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1,024 results for “Spectra”
Zooplankton and macroinvertebrate size spectra, biomass, and community composition; and harvest of bigmouth buffalo and common carp in six shallow lakes in Iowa, USA (2018-2020)
This data product contains biological data collected within six shallow lakes in Iowa, USA between 2018 - 2020, where four lakes were undergoing targeted removals of common carp (Cyprinus carpio) and bigmouth buffalo (Ictiobus cyprinellus). Parts of these data were a portion of Albright et al. 2022 (https://doi.org/10.6073/pasta/1d3797fd573208bae6f78963479445a0), however the data herein include additional survey data from the Ambient Lake Monitoring network instituted through Iowa State University and the Iowa Department of Natural Resources (https://www.iowadnr.gov/environmental-protection/water-quality/water-monitoring/ambient-lake-monitoring#ambient-lake-monitoring-sampling-plan). Data are packaged and formatted specifically for size spectra analysis and compositional analysis.
ClostriTof microflex Biotyper library plugin and associated raw Maldi spectra version 2.0
<p>This dataset contains the ClostriTof microflex Biotyper library plugin, an installation guide as well as the raw spectral data for all library and validation strains used to construct the ClostriTof library plugin.</p> <p>If you use this library for your research, please cite Asare et al., Frontiers in Microbiology, 2023; <a href="https://doi.org/10.3389/fmicb.2023.1104707">https://doi.org/10.3389/fmicb.2023.1104707</a></p> <p>We would like to thank Thomas Maier for his help with assembling version 2.0 of the ClostriTOF Database.</p>
XPS spectra for Li intercalated few-layer MoS2 films
<h3>Description</h3> <p>Dataset for synchrotron-based x-ray photoelectron (XPS) spectra of Li doped MoS<sub>2</sub> nanofilms grown on c-plane sapphire substrate by two techniques: thermally assisted conversion (TAC) and pulsed laser deposition (PLD). Reference spectra for undoped MoS<sub>2</sub> were measured on a commercial MoS<sub>2</sub> powder sample.</p> <p> </p> <p><strong> Data formats</strong></p> <p>The XPS datasets are available in two formats.</p> <ol> <li><a href="https://doi.org/10.1002/sia.740130202">VAMAS</a> (ASCII ISO 14976).</li> <li>Plain text column files: Double-column files (dat) and corresponding metadata files (txt).</li> </ol>
Database of fitted spectra for: Changing-Look AGNs - I. Tracking the transition on the main sequence of quasars
<h3>Results from the spectral fitting for a sample of changing-look active galactic nuclei (AGNs) with SDSS spectroscopy using PyQSOFit.</h3>
Experimental data for "Deep Learning Methods for Colloidal Silver Nanoparticle Concentration and Size Distribution Determination from UV-Vis Extinction Spectra"
<p>Testing data (experimental data) for neural networks published in preprint https://doi.org/10.48550/arXiv.2404.10891</p> <p>The UV-VIS-NIR spectral data was also used in the dissertation of Nadzeya Khinevch, titled "Two-dimensional structures of nanoparticles for elements of surface-enhanced Raman scattering substrates".</p> <p>Emails of the corresponding authors:</p> <p>Tomas Klinavičius tomas.klinavicius@ktu.lt</p> <p>Tomas Tamulevičius tomas.tamulevicius@ktu.lt</p>
QuaLiKiz-v2.6.2 linear instability spectra based on JET experimental plasma profiles
<p>This dataset was used to train the QuaLiKiz-neural-network (QLKNN) model, QLKNN-jetexp-15D, described within the following published article: <a href="https://doi.org/10.1063/5.0038290">https://doi.org/10.1063/5.0038290</a>. It was generated with approximately 33 million standalone evaluations of QuaLiKiz-v2.6.2, each performed with a standard vector of 18 wavenumbers. Only approximately 21 million of these are kept for training due to various consistency checks applied to the code outputs. More information about the QuaLiKiz code can be found at <a href="https://www.qualikiz.com">www.qualikiz.com</a>.</p> <p>The data is saved under 3 keys in HDF5 format: "/input", "/spectrum", and "/wavenumber". The '/input' key contains the inputs used for the QuaLiKiz evaluations, representing the local plasma parameters extracted from experimental measurements from the JET plasma device in Culham, UK, along with variations of select parameters according to propagated experimental uncertainties. The "/spectrum" key contains the linear growth rate and frequency spectra corresponding to the 2 most dominant microinstabilities determined by the calculation (s0 = dominant, s1 = sub-dominant). The "/wavenumber" key contains an array representing the standard set of 18 wavenumbers (<span class="math-tex">\(k_y \rho_s\)</span>) was used to generate the spectra (k0 = lowest wavenumber, k17 = highest wavenumber).</p>
