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106 results for “Chemical structure”
Chemical structures, Cell Painting and transcriptional profiles for compound bioactivity prediction.
<p>This is the related data, both input and produced for the paper <a href="https://doi.org/10.1101/2020.12.15.422887">"Predicting compound activity from phenotypic profiles and chemical structures"</a>.</p> <p>This data can be merged with <a href="https://github.com/CaicedoLab/2023_Moshkov_NatComm">paper's GitHub repository</a> for reproduction.</p> <p>Folders and files and are described below:</p> <pre><code>├── assay_data ├── assay_matrix_discrete_270_assays.csv Assay matrix with hits for assays (270) and compounds (16170). Note that this is the final file that we used to produce splits. ├── assay_metadata.csv Assay metadata ├── broad_ids.txt List of broad ids used in this study. That is an unfiltered list of compounds required by some analysis scripts. ├── smiles.txt Same as broad_ids.txt, but SMILES strings. ├── feature_data (for 16978 compounds, can be masked with ./misc/compounds16978to16170.npy) ├── cp.npz Classical chemical features ├── ge.npz Gene expression features ├── ge_scale.npz Gene expression scaled features ├── mo.npz Morphology features (not batch corrected) ├── mobc.npz Morphology features (batch corrected) ├── misc ├── compound_analysis.npz Compounds in the dataset identified as PAINS ├── compounds16978to16170.npy Used to filter features from the bigger set of compounds to the final one ├── fingerprints.npz Calculated fingerprints of compounds, those were then used to calculate similarity ├── similarity_fingerprints.npz Similarity matrix for compounds (16978) ├── population_normalized.csv.gz Well-level morphological profiles that were used for batch-correction ├── Table for PUMA Excel file with additional data and plots ├── predictions ├── scaffold_median(mean)_AUC.csv Aggregated median(mean) AUC scores over scaffold-based cross-validation splits. In the paper, median results were reported. ├── scaffold_median(mean)_EF.csv Aggregated median(mean) enrichment factor (EF) over scaffold-based cross-validation splits. In the paper, median results were reported. ├── toprank_chemical_cv{}_hitsnorm.csv Those files are needed to create enrichment plots and contain hit rate and top rank hit rate. ├── Each folder here stands for an experiment type, the number in the folder name is a number of the split. Inside each folder there are the following elements: ├── predictions Folder with predictions for each assay-compound pair for each modality ├── 2022_01_evaluation_all_data.csv File with AUC scores for each assay for the test set in the split ├── 2022_01_evaluation_all_data_EF.csv File with enrichment factor (EF) values for each assay for the test set in the split. Those files exist only for *chemical* folders. ├── assay_matrix_discrete_train(test)_old_scaff.csv Training and test subsets of data for the split. The first column contains broad_id. ├── assay_matrix_discrete_train(test)_old_scaff.csv Same, but SMILES strings in the first column. Those files are used as input to ChemProp! Experiments in this folder are the following: - chemical Scaffold-based 5-fold cross-validation splits, the main results in the paper are reported with this series of experiments. - chemical_bal Same splits as in chemical, but training were run with ChemProp built-in data balancing. - chemical_st Same splits as in chemical, but separate models were trained for each assay. - CV Random 5-fold cross-validation splits. - GE 5-fold cross-validation splits based on same-size clustering of gene expression features. - MOBC 5-fold cross-validation splits based on same-size clustering of batch-corrected morphology features. - random 10 random splits, ~80% of compounds in the training set and the rest in the test set. ├── splitting This folder contains numpy files which help to match compounds and features to create training and test sets for a split, which can be reused in the analysis notebook for data preparation. ├── scaffold_based_split.npz Splitting for scaffold-based splits. ├── random_split_{}.npz Random split indices of test set compounds (10 files). ├── cross_validation_indicies.npz Indices for random cross-validation splits ├── GE_clusters_size_constrained.npz Indicies of clusters of same-size clustering for gene-expression features. ├── MOBC_clusters_size_constrained.npz Indices of clusters of same-size clustering for batch-corrected morphology features.</code></pre> <p> </p>
Unveiling the atomistic and electronic structure of NiII–NO adduct in a MOF-based catalyst by EPR spectroscopy and quantum chemical modelling
<p><strong>Description of the dataset: </strong></p> <ul> <li><strong>Data type</strong>: Experimental spectroscopic measurements, computer simulation and analysis</li> <li>Files are with filename extensions: <strong>DSC</strong>, <strong>DAT</strong>, <strong>m</strong>, <strong>txt</strong></li> <li>Information on <strong>origin of the data</strong>:</li> </ul> <ul> <li>EPR spectroscopic measurements with filename extensions <strong>DSC</strong>, <strong>DTA.</strong></li> <li>EPR spectroscopic simulation and analyses with filename extension <strong>m</strong>.</li> <li>EPR spectra are exported as <strong>txt</strong> files in ASCII format.</li> </ul> <ul> <li>X-band CW-EPR spectroscopic measurements were generated by EMX spectrometer equipped with SHQ cavity produced by Bruker.</li> <li><strong>If the dataset includes multiple files that relate to each other:</strong> <ul> <li>Files in <strong>PARACAT_WP4_20230706_01_CW_Xband </strong>folder includes X-band CW-EPR spectroscopic measurements; original data are in DTA/DSC and txt formats.