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1,041 results for “Spectroscopy”
O2-O2, SO2, BrO, and IO differential slant column densities (dSCDs) measured by the University of Colorado Multi-AXis Differential Optical Absorption Spectroscopy (CU MAX-DOAS) instrument at Maido Observatory during April 29, 2018 and May 4, 2018
<p>Description: O<sub>2</sub>-O<sub>2</sub>, SO<sub>2</sub>, BrO, and IO differential slant column densities (dSCDs) measured by the University of Colorado Multi-AXis Differential Optical Absorption Spectroscopy (CU MAX-DOAS) instrument at Maido Observatory during April 29, 2018 and May 4, 2018.</p> <p>Instrument: University of Colorado Multi-AXis Differential Optical Absorption Spectroscopy (CU MAX-DOAS)<br> Instrument reference: Coburn et al. (2011); doi:10.5194/amt-4-2421-2011<br> Instrument contact: Christopher F. Lee (christopher.f.lee@colorado.edu)<br> Instrument PI: Rainer Volkamer (rainer.volkamer@colorado.edu)<br> <br> Measurement site: Maido Observatory, Reunion Island<br> Longitude: 55.384 degrees East<br> Latitude: 21.080 degrees South<br> Altitude: 2160 meters above sea level<br> Azimuth angle: Approximately 100 degrees clockwise from north<br> <br> The detection limit is defined as (2*Measured RMS) / (Maximum differential absorption cross section), where RMS = root-mean-square noise of spectral signal not accounted for by DOAS fit parameters [optical density units]. The maximum differential absorption cross sections used are 7.0e-21 [cm<sup>2</sup>] for SO<sub>2</sub>, 2.6e-17 [cm<sup>2</sup>] for BrO, and 3.5e-17 [cm<sup>2</sup>] for IO. Detection limits for SO<sub>2</sub> dSCDs, BrO dSCDs, and IO dSCDs are only reported during periods of significant SO<sub>2</sub> detection. BrO to SO<sub>2</sub> ratios are only reported during periods when both BrO dSCDs and SO<sub>2</sub> dSCDs are above the detection limit.</p> <p>Local time (RET) is UTC+4.<br> <br> Column 1: UTC start datetime (yyyy-mm-dd HH:MM:SS)<br> Column 2: UTC center datetime (yyyy-mm-dd HH:MM:SS)<br> Column 3: UTC stop datetime (yyyy-mm-dd HH:MM:SS)<br> Column 4: Elevation angle above the horizon (degrees)<br> Column 5: O<sub>2</sub>-O<sub>2</sub> dSCD [molec<sup>2</sup> cm<sup>-5</sup>]<br> Column 6: Spectral fit error for O<sub>2</sub>-O<sub>2</sub> dSCD [molec<sup>-2</sup> cm<sup>-5</sup>]<br> Column 7: SO<sub>2</sub> dSCD [molec cm<sup>-2</sup>]<br> Column 8: Spectral fit error for SO<sub>2</sub> dSCD [molec cm<sup>-2</sup>]<br> Column 9: Detection limit for SO<sub>2</sub> dSCD [molec cm<sup>-2</sup>]<br> Column 10: BrO dSCD [molec cm<sup>-2</sup>]<br> Column 11: Spectral fit error for BrO dSCD [molec cm<sup>-2</sup>]<br> Column 12: Detection limit for BrO dSCD [molec cm<sup>-2</sup>]<br> Column 13: IO dSCD [molec cm<sup>-2</sup>]<br> Column 14: Spectral fit error for IO dSCD [molec cm<sup>-2</sup>]<br> Column 15: Detection limit for IO dSCD [molec cm<sup>-2</sup>]<br> Column 16: Ratio of BrO dSCDs to SO<sub>2</sub> dSCDs<br> Column 17: Error in ratio of BrO dSCDs to SO<sub>2</sub> dSCDs</p>
Core-Hole Spectroscopy of Energy Conversion and Storage-Related Phosphorus Compounds Using Soft and Hard X rays (Experimental XANES dataset)
<p>Dear readers,</p> <p>In the following, we provide the complete experimental X-ray absorption near-edge structure (XANES) spectroscopy dataset given in our investigation of: "Core-Hole Spectroscopy of Energy Conversion and Storage Related-Phosphorus Compounds Using Soft and Hard X-rays". This dataset includes partial fluorescence yield (PFY)-XANES at P <em>K</em>-edge and P <em>L</em><sub>2,3</sub>-edge of 9 solid P-containing compounds with different chemical environments and oxidation states ranging from P(-III) to P(V), namely: GaP, InP, red phosphorus (Red-P), H<sub>3</sub>PO<sub>3</sub>, Na<sub>2</sub>H<sub>2</sub>P<sub>2</sub>O<sub>6</sub>, H<sub>3</sub>PO<sub>4</sub>, KH<sub>2</sub>PO<sub>4</sub>, Na<sub>2</sub>HPO<sub>4</sub>, InPO<sub>4</sub>. Additionally, P <em>K</em>-edge XANES spectra of aqueous P-containing acids: 1 mol dm<sup>-3</sup> H<sub>3</sub>PO<sub>4</sub>, 1 mol dm<sup>-3</sup> H<sub>3</sub>PO<sub>3</sub>, as well as the solution mixture of 1 mol dm<sup>-3</sup> H<sub>3</sub>PO<sub>3</sub> + 1 mol dm<sup>-3</sup> H<sub>3</sub>PO<sub>4</sub>, and 0.1 mol dm<sup>-3</sup> H<sub>3</sub>PO<sub>3</sub> + 1 mol dm<sup>-3</sup> H<sub>3</sub>PO<sub>4</sub>.</p>
