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873 results for “ligands”
DFT Calculated xyz and log Files as well as csv Files for Machine Learning in Support of "Tailoring Phosphine Ligands for Improved C H Activation: Insights from Δ-Machine Learning"
<p>Transition metal complexes have played crucial roles in various homogeneous catalytic processes due to their exceptional versatility. This adaptability stems not only from the central metal ions but also from the vast array of choices of the ligand spheres, which form an enormously large chemical space. For example, Rh complexes, with a well-designed ligand sphere, are known to be efficient in catalyzing the C-H activation process in alkanes. To investigate the structure-property relation of the Rh complex and identify the optimal ligand that minimizes the calculated reaction energy ΔE of an alkane C-H activation, we have applied a Δ-Machine Learning method trained on various features to study 1,743 pairs of reactants (Rh(PLP)(Cl)(CO)) and intermediates (Rh(PLP)(Cl)(CO)(H)(propyl)). Our findings demonstrate that the models exhibit robust predictive performance when trained on features derived from electron density (R<sup>2 </sup>= 0.816), and SOAPs (R<sup>2 </sup>= 0.819), a set of position-based descriptors. Leveraging the model trained on xTB-SOAPs that only depend on the xTB-equilibrium structures, we propose an efficient and accurate screening procedure to explore the extensive chemical space of bisphosphine ligands. By applying this screening procedure, <a>we identify ten newly selected reactant-intermediate pairs with an average ΔE </a>of 33.2 kJ mol<sup>-1</sup>, remarkably lower than the average ΔE of the original data set of 68.0 kJ mol<sup>-1</sup>. This underscores the efficacy of our screening procedure in pinpointing structures with significantly lower energy levels.</p> <p>_______________________________________________________________________</p> <p>The dataset contains three file types:</p> <p>Version 1.0:</p> <ol> <li>xyz files of the final optimized Rh-phosphine complexes; one set for the starting materials denoted as "molecule-XXXX_4-times" and one set for the intermediates after C-H activation denoted as "molecule-XXXX_6-times"</li> <li>Gaussian16 log files for the optimization process; one set for the starting materials denoted as "molecule-XXXX_4-times" and one set for the intermediates after C-H activation denoted as "molecule-XXXX_6-times"</li> <li>csv files containing the per molecule features used for training the different machine learning models. The name of the csv files indicates which property was predicted and which model was used</li> </ol> <p>New in version 1.1 (other data is unchanged):</p> <ol> <li>Gaussian16 log files for the ten newly identified bisphosphine ligands; one set for the product material denoted as "LXX_6-times-axial" and one set for the transition state for the C-H activation denoted as "LXX_C-H-activation_TS"</li> </ol>
Application of optical tweezer technology reveals that PfEBA and PfRH ligands, not PfMSP1, play a central role in Plasmodium-falciparum merozoite-erythrocyte attachment, Supporting Information
<p>This repository contains the dataset and analysis scripts associated with the upcoming publication titled <em>Application of optical tweezer technology reveals that PfEBA and PfRH ligands, not PfMSP1, play a central role in Plasmodium-falciparum merozoite-erythrocyte attachment</em>. The repository includes a comprehensive collection of data and scripts related to optical tweezer experiments, growth assays, qPCR data, and supplementary information. It is organized into several sections, each detailing different aspects of the study:</p> <ul> <li><strong>Growth Assays:</strong> Includes raw and processed data on parasitemia levels, invasion rates, and growth rate assays, along with corresponding Jupyter notebooks and Python scripts for data visualization (e.g., <code>GrowthAssayPlotlib.py</code>, <code>Plot GA1.ipynb</code>, and <code>GA2_df_melted.json</code>).</li> <li><strong>qPCR Data:</strong> Contains results from multiple qPCR runs, including quantification data for various samples, as well as analysis scripts and plotted results (<code>qpcr_plotbench.ipynb</code>, <code>qPCR_plotting.py</code>, etc.). Data files such as <code>.xlsx</code> and <code>.json</code> contain gene expression data and fold changes to NF54.</li> <li><strong>Optical Tweezer Experiments:</strong> Includes detailed results and plots from optical tweezer measurements of attachment forces, time dependence, and multiple merozoite attachments. Notebooks (<code>tweezer_plots.ipynb</code>, <code>Antibody_binding_assay_plots.ipynb</code>) and data files support these analyses.</li> <li><strong>Optical Tweezer Images</strong>: Includes images that were used to measure RBC diameters for deformation and force measurements in <code>.tiff</code> format.</li> <li><strong>Supplementary Information (SI):</strong> Provides additional data and visualizations, such as scatter plots of two stretched RBCs, time post-egress vs. detachment force, and antibody GIA flow data. The accompanying figures (e.g., <code>SupFig1d_egress time vs force_3D7.svg</code>, <code>SupFig5a_GIA.svg</code>) are provided as <code>.svg</code> files.</li> </ul> <p>This repository offers all necessary resources to replicate the findings, including the complete codebase, raw data, and graphical representations of results. Researchers are encouraged to explore the included notebooks and datasets for detailed insights.</p>
