Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
535
datasets available to search
ShareScore release 0.9.0
Dataset results
535 results for “NMR,”
Microscopic understanding of NMR signals by dynamic mean-field theory for spins
<p>Data collection for several plots of the article <a href="https://doi.org/10.1016/j.ssnmr.2024.101936">Microscopic understanding of NMR signals by dynamic mean-field theory for spins</a>. Each hdf5-file contains one or several datasets of a specific figure. The datasets are described by the attribute "description".</p>
Ir and NMR for article Synthesis and Biological Activity Evaluation of New Isatin-Gallate Hybrids as Antioxidant and Anticancer Agents (in vitro) and In-silico Study as Anticancer Agents and Coronavirus Inhibitors
<p>this is IR and NMR data for article titled <strong>Synthesis and Biological Activity Evaluation of New Isatin-Gallate Hybrids as Antioxidant and Anticancer Agents (<em>in vitro</em>) and In-silico Study as Anticancer Agents and Coronavirus Inhibitors </strong></p> <p> </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>
Concise Synthesis of Pseudane IX, its N-Oxide and Novel Carboxamide Analogs with Antibacterial Activity - NMR Data
<p>This archive contains raw 1H/13C FIDs and associated data in Bruker-specific format that can be viewed with Bruker’s TopSpin or other appropriate NMR processing software. The subfolders are named in accordance with the compound numbering in the associated research paper (Concise Synthesis of Pseudane IX, its N-Oxide and Novel Carboxamide Analogs with Antibacterial Activity).</p> <p>Correspondence: angelov@uni-plovdiv.bg</p>
Triple-resonance NMR spectra of Tau 1-239 protein fragment acquired at 5 C, 10 C and 15 C
<p>Dataset used in a paper "Using temperature coefficients to support resonance<br>assignment of intrinsically disordered proteins" by Paulina Putko, Javier Agustin Romero, Christian F. Pantoja, Markus<br>Zweckstetter, Krzysztof Kazimierczuk, and Anna Zawadzka-Kazimierczuk</p> <p>The following spectra have been collected:</p> <table> <tbody> <tr> <td>Experiments<br>T : 5 C</td> <td>NUS points<br>(Recorded)</td> <td>NS (scans)<br>(Recorded)</td> </tr> <tr> <td>HNCO</td> <td>600</td> <td>4</td> </tr> <tr> <td>HN(CA)CO</td> <td>1000</td> <td>16</td> </tr> <tr> <td>HNCA</td> <td>600</td> <td>8</td> </tr> <tr> <td>HN(CO)CA</td> <td>450</td> <td>8</td> </tr> <tr> <td> <p>CBCA(CO)NH</p> <p>(HAHB)CBCA(CO)NH</p> </td> <td>1000</td> <td> </td> </tr> <tr> <td>Experiments<br>T : 10 C</td> <td>NUS points<br>(Recorded)</td> <td>NS (scans)<br>(Recorded)</td> </tr> <tr> <td>HNCO</td> <td>600</td> <td>4</td> </tr> <tr> <td>HN(CO)CA</td> <td>450</td> <td>8</td> </tr> <tr> <td>(HAHB)CBCA(CO)NH</td> <td>1000</td> <td>8</td> </tr> <tr> <td>Experiments<br>T : 15 C</td> <td>NUS points<br>(Recorded)</td> <td>NS (scans)<br>(Recorded)</td> </tr> <tr> <td>HNCO</td> <td> 600</td> <td> 4</td> </tr> <tr> <td>HN(CO)CA</td> <td> 450</td> <td> 8</td> </tr> <tr> <td>(HAHB)CBCA(CO)NH</td> <td> 1000</td> <td> 8</td> </tr> <tr> <td> </td> <td> </td> <td> </td> </tr> </tbody> </table>
Rawdata for publication: Observation of Transient Prenucleation Species of Calcium Carbonate by DNP-Enhanced NMR
<p>This is the raw data set for the publication:</p> <p>Observation of Transient Prenucleation Species of Calcium Carbonate by DNP-Enhanced NMR</p> <p>by</p> <p>Martins Balodis, Yu Rao, Gabriele Stevanato, Matthias Kellner, Josephine Meibom, Mattia Negroni, Bradley F. Chmelka, Lyndon Emsley .</p> <p>https://pubs.acs.org/doi/10.1021/acs.jpclett.4c01588</p>
NMR, HPLC and HRMS data - General Cyclopropane Assembly via Enantioselective Transfer of a Redox-Active Carbene to Aliphatic Olefins
<p>Raw data for the article with the same title.</p> <p>ChemRxiv pre-print (<a href="https://doi.org/10.26434/chemrxiv.7436795">https://doi.org/10.26434/chemrxiv.7436795</a>)</p>
Enantioselective Assembly of Congested Cyclopropanes using Redox-Active Aryldiazoacetates - NMR, HRMS and X-ray Raw Data