</li> <li>Files in <strong>PARACAT_WP4_20230706_02_HYSCORE </strong>and <strong>PARACAT_WP4_20230706_03_ENDOR </strong>folders include X-band HYSCORE and ENDOR data; original data are in DTA/DSC and txt formats.</li> <li>Files in <strong>PARACAT_WP4_20230706_ 04_MATLAB</strong> and<strong> PARACAT_WP4_20230706_ 05_Modelling</strong> folders include matlab and computer simulations/analyses of the EPR measurements; data are in m and txt formats.</li> <li>File <strong>PARACAT_WP4_20230706_ 06_Origin</strong> include origin plotted data</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>Information on</strong>: <ul> <li>specialized abbreviations: <strong>MFU– </strong>MFU-4l:NO<sub>2</sub> MOF material</li> <li>NiNO – NO adsorbed MFU-4l:NO<sub>2</sub> MOF</li> <li>@10K – measured at 10 K</li> <li>definitions of variables: <strong>Magnetic field, Temperature.</strong></li> <li>units of measurement: <strong>Gauss (G), K, degree (°), milliTesla (mT)</strong>.</li> </ul> </li> </ul>
Regional and local variation in chemical, structural, and physical leaf traits for tree species in the northeastern United States, 2016-2023.
This dataset is a compilation of leaf trait measurements for 25 different Northern American tree species in the northeastern United States collected between 2016 and 2023 by the Terrestrial Ecosystems Analysis Lab at the University of New Hampshire. Currently, this dataset contains measurements for 2,006 samples across 18 chemical, physical, and structural traits. Measured traits include stable isotopes for carbon (C) and nitrogen (N), chlorophyll estimates, leaf and petiole dimensions, and leaf and petiole water content. Traits have been measured at plots spanning a wide range of latitude, longitude, elevation, and forest types. A simple table containing these plot descriptions has been included. Additional leaf physiological and optical traits have been measured concurrently on many of these samples and have been or will be published separately. This is a continuous dataset that will be updated on an as needed basis.
Computed surface and chemical potentials, expansion coefficients, structures, models and results for the PMFPredictor Toolkit
<p>The PMFPredictor toolkit enables the prediction of the potentials of mean force describing the interaction between a surface and a small molecule in aqueous solution, which would otherwise be obtained from lengthy metadynamics simulations. This repository contains files to enable the operation of the toolkit, with source code available at https://github.com/ijrouse/PMFPredictor-Toolkit and corresponding to release v0.5-alpha.</p> <p>In PMFPredictor-Repository.zip we provide supplementary data necessary for the operation of the PMFPredictor Toolkit including:</p> <ul> <li>Structures of surfaces ("Structures/Surfaces") and chemicals ("Structures/Chemicals") in a united tabulated (.csv) format, listing x/y/z co-ordinates, atom IDs, mass (in amu), charge (in elementary units), Lennard Jones 6-12 parameters: sigma (in nm) and epsilon (in kJ/mol).</li> <li>Interaction potentials of surfaces ("SurfacePotentials") and chemicals ("ChemicalPotentials") with probe atoms and molecules in tabulated format with distances relative to reference points in nm and energies in kJ/mol. Also included in these folders are the potentials with the molecular probes in individual files.</li> <li>Hypergeometric expansion coefficients of the interaction potentials ("Datasets/SurfacePotentialCoefficientsNoise-1-oct12.csv" and "Datasets/ChemicalPotentialCoefficients-oct10.csv") in tabulated form, corresponding to potentials with units of nm for distance and kJ/mol for energy. Descriptions of the headers are provided in DatasetHeaderDescription.txt, included in the archive.</li> <li>Trained TensorFlow models for the prediction of potentials of mean force from HG interaction coefficients, suitable for loading via the Keras backend.</li> <li>PMFs generated for a range of surfaces and chemicals as output from the trained model, in both text format and figures showing comparisons to training PMFs where available. PMFs are supplied as tabulated data with comma separated values of distance in nm and interaction energies in kJ/mol.</li> <li>Adsorption energies in kJ/mol evaluated at T=300K extracted from all PMFs and compared to the values obtained from known PMFs where available.</li> </ul> <p>The surface_pmfpredictor.zip archive contains PMFs selected for the operation of the UnitedAtom software package for the calculation of protein-nanoparticle interactions. This data is included in the main repository file and provided separately to avoid the download of unnecessary data if only the final PMFs are required. As with the main set, these are provided in tabulated form with distance [nm], energy [kJ/mol] pairs. This repository also contains the sets of figures illustrating these PMFs for each surface. Both archives contain further information on the contents, including descriptions of the surfaces and chemicals for which PMFs are computed. We also supply the training data used to build the model in a separate archive, PMFPredictor-TrainingData.zip, along with a text file containing descriptions of all headers in this file. This training data is quite large when uncompressed, c.a. 7 Gb, hence its exclusion from the main archive.</p> <p>If you use results from this repository please cite the following paper in addition to the repository itself:</p> <p>I. Rouse, V. Lobaskin, Machine-learning based prediction of small molecule -- surface interaction potentials, arXiv:2211.07999<br> https://arxiv.org/abs/2211.07999</p>
Rereferenced Chemical Shift files used to select dihedrals for the 5 secondary structure conformations used to calculate Conformational Variability (ConVa).