Discovery and characterization of pyridine and furan substituted ligands of choline acetyltransferase
<p><span>This repository contains datasets for the manuscript "Discovery and characterization of pyridine and furan substituted ligands of choline acetyltransferase"</span></p> <ul> <li><span>Data set of 1.4 million compounds used for virtual screening are freely available at </span><span><a href="https://vitasmlab.biz/downloads"><span>https://vitasmlab.biz/downloads</span></a></span><span>. Vina-MPI used for the virtual screening protocol is freely available at </span><span><a href="https://github.com/mokarrom/mpi-vina"><span>https://github.com/mokarrom/mpi-vina</span></a></span><span>. </span></li> <li><span>The docking pose and docking score for the screened library with Vina-MPI is available in PDBQT format with their docking scores in the folder “vitas_virtual_screening_800K”.</span></li> <li><span>Selected 5958 compounds from the virtual screening are given as PDBQT with docking scores in folder “top_5K_hits”.</span></li> <li><span>Re-docked docking score (Top_5K_re_docking.sdf) and MMGBSA (Top_250_MMGBSA.sdf) calculation are also available with the structures in SDF format.</span></li> </ul>
Diphosphine ligands in iron
<p>Galaxy RO Crate object containing the workflow and data with the reproduction of the results published in: Antonis M. Messinis, Stephen L. J. Luckham, Peter P. Wells, Diego Gianolio, Emma K. Gibson, Harry M. O’Brien, Hazel A. Sparkes, Sean A. Davis, June Callison, David Elorriaga, Oscar Hernandez-Fajardo & Robin B. Bedford (2019) The highly surprising behaviour of diphosphine ligands in iron-catalysed Negishi cross-coupling Nature Catalysis, 2019, 2, 123-133 DOI: 10.1038/s41929-018-0197-z.</p> <div> <p>This RO is published as part of the research data submitted for the paper <strong>Facilitating Reproducibility in Catalysis Research with Managed Workflows and RO-Crates: A Galaxy Case Study</strong>, ChemCatChem, DOI: 10.1002/cctc.202401676.</p> </div>
Polyubiquitin ligand-induced phase transitions are optimized by spacing between ubiquitin units
<p>These are the original data used to make figures for the manuscript titled "Polyubiquitin ligand-induced phase transitions are optimized by spacing between ubiquitin units" by Sarasi Galagedera et al.</p> <p> </p>
Electrochemical and Spectroscopic Data supported by Computational Models for Exploring the Metal- and Ligand-Based Oxidation of Mackinawite Nanoparticles
<p>Supporting information to our study, where under anaerobic conditions, ferrous iron reacts with sulfide producing FeS precipitate, which can then undergo a temperature, redox potential, and pH dependent maturation process resulting in the formation of oxidized mineral phases such as gregite or pyrite. The dataset provide information about the chemical speciation of iron-sulfide by cyclic voltammetry, Raman and X-ray absorption spectroscopic techniques. Nanoparticulate FeS was found to get oxidized to a Fe<sup>3+</sup> containing FeS phase at -0.5 V vs. Ag/AgCl (pH = 7) and in a concomitant oxidation step, polysulfides are proposed to give a material described as Fe<sup>2+</sup><sub>(1−3x)</sub>Fe<sup>3+</sup><sub>(2x)</sub>S<sup>2-</sup><sub>(1-y)</sub>(S<sub>n</sub><sup>2-</sup>)<sub>y</sub>. The thermodynamic differences between ligand- and metal-based oxidation processes from density functional theory can be used to describe one- and two-electron electronic and structural transformations. These findings together point to the existence of a previously unknown, metastable FeS phase located between FeS and greigite (Fe<sup>2+</sup>Fe<sup>3+</sup><sub>2</sub>S<sup>2-</sup><sub>4</sub>) along a metal oxidation path, and Fe<sup>2+</sup>S<sup>2-</sup> and pyrite (Fe<sup>2+</sup>S<sub>2</sub><sup>2-</sup>) along a ligand oxidation path, respectively.</p>
Data for: "Carbon dioxide reduction by lanthanide(III) complexes supported by redox-active Schiff base ligands"
<p>RAW DATA FOR ARTICLE</p> <p>DATE: NOVEMBER 2022</p> <p>TITLE: Carbon dioxide reduction by lanthanide(III) complexes supported by redox-active Schiff base ligands</p> <p>AUTHORS: Nadir Jori, Davide Toniolo, Bang C. Huynh, Rosario Scopelliti, and Marinella Mazzanti*</p> <p>JOURNAL: Inorganic Chemistry Frontiers (RSC) 2020</p> <p>DOI: <a href="https://doi.org/10.1039/D0QI00801J">10.1039/D0QI00801J</a> </p> <p> </p>
Raw MS data for "Ligand-specific changes in conformational flexibility mediate long-range allostery in the lac repressor"
<p>These are the raw HDX/MS data for our paper: "Ligand-specific changes in conformational flexibility mediate long-range allostery in the lac repressor."</p>
fingeRNAt—A novel tool for high-throughput analysis of nucleic acid-ligand interactions - supplementary data.
