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535 results for “NMR”
NMR screen reveals the diverse structural landscape of a G- quadruplex library
<p>This is the NMR dataset for the manuscript '<span>NMR screen reveals the diverse structural landscape of a G-</span><br><span>quadruplex library</span>'</p> <p>Abstract</p> <p><span>G-quadruplexes are noncanonical nucleic acid structures</span><br><span>formed by stacked guanosine tetrads. Despite their functional and</span><br><span>structural diversity, a single consensus model is typically used to</span><br><span>describe</span><span> </span><span>sequences</span><span> </span><span>with</span><span> </span><span>the</span><span> </span><span>potential</span><span> </span><span>to</span><span> </span><span>form</span><span> </span><span>G-quadruplex</span><br><span>structures. We are interested in developing more specific sequence</span><br><span>models</span><span> </span><span>for</span><span> </span><span>G-quadruplexes.</span><span> </span><span>In</span><span> </span><span>previous</span><span> </span><span>work,</span><span> </span><span>we</span><span> </span><span>functionally</span><br><span>characterized each sequence in a 496-member library of variants of a</span><br><span>monomeric</span><span> </span><span>reference</span><span> </span><span>G-quadruplex</span><span> </span><span>for</span><span> </span><span>the</span><span> </span><span>ability</span><span> </span><span>to</span><span> </span><span>bind</span><span> </span><span>GTP,</span><br><span>promote a model peroxidase reaction, generate intrinsic fluorescence,</span><br><span>and to form multimers. Here we used NMR to obtain a broad overview</span><br><span>of the structural features of this library. After determining the</span><span> </span><span>1</span><span>H NMR</span><br><span>spectrum of each of these 496 sequences, spectra were sorted into</span><br><span>multiple classes, most</span><span> </span><span>of</span><span> </span><span>which could be rationalized based on</span><br><span>mutational patterns in the primary sequence. A more detailed screen</span><br><span>using representative sequences provided additional information about</span><br><span>spectral classes, and confirmed that the classes determined based on</span><br><span>analysis of</span><span> </span><span>1</span><span>H NMR spectra are correlated with functional categories</span><br><span>identified in previous studies. These results provide new insights into</span><br><span>the surprising structural diversity of this library. They also show how</span><br><span>NMR can be used to identify classes of sequences with distinct</span><br><span>mutational signatures and functions.</span></p> <p><span>Link to journal article: <a href="https://doi.org/10.1002/chem.202401437"><span>https://doi.org/10.1002/chem.202401437</span></a></span></p>
MS and NMR data of in situ Captured Marine Exometabolites
<p>This folder contains the raw data pertaining to the article <i><strong>In Situ</strong></i> <strong>Capture and Real Time Enrichment of Marine Chemical Diversity </strong></p><p><a href="https://doi.org/10.1021/acscentsci.3c00661">https://doi.org/10.1021/acscentsci.3c00661</a></p><p>Data are organized in folders corresponding to each figure. Briefly, this folder contains the raw mass spectrometry (MS) data, the cytoscape files of the full molecular network (Fig3), the xcel spreadsheets of annotated MS spectra related to each investigated specialized exometabolites from the Mediterranean sponges <i>Aplysina cavernicola </i>(AC, Fig4), <i>Spongia officinalis </i>(SO, Fig5)<i>, </i>and <i>Agelas oroides </i>(AO, Fig6)<i>, </i>the raw 1H NMR data from each sponge exometabolite (EM) extract with their corresponding crude extract (CR).