of the sea lamprey (Petromyzon marinus) in north-western Africa (grey squares) and the record from the mouth of Oued Moulouya (black square). References for previous records: 1: Furnestin et al. (1958); 2: Boutellier (1918), adjacent records; 3: Dollfus (1955); 4: Bacha and Amara (2007); 5: records compiled by Renaud (2011).
Acid fractionation during carbonate digestion with phosphoric acid – Assessment of two different techniques applied for clumped and stable isotope analysis using a Tuneable Infrared Laser Differential Absorption Spectrometer (TILDAS) [dataset]
<p>Date files : </p> <table> <tbody> <tr> <td> <p>Data 1 (Relationship between Mixing ratio difference of sample and WRG and D638 value).xlsx;Data 2 (Role of bulk isotope composition (δ628 & δ636) on Δ638).xlsx;Data 3 (CDES scale conversion).xlsx;Data 4 (VPDB scale conversion for d628 and d636).xlsx;Data 5 (Reference material values).xlsx;Data 6 (Break seal method acid fractionation factor of calcite).xlsx;Data 7 (IAB method acid fractionation factor of calcite).xlsx</p> </td> </tr> </tbody> </table>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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