<p>NMR, HRMS and single crystal X-ray diffraction raw data for the compounds in the manuscript ACS Catalysis 2019, DOI: <a href="https://doi.org/10.1021/acscatal.9b02615">https://doi.org/10.1021/acscatal.9b02615</a></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>
Distinct association of HRAS and KRAS with Mn(II) ion illustrated by paramagnetic NMR
<p><a name="OLE_LINK4"></a><span>RAS proteins are among crucial oncogenic proteins and are involved in several essential intracellular processes. The RAS protein has an intrinsic metal binding site for Mg<sup>2+</sup>, which is important for the conformational stability of the active site. Recently, it was reported that a second metal ion binding site, located further from the active site in HRAS, binds Ca<sup>2+</sup> with millimolar affinity.</span><span> As one of the most abundant metal ions in cells, Mn<sup>2+</sup> is a potential candidate for the second metal ion binding site in RAS proteins. Here, we examined the interaction of Mn<sup>2+</sup> with HRAS and KRAS using high resolution NMR spectroscopy. The NMR data showed that both the second metal ion binding site and the switch I and II regions bind Mn<sup>2+</sup> in the RAS proteins. Furthermore, our paramagnetic NMR results disclosed the conformational differences in helix </span><span>a</span><span>3 and the following loop between HRAS and KRAS, accompanied by the association with metal ion binding. These results provide new insights into the interaction of RAS proteins and Mn<sup>2+</sup> in the respective biological processes in cells.</span></p>
A combined NMR and Deep Neural Network approach for enhancing the spectral resolution of aromatic side chains in proteins
<p><span><span>Nuclear magnetic resonance (NMR) spectroscopy has become an important technique in structural biology for characterising the </span><span>structure, </span><span>dynamics</span> <span>and interactions </span><span>of </span><span>macromolecules. Wh</span><span>ile</span><span> a plethora of </span><span>NMR </span><span>methods are </span><span>now </span><span>available to inform on backbone and methyl-bearing </span><span>side-chains</span><span> of proteins, </span><span>a </span><span>characterisation</span><span> of</span><span> aromatic side chains is more challenging and often require</span><span>s</span><span> specific labelling or </span></span><span><span>13</span></span><span><span>C-detection. Here we </span><span>present</span><span> a deep neural network (DNN)</span> <span>named FID-Net-2</span><span>, which transforms NMR spectra recorded on simple uniformly </span></span><span><span>13</span></span><span><span>C labelled samples to yield high-quality </span></span><span><span>1</span></span><span><span>H</span><span>-</span></span><span><span>13</span></span><span><span>C correlation spectra of the aromatic side chains. </span><span>Key to the success of the DNN is the design of a </span><span>complementary</span><span> set of</span> <span>NMR experiment</span><span>s</span><span> that </span><span>produce</span> <span>spectra with </span><span>unique </span><span>features</span><span> to aid the</span> <span>DNN </span><span>prod</span><span>uce</span><span> high-resolution aromatic </span></span><span><span>1</span></span><span><span>H-</span></span><span><span>13</span></span><span><span>C correlation spectra with </span><span>accurate</span><span> intensities. </span><span>The </span><span>reconstructed spectra can be used for quantitative </span><span>purposes as FID-Net-2</span><span> predicts uncertainties </span><span>in </span><span>the </span><span>resulting</span> <span>spectra</span><span>. </span><span>We </span><span>have </span><span>validated</span> <span>the new </span><span>methodology</span><span> experimentally</span> <span>on protein</span><span> samples</span><span> ranging from 7 to 40 </span><span>kDa</span><span> in size. We </span><span>demonstrate</span><span> that the method can</span> <span>accurately