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255 results for “Raman”
System transferability of a Raman-based oesophageal tissue classification
<p>This dataset contains 560819 Raman spectra (Uncorrected_MedianFiltered_SizeMatched_SMART_x) taken from 61 oesophageal samples (sampleID) representing 51 patients (patientID). The data was acquired across three independent sites (centre) using the same make of spectrometer - Renishaw RA816 Biological Analyser (Renishaw plc, Wotton-under-edge, UK). Ostensibly the same sample was collected by all three sites (three adjacent FFPE tissue slices were obtained and regions of interest were identified by a histopathologist on one of the centres). The samples belong to one of 5 pathology classes: NSQ (0), IM(1), LGD(2), HGD(3) and AC(4). </p> <p>Included in this version is the protocol used to collect this data, particularly focused on the aquisition of Raman spectra.</p>
Dataset: Label-free detection of methicillin resistance in Staphylococcus aureus using different Raman-spectroscopy approaches
<p>This is the dataset accompanying the submission of the manuscript: Label-free detection of methicillin resistance in Staphylococcus aureus using different Raman-spectroscopy approaches in the journal Microbiology Spectrum.</p> <p>The data description is the following:</p> <p>Strains<br> 16859MRSA= Strain AUSTR-07-16859 MRSA<br> 16859MSSA= Strain AUSTR-07-16859 MSSA<br> CC8MRSA= Strain 08V15773<br> CC8MSSA= Strain MRSA2010-174<br> AUSTR05MRSA= Strain AUSTR-05-15441 MRSA<br> AUSTR05MSSA= Strain AUSTR-05-15441 MSSA<br> CC361MRSA= Strain UAE-Abu Dhabi-020<br> CC361MSSA= Strain UAE-Dubai-80-MS 1368.9/09</p> <p>Datasets<br> UVRR: UV-Resonance Raman with 244 nm excitation on bulk samples, calibration standard Polystyrene, measurements were time series of 10 consecutive spectra, for each strain and batch 25 time series were collected from 3 different slides<br> 532nm: Single cell analysis with 532nm excitation, calibration standard 4AAP, one spectrum per bacterial cell was collected<br> 785nm: Bulk analysis of bacterial colonies using 785 nm excitation and a Raman fibre probe, calibration standard 4AAP, bulk analysis, individual spectra of colonies were collected</p> <p>Data structure is in the metadata files.<br> Individual spectra are in the folders sorted by the date they were measured.</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>
Atomic spin-controlled non-reciprocal Raman amplification of fibre-guided light
<p>This repository contains the data used in an experiment that demonstrates atomic spin-controlled non-reciprocal Raman amplification of fibre-guided light. For more information, see the following publication:</p> <ul> <li><a href="https://doi.org/10.1038/s41566-022-00987-z">10.1038/s41566-022-00987-z</a></li> <li><a href="https://doi.org/10.48550/arXiv.2107.07272">10.48550/arXiv.2107.07272</a></li> </ul> <p>We provide the data in text files encoded in the Unicode standard UTF-8. In the following, we describe the files in more detail.</p> <p>The measured evolution of the signal transmission presented in Fig. 2<strong>b</strong> is provided in the file “source_data_fig2b.txt”. The file has five columns that are separated by the delimiter “, ”:</p> <ul> <li>the time in microseconds,</li> <li>the signal transmission in the 1→2 direction,</li> <li>the error of the signal transmission in the 1→2 direction,</li> <li>the signal transmission in the 1→2 direction,</li> <li>and the error of the signal transmission in the 1→2 direction.</li> </ul> <p>We provide the theory data in the additional file “theory_fig2b.txt”. It contains three columns that are separated by the delimiter “, ” :</p> <ul> <li>the time in microseconds,</li> <li>the calculated signal transmission in the 1→2 direction,</li> <li>the calculated signal transmission in the 2→1 direction.</li> </ul> <p>In the files “source_data_fig2c.txt”, “source_data_fig2d.txt”, and “source_data_fig3b.txt”, we provide the data of the bar plots in Fig. 2<strong>c</strong>, 2<strong>d</strong>, and 3<strong>b</strong>, respectively. In every file, the first column indicates the measurement direction. The following columns contain the detected mean signal transmission with the corresponding errors for various initial atomic spin states defined by the magnetic quantum number <em>m<sub>F</sub></em>.</p>
Bolaform Surfactant-Induced Au Nanoparticle Assemblies for Reliable Solution-Based Surface-Enhanced Raman Scattering Detection