Time series of in situ Uv-Vis absorbance spectra and high-frequency predictions of total and soluble Fe and Mn concentrations measured at multiple depths in Falling Creek Reservoir (Vinton, VA, USA) in 2020 and 2021
High-frequency measurements of light absorbance were collected at multiple depths in Falling Creek Reservoir (FCR; Vinton, VA, USA) using a s::can Spectrolyser UV-Visible spectrophotometer coupled with a multiplexor pumping system. The system pumps water samples from individual depths into a flow-through cuvette where the UV-vis absorbance spectra of the sample are measured by the spectrophotometer. The system used in our study collected measurements of light absorbance every 2.5 nm wavelengths from 200 nm to 732.5 nm (optical path length of 10 mm) approximately at an hourly time step for seven monitoring depths in the reservoir. Data was collected during two periods; the first deployment (16 October to 9 November 2020) was to observe changes in Fe and Mn concentrations before, during, and after reservoir fall turnover and the second deployment (26 May to 21 June 2021) was to observe the effects of engineered hypolimnetic oxygenation on Fe and Mn concentrations. Partial least squares regression models were developed to generate predictions of total and soluble Fe and Mn concentrations based on the correlation between absorbance spectra and sampling data.
Vegetation indices calculated from reflectance spectra collected at LTER plots at Toolik Lake, Alaska during the 2007-2019 growing seasons.
Vegetation indices calculated from reflectance spectra collected at Arctic LTER experimental plots at Toolik Lake, Alaska during the 2007-2019 growing seasons. Long term experimental plots span several different vegetation types: Heath (HTH89), Moist Acidic Tussock (MAT89 and Low Fert), Moist Non-Acidic Tussock (MNAT), Non-Acidic Non-Tussock (NANT), Shrub (SHB), and Wet Sedge (WSG). Plots are differentiated by their experimental treatment and are located in replicate blocks.Canopy reflectance is measured by hand-held spectrophotometer and several indices of interest (NDVI, EVI, EVI2, PRI, WBI, and Chlorophyll index) are calculated.
Raw spectra measurements of scattered sunlight collected using a MAX-DOAS (Multi-Axis Differential Optical Absorption Spectroscopy) instrument in the austral summer of 2016/17 during the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>To achieve the objectives of the project, we installed a MAX-DOAS (Multi-AXis Differential Optical Absorption Spectroscopy) instrument on the vessel “Akademik Tryoshnikov”. This instrument is based on the DOAS technique, which is used to measure trace gas concentrations in the atmosphere. The method consists of the analysis of the spectral absorption lines that each trace gas produces in the solar spectra. The DOAS technique uses the narrowband features that every trace gas has in their spectral absorption coefficients. This differential cross section is unique and acts like a fingerprint for the trace gases, allowing to differentiate between them and to estimate their concentrations (for further details see Platt and Stutz, 2008).</p> <p>In the past decades, atmospheric chemists have come to realize that halogen species (like Cl, Br or I and their oxides ClO, BrO and IO) exert a powerful influence on the chemical composition of the troposphere and through that influence affect the evolution of pollutants, hence having a significant impact on climate. These reactive halogen species are potent oxidizers for organic and inorganic compounds throughout the troposphere. In particular, halogen cycles can act on several compounds (such as methane, ozone, particles…), all of which are climate forcing agents through direct and indirect radiative effects. Dynamic exchange of halogens between the ocean, sea ice, snowpack and atmosphere is the main driver for the frequent occurrence of Ozone Depletion Events (ODEs) and Atmospheric Mercury Depletion Events (AMDEs) (Saiz-Lopez and von Glasow, 2012).