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277 results for “infrared spectroscopy”
Fourier transform infrared spectroscopy as a non-destructive method for analysing herbarium specimens
Open the record for dataset details and reuse information.
DATA for "An Acoustic Trap for Bead Injection Attenuated To-tal Reflection Infrared Spectroscopy"
<p>Data used for: doi/10.1021/acs.analchem.9b00611</p>
Data and 3D-files for: 3D Printing for Low-Cost and Versatile Attenuated Total Reflection Infrared Spectroscopy
<p>Data and 3D files for :<a href="https://doi.org/10.1021/acs.analchem.9b04043">https://doi.org/10.1021/acs.analchem.9b04043</a></p>
Vibrational Probe at the Electrochemical Interface: Dependence on Plasmon Coupling and Potential on the Lineshape in Two-Dimensional Infrared Spectroscopy
<p>These files contain the data presented in the research article: Vibrational Probe at the Electrochemical Interface: Dependence on Plasmon Coupling and Potential on the Lineshape in Two-Dimensional Infrared Spectroscopy by Melissa Bodine, Vepa Rozyyev, Jeffrey W. Elam, Andrei Tokmakoff and Nicholas H. C. Lewis J. Phys. Chem. Lett., (2023)</p>
DATA SET: Near-infrared diffuse optical characterization of human thyroid using ultrasound-guided hybrid time-domain and diffuse correlation spectroscopies
<p>This repository contains the data sets of the article:</p> <p>P. Fernández Esteberena et al. (2024). Near-infrared diffuse optical characterization of human thyroid using ultrasound-guided hybrid time-domain and diffuse correlation spectroscopies. <em>Biomedical Optics Express</em>. <a href="https://doi.org/10.1364/BOE.538141">https://doi.org/10.1364/BOE.538141</a></p>
Predicting methane emission in Canadian Holstein dairy cattle using milk mid-infrared reflectance spectroscopy and other commonly available predictors via artificial neural networks
<p>Supplementary Tables - Version 2</p>
Predicting dry matter intake in Canadian Holstein dairy cattle using milk mid-infrared reflectance spectroscopy and other commonly available predictors via artificial neural networks
<p>Supplementary Tables</p>
From Networked to Isolated: Observing Water Hydrogen Bonds in Concentrated Electrolytes with Two Dimensional Infrared Spectroscopy
<p>The files contain the data that are shown in the figures of the Main Text and of the Supplementary Materials of the research article:</p> <p>From Networked to Isolated: Observing Water Hydrogen Bonds in Concentrated Electrolytes with Two Dimensional Infrared Spectroscopy</p> <p>Nicholas H. C. Lewis, Bogdan Dereka, Yong Zhang, Edward J. Maginn, and Andrei Tokmakoff</p> <p>J. Phys. Chem. B, (2022)</p>
Raw Data for "Frequency stabilisation and SI tracing of mid-infrared quantum-cascade lasers for precision molecular spectroscopy"
<p>The MasterRunner.m Matlab file in the main folder can execute individual Matlab scripts in different folders in order to analyze the data and generate the figures in the papers.</p> <p>The folder BeatNotes contains the raw data and the corresponding analysis scripts for getting the beats of the 729 laser, QCL, and the US laser. </p> <p>The folder AllanDeviation contains the Allan deviation measurements of the QCL and the 729 nm laser with respect to the GPS disciplined oscillator and the US laser. The file Analysis_v2.m can be executed in order to analyze the Allan deviation data.</p> <p>The folder TrackingOscillatorSetup contains the electronic schematic, list of components and a photo of the complete home-built VCO setup.</p>
Classification of apricot varieties by Infrared Spectroscopy
Open the record for dataset details and reuse information.
A near infrared spectroscopy dataset of coal and coal-measure rock under diverse conditions
<p>we selected 24 representative coal and coal-measure rock samples, created sub-samples with 11 different granularity for each sample, and collected spectral data from the sub-samples under 5 different detection azimuths, 18 different detection zeniths, and 8 different light source zeniths. Our near infrared spectroscopy dataset of coal and coal-measure rock will provide valuable data support for coal and coal-measure rock identification based on near-field spectroscopy.</p>
FTIR-Plastics: a Fourier Transform Infrared Spectroscopy dataset for the six most prevalent industrial plastic polymers.