<p>Related publication: García-Lojo, D; Méndez-Merino, D; Pérez-Juste, I; Acuña, A; García-Río, L; Rodríguez-Patón, A; Pastoriza-Santos, I; Pérez-Juste, J. Bolaform surfactant-induced Au nanoparticle assemblies for reliable solution-based SERS detection. Adv.Mater. Technol. 2022, 2101726. <a href="https://doi.org/10.1002/admt.202101726">https://doi.org/10.1002/admt.202101726</a></p> <p> </p> <p> </p> <p>Abstract:</p> <p>Solution-based surface-enhanced Raman scattering (SERS) detection typically involves the aggregation of citrate-stabilized Au nanoparticles into colloidal assemblies. Although this sensing methodology offers excellent prospects for sensitivity, portability, and speed, it is still challenging to control the assembly process by a salting-out effect, which affects the reproducibility of the assemblies and, therefore, the reliability of the analysis. This work presents an alternative approach that uses a bolaform surfactant, B<sub>20</sub>, to induce the plasmonic assembly. The decrease of the surface charge and the bridging effect, both promoted by the adsorption of B<sub>20</sub>, are hypothesized as the key points governing the assembly. Furthermore, molecular dynamic simulations supported the bridging effect of the B<sub>20</sub> by showing the preferential bridging of surfactant monomers between two adjacent Au(111) slabs. The colloidal assemblies showed excellent SERS capabilities towards the rapid, on-site detection and quantification of beta-blockers and analgesic drugs in the nanomolar regime, with a portable Raman device. Interestingly, the application of state-of-the-art convolutional neural networks, such as ResNet, allows a 100% accuracy in classifying the concentration of different binary mixtures. Finally, the colloidal approach was successfully implemented in a millifluidic chip allowing the automation of the whole process, as well as improving the performance of the sensor in terms of speed, reliability, and reusability without affecting its sensitivity.</p>
Forward and backward Raman scattering photon counts in a FTTH topology.
<p>This set comprises a simulation tool in Mathematica for the generated Raman noise in a<br> GPON-based FTTH topology. This set takes into consideration various FTTH parameters,<br> such as the number of Optical Network Terminals (ONTs) and the splitting ratios, as well<br> as the drop and feeder fiber lengths, as it is depicted in Figure 1, providing in the output<br> the expected Raman noise counts calculated in counts per second (cps) detected in a<br> Single Photon Detector (SPD). The user of the code can manipulate various setup<br> parameters, such as the filtering passband and loss, as well as the specific SPAD<br> parameters which can be selected to be operating either in gated or free running mode.<br> The code provides as an output the expected noise count rates (cps) associated with the<br> forward and backward Raman scattering effect.<br> </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>
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>
Raw dataset for: High-throughput multimodal wide-field Fourier-transform Raman microscope
<p>This is the Raw spectral dataset of the data published in 10.1364/OPTICA.488860</p> <p>Data are arranged as follows:</p> <p>wavenumber [Nx1]</p> <p>Hyperspectrum_cube [Nx2, A, B]: hyperspectral datacube, where: Hyperspectrum_cube (1:N, :, :) is the real part; Hyperspectrum_cube (N+1:2N, :, :) is the imaginary part</p> <p>maximum [1x1]</p> <p>minimum [1x1].</p> <p>N: number of spectral bands</p> <p>A and B: size of the spatial coordinates</p> <p>Spectral amplitudes are obtained by: Hyperspectrum_cube=double(Hyperspectrum_cube)./(2.^16-1).*maximum+minimum</p>
Supplementary material - Optical Diffraction Tomography and Raman Confocal Microscopy for the Investigation of Vacuoles Associated with Cancer Senescent Engulfing Cells
<p>Supplementary material containing the data used in the manuscript "Optical Diffraction Tomography and Raman Confocal Microscopy for the Investigation of Vacuoles Associated with Cancer Senescent Engulfing Cells"</p>
Dataset: Submicron‐ and Nanoplastic Detection at Low Micro‐ to Nanogram Concentrations Using Gold Nanostar‐Based Surface‐Enhanced Raman Scattering (SERS) Substrates
<p>ABSTRACT</p> <p>The presence of submicron- (1 µm – 100 nm) and nanoplastic (< 100 nm) particles within various sample matrices, ranging from marine environments to foods and beverages, has become a topic of increasing interest in recent years. Despite this interest, very few analytical techniques remain that allow for the detection of these small plastic particles in the low concentration ranges that they are anticipated to be present at. Research focused on optimizing surface-enhanced Raman scattering (SERS) to enhance signal obtained in Raman spectroscopy has been shown to have great potential for the detection of plastic particles below conventional resolution limits. In this study, we produce SERS substrates composed of gold nanostars and assess their potential for submicron- and nanoplastic detection. The results show 33 nm polystyrene could be detected down to 1.25 µg/mL while 36 nm poly(ethylene terephthalate) was detected down to 5 µg/mL. These results confirm the promising potential of the gold nanostar-based SERS substrates for nanoplastic detection. Furthermore, combined with findings for 121 nm polypropylene and 126 nm polyethylene particles, they highlight potential differences in analytical performance that depend on the properties of the plastics being studied.</p>
Detection of Submicron- and Nanoplastics Spiked in Environmental Fresh- and Saltwater with Raman Spectroscopy