</p> <p>In this dataset we present the raw spectra measurements of scattered sunlight recorded by the MAX-DOAS onboard a research vessel in the Southern Ocean and Atlantic Ocean. Included are position and vessel inclination data. Data coverage is from December 2016 to April 2017.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ace_maxdoas_gps.zip</li> <li>GPS_JDDD.txt, data file, ASCII text</li> <li>ace_maxdoas_inclination.zip</li> <li>Inclination_JDDD.txt, data file, ASCII text</li> <li>ace_maxdoas_spectra-YYYY-MM.zip</li> <li>- MAXDOAS<br> - - WWW<br> - - - JDDD<br> - - - - LiveInfo_DDDhhmmss.WWW, data file, ASCII text<br> - - - - Atmos<br> - - - - - DDDhhmmss_90.WWW, data file, ASCII text<br> - ZENITH<br> - - WWW<br> - - - JDDD<br> - - - - LiveInfo_DDDhhmmss.WWW, data file, ASCII text<br> - - - - Atmos<br> - - - - - DDDhhmmss_90.WWW, data file, ASCII text</li> <li>README.txt, metadata, text</li> <li>data_file_header_gps.txt, metadata, text</li> <li>data_file_header_inclination.txt, metadata, text</li> <li>data_file_header_spectra_atmos.txt, metadata, text</li> <li>data_file_header_spectra_liveinfo.txt, metadata, text</li> </ul> <p>where YYYY is the year and MM is the month. JDDD is the day of the year (Julian day) YYYY in which the file was recorded. hhmmss is the time. WWW is the central wavelength of the measured spectrum in the UV or VIS region.</p> <p><strong>Dataset license</strong></p> <p>This dataset of raw spectra of scattered sunlight measurements from ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Raman spectra of the Adenoma-Carcinoma-Sequence in a mice model
<p>In the following, a short desciption for each csv files:</p> <ol> <li>Meta data: includes information about mice ID, scans collected from each mouse, location of extracted scans, activity of P53 gene, mouce gender, tissue type.</li> <li>MSpectra: contains mean spectra of tissue types with respect to each extracted scan.</li> <li>TissueLabels: describes different divisions of tissue types;e.g. normal vs abnormal, normal vs HB vs Karzinom, normal vs HB vs adenoma vs carcinoma</li> <li>Wavenumbers: includes Raman spectra wavenumbers. </li> </ol>
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>
Supercontinuum Spectra
<p>This dataset contains 51429 supercontinuum spectra generated by numerically simulating the propagation of short optical pulses in an optical fiber. The parameters characterizing both the input pulse and the optical fiber are provided below.</p> <p>More information can be found in the provided README file.</p>
Raman spectra dataset of hydrous glasses of Le Losq et al., 2012, Am. Min 97:779-790
<p>This dataset contains Raman spectra of hydrous glasses used in the publication of Le Losq et al. (2012) to implement a chemical-independent method to quantify water content of glasses with Raman spectroscopy.</p> <p>Spectra are unprocessed. They were acquired with a T64000 Jobin-Yvon triple grating Raman spectrometer equipped with a confocal system, a 1024 CCD detector cooled by liquid nitrogen and an Olympus microscope. The optimal spatial resolution allowed by the confocal system is 1–2 μm<sup>2</sup> with a 100× Olympus objective. The spectral resolution of the spectrometer is 0.7 cm<sup>–1</sup>. A Coherent laser 70-C5 Ar+, having a wavelength of 514.532 nm, is used for the excitation line.</p> <p>The file dataliste.csv contains a list of the spectra together with the sample name and water contents in wt%. See Tables 1 and 2 in Le Losq et al. (2012) for corresponding sample chemical composition and errors on water concentrations, as well as supplementary information for a table containing the regions of interest for background fitting.</p> <p>Reference</p> <p>Le Losq, C., Neuville, D.R., Moretti, R., Roux, J., 2012. Determination of water content in silicate glasses using Raman spectrometry: Implications for the study of explosive volcanism. American Mineralogist 97, 779–790. <a href="https://doi.org/10.2138/am.2012.3831">https://doi.org/10.2138/am.2012.3831</a></p>