Data for Transient 2D IR spectroscopy and multiscale simulations reveal vibrational couplings in the Cyanobacteriochrome Slr1393-g3
<p>Data used in the Manuscript Transient 2D IR spectroscopy and multiscale simulations reveal vibrational couplings in the Cyanobacteriochrome Slr1393-g3</p>
Fluorescence correlation spectroscopy TCSPC data with and without peak artifacts - PEX5 applied experiment
<p>This is a dataset of Fluorescence Correlation Spectroscopy (FCS) Time-Correlated Single Photon Counting (TCSPC) data with and without peak artifacts. The provenance of the data is recorded in <a href="https://github.com/aseltmann/fluotracify/blob/data/data/exp-220120-correlate-ptu/LabBook-exp-220120-correlate-ptu.org">this file</a> (see a rendered version <a href="https://aseltmann.github.io/fluotracify/data/LabBook-all.html#sec-2-5">here</a>). This parent project (<a href="https://github.com/aseltmann/fluotracify">https://github.com/aseltmann/fluotracify</a>) also contains examples of how to use these models and related Python code.</p> <p>The following connected paper is currently under review and should be cited together with these model versions: <em>Seltmann, A.; Carravilla, P.; Reglinski, K.; Eggeling, E.; Waithe, D. Neural Network Informed Photon Filtering Reduces Artifacts in Fluorescence Correlation Spectroscopy Data. 2023 (currently under review)</em></p> <p><strong>Note on the file formats and notation:</strong></p> <p>"Primary data" refers to the <code>.ptu</code> files, so the actual TCSPC data. "Secondary data" refers to the <code>.pqres</code> files, which are derived files by the proprietary PicoQuant software.</p> <p><strong>Note on sample preparation (from the Supplementary Note of the paper above):</strong></p> <p>The peak artifacts measurements were produced by 20 nM Trypanosoma brucei-PEX5 N-term fused to eGFP in solution, and the corresponding control measurements by 5 nM Homo sapiens-PEX5 N-term fused to eGFP in solution. The detailed sample preparation is described elsewhere. We prepared the samples on #1.5 coverslips mounted on Attofluor Cell Chambers (Thermo Fisher Scientific). We acquired the data on a MicroTime 200 microscope (PicoQuant) equipped with an Olympus UPlanSApo 60× 1.2NA water immersion objective lens and a HydraHarp 400 TCSPC module (PicoQuant). Excitation was achieved with a 488 nm pulsed laser (PicoQuant) with a power of 5 mW measured at the sample plane. We used a 20 nM solution of Alexa Fluor for calibrating the correction collar. One TCSPC measurement had a length of 20 s for PEX5 experiments.</p>
Fluorescence correlation spectroscopy TCSPC data with and without peak artifacts - AlexaFluor 488 applied experiment
<p>This is a dataset of Fluorescence Correlation Spectroscopy (FCS) Time-Correlated Single Photon Counting (TCSPC) data with and without peak artifacts. The provenance of the data is recorded in <a href="https://github.com/aseltmann/fluotracify/blob/data/data/exp-220120-correlate-ptu/LabBook-exp-220120-correlate-ptu.org">this file</a> (see a rendered version <a href="https://aseltmann.github.io/fluotracify/data/LabBook-all.html#sec-2-5">here</a>). This parent project (<a href="https://github.com/aseltmann/fluotracify">https://github.com/aseltmann/fluotracify</a>]) also contains examples of how to use these models and related Python code.