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42 results for “spectral analysis”
Nonlinear spectral analysis of ion acoustic solitons arising from a streaming charged object using the numerical inverse scattering transform data
<p>Data files used in the publication: "Nonlinear spectral analysis of ion acoustic solitons arising from a streaming charged object using the numerical inverse scattering transform", submitted to Physics of Plasma August 2022. To be used in conjunction with analysis software KVIST.</p> <p>KVIST can be found at:</p> <ul> <li>https://doi.org/10.5281/zenodo.7017043</li> <li>https://github.com/Planetary-Surfaces-and-Spacecraft-Lab/KVIST</li> </ul> <p>Data files generated with:</p> <p>Truitt, A. (2020). Simulation of Forced Korteweg De Vries Equation as Applied to Small Orbital Debris. Digital Repository at the University of Maryland. https://doi.org/10.13016/FOR0-XJYD</p>
Development of a spectral library for the discovery of altered genomic events in Mycobacterium avium associated with virulence using mass spectrometry-based proteogenomic analysis
<p><em>Mycobacterium avium</em> is one of the prominent disease-causing bacteria in humans. It causes lymphadenitis, chronic and extrapulmonary, and disseminated infections in adults, children, and immunocompromised patients. <em>M. avium</em> has ~4,500 predicted protein-coding regions on an average, which can be helpful in discovering several variants at the proteome level. Many of them are potentially associated with virulence, thus identifying such proteins can be a helpful feature in the development of panel-based theranostics. In line with such a long-term goal, we carried out an in-depth proteomic analysis of <em>M. avium</em> with both data-dependent and data-independent acquisition methods. Further, a set of proteogenomic investigations were carried out using the protein database for <em>Mycobacterium tuberculosis,</em> and a genome six-frame translated database and a variant protein database of <em>M. avium</em>. A search of mass spectrometry data analysis against <em>M. avium</em> protein database resulted in the identification of 2,954 proteins. Further, proteogenomic analyses aided in the identification of 1,301 novel peptide sequences and correction of translation start sites for 15 proteins. At the end, we created a spectral library of <em>M. avium</em> proteins including novel genome search-specific peptides and variant peptides detected in this study. We validated the spectral library by a data-independent acquisition of the <em>M. avium</em> proteome. Thus, we present a <em>M. avium </em>spectral library of 29,033 peptide precursors supported by 0.4 million fragment ions for further use by the biomedical community.</p>
Spectral Cluster Supertree: Analysis Data
<p>Contains all datasets used in the Spectral Cluster Supertree paper. The datasets are composed of a set of rooted model trees, and rooted source trees to predict them. Please cite the appropriate papers, depending on which of the datasets you use.</p> <p>The <code>birth_death</code> folder contains our own dataset generated for our paper (where the generation process is explained), it aims to mimic what may be seen through divide and conquer algorithms for phylogenetic reconstruction. Parameters used to simulate an alignment were simulated under parameters estimated from a sequence alignment of 3 bacterial species (Kaehler et al., 2015) - see <code>alignment</code> folder.</p> <p>The <code>SMIDGenOutgrouped</code> folder contains both the SMIDGenOG (Fleischauer and Böcker, 2016) and SMIDGenOG-5500 dataset (Fleischauer and Böcker, 2017).</p> <p>The <code>SuperTriplets</code> folder contains the SuperTriplets dataset (Ranwez et al, 2010).</p> <p> </p>
Spectral evolution of hot hybrid white dwarfs I. Spectral analysis
<p>Hydrogen-rich white dwarfs (WDs) comprise the majority of the WD population, but are only rarely found at the very hot end of the WD cooling sequence. A small subgroup that exhibits both hydrogen and helium lines in their spectra, the so-called hybrid (or DAO) WDs, represents the majority of hydrogen-rich WDs at effective temperatures <em>T</em>eff<em> ≈ </em>100 kK. We aim to understand the spectral evolution of hot hybrid WDs. Although small in number, they represent an evolutionary phase for most (≈ 75 %) WDs. We conducted a nonlocal thermodynamic equilibrium (NLTE) analysis with fully metal line blanketed model atmospheres for the ultraviolet (UV) and optical spectra of a sample of 19 DA and 13 DAO WDs with <em>Teff </em>> 60 kK. The UV spectra allow us to precisely measure the temperature through model fits to metal lines in different ionization stages, which enables us to place the WDs accurately on the cooling sequence. Here we present model fits to the UV and optical spectra in our sample. Aditionally, the <em>T</em>eff, log <em>g</em>, and abundance values of our sample objectss are compared to previous studies. </p>
Derived data supporting the analysis of surface albedo changes from Mars 2020 observations: Probabilistic distribution of the Amplitude Spectral Densities of Supercam microphone recordings and Monte-Carlo dust devil simulations.