Reflection Spectra Repository for Cool Giant Planets
<p>Supplementary material for <a href="http://iopscience.iop.org/article/10.3847/1538-4357/aabb05"><em>Exploring H2O Prominence in Reflection Spectra of Cool Giant Planets</em></a> - ApJ 858, 69 (2018).</p> <p>This repository contains 65520 model reflection spectra of cool giant planets. The grid explores the influence of metallicity, gravity, effective temperature, and sedimentation efficiency on H<sub>2</sub>O absorption signatures in giant planet atmospheres. We also include two animations to visualise how the prominence of H<sub>2</sub>O absorption evolves over this parameter space. The included models range over:</p> <p>*m => 1-100 x solar (log(m) @ 0.0, 0.5, 1.0, 1.5, 1.7, 2.0 dex) <-- log(m) = 1.7 new for V2 of the database.<br> *g => 1-100 m/s<sup>2</sup> (evenly over log(g) in steps of 0.1 dex)<br> *T<sub>eff</sub> => 150-400 K (linearly in steps of 10 K)<br> *f<sub>sed</sub> => 1-10 (linearly in steps of 1)</p> <p>(V 1.0, March 30th 2018):</p> <blockquote> <p>Initial release of the reflection spectra repository. </p> </blockquote> <p>(V 2.0, Oct 1st 2019): </p> <blockquote> <p>The cool giant reflection spectra grid has been re-computed using the latest version of the PICASO albedo code (doi: <a href="https://arxiv.org/ct?url=https%3A%2F%2Fdx.doi.org%2F10.3847%2F1538-4357%2Fab1b51&v=77076c4a">10.3847/1538-4357/ab1b51</a>). This fixes a few bugs and adds new model features (e.g. Raman scattering, see Batalha+2019).</p> <p>The new grid is packaged as a HDF5 file with an accompanying python script 'Open_Albedo_Database.py'. The python script is provided to show how to open the albedo database, plot the spectra, and save spectra as a .txt file. The user need only change 4 lines (specifying log(m), log(g), T<sub>eff</sub>, f<sub>sed</sub>) and run the python script to produce a plot of the albedo spectra (both with and without H<sub>2</sub>O absorption).</p> </blockquote> <p><strong>NEW</strong>: (V 2.1, Oct 3rd 2019): </p> <blockquote> <p>Fixed a bug causing models with log(g) = 3.4 or 3.9 to not display cloud opacity.</p> </blockquote>
Feature Template Angular Power Spectra
<p>This data was used in the machine learning analysis of the Cosmic Microwave Background data in: https://github.com/IndiraOcampo/CMB_ML_based_model_selection.git and https://dx.doi.org/10.1088/1475-7516/2025/02/004</p> <p>The objective is to train a neural network architecture on the different polarization modes (TT, TE, EE and joint) to perform model selection between the standard cosmological model, ΛCDM and a model that introduces a Feature Template (FT) in the primordial power spectrum - related to the early Universe physics.</p> <p>The first row corresponds to the multipole moment "\ell" and the remaining ones correspond to the different components of the Cl's angular power spectrum, for the different values of A_lin (the feature oscilation parameter). While A_0 = 10^-2 is a reasonable value that still agrees with observations, A_0 = 0 corresponds to the ΛCDM model.</p> <p>Finally, our aim is to apply SHAP to perform feature importance (interpretability) in our results.</p>
FTIR-ATR spectra of culinary grain legumes (pulse) flours
<p>FTIR-ATR spectra of 5 culinary grains: </p> <p><br> 1. chickpea (Cicer arietinum n=87)<br> 2. lentil (Lens culinaris n=93))<br> 3. grass pea (Lathyrus sativus n=116)<br> 4. pea (Pisum sativus n=119)<br> 5. faba bean (Vicia faba n=93)</p> <p>Grains were dried at 40 °C and milled using a miller Retsch cyclone mill with a particle size under 0.8 mm. The different flours were stored at -20 °C. For FTIR-ATR analysis there was no need for further sample preparation.</p> <p> </p> <p>Files: </p> <p><a href="https://zenodo.org/api/files/9c2c7a63-966d-4b4b-a1bf-5b8d8228ac48/FTIRATR_Pulse.mat">FTIRATR_Pulse.mat</a>: Matlab structure with, as fields:<br> <Data> : a 491x1734 matrix, each line corresponds to a spectra<br> <Wavelength>: a 1730 length vector, Wavelength of the incident light<br> <Tag> : a 491 length vector, labelling of the samples<br> <Label4Rag> : Names of the label.</p> <p><a href="https://zenodo.org/api/files/9c2c7a63-966d-4b4b-a1bf-5b8d8228ac48/FTIRATR_pulse_data.csv">FTIRATR_pulse_data.csv</a>: csv file with the data, wavelength and tag fields</p> <p> <a href="https://zenodo.org/api/files/9c2c7a63-966d-4b4b-a1bf-5b8d8228ac48/FTIRATR_pulse_labels.csv">FTIRATR_pulse_labels.csv</a>: Names of the label</p> <p> </p> <p><br> </p> <p> </p> <p> </p>