</p> <p>The following connected paper is currently under review and should be cited together with these model versions: <em>Seltmann, A.; Carravilla, P.; Reglinski, K.; Eggeling, E.; Waithe, D. Neural Network Informed Photon Filtering Reduces Artifacts in Fluorescence Correlation Spectroscopy Data. 2023 (currently under review)</em></p> <p><strong>Note on the file formats and notation:</strong></p> <p>"Primary data" refers to the <code>.ptu</code> files, so the actual TCSPC data. "Secondary data" refers to the <code>.pqres</code> files, which are derived files by the proprietary PicoQuant software.</p> <p><strong>Note on sample preparation (from the Supplementary Note of the paper above):</strong></p> <p>The peak artifact measurements were produced by mixing 20 nM of the small dye Alexa Fluor 488 (Thermo Fisher Scientific) with 10 μM of slow, large (100 nm) unilamellar vesicles (LUVs) labelled with DiO in the membrane (0.1 % mol lipid:dye). The corresponding control measurements were 20 nM Alexa Fluor 488 in solution. We prepared the LUVs by the extrusion method: 1−palmitoyl−2−oleoyl−glycero−3−phosphocholine (POPC, Avanti Polar Lipids) was vacuum dried on a glass vial for 1 h and resuspended on phosphate buffered saline to form lipid vesicles. The vesicles were extruded 30 times through 100 nm pore size polycarbonate membranes using a Mini-Extruder (Avanti Polar Lipids). We prepared the samples on #1.5 coverslips mounted on Attofluor Cell Chambers (Thermo Fisher Scientific). We acquired the data on a MicroTime 200 microscope (PicoQuant) equipped with an Olympus UPlanSApo 60× 1.2NA water immersion objective lens and a HydraHarp 400 TCSPC module (PicoQuant). Excitation was achieved with a 488 nm pulsed laser (PicoQuant) with a power of 5 mW measured at the sample plane. We used a 20 nM solution of Alexa Fluor for calibrating the correction collar. One TCSPC measurement had a length of 10 s for AF488 experiments.</p>
Fluorescence correlation spectroscopy time-series data with and without peak artifacts - simulated data
<p>This is a dataset of FCS time-series with and without peak artifacts. It was created by 2D Monte Carlo simulations of diffusing particles. The provenance of the data is recorded in <a href="https://github.com/aseltmann/fluotracify/blob/data/data/exp-201231-clustsim/LabBook-exp-201231-clustsim.org">this file</a> (see a rendered version <a href="https://aseltmann.github.io/fluotracify/data/LabBook-all.html#sec-2-2">here</a>). This parent project (<a href="https://github.com/aseltmann/fluotracify">https://github.com/aseltmann/fluotracify</a>]) also contains examples of how to use this data and related Python code to load it.</p> <p>The following connected paper is currently under review and should be cited together with this dataset: Seltmann, A.; Carravilla, P.; Reglinski, K.; Eggeling, E.; Waithe, D. Neural Network Informed Photon Filtering Reduces Artifacts in Fluorescence Correlation Spectroscopy Data. 2023 (currently under review)</p> Data structure inside each .csv file <table><tbody> <tr> <td><header></td> <td> <p>10 to 12 lines, contains metadata</p> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>...</td> <td>...</td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td><source_1></td> <td><target_1a></td> <td><target_1b></td> <td><source_2></td> <td><target_2a></td> <td><target_2b></td> <td>...</td> </tr> <tr> <td> <p>FCS time-series with artifact</p> </td> <td>Artifact time series</td> <td> <p>FCS time series without artifact</p> </td> <td> <p>FCS time series with artifact</p> </td> <td>Artifact time series</td> <td> <p>FCS time-series without artifact</p> </td> <td>...</td> </tr> <tr> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> </tr> </tbody> </table> <p> </p>
Dataset: vacuum-ultraviolet laser source for spectroscopy of trapped thorium ions
<p>This is a data set accompanying the publication "Vacuum-ultraviolet laser source for spectroscopy of trapped thorium ions" in the New Journal of Physics. https://doi.org/10.1088/1367-2630/aced1b</p>
Classification and quantification of sucrose from sugar beetand sugarcane using optical spectroscopy and chemometrics
