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85 results for “NMR Spectroscopy”
Metabomatching: Using Genetic Association to Identify Metabolites in Proton NMR Spectroscopy. CoLaus Pseudospectra.
<p>Summary statistics between urine NMR metabolome features and genotypes in the CoLaus cohort. Used as test pseudospectra for metabomatching, a method for metabolite identification using genetic spiking.</p>
Metabomatching: Using Genetic Association to Identify Metabolites in Proton NMR Spectroscopy. SHIP Pseudospectra.
<p>Summary statistics between urine NMR metabolome features and genotypes in the SHIP cohort. Used as test pseudospectra for metabomatching, a method for metabolite identification using genetic spiking.</p>
An assessment of acd_lotus for the structural dereplication of natural products using 13C NMR spectroscopy data.
<p> A method that relies on carbon-13 nuclear magnetic resonance (NMR) spectroscopy, elaborated in earlier works of the author's research group, requires the availability of a dedicated database that establishes relationships between chemical structures, biological and chemical taxonomy, and spectroscopy. The construction of such a database, called <a href="https://doi.org/10.5281/zenodo.6621129">acd_lotus</a>, was reported earlier and its usefulness was only illustrated by <a href="https://doi.org/10.1002/cmtd.202200054">three examples</a>. This dataset provides the results of structure searches carried out starting from 58 carbon-13 NMR data sets recorded on compounds selected in the metabolomics section of BMRB, the biological magnetic resonance bank.</p> <p>Correction in the JSON file of ascochitine.</p> <p>Addition of a CSV file according to <a href="https://doi.org/10.1186/s13321-021-00520-4">Schymanski and Bolton</a> for the description of the selected compounds.</p> <p>Better PNG drawings for compounds 28, 38, 46, and 57.</p> <p>Better PNG drawing for compounds 7 and 26.</p>
Data for: Nawrocka et al., Variable-temperature NMR spectroscopy for metabolite identification in biological materials, RSC Adv., 2021, 11, 35321, DOI: 10.1039/d1ra05626c
<p>Raw NMR spectrometer data (Varian/Agilent format) for the paper Nawrocka et al., Variable-temperature NMR spectroscopy for metabolite identification in biological materials, RSC Adv., 2021, 11, 35321, DOI: 10.1039/d1ra05626c</p> <p>Includes variable-temperature series for artificial and natural mixtures of metabolites and spectra of separate metabolites.</p>
Assessment of Hydrophilicity/Hydrophobicity in Mesoporous Silica by combining Adsorption, Liquid Intrusion and solid-state NMR spectroscopy
<p>This data publication is based on the metadata and datasets underlying the manuscript "</p> <p><span>Assessment of Hydrophilicity/Hydrophobicity in Mesoporous Silica by Combining Adsorption, Liquid Intrusion, and Solid-State NMR Spectroscopy (</span>"https://doi.org/10.1021/acs.langmuir.3c03516")</p> <p>Included are the datasets used, raw and processed data of Adsorption measurements (Water, Ar 87K), Water Intrusion measurements, solid state MAS NMR measurements. and molecular dynamics simulations. </p>
Maximizing Relayed 1H Hyperpolarization Transfer by Slow-Fast MAS NMR Spectroscopy
<p>NMR raw data, matlab scripts, and data related to publication: https://doi.org/10.1021/acs.jpca.4c02452.</p> <p>The raw data content is described in the README files provided within the folders.</p>
Raw data used in the manuscript titled "Metabolomic Analysis of Histological Composition Variability of High-Grade Serous Ovarian Cancer Using 1H HR MAS NMR Spectroscopy "
<p>The folder contains raw data used in the manuscript titled "Metabolomic Analysis of Histological Composition Variability of High-Grade Serous Ovarian Cancer Using <sup>1</sup>H HR MAS NMR Spectroscopy ".</p> <p> </p> <p> Raw data measured on Bruker Avance III 400 MHz NMR spectrometer:</p> <p>- 1D <sup>1</sup>H HR MAS NMR spectra (path: <em>Patient_code – Sample_code/500/fid</em>)</p> <p>- 2D <sup>1</sup>H-<sup>1</sup>H J-resolved HR MAS NMR spectra (path: <em>Patient_code – Sample_code/600/ser</em>).</p> <p> </p> <p>Metadata is included in <em>Metadata.xlsx</em> file.</p> <p>Each sample is described with the following parameters:</p> <p>- patient code (after anonymization),</p> <p>- sample code (the label <em>l</em> or <em>r</em> denotes the <em>left</em> or <em>right</em> ovary in patients from whom samples were obtained bilaterally),</p> <p>- sample weight,</p> <p>- clinic-pathological parameters (such as: age, BMI, menopausal status, diagnosis, FIGO stage),</p> <p>- percentage tissue content obtained from histopathological analysis after HR MAS NMR studies (cancer cells, epithelial compartment within benign tumors, necrosis, inflammation, fibrosis, calcification, normal ovary, vessels, fatty tissue).