<p><b>fingeRNAt—A novel tool for high-throughput analysis of nucleic acid-ligand interactions - supplementary data.</b></p><p>Computational methods play a pivotal role in drug discovery and are widely applied in virtual screening, structure optimization, and compound activity profiling. Over the last decades, almost all the attention in medicinal chemistry has been directed to protein-ligand binding, and computational tools have been created with this target in mind. With novel discoveries of functional RNAs and their possible applications, RNAs have gained considerable attention as potential drug targets. However, the availability of bioinformatics tools for nucleic acids is limited. Here, we introduce fingeRNAt—a software tool for detecting non-covalent interactions formed in complexes of nucleic acids with ligands. The program detects nine types of interactions: (i) hydrogen and (ii) halogen bonds, (iii) cation-anion, (iv) pi-cation, (v) pi-anion, (vi) pi-stacking, (vii) inorganic ion-mediated, (viii) water-mediated, and (ix) lipophilic interactions. However, the scope of detected interactions can be easily expanded using a simple plugin system. In addition, detected interactions can be visualized using the associated PyMOL plugin, which facilitates the analysis of medium-throughput molecular complexes. Interactions are also encoded and stored as a bioinformatics-friendly Structural Interaction Fingerprint (SIFt)—a binary string where the respective bit in the fingerprint is set to 1 if a particular interaction is present and to 0 otherwise. This output format, in turn, enables high-throughput analysis of interaction data using data analysis techniques. We present applications of fingeRNAt-generated interaction fingerprints for visual and computational analysis of RNA-ligand complexes, including analysis of interactions formed in experimentally determined RNA-small molecule ligand complexes deposited in the Protein Data Bank. We propose interaction fingerprint-based similarity as an alternative measure to RMSD to recapitulate complexes with similar interactions but different folding. We present an application of interaction fingerprints for the clustering of molecular complexes. This approach can be used to group ligands that form similar binding networks and thus have similar biological properties. The fingeRNAt software is freely available at https://github.com/n-szulc/fingeRNAt.</p>
Structural Interaction Fingerprints and Machine Learning for predicting and explaining binding of small molecule ligands to RNA: a benchmark dataset
<p><b>Structural Interaction Fingerprints and Machine Learning for predicting and explaining binding of small molecule ligands to RNA: a benchmark dataset.</b></p><p>Ribonucleic acids (RNA) play crucial roles in living organisms as they are involved in key processes necessary for proper cell functioning. Some RNA molecules, such as bacterial ribosomes and precursor messenger RNA, are targets of small molecule drugs, while others, e.g., bacterial riboswitches or viral RNA motifs are considered as potential therapeutic targets. Thus, the continuous discovery of new functional RNA increases the demand for developing compounds targeting them and for methods for analyzing RNA—small molecule interactions. We recently developed fingeRNAt - a software for detecting non-covalent bonds formed within complexes of nucleic acids with different types of ligands. The program detects several non-covalent interactions, such as hydrogen and halogen bonds, ionic, Pi, inorganic ion- and water-mediated, lipophilic interactions, and encodes them as computational-friendly Structural Interaction Fingerprint (SIFt). Here we present the application of SIFts accompanied by machine learning methods for binding prediction of small molecules to RNA targets. We show that SIFt-based models outperform the classic, general-purpose scoring functions in virtual screening. We discuss the aid offered by Explainable Artificial Intelligence in the analysis of the binding prediction models, elucidating the decision-making process, and deciphering molecular recognition processes.</p>
Differential response of α-synuclein expression to bacterial ligands and metabolites in mouse enteroendocrine cells
<p>Dataset for manuscript <em>"<strong> </strong></em><strong>α</strong><strong>-synuclein expression in response to bacterial ligands and metabolites in gut enteroendocrine cells</strong><em>". </em>Tabs in excel file are title with the figure number. </p>
Dataset for: All-atom simulations reveal the intricacies of signal transduction upon binding of HLA-E ligand to the transmembrane inhibitory CD94/NKG2A receptor
<p>This dataset contains relevant structures, input and other files that are associated with our article "<em>All-atom simulations reveal the intricacies of signal transduction upon binding of HLA-E ligand to the transmembrane inhibitory CD94/NKG2A receptor", available at https://pubs.acs.org/doi/full/10.1021/acs.jcim.3c00249</em></p>
Supplementary information: How robust is the ligand binding transition state?