</p><ul><li>All MS2 data were acquired on a Bruker Impact II qTOF (ESI positive, collision energy 20-40eV) also deposited here : MSV000091465</li><li>SIRIUS software and CANOPUS were used to further annotate the chemodiversity of captured marine EMs</li><li>All NMR data were acquired on a BRUKER avance II+ instrument (600 MHz, cryoprobe) in CD<i>3</i>OD</li></ul><p>-------------------------</p><p><strong>References related to in silico MS annotation tools:</strong></p><ul><li>Kai Dührkop, Louis-Félix Nothias, Markus Fleischauer, Raphael Reher, Marcus Ludwig, Martin A. Hoffmann, Daniel Petras, William H. Gerwick, Juho Rousu, Pieter C. Dorrestein and Sebastian Böcker <i>Systematic classification of unknown metabolites using high-resolution fragmentation mass spectra</i>. Nature Biotechnology, 2020. https://doi.org/10.1038/s41587-020-0740-8</li><li>Yannick Djoumbou Feunang, Roman Eisner, Craig Knox, Leonid Chepelev, Janna Hastings, Gareth Owen, Eoin Fahy, Christoph Steinbeck, Shankar Subramanian, Evan Bolton, Russell Greiner, David S. Wishart <i>ClassyFire: automated chemical classification with a comprehensive, computable taxonomy </i>J Cheminf, 8, 2016. https://doi.org/10.1186/s13321-016-0174-y</li><li>Kim, Hyun Woo and Wang, Mingxun and Leber, Christopher A. and Nothias, Louis-Félix and Reher, Raphael and Kang, Kyo Bin and van der Hooft, Justin J. J. and Dorrestein, Pieter C. and Gerwick, William H. and Cottrell, Garrison W. NPClassifier:<i> A Deep Neural Network-Based Structural Classification Tool for Natural Products. </i>Journal of Natural Products, 84, 2021. https://doi.org/10.1021/acs.jnatprod.1c00399</li></ul>
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>
NMR and MS data of identified bromotyrosine alkaloids produced and released by Aplysina cavernicola
<p>This folder contains NMR and MS datasets of each identified bromotyrosine spiroisoxazoline pertaining to the publication<em> </em>entitled:</p> <p><strong>Diving into the molecular diversity of <em>Aplysina cavernicola’s </em>exo-metabolites: contribution of bromo-spiroisoxazoline alkaloids.</strong> <em>ACS Omega</em> 2022 <strong> <a href="https://pubs.acs.org/doi/10.1021/acsomega.2c05415"> </a></strong><a href="https://pubs.acs.org/doi/10.1021/acsomega.2c05415">https://doi.org/10.1021/acsomega.2c05415</a></p> <ul> <li>All NMR data were acquired in CD<sub>3</sub>OD at 600 MHz (Bruker Avance III, cryosonde TCI) using 2 mm NMR tubes</li> <li>All MS<sup>2</sup> data were acquired on a Bruker Impact II qTOF (ESI positive, collision energy 20-40eV)</li> </ul> <p>The compressed folder of the newly described Aplysine1 contains also raw data related to circular dichroism (CD) and infrared (IR) analyses, as well as quantum mechanical calculations of <sup>13</sup>C NMR shifts using GIAO NMR and DP4+ analyses.</p> <p>The Excel spreadsheet for DP4+ analyses were obtained from: Grimblat N et al. “Beyond DP4: An Improved Probability for the Stereochemical Assignment of Isomeric Compounds Using Quantum Chemical Calculations of NMR Shifts.” <em>The Journal of Organic Chemistry</em> 80, no. 24 (December 18, 2015): 12526–34. <a href="https://doi.org/10.1021/acs.joc.5b02396">https://doi.org/10.1021/acs.joc.5b02396</a>.</p> <p>All MS data are also made Freely available at the UCSD Center for Computational Mass Spectrometry database with the MassIVE identifier <a href="https://massive.ucsd.edu/ProteoSAFe/dataset.jsp?task=4f6d3c00539a412a9c6d7fac0f7f2a81">MSV000089502</a> .</p> <p>NOTE: 3,5 dibromotyrosine was not identified neither in<em> Aplysina cavernicola </em>crude extract nor as exo-metabolites but was used for MS dereplication purposes.</p>
NMR data for "Rapid and simple 13C-hyperpolarization by 1H dissolution dynamic nuclear polarization followed by an in-line magnetic field inversion"