reconstruct</span> <span>high resolution </span><span>two-dimensional </span><span>aromatic </span></span><span><span>1</span></span><span><span>H-</span></span><span><span>13</span></span><span><span>C correlation </span><span>maps</span><span>,</span> <span>high resolution </span><span>three-dimensional aromatic</span><span>-</span><span>methyl NOESY spectra to </span><span>facilitate</span><span> aromatic </span></span><span><span>1</span></span><span><span>H-</span></span><span><span>13</span></span><span><span>C assignments</span><span>,</span><span> and </span><span>that the intensities of peaks from the reconstructed</span> <span>aromatic </span></span><span><span>1</span></span><span><span>H-</span></span><span><span>13</span></span><span><span>C correlation maps </span><span>can be used to </span><span>quantitatively </span><span>characterise</span><span> the kinetics of protein folding</span><span>. </span><span>More generally, w</span><span>e believe that this strategy of</span><span> devising new</span><span> NMR</span><span> experiments</span><span> specifically</span> <span>for analysis</span> <span>using customised </span><span>DNN</span><span>s </span><span>represents</span><span> a </span><span>substantial</span><span> advance </span><span>that</span> <span>will</span><span> have a major impact on the study of molecules </span><span>using NMR </span><span>in the years to come.</span></span><span> </span></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>
NMR spectra for ambocidin A, A1, B and B1.
<p>This dataset contains raw NMR spectra for the calcium dependent lipopeptide antibiotics ambocidin A, A1, B and B1. Zipped folders contain FIDs for the compounds indicated in the file name. Discovery of these compounds is described in<em> Calcium-Dependent Lipopeptide Antibiotics against Drug-Resistant Pathogens Discovered via Host-Dependent Heterologous Expression of a Cloned Biosynthetic Gene Cluster,</em> Angew. Chem. Int. Ed. 2024, e202410286.</p>
Raw data and figures for "Increasing Sensitivity and Versatility in NMR Supersequences with New HSQC-based Modules"
<p>Datasets, pulse programmes, processing scripts, and figures for the publication "Increasing Sensitivity and Versatility in NMR Supersequences with New HSQC-based Modules" (J. Magn. Reson., accepted, 2021).</p> <p>Please see the README.txt file for more information.</p> <p>If you are only interested in pulse programmes and processing scripts, then this dataset will likely be too big for your liking (it is ca. 9 GB because there is a lot of 2D data). You can download the pulse programmes and processing scripts from the Bruker User Library https://www.bruker.com/en/services/bruker-user-library.html.</p> <p><strong>Changelog</strong></p> <p>v1.0.1: some quality of life improvements to the Python NUS script to stop it from being accidentally used on old pulse programmes.</p> <p>v1.0.2: fixed a bug in the v1.0.1 Python script. It now also resets FnTYPE in case the user has changed it.</p>
Dataset for: Photoreactivity of an Exemplary Anthracene Mixture Revealed by NMR Studies, Including a Kinetic Approach
<p>Anthracenes are an important class of acenes. They are being utilized more and more often in chemistry and materials science, due to their unique rigid molecular structure and photoreactivity. In particular, photodimerization can be harnessed for the fabrication of novel photoresponsive materials. Photoreactions between the same anthracenes have been investigated and utilized in various fields, while reactions between varying anthracenes have barely been investigated. Here, Nuclear Magnetic Resonance (NMR) spectroscopy is employed for the investigation of the photodimerization of two exemplary anthracenes: anthracene (A) and 9-bromoanthracene (B), in the solutions with only A or B, and in the mixture of A and B. Estimated k values, derived from the presented kinetic model, showed that the dimerization of A was 10 times faster in comparison with B when compounds were investigated in separate samples, and 2 times faster when compounds were prepared in the mixture. Notably, the photoreaction in the mixture, apart from AA and BB, also yielded a large amount of the AB mixdimer. The investigation of a mixture with different anthracenes can deliver relative reactivity under the same experimental conditions. This results in a better understanding of the photoisomerization processes, which is essential for the practical utilization of the photodimerization of anthracenes.