Avoided crossing in gravitational wave spectra from protoneutron star
<p>The data of the gravitational wavefroms of core-collapse supernovae, which are used in Sotani and Takiwaki (2020), Monthly Notices of the Royal Astronomical Society, Volume 498, Issue 3, pp.3503-3512.</p> <p>Data Format:</p> <p>The data are in ASCII format and the two columns are1:time time since bounce in sec</p> <p>2:hplus plus polarization of the GW amplitude. We assume the source distance of 10 kpc.</p> <p>The data are sampled at ~10 kHz, but, the sampling is not uniform in time. Therefore resampling might be necessary.</p>
Generalised Oscillator Strengths for the simulation of EELS spectra, with a broader coverage of high energy and minor edges
<p>This deposit contains a tabulated set of generalised oscillator strengths, which are required to compute the double differential cross sections for the inelastic scattering of fast electrons by atoms, i.e. for the simulation of EELS spectra.</p> <p>These tabulated values are calculated self-consistently within the local density approximation using the exchange correlation potential after Perdew [1]. For this a modified version of a program by Hamann is used [2]. Using this atomic potential the wave function of the ejected free electron is calculated, which is normalised by matching it to spherical Bessel and Neumann functions at large distances from the core [3]. The remaining integral constitutes a spherical Bessel transform. Using the convolution theorem this integral is solved with the fast Fourier transformation routine as done in [4]. A further discussion is available along with the code (see below), or more in-depth (but in German) in the <a href="https://www.uni-muenster.de/imperia/md/content/physik_pi/kohl/abschlussarbeiten/lsegger-bsc-arbeit.pdf">Thesis of L. Segger</a>.</p> <p><strong>This updated version offered here greatly expands the number of available edges</strong>, but is otherwise identical to the earlier version uploaded at <a href="https://zenodo.org/record/6599071">https://zenodo.org/record/6599071</a>.</p> <p> </p> <p>The data offered here is in the GOSH file format, a file format developed for the distribution of such datasets. A description of the file format as used here is included in the file `gosh.md`, while an up to date version can be found at:</p> <p><a href="https://gitlab.com/gguzzina/gosh">https://gitlab.com/gguzzina/gosh</a></p> <p>The code used to compute the GOS is publicly available, along with a discussion of the approach and methods, at:</p> <p><a href="https://github.com/Br0Fi/goscalc">https://github.com/Br0Fi/goscalc</a></p>
XPS and XANES spectra for Li doped MoS2 few-layer films
<p><strong>Description</strong></p> <p>Dataset for x-ray photoelectron (XPS) and absorption near-edge structure (XANES) spectra of Li doped MoS<sub>2</sub> nanofilms grown by one-zone sulfurization on c-plane sapphire substrate.</p> <p> </p> <p><strong>XPS Data formats</strong></p> <p>The same XPS datasets are saved into two different data formats.</p> <ol> <li><a href="https://doi.org/10.1002/sia.740130202">VAMAS</a> (ASCII ISO 14976)</li> <li>Plain text column files: Double-column files (dat) and corresponding metadata files (txt)</li> </ol> <p> </p> <p><strong>XANES Data formats</strong></p> <ol> <li>XDI (<a href="http://github.com/XraySpectroscopy/XAS-Data-Interchange">XAS Data Interchange</a>): raw XANES data</li> <li>PRJ (Athena Project File): normalized XANES data processed in <a href="http://bruceravel.github.io/demeter/">ATHENA free software package.</a></li> </ol> <p> </p>
Raman Spectra of K2ReCl6 and K2SnCl6, published in PRB 107, 214301 (2023)
<p>Raman Spectra of K2ReCl6 from 5 K to room temperature, as well as K2SnCl6 at room temperature, in c(aa)c' and c(ab)c' configuration. Spectra shown and discussed in Phys. Rev. B <strong>107 </strong>214301 (2023), also available as preprint https://arxiv.org/abs/2209.05866. </p>
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.