<p>Liquid-state and solid-state NMR data for "Rapid and simple 13C-hyperpolarization by 1H dissolution dynamic nuclear polarization followed by an in-line magnetic field inversion".</p> <p>The data enclosed are NMR data generated by the software Topspin by Burker Biospin. The experiments are dDNP runs that come in two parts: a solid-state and a liquid-state part.</p> <ul> <li>Experiments from 1 to 9 are reference experiments used to quantify polarization in other experiments</li> <li>Experiments 11-19, 21-29, 31-39, ... 61-69 correspond to 6 dDNP runs performed a different samples from the same batch. The numbers correspond between solid and liquid-state datasets</li> </ul> <p>The codes used to analyze the data are available at in a next upload.</p> <p>Refer to the main text of the paper and its supplementary material at 10.26434/chemrxiv-2023-6gd0l for more information.</p>
Ilm-NMR-P31
<p>This publication introduces a novel open-access 31P Nuclear Magnetic Resonance (NMR) shift database designed to bridge the gap between commercial and open-access resources. With 15,967 entries encompassing 15,405 distinct molecules from 3,996 references, this database offers a comprehensive repository of organic and inorganic compounds. Emphasizing single-phosphorus atom compounds, the database facilitates data mining and machine learning endeavors, particularly in signal prediction and Computer-Assisted Structure Elucidation (CASE) systems.</p> <p> </p> <p><span lang="EN-US">In Version 2.0 of this dataset the following changes were made:</span></p> <ol> <li><span lang="EN-US">66 new single phosphorous containing molecules as well as 1,825 components which contain more than one phosphorous atom, but which are symmetric, so that all phosphorous atoms show the same 31P NMR shift were added.</span></li> <li><span lang="EN-US">For 10,111 molecules Density Functional Theory (DFT)-derived 31P NMR shifts and shielding constants are available. This includes single point calculations in vacuum as well as in five implicit solvents (chloroform, dimethyl sulfoxide, water, toluene, acetonitrile). Furthermore, also Boltzmann-weighted NMR shifts from up to 20 conformers are available. </span></li> <li><span lang="EN-US">The repository also lists the DFT-optimized xyz coordinates for each of the 10,111 molecules, including all xyz information for the up to 20 conformers considered. The xyz files are named as the structures in the dataset.</span></li> </ol> <p> </p> <p>The dataset is available in 3 different formats:</p> <ol> <li>The molecular structures are available as either a single large SDF file, which includes all database information or as multiple smaller SDF (Structure Data Format) files, where each SDF file lists the information for one molecule. The NMR data is stored in the tag section of the SDF files in the format proposed by the NMReData initiative (http://nmredata.org/, V1.1). This includes the 31P shift the solvent, where available.</li> <li>The data is also available as CSV file. The file does not contain the molecular structures but rather lists important information (sum formula, 31P shift, solvent, molecular weight, number of carbon, nitrogen, phosphorous and oxygen atoms) of which the most important is the canonical SMILES string generated by OpenBabel V3.1.1.</li> <li>The same information as in the CSV and SDF files is also available in a RDA file, which is the R Data Format for the script language R (https://www.r-project.org/). The data is stored as a "tibble" (https://tibble.tidyverse.org/) which is a special data frame format in R.</li> </ol> <p> </p>
Data for "Dynamic of binary molecular systems – advantages and limitations of NMR relaxometry"
<p>Raw data for "Dynamic of binary molecular systems – advantages and limitations of NMR relaxometry". DOI of article: https://doi.org/10.1063/5.0188257</p>
Data for "Detection of metabolite-protein interactions in complex biological samples by high-resolution relaxometry: towards interactomics by NMR"