</p> <p>---------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>The study employs 400 MHz 1H-NMR spectroscopy for the investigation of the photodimerization of two exemplary anthracenes: anthracene (A) and 9-bromoanthracene (B) in the solutions with only A or B and in the mixture of A and B. Experiments were performed using different concentrations and oxygenation level of the sample. CD2Cl2 was used as a solvent. </p> <p> </p> <p><strong>Given data include eight 1H-NMR experiments processed by TopSpin 4.0.7 software of:</strong></p> <ol> <li> 4.5 mM of A (File: 1_A_concentration I.fid.zip)</li> <li> 2.25 mM of A (File: 2_A_concentration II.fid.zip)</li> <li> 2.25 mM of A with oxygen (File: 3_A_concentration II_Oxygen.fid.zip)</li> <li> 4.5 mM of B (File: 4_B_concentration I.fid.zip)</li> <li> 2.25 mM of B (File: 5_B_concentration II.fid.zip)</li> <li> 2.25 mM of B with oxygen (File: 6_B_concentration II_Oxygen.fid.zip)</li> <li> Mixture of A and B with molar mixing ratio 1:1.3 (File: 7_Mixture_ratio I.fid.zip)</li> <li> Mixture of A and B with molar mixing ratio 1:2.3 (File: 8_Mixture_ratio II.fid.zip)</li> </ol> <p>-------------------------------------------------------------</p>
Fluorine NMR study of proline-rich sequences using fluoroprolines
<p>Experimental NMR dataset related to the publication in Magnetic Resonance 2021 entitled:</p> <p>"Fluorine NMR study of proline-rich sequences using fluoroprolines"</p> <p>by</p> <p>Davy Sinnaeve, Abir Ben Bouzayene, Emile Ottoy, Gert-Jan Hofman, Eva Erdmann,<br> Bruno Linclau, Ilya Kuprov, José C. Martins, Vladimir Torbeev and Bruno Kieffer</p> <p><strong>Abstract.</strong> Proline homopolymer motifs are found in many proteins; their peculiar conformational and dynamic properties are often directly involved in those proteins' functions. However, the dynamics of proline homopolymers is hard to study by NMR due to lack of amide protons and small chemical shift dispersion. Exploiting the spectroscopic properties of fluorinated prolines opens interesting perspectives to address these issues. Fluorinated prolines are already widely used in protein structure engineering – they introduce conformational and dynamical biases – but their use as <sup>19</sup>F NMR reporters of proline conformation has not yet been explored. In this work, we look at model peptides where Cγ-fluorinated prolines with opposite configurations of the chiral Cγ centre have been introduced at two postions in distinct polyproline segments. By looking at the effects of swapping these (4<em>R</em>)- and (4<em>S</em>)-4-fluoroprolines within the polyproline segments, we were able to separate the intrinsic conformational properties of the polyproline sequence from the conformational alterations instilled by fluorination. We assess the fluoroproline <sup>19</sup>F relaxation properties, and exploit the latter in elucidating binding kinetics to the SH3 domain</p> <p>CONTENT OF THE REDEPOSITORY</p> <p><strong>Peptide assignment </strong></p> <p>To assign MpRS and MpSR peptides we recorded at 298 K the following experiments:</p> <ul> <li>H-<sup>13</sup>C HSQC. Recorded at 700MHz. The corresponding data are in the following datasets: MpRS_HSQC and MpSR_HSQC</li> <li>H-<sup>1</sup>H TOCSY. Recorded at 600MHz. The corresponding data are in the following datasets: MpRS_TOCSY and MpSR_TOCSY</li> <li>H-<sup>13</sup>C HSQC-NOESY. Recorded at 700MHz. The corresponding data are in the following datasets: MpRS_HSQC_NOESY and MpSR_HSQC_NOESY</li> <li>H-<sup>1</sup>H NOESY. Recorded at 600MHz. The corresponding data are in the following datasets: MpRS_NOESY and MpSR_NOESY</li> </ul> <p> </p> <p><strong>Homonuclear scalar coupling constants </strong></p> <p>Homonuclear <sup>3</sup>J<sub>H-H </sub>scalar couplings were measured using SERF experiments on a 700MHz spectrometer at 298 K. The corresponding data can be examined in the followed datasets: MpRS_SERF and MpSR_SERF for both MpRS and MpSR peptides, respectively.