<p>We have used the REVO weighted ensemble approach followed by Markov state models to identify the ligand unbinding transition states for five ligands unbinding from the enzyme soluble epoxide hydrolase (sEH). This repo provides the <em><strong>counts matrices, properties and state (cluster) labels</strong></em> of the markov state models. The counts matrices can be converted to conformation space networks using CSNAnalysis software (<a href="https://github.com/ADicksonLab/CSNAnalysis">https://github.com/ADicksonLab/CSNAnalysis</a>). The <em><strong>networks</strong></em> are also provided in the gexf formatted files to be visualized in gephi (<a href="https://github.com/gephi/gephi">https://github.com/gephi/gephi</a>).</p>
Molecular dynamics simulation data of designed cyclic peptide (ligand-only)
<p>Trajectories of <strong>ligand-only </strong>simulation and simulation set-up files of designed cyclic peptide as MDM2 binders. <br> The original paper of these designed cyclic peptide: Danelius, E., Pettersson, M., Bred, M., Min, J., Waddell, M. B., Guy, R. K., et al. (2016). Flexibility is important for inhibition of the MDM2/p53 protein–protein interaction by cyclic β-hairpins. <em>Org. Biomol. Chem.</em>, <em>14</em>(44), 10386–10393. http://doi.org/10.1039/C6OB01510G</p>
PDEStrIAn: A phosphodiesterase structure and ligand interaction annotated database as a tool for structure-based drug design
<p>A systematic analysis is presented of the 220 phosphodiesterase (PDE) catalytic domain crystal structures present in the Protein Data Bank (PDB) with a focus on PDE-ligand interactions. The consistent structural alignment of 57 PDE ligand binding site residues enables the systematic analysis of PDE-ligand Interaction FingerPrints (IFPs), the identification of subtype-specific PDE-ligand interaction features, and the classification of ligands according to their binding modes. We illustrate how systematic mining of this phosphodiesterase structure and ligand interaction annotated (PDEStrIAn) database provides new insights into how conserved and selective PDE interaction hot spots can accommodate the large diversity of chemical scaffolds in PDE ligands. A substructure analysis of the co-crystalized PDE ligands in combination with those in the ChEMBL database provides a toolbox for scaffold hopping and ligand design. These analyses lead to an improved understanding of the structural requirements of PDE binding that will be useful in future drug discovery studies.</p>
Interformer: An Interaction-Aware Model for Protein-Ligand Docking and Affinity Prediction
<p>The code, dataset, and model weights are described in the paper "Interformer: An Interaction-Aware Model for Protein-Ligand Docking and Affinity Prediction."</p> <p> </p> <p><strong>experiment_results.zip:</strong> Contains generated results that can reproduce the result from the reported paper.</p> <p><strong>benchmark.zip:</strong> Contains docking and affinity input data of the interformer. You can use the source code to make predictions and reproduce the number of the reported paper.</p> <p><strong>checkpoints.zip: </strong>Contains one weight for the Energy and four PoseScore and Affinity models.</p> <p><strong>source_code_1.0.zip:</strong> Contains the initial version of the source code.</p> <p><strong>interformer_train.tar.gz:</strong> Contains prepared training data for interformer. poses/ contains all structure need for training, poses/ligand contains the re-docking poses generated by interformer energy, poses/ligand/rcsb contains the conformation of reference ligand, poses/pocket contains all pocket extract by raw PDB from rcsb, poses/uff contains all ligand conformation minimized using UFF from reference ligand, and train/ contains the training csv.</p> <p><strong>baseline_results.tar.gz:</strong> Contains the predictions from three methods: Interformer, DiffDock, and DeepDock. The results align with the exact numbers reported in the paper. For further details, please refer to the <em>eda/ </em>directory.</p> <p> </p> <p>You can also find the newest version of the source code at <a href="https://github.com/tencent-ailab/Interformer" target="_blank" rel="noopener">https://github.com/tencent-ailab/Interformer</a></p> <p> </p>
Data for "Impact of Ligand Substitution and Metal Node Exchange in the Electronic Properties of Scandium Terephthalate Frameworks"
<p>The AiiDA archives of the high-throughput calculations presented in the paper "Impact of Ligand Substitution and Metal Node Exchange in the Electronic Properties of Scandium Terephthalate Frameworks".</p><p>The file "MOF_workflows.aiida" contains the actual calculation data and the files with suffix "*.yaml" contain configuration files of the workflows.</p>
Kinodata-3D: an in silico kinase-ligand complex dataset for kinase-focused machine learning.