<p>Raw NMR data for relaxometry experiments, divided by donor sample. For every donor sample 2 or 3 different samples were used in order to record data at 19 different magnetic fields.</p> <p>Data from fast field-cycling relaxometry. All the data is in one xlsx file, divided by donor sample.</p> <p>Relaxometry results for alanine, lactate, creatinine and glutamine, obtained from the fitting of their relaxation decays recorded at 19 different fields, divided by donor sample.</p>
NMR Data for "Unveiling a Key Catalytic Pocket for the Ruthenium NHC-Catalysed Asymmetric Heteroarene Hydrogenation" (DOI: 10.1039/D1SC06409F)
<p># NMR Data for "Unveiling a Key Catalytic Pocket for the Ruthenium NHC-Catalysed Asymmetric Heteroarene Hydrogenation" (DOI: 10.1039/D1SC06409F)</p> <p>In the following, the original NMR Data for the publication "Unveiling a Key Catalytic Pocket for the Ruthenium NHC-Catalysed Asymmetric Heteroarene Hydrogenation" (DOI: 10.1039/D1SC06409F) is provided. </p> <p>## Experimental methodology and associated data</p> <p>### Dataset 200113.40a</p> <p>Inside an argon filled glovebox, 4.6 mg of **1-A** (5.4 µmol) was dissolved in 0.65 mL THF-d<sub>8</sub> (distilled over sodium/benzophenone and stored over 3 Å molecular sieves), yielding a clear, dark yellow solution. The solution was then transferred into a medium pressure J Young NMR tube and sealed. Subsequently, initial NMR spectra under argon atmosphere were recorded (t = 0 h).</p> <p>Afterwards, 2 bar of H2 pressure were applied to the J Young NMR tube, resulting in a partial H<sub>2</sub> pressure of 1 bar (due to the presence of 1 bar argon). After shaking to dissolve the added H<sub>2</sub>, a slow color change to orange could be observed. The reaction was monitored using 1H NMR (Bruker AV 400, 400 MHz), showing consumption of dissolved H<sub>2</sub> as evidenced by a decrease in intensity for the H<sub>2</sub> signal at δ(1H) = 4.55 ppm. Six hours after the first addition of H<sub>2</sub>, the J Young NMR tube was re-pressurized with 2 bar H<sub>2</sub>, and again after 36 h, shaking the NMR tube regularly to dissolve H<sub>2</sub>. After the third pressurization, no further decrease of dissolved H<sub>2</sub> could be observed.</p> <p>#### NMR experiments and timestamps</p> <p>File name | NMR Experiment | Time stamp / h (relative to H<sub>2</sub> addition)<br> --- | --- | ---<br> 200113.40a.1 | 1H | 0<br> 200113.40a.2 | 1H | 0<br> 200113.40a.3 | 1H | 0.5<br> 200113.40a.4 | 1H | 1<br> 200113.40a.5 | 1H | 2.5<br> 200113.40a.6 | 1H | 2.5<br> 200113.40a.7 | 1H | 5 <br> 200113.40a.8 | 1H COSY-45 | 5<br> 200113.40a.9 | 1H | 6.5<br> 200113.40a.10 | 1H | 22.5<br> 200113.40a.11 | 1H | 30<br> 200113.40a.12 | 1H | 31<br> 200113.40a.13 | 29Si-inept | 31<br> 200113.40a.14 | 1H-29Si HMBC | 31<br> 200113.40a.15 | 1H | 45.5<br> 200113.40a.16 | 1H-29Si HMBC | 45.5<br> 200113.40a.17 | 1H | 55<br> 200113.40a.18 | 1H | 69.5<br> 200113.40a.110 | 1H (larger measurement window) | 22.5<br> 200113.40a.111 | 1H (larger measurement window) | 30<br> 200113.40a.115 | 1H (larger measurement window) | 45.5<br> 200113.40a.117 | 1H (larger measurement window) | 55<br> 200113.40a.118 | 1H (larger measurement window) | 69.5</p> <p>### Dataset 200117.40a</p> <p>69.5 h after the first addition of H<sub>2</sub> no significant changes could be observed in the 1H NMR spectra anymore. At this point, 0.15 mL of a 0.052 M solution of benzofuran in THF-d<sub>8</sub> (7.8 µmol benzofuran, ca. 1.5 equivalents relative to **1-A**) were added to the NMR tube while applying 2 bar of H<sub>2</sub> pressure. No significant color change was observed upon addition of the substrate. Subsequently, the NMR tube was sealed and the reaction was monitored using 1H NMR for an additional 119 h, especially following the hydride signals at δ(1H) = −3.7 ppm and δ(1H) = −3.8 ppm as well as the signals of 2,3-dihydrobenzofuran at δ(1H) = 4.48 ppm and δ(1H) = 3.15 ppm. After 119 h of reaction time, the color of the reaction solution had changed to light orange.</p> <p>#### NMR experiments and timestamps</p> <p>File name | NMR Experiment | Time stamp / h (relative to substrate addition)<br> --- | --- | ---<br> 200117.40a.1 | 1H | 0.5<br> 200117.40a.2 | 1H | 1.5<br> 200117.40a.3 | 1H | 7<br> 200117.40a.4 | 1H | 24<br> 200117.40a.5 | 1H | 24.5 <br> 200117.40a.6 | 1H | 72 <br> 200117.40a.7 | 1H COSY-45 | 72<br> 200117.40a.101 | 1H (larger measurement window) | 0.5<br> 200117.40a.103 | 1H (larger measurement window) | 7<br> 200117.40a.104 | 1H (larger measurement window) | 24</p>