<p>Sucrose, obtained from either sugar beet or sugarcane, is one of the main ingredients used in the food industry. Due to the same molecular structure, chemical methods cannot distinguish sucrose from both sources. More practical and affordable methods would be valuable. Sucrose samples (cane and beet) were collected from nine countries, 25% (w/w) aqueous solutions were prepared and their absorbances recorded from 200 to 1380 nm. Spectral differences were observable in the ultraviolet–visible (UV–Vis) region from 200 to 600 nm due to impurities in sugar. Linear discriminant analysis (LDA), classification and regression trees, and soft independent modeling of class analogy were tested for the UV–Vis region. All methods showed high performance accuracies. LDA, after selection of five wavelengths, gave 100% correct classification with a simple interpretation. In addition, binary mixtures of the sugar samples were prepared for quantitative analysis by means of partial least squares regression and multiple linear regression (MLR). MLR with first derivative Savitzky–Golay were most accept- able with root mean square error of cross-validation, prediction, and the ratio of (standard error of) prediction to (standard) deviation values of 3.92%, 3.28%, and 9.46, respectively. Using UV–Vis spectra and chemometrics, the results show promise to distinguish between the two different sources of sucrose. An affordable and quick analysis method to differentiate between sugars, produced from either sugar beet or sugarcane, is suggested. This method does not involve complex chemical analysis or high-level experts and can be used in research or by industry to detect the source of the sugar which is important for some countries’ agricultural policies.</p>
Dataset for: Laser absorption spectroscopy measurements of different pulmonary oxygen gas concentrations in transmittance and remittance geometry – phantom study
<p><strong>Significance</strong></p> <p>GASMAS technique has the potential for continuous, clinical monitoring of pre-term infant lung function, removing the need to X-ray diagnosis and reliance on indirect and relatively slow measurement of blood oxygenation.</p> <p><strong>Aim</strong></p> <p>To determine optimal source-detector configuration for reliable path lengths calculation and to estimate the oxygen gas concentration inside the lung cavities filled with humidified gas with four different oxygen gas concentrations ranging between 21% and 100%.</p> <p><strong>Approach</strong></p> <p>Anthropomorphic optical phantoms of neonatal thorax with two different geometries were used to acquire Gas in Scattering Media Absorption Spectroscopy (GASMAS) signals, for 30 source-detector configurations in transmittance and remittance geometry of phantoms in two sizes.</p> <p><strong>Results</strong></p> <p>The results show that an internal light administration is more likely to provide a high GASMAS signal-to-noise ratio (SNR). In general, better SNRs were obtained with the smaller set of phantoms. The values of path length and O<sub>2</sub> concentrations calculated with signals from the phantoms with optical properties at 820 nm, exhibit higher variations than signals from the phantoms with optical properties at 764 nm.</p> <p><strong>Conclusion</strong></p> <p>The study shows that by moving the source and detector over the thorax, most of the lung volumes can potentially be assessed using GASMAS technique.</p>
Examining Validity and Sensitivity of Pressure-Mediated Reflection Spectroscopy
ClinicalTrials.gov study NCT04056624. IPD Sharing: YES. Countries: 1. Publications: 3.
In vivo and in vitro electrochemical impedance spectroscopy analysis of acute and chronic intracranial electrodes
Open the record for dataset details and reuse information.
Models for Rapid estimates of leaf litter chemistry using reflectance spectroscopy
Open the record for dataset details and reuse information.