<p><span><span>Two datasets are presented: FTIR-Plastics-C4 and FTIR-Plastics-C8, comprising 6,000 spectra obtained through Fourier Transform Infrared Spectroscopy (FTIR) applied to the six most used synthetic polymers: Polyethylene Terephthalate (PET), High-Density Polyethylene (HDPE), Polyvinyl Chloride (PVC), Low-Density Polyethylene (LDPE), Polypropylene (PP), and Polystyrene (PS). The key feature of the datasets lies in the FTIR analysis, which reports the percentage transmittance as the intensity measure as a function of the wavelength of an Infrared light source, expressed as wavenumber (with units in cm</span></span><sup><span><span>-1</span></span></sup><span><span>). FTIR analysis was performed using a Jasco FTIR PRO 4x spectrophotometer with a wavenumber resolution setting of 8 cm</span></span><sup><span><span>-1</span></span></sup><span><span> for FTIR-Plastics-C8 and 4 cm</span></span><sup><span><span>-1</span></span></sup><span><span> for FTIR-Plastics-C4, both employing a configuration of 32 scans and a range from 4000 to 400 cm</span></span><sup><span><span>-1</span></span></sup><span><span>. The datasets are presented in CSV (comma-separated values) format, including the following information (per each column):</span></span></p> <ul> <li> <p><span><span><strong>IDE</strong></span></span><span><span>: unique identifier of the sample.</span></span></p> </li> <li> <p><span><span><strong>Polymer: </strong></span></span><span><span>type of synthetic polymer (PET, HDPE, PVC, LDPE, PP, or PS).</span></span></p> </li> <li> <p><span><span><strong>Technique: </strong></span></span><span><span>Type of technique used (FTIR).</span></span></p> </li> <li> <p><span><span><strong>Sample: </strong></span></span><span><span>polymer sample number.</span></span></p> </li> <li> <p><span><span><strong>BR</strong></span></span><span><span>: scanning configuration (32).</span></span></p> </li> <li> <p><span><span><strong>RST</strong></span></span><span><span>: resolution configuration (8 or 4 cm</span></span><sup><span><span>-1</span></span></sup><span><span>).</span></span></p> </li> <li> <p><span><span><strong>Data (x) y Data(y): </strong></span></span><span><span>1884 pairs of columns for FTIR-Plastics-C8 and 3751 pairs of columns for FTIR-Plastics-C4, representing values on the "x" axis (wavenumber) and the "y" axis values associated with molecular vibration intensities, indicating the transmittance (%), which differentiates each polymer.</span></span></p> </li> </ul> <p><span><span>Additionally, the files generated by the Jasco spectrophotometer for each polymer are provided, which were standardized by adding a header with the following structure:</span></span></p> <ul> <li> <p><span><span>TITLE SAMPLE NAME: referring to the name of the analyzed polymer.</span></span></p> </li> <li> <p><span><span>DATA TYPE: specifying the characterization technique.</span></span></p> </li> <li> <p><span><span>MEASUREMENT INFORMATION: equipment used for data collection.</span></span></p> </li> <li> <p><span><span>MODEL NAME: name of the equipment used.</span></span></p> </li> <li> <p><span><span>SERIAL No: serial number assigned to the equipment used.</span></span></p> </li> <li> <p><span><span>ACCESSORY: complementary device integrated into the equipment.</span></span></p> </li> <li> <p><span><span>LIGHT SOURCE: standardized light source related to the DLATGS detector.</span></span></p> </li> <li> <p><span><span>RESOLUTION: parameters are used to distinguish the wavenumber in the analyzed materials.</span></span></p> </li> <li> <p><span><span>XUNIT/HORIZONTAL AXIS: referring to the unit’s title assigned on the x-axis.</span></span></p> </li> <li> <p><span><span>YUNITS/VERTICAL AXIS: referring to the unit’s title designated on the y-axis.</span></span></p> </li> <li> <p><span><span>FIRSTX: initial value set for the x-axis.</span></span></p> </li> <li> <p><span><span>FIRSTY: initial value set for the y-axis.</span></span></p> </li> <li> <p><span><span>LASTX: final value set for the x-axis.</span></span></p> </li> <li> <p><span><span>LASTY: final value set for the y-axis.</span></span></p> </li> <li> <p><span><span>NPOINTS: total data points in the file.</span></span></p> </li> </ul> <p><span><span>Data collection was carried out meticulously, following specific steps to ensure the accuracy and reliability of the results. The calibration certificates issued by the supplier (calibration_certificate.pdf) corresponding to the equipment used in the experiments and data collection that give rise to these databases are attached.</span></span></p>