Predicted 13C NMR Chemical Shifts of Natural Products
<p>The Natural Product structures are those from the <a href="https://zenodo.org/record/5336220">COCONUTv5 database</a> .</p> <p>Predictions were obtained by means of the "Check Chemical Shifts" method from <a href="https://www.acdlabs.com/">ACD/Labs</a> C+H NMR Predictors and DB software, version 2020.1.0.</p> <p>The acd_coconut.zip archive contains a single file, acd_coconut.sdf, a collection of 2D structures from COCONUT supplemented by <sup>13</sup>C NMR chemical shifts values from ACD/Labs CNMR Predictor in verification mode.</p> <p>The file mol1.sdf describes the first compound in acd_coconut.sdf and indicates how chemical shift values are encoded.</p> <p>SDF tags related to NMR:</p> <ul> <li><CNMR_SHIFTS> for ACD/Labs DB software</li> <li><Predicted 13C shifts>, <Quaternaries>, <Tertiaries>, <Secondaries>, <Primaries> for <a href="https://sourceforge.net/projects/mixonat/">MixONat</a></li> <li><NMREDATA_ASSIGNMENT>, <NMREDATA_ORIGIN> in the style of <a href="https://nmredata.org/">NMReDATA</a></li> </ul> <p>The calculation workflow is based on tools developped <a href="https://github.com/nuzillard/KnapsackSearch/">here</a>.</p> <p>No attempt was made to change unlikely tautomers (like aliphatic iminols standing for aliphatic amides). Unlikely structures are likely associated to unlikely predicted chemical shift value sets.</p>
Glucosinolate and Desulfoglucosinolate NMR Data and Structures
<p><sup>1</sup>H, <sup>13</sup>C, <sup>15</sup>N NMR spectra, NMR parameters, and structures of a set of 31 glucosinolates or desulfoglucosinolates</p>
Titration_DB: NMR-monitored protein pH titration chemical shift data.
<p>This archive contains a backup of the NMR titration_db database raw chemical shift data.<br> Reference: D. Farrell et al., “Titration_DB: Storage and analysis of NMR-monitored protein pH titration curves.,” Proteins, vol. 78, no. 4, pp. 843–857, 2009.</p> <p>This data was formerly hosted on an interactive website which is no longer active. The files are stored as csv format, one per protein. They are also split into three folders according to the nuclei whose chemical shifts were used to detect the measurement (1H,13C or 15 N). Much of this data was obtained from literature. A table in the zip file (titration_dataset_info.csv) associates the protein name with the original paper. </p> <p>Note that the database stored the pka values fit using a model to each residue dataset. This is not stored here.</p> <p>Also present is a file titdb.fs. This is a binary file that can only be opened using the PEATDB software. PEATDB (Protein Engineering Analysis Tool - Database) was an application for sharing, analysing and storing experimental and theoretical data from Protein Engineering experiments.<br> This is now legacy software and no longer maintained. It was used to allow interactive fitting of the titration data using the Ekin module.<br> It is still possible to install this software if you really need to. It is stored on github at https://github.com/dmnfarrell/peat.</p> <p>D. Farrell May 2019</p>
NMR data - Mechanism and regioselectivity of the anionic oxidative rearrangement of 1,3-diketones towards all-carbon quaternary carboxylates
<p>NMR characterisation raw data for the publication:</p> <p>Mechanism and regioselectivity of the anionic oxidative rearrangement of 1,3-diketones towards all-carbon quaternary carboxylates</p>
Critical Assessment of automated Structure Determination of Proteins by NMR