</p> <p> </p> <p><strong><sup>1</sup></strong><strong>D <sup>19</sup>F spectra </strong></p> <p> </p> <p><sup>1</sup>D <sup>19</sup>F spectra of MpRS and MpSR peptides were recorded on a 600MHz spectrometer at 298 K, with samples dissolved in D2O. 3mm tubes were used. The data are stored in the following datasets: MpRS_1D_F and MpSR_1D_F for both MpRS and MpSR peptides respectively.</p> <p> </p> <p><strong><sup>19</sup></strong><strong>F Relaxation </strong></p> <p>Longitudinal and transverse relaxation rates of fluorine were measured for both MpRS and MpSR peptides. Standard inversion recovery, spin-echo and CPMG experiments were collected for both MpSR and MpRS peptides on a 600MHz at 298 K. The corresponding data may be examined in these datasets:</p> <p>For MpRS: MpRS_R1, MpRS_R2_spin_echo, MpRS_R2_CPMG</p> <p>For MpSR: MpSR_R1, MpSR_R2_spin_echo, MpSR_R2_CPMG</p> <p> </p> <p><sup>1</sup>HG-<sup>19</sup>F heteronuclear-nOes were measured using nOe buildup experiments recorded on a 600MHz spectrometer at 298 K. The experimental data of both peptides are in the following datasets: MpRS_HGF_NOE and MpSR_HGF_NOE</p> <p> </p> <p><strong>Titration experiments with Vinexin SH3.3 domain</strong></p> <p>Titration experiments were performed on a 600MHz spectrometer at 298K using 3 mm tubes. The respective volumes of peptide and SH3 protein are given in the title file. The 1D <sup>19</sup>F spectra of MpRS and MpSR peptides are stored in titrMpRS_1D_F and titrMpSR_1D_F, respectively. The <sup>1</sup>H-<sup>15</sup>N HSQC spectra are stored in titrMpRS_HSQC and titrMpSR_HSQC datasets.</p> <p>Stock solutions were:</p> <p>SH3: 314 µM</p> <p>MpRS S0: 5.67 mM</p> <p>MpSR S0: 5.1 mM</p> <p>S1 stocks resulted from S0 by a factor 2 dilution.</p>
Plasma NMR metabolomic of rainbow trout fed diets with different levels of marine and plant ingredients.
<p>The NMR and biochemical datasets used in the manuscript draft entiteld "Intestinal microbiota in rainbow trout, <em>Oncorhynchus mykiss</em>, fed diets with different levels of marine and plant ingredients: A correlative approach with some plasma metabolites."</p> <p>François-Joël Gatesoupe1*, Benoît Fauconneau1, Catherine Deborde2,3**, Blandine Madji Hounoum1,2, Daniel Jacob2,3,Annick Moing2,3, Geneviève Corraze1, Françoise Médale1. 1 UMR 1419 NuMeA, INRA, Université de Pau et des Pays de l’Adour, Saint Pée sur Nivelle, France 2 Bordeaux Metabolome Facility, CGFB, MetaboHUB, Centre INRA Nouvelle -Aquitaine-Bordeaux, Villenave d'Ornon, France 3 UMR1332 Biologie du Fruit et Pathologie, Centre INRA Nouvelle -Aquitaine-Bordeaux, Villenave d'Ornon, France<br> *Corresponding author Joel.Gatesoupe@partenaire-exterieur.ifremer.fr</p> <p>**NMR dataset Corresponding author catherine.deborde@inra.fr</p> <p> </p> <p> </p> <p> </p>
EMERGE 2016 Autochamber Sites NMR
METHODS:<br>To follow bio-available metabolites, we used NMR on the water extracted supernatant samples. Supernatant samples (180 µL) were combined with 2,2-dimethyl-2-silapentane-5-sulfonate-d6 (DSS-d6) in D2O (20 µL, 5 mM) and thoroughly mixed prior to transfer to 3 mm NMR tubes. NMR spectra were acquired on a Bruker Neo spectrometer operating at 18.8T (1H ν0 of 800.30 MHz) equipped with a 5mm Bruker TCI/CP HCN (inverse) cryoprobe with Z-gradient.at a regulated temperature of 298.0 K. The 90° 1H pulse was calibrated prior to the measurement of each sample. The one-dimensional 1H spectra were acquired using a nuclear Overhauser effect spectroscopy (noesypr1d) pulse sequence with a spectral width of 20.1 