Chemical and Structural In-Situ Characterization of Model Electrocatalysts by Combined Infrared Spectroscopy and Surface X-Ray Diffraction
<p>Raw and treated data</p>
Dataset: Infrared Spectroscopy and Quadrupole Mass Spectrometry during Temperature Programmed Desorption of Mixed Hyper-volatile (CO, N2, Ar) and Amorphous Ices (H2O, CO2)
<p>IR spectra of H2O:CO and CO2:CO ices mixed at 2 concentrations (5:1 and 15:1) and grown to 7 thicknesses (50 to 3000 ML). Spectra are also included for a smaller set of H2O:N2, H2O:Ar, CO2:N2, and CO2:Ar ices. </p> <p>The files are .txt files, where the first column is wavenumber (cm-1) and the second is IR absorbance.</p> <p>QMS data taken during TPD of H2O:CO and CO2:CO ices mixed at 2 concentrations (5:1 and 15:1) and grown to 7 thicknesses (50 to 3000 ML). Data are also included for a smaller set of H2O:N2, H2O:Ar, CO2:N2, and CO2:Ar ices.</p> <p>Constructing TPD curves require 2 files for each ice, a .asc file and a .xls file. The .asc files contain the relative time (s) and ion count for many relevant m/z. The .xls files contain time (s) and temperature (K). Time from each file can be interpolated to produce ion count as a function of temperature.</p>
ON THE CAPABILITY OF THE LINEAR DICHROIC INFRARED SPECTROSCOPY - dataset of experimental spectra
<p>Experimental dataset</p>
Competing Technologies: Determining the Geographical Origin of Strawberries (Fragaria × ananassa) using laboratory based Near-Infrared Spectroscopy compared to a simple portable device
<p><span>The application and development of fast and simple screening methods for the authentication of foods has increased continuously in recent years. A widely used analytical technique is Fourier transform near-infrared spectroscopy (FT-NIR). Despite the simple application of FT-NIR analysis, the analyses are usually carried out on benchtop devices in the laboratory. However small, inexpensive and mobile NIR devices could be used on-site. Despite the simple use of FT-NIR analysis, the examinations are usually carried out on a stationary benchtop device in a laboratory. However, in order to be able to perform the application directly on site, the application of small, cost-effective and mobile NIR devices for food analysis is crucial. In this study, both, a benchtop NIR instrument and a handheld NIR device with a lower resolution and analyzed wavenumber range were applied for the differentiation of strawberries from different geographical origins. Distinguishing German and non-German strawberries using linear discriminant analysis (LDA) yielded an accuracy of 91.9 % and 84.0 % using the benchtop and the handheld devices, respec-tively. Relevant variables could be assigned to lipids, carbohydrates and proteins. Overall, our study demonstrated for the first time that analyzing the geographical origin of strawberries using NIR spectroscopy is also possible by means of a handheld device.</span></p>
FIGURE 5 in Barcoding without DNA? Species identification using near infrared spectroscopy
FIGURE 5. Discriminant analysis of the entire spectrum in which each species has a unique symbol. A) Note that intraspecific variation is so small that the entire set of points per individual is encompassed by the one point for the species. B) Amplification of one point (one species, N. neoaustralis) illustrates the small intraspecific variation. DF (1, 2 and 3) indicate the discriminant functions.
FIGURE 3 in Barcoding without DNA? Species identification using near infrared spectroscopy
FIGURE 3. NIR spectra of nine species of Neodexiopsis after pre-processing (see text) in which each line is the spectra of each of the 67 specimens. The regular divisions are the minimal intervals that have information to discriminate the nine species simultaneously in the DA.
FIGURE 2 in Barcoding without DNA? Species identification using near infrared spectroscopy
FIGURE 2. Illustrative diagram of the use of NIR spectroscopy with insects. A) Light source, B) Detector, C) Diffuse reflectance accessory (see text).
FIGURE 4. A in Barcoding without DNA? Species identification using near infrared spectroscopy
FIGURE 4. A) PCA of three species based on the spectra without transformations. Yellow triangles (N. paranensis), green circles (N. rustica) and purple circles (N. vulgaris). Explained variation: PC1=99%, PC2= 0.9%, PC3<0.01%. B) PCA of the same species as Figure 4A, based on the spectra with transformations. Explained variation: PC1=68%, PC2=10%, PC3=4%.
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.