</p> <p> </p> <p>Some samples were considered representative of particular tissue components:</p> <p>- cancer (HGSOC) compartment,</p> <p>- fibrotic stroma within malignant (HGSOC) tumors,</p> <p>- fibrotic stroma within benign tumors,</p> <p>- normal ovary tissue (the samples collected from the control group),</p> <p>- normal ovary tissue (the samples collected from the cancer patients),</p> <p>- necrosis,</p> <p>- non-tumoral fibrous tissue / fibrous tumor capsule (obtained from the patients with benign non-neoplastic lesions)</p> <p>- corpus albicans</p> <p>The assignment of the samples to these categories is indicated in the column <em>Tissue components.</em></p> <p><em> </em></p> <p>The samples classified as outliers in PCA model 1 are indicated in the column <em>Outliers</em>.</p> <p>The samples included in multivariate models are indicted in the columns: <em>PCA 2, PCA 3, PCA 4, PCA 5, PCA 5a, PCA 6, OPLS-DA 1, OPLS-DA 2, OPLS-DA 3, OPLS-DA 4, OPLS-DA 5, OPLS-DA 6 and OPLSR.</em></p> <p><em> </em></p>
A complete picture of cation dynamics in hybrid perovskite materials from solid-state NMR spectroscopy
<p> Raw, collated NMR and XRD data for the article "A Complete Picture of Cation Dynamics in Hybrid Perovskite Materials from Solid-State NMR Spectroscopy". For further details see the readme.txt file.</p>
Training data for benchtop NMR and UV/vis spectroscopy for Artificial Neural Networks
<p>Data set of low-field NMR spectra and UV/vis spectra for the synthesis of mesalazine intermediates, which were used as training or validation data for data processing with artificial neural networks development</p> <p><strong>Low-field NMR spectra for the nitration step:</strong></p> <p>The pure component spectrum of 2ClBA, 3N-2ClBA, and 5N-2ClBA are marked as NMR_pure_spectrum. The concentration levels for 2ClBA, 3N-2ClBA and 5N-2ClBA are in row 1, 2, and 3, respectively.</p> <p>The data sets marked as NMR_ represents low-field NMR-spectra recorded. The reference values for 2ClBA, 3N-2ClBA and 5N-2ClBA are in column 1, 2, and 3, respectively.</p> <p><strong>Datafusion data sets for the hydrolysis and nitration step</strong></p> <p>The NMR data are either recorded or simulated from the pure NMR spectrum of each individual component. The reference values for 2ClBA, 3N-2ClBA, 5N-2ClBA, 3-NSA and 5-NSA are either assigned with UHPLC measurements or calculated from the prepared solutions.</p> <p>The NMR spectra are depicted in datafusion_NMR_training. The reference values for 2ClBA, 3N-2ClBA and 5N-2ClBA are in column 1, 2, and 3, respectively.</p> <p>The UV/vis spectra are depicted in datafusion_UVvis_training. The reference values for 2ClBA, 3N-2ClBA, 5N-2ClBA, 3-NSA and 5-NSA are in column 1, 2, 3, 4, and 5, respectively.</p> <p><strong>Process data</strong></p> <p>The NMR spectra for the stability run and the run with dynamic changes are depicted in process_NMR_. The first column is the time stamp.</p> <p>The UV/vis spectra for the stability run and the run with dynamic changes are depicted in process_UV_. The first column is the time stamp.</p>
Sensitivity-enhanced multidimensional solid-state NMR spectroscopy by optimal-control-based transverse mixing sequences
<p>The dataset here contains the raw data and pulse programs used for the publication "Sensitivity-enhanced multidimensional solid-state NMR spectroscopy by optimal-control-based transverse mixing sequences" submitted to JACS.</p> <p>All data is in a native Bruker TopSpin format. Data is organized in folders corresponding to Figures of the original publication. Detailed description is included in the file description.txt.</p> <p>We reccomend to visit our website optimal-nmr.net for additional information about optimal control methods applied to pulse sequence development for solid-state magic-angle-spinning NMR studies of proteins.</p>
NMR spectroscopy-based metabolomics of organotypic retinal explants