<p>These files contain derived data used in the analysis submitted for publication in Journal of Geophysical Research: Planets, entitled "Dust Lifting Through Surface Albedo Changes at Jezero Crater, Mars" by Vicente-Retortillo et al. The article was initially submitted on November 14, 2022, and the revised version on March 1, 2023.</p> <p>Files include the derived data and information needed to generate Figures 4 (Microphone_Data.mat and Plot_ASD_from_Microphone_Data) and 6 (remaining files) of the article.</p>
Data set for spectral and FT-ICR-MS analysis of dissolved organic matter in sediments of the New Britain Trench axis station
<p> </p> <p>数据集包括原始数据和简单处理所需的数据、图表和文章。</p> <p> </p> <p>表格包含样品数量、收集深度、样品名称以及相应光谱和 FT-ICR-MS 数据的名称。</p> <p> </p> <p>例如,光谱数据包括荧光相对强度、荧光指数,而 FT-ICR-MS 光谱数据包括化合物类型相对强度以及化学式、元素比、等效双键数 (DBE) 和每个样品的芳香指数 (AImod)。</p> <p> </p>
Deep learning for enhanced spectral analysis of MA-XRF datasets of Paintings
<h1>Synthetic and Experimental XRF datasets</h1> <h3>Synthetic datasets</h3> <p>Files starting with RH231 are synthetic datasets. The synthetic spectra are stored as 'data' and the XMI counts as 'labels'. The corresponding elemental lines names are stored in metadata: 'labels'.</p> <h3><strong>Experimental datasets</strong></h3> <p>4 datasets of the entire painting of the God The Father by Raphael: EternoA, EternoB, EternoC, EternoD</p> <p>2 datasets of the detail of painting of the God The Father by Raphael: EternoVolotA, EternoVoltoB</p> <p>1 dataset of the painting of the Virgin Mary</p> <p>1 dataset if the architectural detail of the painting of the Virgin Mary</p> <p> </p> <p>The experimental data are obtained within MOLAB network.</p>
Active and low-cost hyperspectral imaging for spectral analysis in low lighting environment
<p>Hyperspectral imaging can capture information beyond conventional RGB cameras; thus, it has many applications, such as material identification and spectral analysis. However, like many camera systems, most of the existing hyperspectral cameras are still passive imaging systems: they require external light sources to illuminate the objects to capture the spectral intensity. As a result, the collected images highly depend on the environment lighting, and the imaging system cannot function in a dark or low-lighting environment. This work develops a prototype system for active hyperspectral imaging, which actively emits different single-wavelength lights at different frequencies when imaging. This concept has several advantages: first, using the controlled lighting, the magnitude of the individual bands is normalized to extract reflectance information; second, the system is capable of collecting information at the desired spectral range by tailoring the light sources; third, an active system is mechanically easier to make, since it does not require complex band filters as used in passive systems; last, such a system may work under low light or dark environments, which greatly facilitate underground/subsurface sensing applications such as borehole based mining exploration. This prototype is achieved by using an array of low-cost and single-wavelength LED (Light Emitting Diode) lights, a remote control module controlling the LED illuminator, and the shutter of a full spectrum camera. We demonstrate that such design is feasible and could yield informative hyperspectral images for spectral analysis and machine learning-based object identification in low light or dark environments, having great potential to benefit both the academic and industry such as in geochemistry, earth science, subsurface energy, and mining.</p>
Snow cover from spectral mixture analysis algorithm SCAG: OLI and MODIS