<p><strong>Project Description</strong></p> <p>Drug discovery pipelines nowadays rely on machine learning models to explore and evaluate large chemical spaces. While the inclusion of 3D complex information is considered to be beneficial, structural ML for affinity prediction suffers from data scarcity. <br>We provide kinodata-3D, a dataset of <strong>~138 000</strong> docked complexes to enable more robust training of 3D-based ML models for kinase activity prediction (see <a href="https://github.com/volkamerlab/kinodata-3D-affinity-prediction">github.com/volkamerlab/kinodata-3D-affinity-prediction</a>).</p> <h2>Dataset</h2> <h3>1. Data</h3> <p>This data set consists of three-dimensional protein-ligand complexes that were generated using computational docking from the OpenEye toolkit. The modeled proteins cover the kinase family for which a fair amount of structural data, i.e. co-crystallized protein-ligand complexes in the PDB, enriched through KLIFS annotations, is available. This enables us to use template docking (OpenEye’s POSIT functionality) in which the ligand placement is guided according to a similar co-crystallized ligand pose. The kinase-ligand pairs to dock are sourced from binding assay data via the public ChEMBL archive, version 33. In particular, we use kinase activity data as curated through the <a href="https://github.com/openkinome/kinodata">OpenKinome kinodata</a> project. The final protein-ligand complexes are annotated with a predicted RMSD of the docked poses. The RMSD model is a simple neural network trained on a <a href="https://github.com/openkinome/kinase-docking-benchmark">kinase-docking benchmark</a> data set using ligand (fingerprint) similarity, docking score (ChemGauss 4), and Posit probability (see <a href="https://github.com/volkamerlab/kinodata-3D" target="_blank" rel="noopener">kinodata-3D repository</a>).</p> <p>The final data set contains in total <strong>138 286</strong> deduplicated kinase-ligand pairs, covering <strong>~98 000</strong> distinct compounds and ~<strong>271</strong> distinct kinase structures.</p> <h3>2. File structure</h3> <p>The archive <strong>kinodata_3d.zip </strong>uses the following file structure</p> <blockquote> <p>data/raw<br> | kinodata_docked_with_rmsd.sdf.gz<br> | pocket_sequences.csv<br> | mol2/pocket<br> | 1_pocket.mol2<br> | ...</p> </blockquote> <p>The file <strong>kinodata_docked_with_rmsd.sdf.gz</strong> contains the docked ligand poses and the information on the protein-ligand pair inherited from <em>kinodata</em>. The protein pockets located in <strong>mol2/pocket</strong> are stored according to the MOL2 file format.</p> <p>The pocket structures were sourced from KLIFS (<a href="https://klifs.net" target="_blank" rel="noopener">klifs.net)</a> and complete the poses in the aforementioned SDF file. The files are named <strong>{klifs_structure_id}_pocket.mol2</strong>. The structure ID is given in the SDF file along with the ligand poses.</p> <p>The file <strong>pocket_sequences.csv </strong>contains all KLIFS pocket sequences relevant to the kinodata-3D dataset.</p> <h3>3. Related code</h3> <p>The code used to create the poses can be found in the <a href="https://github.com/volkamerlab/kinodata-3D" target="_blank" rel="noopener">kinodata-3D repository</a>. The docking pipeline makes heavy use of the <a href="https://github.com/openkinome/kinoml" target="_blank" rel="noopener">kinoml</a> framework, which in turn uses <a href="https://www.eyesopen.com" target="_blank" rel="noopener">OpenEye's</a> Posit template docking implementation. The details of the original pipeline can also be found in the manuscript by <a href="https://www.biorxiv.org/content/10.1101/2023.09.11.557138v1">Schaller et al. (<strong>2023</strong>). Benchmarking Cross-Docking Strategies for Structure-Informed Machine Learning in Kinase Drug Discovery. <em>bioRxiv</em>.</a></p>
Code for manuscript "Organic ligands in whale excrement support iron availability and reduce copper toxicity to the surface ocean" by Monreal et al.