Spectral and trait data for Rapid estimates of leaf litter chemistry using reflectance spectroscopy
Open the record for dataset details and reuse information.
Evaluating the use of Fourier transform Raman spectroscopy for pollen chemical characterization
Open the record for dataset details and reuse information.
Data from: Non-invasive estimation of absorbed ionizing radiation dose in mice using Near-Infrared Spectroscopy (NIRS) and aquaphotomics
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Supporting data files for "Binding of Biologically Relevant Divalent Cations to Aqueous Carboxylates: Molecular Simulations Guided by Raman Spectroscopy"
<p>Parameter files and typical simulation input files that allow replication of the computational work presented in the paper "Binding of Biologically Relevant Divalent Cations to Aqueous Carboxylates: Molecular Simulations Guided by Raman Spectroscopy", authored by Denilson Mendes de Oliveira, Samual R. Zukowski, Vladimir Palivec, Jérôme Hénin, Hector Martinez Seara, Dor Ben Amotz, Pavel Jungwirth and Elise Duboué-Dijon</p>
Nanophotonic supercontinuum-based mid-infrared dual-comb spectroscopy - Dataset and Codes
<p>Here we publish the source data and the codes for simulation and data processing regarding the article "Nanophotonic supercontinuum-based mid-infrared dual-comb spectroscopy" that is published on Optica (<a href="https://doi.org/10.1364/OPTICA.396542">https://doi.org/10.1364/OPTICA.396542</a>).</p>
What Can MR Spectroscopy Measures of Occipital GABA tellabout Visual Plasticity in Human Adult?: Exp3 Dataset
<p>Dataset from Exp3 in "Proulx, Sébastien, Sheynin, Yasha, Hess, Robert, & Farivar, Reza. (2020). What Can MR Spectroscopy Measures of Occipital GABA tell about Visual Plasticity in Human Adult?. Zenodo. http://doi.org/10.5281/zenodo.4034898".</p> <p>Data was collected in 3-min measures: 3 pre-deprivation seperated by 3-min breaks and 3 deprivation measured at 30, 50 and 70 minutes into deprivation.</p> <p>The 'data' variable from the exp3Data.mat file stores percept durations that were logged then averaged within measures.</p> <p>The 'dataTot' variable from the exp3Data.mat file is the same data as in the 'data' variable, but processed differently as the fraction of a measure time (180min) a given percept is perceived.</p> <p>Metadata is included in the data and dataTot variable structure.</p>
Confocal Raman spectroscopy data from native and polyethylene glycol-containing wood
<p>Confocal Raman mapping data from native pine wood and pine wood with polyethylene glycol (PEG) of three different molecular weights in deuterated water.</p> <p>The spectroscopic data consists of 4 text files, each of which contains in total 1600 2D arrays of Raman intensity corresponding to different wavenumber values. Each of these 2D arrays consists of 175 rows with 175 comma-separated values on each row. The 2D arrays corresponding to different wavenumbers are separated by a line starting with '#' and specifying the wavenumber of the subsequent 2D array (unit inverse cm). The wavenumber axis is also given in a separate file. The image size is 45 µm x 45 µm (175 x 175 pixels) and the wavenumber axis consists of 1600 points.</p> <p>For further details on the samples and data collection, see the following reference:<br> Paavo A. Penttilä, Michael Altgen, Muhammad Awais, Monika Österberg, Lauri Rautkari, & Ralf Schweins. Bundling of cellulose microfibrils in native and polyethylene glycol-containing wood cell walls revealed by small-angle neutron scattering. <em>Scientific Reports</em> <strong>10, </strong>20844 (2020). https://doi.org/10.1038/s41598-020-77755-y</p>
Figures, plotting scripts, and data for "Resolving non-uniform temperature distributions with single-beam absorption spectroscopy. Part I: Theoretical capabilities and limitations"
<p>This dataset contains the necessary materials to recreate the figures in "Resolving nonuniform temperature distributions with single-beam absorption spectroscopy: Part I: Theoretical capabilities and limitations."</p> <p>The code included in this dataset is released under the BSD 3-Clause License. The figures are shared under the Creative Commons Attribution 4.0 International License (CC BY 4.0, https://creativecommons.org/licenses/by/4.0/).</p>
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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)
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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.