<p>ABSTRACT</p> <p>Detection of small plastic particles in environmental water samples has been a topic of increasing interest in recent years. A multitude of techniques, such as variants of Raman spectroscopy, have been employed to facilitate their analysis in such complex sample matrices. However, these studies are often conducted for a limited number of plastic types in matrices with relatively little additional materials. Thus, much remains unknown about what parameters influence the detection limits of Raman spectroscopy for more environmentally relevant samples. To address this, this study utilizes Raman spectroscopy to detect six plastic particle types; 161 and 33 nm polystyrene, < 450 nm and 36 nm poly(ethylene terephthalate), 121 nm polypropylene, and 126 nm polyethylene; spiked into artificial saltwater, artificial freshwater, North Sea, Thames River, and Elbe River water. Overall, factors such as plastic particle properties, water matrix composition, and experimental setup were shown to influence the final limits of detection.</p>
Carbon x-ray Raman scattering mapping and spectroscopy of a fragment of Lepidodendron trunk from the Upper Carboniferous
<p>Carbon x-ray Raman scattering mapping and spectroscopy of a fragment of Lepidodendron trunk from the Upper Carboniferous (ca. 305 Mya) of Noyelles-lez-Lens, France</p>
Dataset of electronic Raman scattering in rhombohedral graphite
<p>The dataset contains the raw data, used to generate the figures of our paper (Carbon 2024 and arxiv: 2401.17779).</p> <p>Each folder contains the raw data in text files as well as the python Jupyter notebooks used to analyse the data and generate the figures.</p> <p>To work with the data, open the Jupyter notebooks (*.ipynb files). The notebooks can be previewed by opening the html exported format of each notebook.</p>
Raman spectra of rhenium heptoxide Re2O7 and its water adducts: Re2O7.2H2O and Re2O7.2D2O
<p>Raw .txt files of the Raman spectra of Re2O7, Re2O7.2H2O and Re2O7.2D2O obtained by the controlled hydration of Re2O7.</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>
Dataset: Correlative Light, Electron Microscopy and Raman Spectroscopy Workflow to Detect and Observe Microplastic Interactions with Whole Jellyfish
<p>ABSTRACT</p> <p>Many researchers have turned their attention to understanding microplastic interaction with marine fauna. Efforts are being made to monitor exposure pathways and concentrations, and to assess the impact such interactions may have. To answer these questions, it is important to select appropriate experimental parameters and analytical protocols. This study focuses on medusae of <em>Cassiopea andromeda</em> jellyfish: a unique benthic jellyfish known to favor (sub-)tropical coastal regions which are potentially exposed to plastic waste from land-based sources. Juvenile medusae were exposed to fluorescent poly(ethylene terephthalate) and polypropylene microplastics (< 300 µm), resin embedded, and sectioned before analysis with confocal laser scanning microscopy as well as transmission electron microscopy and Raman Spectroscopy. Results show the fluorescent microplastics were stable enough to be detected with the optimized analytical protocol presented, and that their observed interaction with medusae occurs in a manner which is likely driven by the microplastic properties (<em>e.g.</em> density, hydrophobicity).</p>
Reference data to the low-wavenumber Raman spectral database of pharamceutical excipients
<p>Supplementary LF-785 and FT-Raman data of all the excipient samples included in the database. More information available in the following paper: <a href="https://doi.org/10.1016/j.vibspec.2020.103021">https://doi.org/10.1016/j.vibspec.2020.103021</a></p> <p>This version also contains all spectra in the .spc file format.</p>
Raman spectroscopic data derived from Calluna vulgaris charcoals, experimentally generated across a range of natural wildfire temperatures
<p>This data has been derived from deconvolved Raman spectra, utilising two first order bands - D (Disordered) and G (Graphitic). Spectra were collected from experimentally pyrolysed charcoals, made from Calluna vulgaris (Ling Heather) separated into three main components; stem, root and flower. For each component at 250, 400, 600 and 800 degrees centigrade respectively, 5 charcoal samples (A, B, C, D, E) were analysed. Following deconvolution, median values for each spectra were produced. These correspond to parameters derived from the Raman data, including D- and G-band width (FWHM), intensity (ID/IG or 'R1') and area (AD/AG) ratios, band separation (G-D or 'RBS'), and band width ratios (D-FWHM/G-FWHM). All parameters have been compiled for each component material, and displayed graphically within this dataset.</p>
Biochemical Characterization of Mouse Retina of an Alzheimer's Disease Model by Raman Spectroscopy
<p>Raman raw data for the paper "Biochemical Characterization of Mouse Retina of an Alzheimer’s Disease Model by Raman Spectroscopy"</p> <ul> <li>two datasets of Raman images from cross-sectional and en face mouse retinas without processing</li> </ul>
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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.