<p>.zip file containing GitHub repository titled "ligands-in-whale-excrement" (<a href="https://github.com/patrickmon38/ligands-in-whale-excrement/tree/main">https://github.com/patrickmon38/ligands-in-whale-excrement/tree/main</a>)<br><br><strong>README from GitHub: </strong></p> <div> <h3>Code used to generate figures for the manuscript "Organic ligands in whale excrement support iron availability and reduce copper toxicity to the surface ocean" by Monreal et al. are found in this repository.</h3> </div> <div> <p>In press at Communications Earth & Environment</p> <p> </p> </div> <p>Most data (all except .mzXML data) called in code is from Github_Data_For_Whale_Ligand_Manuscript.xlsx in this repository.</p> <p> </p> <p>Mass spec data from .mzXML files are has been depositied and is available for download in the Mass Spectrometry Interactive Virtual Environment (MassIVE). LC-ESI-MS (Orbitrap) and LC-FT-ICR-MS raw data can be accessed there under MSV000094994 (doi:10.25345/C50000B5D) and MSV000094995 (doi:10.25345/C5V98034P), respectively.</p> <p> </p> <p>html output from Rmarkdown file can be viewed directly at <a href="https://html-preview.github.io/?url=https://github.com/patrickmon38/ligands-in-whale-excrement/blob/main/Figures_for_GitHub_Whale_Excrement_Manuscript.html" rel="nofollow">https://html-preview.github.io/?url=https://github.com/patrickmon38/ligands-in-whale-excrement/blob/main/Figures_for_GitHub_Whale_Excrement_Manuscript.html</a>.</p> <p>If trying to run Rmarkdown on your own system, you will need to download the .xlsx and .mzXML files and change paths accordingly.</p> <p>Co-authors of this manuscript:</p> <h5>Patrick J. Monreal (University of Washington)</h5> <h5>Matthew S. Savoca (Stanford University)</h5> <h5>Lydia Babcock-Adams (National High Magnetic Field Laboratory)</h5> <h5>Laura E. Moore (University of Washington)</h5> <h5>Angel Ruacho (Univesrity of Washington)</h5> <h5>Dylan Hull (University of Washington)</h5> <h5>Logan J. Pallin (Unversity of California, Santa Cruz)</h5> <h5>Ross C. Nichols (Unversity of California, Santa Cruz)</h5> <h5>John Calambokidis (Cascadia Research Collective)</h5> <h5>Joseph A. Resing (Unviersity of Washington/CICOES/NOAA)</h5> <h5>Ari S. Friedlaender (Unversity of California, Santa Cruz)</h5> <h5>Jeremy Goldbogen (Stanford University)</h5> <h5>Randelle M. Bundy (University of Washington)</h5> <p> </p> <div> </div> <div><strong>If there are issues or questions with the code, author for contact is Patrick Monreal (<a href="mailto:pmonreal@uw.edu">pmonreal@uw.edu</a>).</strong></div>
Dataset of the publication: Dataset of the publication: Chiral spin-crossover complexes based on an enantiopure Schiff base ligand with three chiral carbon centers. Dalton Trans. 2024, 53, 10637.
<p><span><span><span>Dataset of the publication: Chiral spin-crossover complexes based on an enantiopure Schiff base ligand with three chiral carbon centers<br></span></span></span></p> <p><span><span> A. Regueiro, V. García-López, A. Forment-Aliaga, M. Clemente-León, <em>Dalton Trans.</em> <strong>2024</strong>, <em>53</em>, 10637. </span> </span></p> <p><span><span>doi: 10.1039/d4dt00924j</span></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)
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.