<p>This data is snow cover fraction from the Snow Covered Area and Grain Size (SCAG) model for Landsat OLI and Terra MODIS. Terra MODIS data are gap filled to better represent on the ground snow. The data was used in the a publication for The Cyrosphere titled Landsat, MODIS, and VIIRS snow cover mapping algorithm performance as validated by airborne lidar datasets, doi.org/10.5194/tc-2022-159. Geotiffs and PNG files for Landsat 8 are self describing. The .mat files for Terra MODIS contain three variables:</p> <p>snow_fraction: the gap filled snow fraction stored as uint8 with 255 as the NoData value and valid values between and including 0 to 100.</p> <p>mstruct: projection structure describing the standard MODIS tile projection structure. The data represent data from tile h08v05 and h09v05</p> <p>RefMatrix: affine spatial referencing matrix for the snow_fraction grid with the projection described by mstruct</p>
ALIMA range-corrected photon count profiles and spectral analysis output
<p>The dataset includes a .nc file of range-corrected and background-corrected photon counts measured by ALIMA (Airborne Lidar for Middle Atmosphere research) from six research flights (ST08, ST09, ST10, ST11, ST12, ST14) during the SouthTRAC-GW campaign in September 2019. These corrected photons counts are proportional to the atmospheric density. The data resolution (t, z) is (10s, 100m) but not yet vertically smoothed nor temporally integrated. A vertical smoothing of 900 m and a temporal integration of 1 min is recommended to obtain a sufficient signal-to-noise-ratio.</p> <p>Furthermore, .nc files containing spectral power density (PSD) for selected legs of each research flight averaged over 10 km altitude ranges based on vertically smoothed and temporally integrated range- and background-corrected photon counts by ALIMA are included.</p>
Active and low-cost hyperspectral imaging for spectral analysis in low lighting environment
Open the record for dataset details and reuse information.
Data from: Mapping tropical dry forest succession using multiple criteria spectral mixture analysis
Tropical dry forests (TDFs) in the Americas are considered the first frontier of economic development with less than 1% of their total original coverage under protection. Accordingly, accurate estimates of their spatial extent, fragmentation, and degree of regeneration are critical in evaluating the success of current conservation policies. This study focused on a well-protected secondary TDF in Santa Rosa National Park (SRNP) Environmental Monitoring Super Site, Guanacaste, Costa Rica. We used spectral signature analysis of TDF ecosystem succession (early, intermediate, and late successional stages), and its intrinsic variability, to propose a new multiple criteria spectral mixture analysis (MCSMA) method on the shortwave infrared (SWIR) of HyMap image. Unlike most existing iterative mixture analysis (IMA) techniques, MCSMA tries to extract and make use of representative endmembers with spectral and spatial information. MCSMA then considers three criteria that influence the comparative importance of different endmember combinations (endmember models): root mean square error (RMSE); spatial distance (SD); and fraction consistency (FC), to create an evaluation framework to select a best-fit model. The spectral analysis demonstrated that TDFs have a high spectral variability as a result of biomass variability. By adopting two search strategies, the unmixing results showed that our new MCSMA approach had a better performance in root mean square error (early: 0.160/0.159; intermediate: 0.322/0.321; and late: 0.239/0.235); mean absolute error (early: 0.132/0.128; intermediate: 0.254/0.251; and late: 0.191/0.188); and systematic error (early: 0.045/0.055; intermediate: −0.211/−0.214; and late: 0.161/0.160), compared to the multiple endmember spectral mixture analysis (MESMA). This study highlights the importance of SWIR in differentiating successional stages in TDFs. The proposed MCSMA provides a more flexible and generalized means for the best-fit model determination than common IMA methods.