ppm and 2048 transients. The NOESY mixing time was 100ms and the acquisition time was 4s followed by a relaxation delay of 1.5 s during which presaturation of the water signal was applied. The 1D 1H spectra were manually processed, assigned metabolite identifications and quantified using Chenomx NMR Suite 9.0. Time domain free induction decays (65536 total points) were zero filled to 131072 total points prior to Fourier transform, followed by exponential multiplication (0.3 Hz line-broadening), and semi-automatic multipoint smooth segments baseline correction. Chemical shifts were referenced to the 1H methyl signal in DSS-d6 at 0 ppm. Metabolite identification was based on matching the chemical shift, J-coupling and intensity of experimental signals to compound signals in the Chenomx, HMDB and custom in-house databases (Supplementary Data X). Quantification was based on fitted metabolite signals relative to the internal standard (DSS-d6). Signal to noise ratios (S/N) were checked using MestReNova 14.1 with the limit of quantification equal to a S/N of 10 and the limit of detection equal to a S/N of 3.<br><br><br>FUNDING:<br>This research is a contribution of the EMERGE Biology Integration Institute, funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070.<br>We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council's grant 4.3-2021-00164.<br>This study was also funded by the Genomic Science Program of the United States Department of Energy Office of Biological and Environmental Research, grant #s DE-SC0010580 and DE-SC0016440. A portion of this research was performed under the Facilities Integrating Collaborations for User Science (FICUS) exploratory effort and used resources at the Environmental Molecular Sciences Laboratory (proposal ID 51858), which are DOE Office of Science User Facilities. A portion of the research has been performed using EMSL (grid.436923.9), a DOE Office of Science User Facility sponsored by the Office of Biological and Environmental Research.
NMR data of alkaloids from Urceolina peruviana
<p>This archive provides the description of the NMR data recorded for 13 alkaloids identified in an extract of the bulb of the South American plant <em>Urceolina peruviana</em> (C. Presl) J.F. Macbr (Amaryllidaceae):</p> <p>Tazettine <strong>1</strong><br> Albomaculine <strong>2</strong><br> Haemanthamine <strong>3</strong><br> Crinine <strong>4</strong><br> Trisphaeridine <strong>5</strong><br> 3-Epimacronine <strong>6</strong><br> 3-Methoxy-8,9-methylenedioxy-3,4-dihydrophenanthridine <strong>7</strong><br> Crinine acetate <strong>8</strong><br> 6α-Hydroxybuphanisine <strong>9</strong><br> Nerinine <strong>10</strong><br> Pretazettine(6β-OH) <strong>11A</strong><br> Pretazettine(6α-OH) <strong>11B</strong><br> 6-Dehydroxy-6-acetamido-nerinine <strong>12</strong></p> <p>See the README.txt file for more details.</p> <p> </p>
Dataset: 1H NMR metabolomic study of auxotrophic starvation in yeast using Multivariate Curve Resolution-Alternating Least Squares for Pathway Analysis
<p>This dataset contains the set of 1H NMR data used in https://doi.org/10.1038/srep30982.</p> <p>Yeast was grown in five different liquid media and their metabolism was characterized at 6 different time-points during 24 h.</p> <p>The media used were YSC (Yeast nitrogen base Synthetic Complete) and four Drop-Out (DM) medium that do not contain one of the following nutrients (L-histidine, L-leucine, L-methionine and uracil). Since the used yeast strain does not encode in its genome some genes relative to the biosynthesis of these four nutrients, some gene de-regulations process will occur, detectable at the metabolome level.</p> <p>In this study, we have characterized the metabolome using <sup>1</sup>H NMR spectroscopy, detecting more than 40 metabolites, and the evolution of this metabolome along the measured time-points was described by application of PCA, ASCA and MCR-ALS chemometric methods.</p>
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.