<p>The community-wide initiative "Critical Assessment of Automated Structure Determination of Proteins by NMR (<strong>CASD-NMR</strong>)" was launched in 2009 to to evaluate the ability of automated methods to produce 3D protein structures from NMR data that closely match structures manually determined by experts.</p> <p>This dataset includes all the experimental data made available to the participants of CASD-NMR in the two completed rounds of the initiative.</p> <p>Also refer to http://www-nmr.cabm.rutgers.edu/blindtest/blind.html for additional details, including first release date and link to each final PDB entry</p>
NMR data of all compounds appearing in the study "Visible Light-Mediated Formal Alkylation and [4+1]-Cycloaddition Strategies of Silyl Enol Ethers with Aryldiazoacetates"
<p>FID files of NMR data (<em>e.g.</em> 1H, 13C{1H}, 19F{1H}, COSY, HSQC, DEPT135 and HMBC) associated with all compounds synthesized during the study entitled "Visible Light-Mediated Reactions of Silyl Enol Ethers with Aryldiazoacetates: Formal Alkylation and [4+1]-Cycloaddition Strategies" (a research project in organic synthesis)</p>
2D NMR HSQC spectra of proteins and mouse urine with peaks picked by DEEP Picker
<p>2D 15N-1H HSQC NMR spectra of Im7 and a-synuclein with peak lists produced by DEEP Picker.</p> <p>2D 13C-1H HSQC NMR spectrum of mouse urine with peak lists determined by DEEP Picker.</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>
Raw NMR FID data of cryptoporic acid derivatives isolated from Cryptoporus volvatus fruiting bodies
<p>This is a NMR FID data of cryptoporic acid derivatives isolated from fruiting bodies of <em>Cryptoporus volvatus</em> (Polyporaceae).</p> <p>Isolation and structural elucidation of these compounds will be reported in the article titled "Species prioritization based on spectral dissimilarity: A case study of polyporoid fungi species", which is currently under revision.</p>
MD Data for Patterns in protein flexibility: a comparison of NMR "ensembles", MD trajectories and crystallographic B-factors
<p>This data set comprises five zipped directories that contain the scripts and intermediate molecular dynamics (MD) results used in (initially as of April 24, 2017, updated with additional directories on December 15, 2020) a soon to be submitted paper, "Patterns in protein flexibility: a comparison of NMR 'ensembles', MD trajectories and crystallographic B-factors" written by the authors of this entry. An earlier version of this paper is available via BioRxiv, DOI: https://doi.org/10.1101/240655.</p> <p>This paper explores patterns in coordinate variance and coordinate uncertainty in MD trajectories and in protein structures derived from NMR and compares coordinate variances/uncertainties with those crystallographic B-factors. The files, MD_data.zip and MD_data2.zip, each unzip to contain input files and scripts for reproducing the MD trajectories used in this paper (using DESMOND): MD_data.zip contains input files/scripts for the MD trajectories used in the preprint; MD_data2.zip contains input files/scripts for trajectories ran following publication of the preprint. The file btab_analysis_scripts.zip contains key scripts for analyzing those trajectories (following file conversion with VMD and superimposition with THESEUS) in MATLAB (this analysis assumes the presence of the FindCore Toolbox, written by David Snyder and available via the MATLAB Central File Exchange, as well as the MATLAB Statistics and Machine Learning Toolbox). And the files, superimposed_MD_trajectories.zip and superimposed_MD_trajectories2.zip, each unzip to yield the trajectories (superimposed using THESEUS and in PDB multimodel file format) analyzed in the soon to be submitted paper: superimposed_MD_trajectories.zip contains trajectories reported in the preprint and superimposed_MD_trajectories2.zip contains the results of subsequent simulations. </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.