Supporting data for "The benefit of in silico predicted spectral libraries in data-independent acquisition data analysis workflows"
Open the record for dataset details and reuse information.
Sequential LIBS Spectral Analysis of Artemisia absinthium: Dataset Series from Eight Repeated Measurements
<p>Sequential LIBS Spectral Analysis of Artemisia absinthium: Dataset Series from Eight Repeated Measurements, Raw and Normalized Data</p>
Results of long-slit spectral analysis of UGC1378
<p>The results of the analysis of long-slit spectral data of UGC1378 for emission and absorption lines. The description of the data containing in the columns is given in each table.</p>
Online Repository for One-dimensional Power Spectral Analysis of Fresh Lunar Impact Craters
<p>This dataset includes:</p> <p>(1) Shape files of the rim crest, floor, and rim flank outlines of our selected fresh lunar craters.</p> <p>(2) Power spectral densities of the rim crest, floor, and rim flank outlines of our selected fresh lunar craters.</p> <p>(3) Python scripts to generate synthetic fresh lunar craters.</p> <p>(4) A spreadsheet that contains the morphometric parameters and the breakpoints (wavelengths and powers).</p>
Random Forest fused MODIS and Landsat snow cover from spectral mixture analysis in the Sierra Nevada, USA
<p>This data is snow cover fraction from the Snow Covered Area and Grain Size (SCAG) model for Landsat OLI and Terra MODIS and well as a 2-stage random forest model to fuse the 2 datasets for improved temporal/spatial resolution. There are 170 scenes in 2001 to 2012. It was used in the a publication for Remote Sensing of the Environment titled: Multi-sensor fusion using random forests for daily fractional snow cover at 30 m, doi: to be assigned.</p> <p><strong>Inputs</strong>: [YYYYMMDD is year month day of month, $num is 5 or 7 for Landsat platform, $sens is sensor TM or ETM+]</p> <p>Landsat.zip:</p> <p>Snow cover from Landsat: SSN.p042r034_YYYYMMDD.Landsat$num-$sens.canopyadjusted_mask.v01.tif </p> <p> </p> <p>MODIS.zip:</p> <p>Snow cover from MODIS: SSN.SN_W$YYYYMMDD_$YYYYMMDD.Terra-MODIS.snow_cover_percent.v01.tif</p> <p> </p> <p>Predictors.zip<strong> </strong></p> <p>Static predictors (see RSE publication Table 2): SouthernSierraNevada*.tif [* here is the variable name]</p> <p><strong>Outputs [</strong> [YYYYMMDD is year month day of month]</p> <p>ProbabilityNot0Not100.zip</p> <p>SSN.prob.btwn.YYYYMMDD.v3.tif - from classification random forest, probability of being between 0 and 100</p> <p> </p> <p>Probability100fSCA</p> <p>SSN.pro.hundred.YYYYMMDD.v3.tif - from classification random forest, probability of being 100</p> <p> </p> <p>RegressionResult.zip</p> <p>SSN.regression.YYYYMMDD.v3.tif - from prediction random forest</p> <p> </p> <p>Final_Downscaled.zip</p> <p>SSN.downscaled.YYYYMMDD.v3.3e+05.tif - final product (combination of classification and prediction)</p>
Effect of Pantoprazole 40mg Daily vs Placebo on Power Spectral Analysis of the Sleep EEG of Patients With GERD.
ClinicalTrials.gov study NCT00674245. IPD Sharing: Not stated. Countries: 1. Publications: 6.
Spectral Analysis of Central Venous Pressure Waveform
ClinicalTrials.gov study NCT04733547. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Prognostic Value of Ventricular Fibrillation Spectral Analysis in Sudden Cardiac Death
ClinicalTrials.gov study NCT03248557. IPD Sharing: NO. Countries: 1. Publications: 3.
ScienceDex guides
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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.