<p>The retina consumes massive amounts of energy, yet its metabolism and substrate exploitation remain poorly understood. Here, we used a murine explant model to manipulate retinal energy metabolism under entirely controlled conditions and utilized <sup>1</sup>H-NMR spectroscopy-based metabolomics, in situenzyme detection, and cell viability readouts to uncover the pathways of retinal energy production. Our experimental manipulations resulted in varying degrees of photoreceptor degeneration, while the inner retina and retinal pigment epithelium were essentially unaffected. This selective vulnerability of photoreceptors suggested very specific adaptations in their energy metabolism. Rod photoreceptors were found to rely strongly on oxidative phosphorylation, but only mildly on glycolysis. Conversely, cone photoreceptors were dependent on glycolysis but insensitive to electron transport chain decoupling. Importantly, photoreceptors appeared to uncouple glycolytic and Krebs-cycle metabolism via three different pathways: 1) the mini-Krebs-cycle, fueled by glutamine and branched-chain amino acids, generating N-acetylaspartate; 2) the alanine-generating Cahill-cycle; 3) the lactate-releasing Cori-cycle. Moreover, the metabolomic data indicated a shuttling of taurine and hypotaurine between the retinal pigment epithelium and photoreceptors, likely resulting in an additional net transfer of reducing power to photoreceptors. These findings expand our understanding of retinal physiology and pathology and shed new light on neuronal energy homeostasis and the pathogenesis of neurodegenerative diseases.</p>
Loading-Dependent Structural Model of Polymeric Micelles Encapsulating Curcumin by Solid-State NMR Spectroscopy
<p>(Raw) experimental and calculation data, which was the basis for this publication.</p> <ul> <li>DOSY</li> <li>solid-state NMR</li> <li>PXRD</li> <li>Dissolution Rates</li> <li>GIPAW (CASTEP) calculations</li> </ul>
Applications of Fast Iterative Filtering in NMR spectroscopy [NMR DATASETS]
<p>Employed NMR spectra for the application of denoising and baseline correction method described in the manuscript.</p>
Rawdata for publication: An Efficient and Stable Polarizing Agent for In-Cell Magic-Angle Spinning Dynamic Nuclear Polarization NMR Spectroscopy
<p>This is the raw dataset for the publication: An Efficient and Stable Polarizing Agent for In-Cell Magic-Angle Spinning Dynamic Nuclear Polarization NMR Spectroscopy. It contains the EPR and NMR data used in this publication. DOI of this pubcliation: 10.1021/acs.jpclett.4c02709</p>
Data for "Optically Enhanced Solid-State 1H NMR Spectroscopy"
<p>Raw 1H NMR and photo-CIDNP-enhanced NMR data for "Optically Enhanced Solid-State 1H NMR Spectroscopy". A Mathematica notebook for data processing is also included.</p>
NMR spectroscopy-based metabolomics of organotypic retinal explants
Open the record for dataset details and reuse information.
NMR spectroscopy of coin cell batteries with metal casings
<p>Raw NMR data in Bruker format for experiments on coin cells with metal casings.</p> <p>Operando experiment on COTS cell: 20201112-LiAlCell3/19</p>
Data for Observation of 1H-1H J-couplings in fast magic-angle-spinning solid-state NMR spectroscopy
<p>Supporting data for Observation of 1H-1H J-couplings in fast magic-angle-spinning solid-state NMR spectroscopy.</p> <p>Raw and processed NMR data and fitting codes.</p> <p>See individual README.txt in each zip file for details.</p>
NMR spectroscopy data (600 and 950 MHz) of oxidised triolein, trilinolein, trilinolenin, rapeseed oil, and sunflower oil
<p>Dataset containing NMR (nuclear magnetic resonance) spectroscopy of oxidised triacylglycerides (triolein, trilinolein, and trilinolenin) and oils (rapeseed oil and sunflower oil). The dataset was used for the annotation of substructures observed in oxidised vegetable oil. It includes 1D proton, 1D selective proton, 2D (band-selective) HSQC, 2D (band-selective) HMBC, 2D TOCSY, 2D ROESY, 2D HSQC-TOCSY spectra.</p> <p>This dataset was used to develop a NMR-based oxylipidomics worksflow. We describe a workflow that can be used to study lipid oxidation mechanistically in a paper that can be found here: https://www.sciencedirect.com/science/article/abs/pii/S0963996924016831.</p> <p>The NMR data was acquired on two systems. A 950 MHz with cryoprobe from the NMR facility at Radboud University (Nijmegen, the Netherlands) and a 600 MHz from the MAGNEFY centre (Wageningen University, the Netherlands). </p>
NMR data: Dynamic nuclear polarization solid-state NMR spectroscopy as a tool to rapidly determine degree of modification in dialcohol cellulose
<p>Solid-state NMR data underlying figures and tables in the article: <span>Dynamic nuclear polarization solid-state NMR spectroscopy as a tool to rapidly determine degree of modification in dialcohol cellulose</span></p>
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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)
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DANDI Archive for NWB datasets
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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.