<p>These Chemical Shifts in this repository were processed with ShiftCrypt (1) and then processed as described in the manuscript. </p> <p> </p> <p>1- Gabriele Orlando, Daniele Raimondi, Luciano Porto Kagami, Wim F Vranken, ShiftCrypt: a web server to understand and biophysically align proteins through their NMR chemical shift values, <em>Nucleic Acids Research</em>, Volume 48, Issue W1, 02 July 2020, Pages W36–W40, <a href="https://doi.org/10.1093/nar/gkaa391">https://doi.org/10.1093/nar/gkaa391</a></p>
Chemical-genetic interrogation of RNA polymerase mutants reveals structure-function relationships and physiological tradeoffs
<p>The multi-subunit bacterial RNA polymerase (RNAP) and its associated regulators carry out transcription and integrate myriad regulatory signals. Numerous studies have interrogated the inner workings of RNAP, and mutations in genes encoding RNAP drive adaptation of <i>Escherichia coli</i> to many health- and industry-relevant environments, yet a paucity of systematic analyses has hampered our understanding of the fitness benefits and trade-offs from altering RNAP function. Here, we conduct a chemical-genetic analysis of a library of RNAP mutants. We discover phenotypes for non-essential insertions, show that clustering mutant phenotypes increases their predictive power for drawing functional inferences, and demonstrate that some RNA polymerase mutants both decrease average cell length and confer insensitivity to killing by cell-wall targeting antibiotics. Our findings demonstrate that RNAP chemical-genetic interactions provide a general platform for interrogating structure-function relationships <i>in vivo</i> and for identifying physiological trade-offs of mutations, including those relevant for disease and biotechnology. This strategy should have broad utility for illuminating the role of other important protein complexes.</p>
Figure 1: The chemical structure of TSPP
<p>5,10,15,20-tetra-(4-sulfonatophenyl)porphyrin (TSPP), Figure 1, was syn-<br> thesized and puri¯ed in the laboratory after the literature methods [2]. It was<br> solubilized in water at 10¡4 M concentration. All the stock solutions were<br> stored at 4oC in the dark and used in the 14 days interval.</p>
◂Fig. 13 Crystallographic structure on the columellar lamellae of Dinaride Zospeum and Iberozospeum shells; (a) Zospeum spelaeum, (AJC 847), Betalov Spodmol jama, Slovenia (45.7922 14.1877), pattern of low, non-overlapping, wedges of crystallographic structure on the lamella; (b) Zospeum spelaeum, (MCBI CSR SASA 37049a), Velika Pasica, Slovenia (N45.9189 E14.4934), non-overlapping wedges of crystallographic structure on lamella in old shell; (c) Iberozospeum sp., (RMNH.MOL. 234,120), Cueva Refugio, Trucios, overview of dense, overlapping, scale-like wedges of localized, crystallographic structure on upper part of the lower lamella; (d) ibid., closeup view of c; (e) Iberozospeum sp., (RMNH.MOL. 234,104), Cueva del Comediante, Santander, upper part of the lamella of chemically treated shell showing dense, overlapping wedges of localized, crystallographic structure; (f) Iberozospeum sp., (RMNH. MOL. 234,141), Cueva a Sul, Oviedo, localized, overlapping wedges of crystallographic structure on lamella of chemically treated shell; (g) Iberozospeum vasconicum, (AJC 1849), Cueva Arrikrutz, overview of dense, localized, crystallographic structure on lower part of the lamella; h, ibid., closeup view of g. — Magnification varies for each perspective, see scale bars; Figs. a–b, g–h) imaged by M. Ruppel, (ret.) Goethe University Frankfurt am Main; Figs. c–f imaged by Dirk Vendermarel, Naturalis Biodiversity Center in Molecular investigation and description of Iberozospeum n. gen., including the description of one new species (Eupulmonata, Ellobioidea, Carychiidae)
◂Fig. 13 Crystallographic structure on the columellar lamellae of Dinaride Zospeum and Iberozospeum shells; (a) Zospeum spelaeum, (AJC 847), Betalov Spodmol jama, Slovenia (45.7922 14.1877), pattern of low, non-overlapping, wedges of crystallographic structure on the lamella; (b) Zospeum spelaeum, (MCBI CSR SASA 37049a), Velika Pasica, Slovenia (N45.9189 E14.4934), non-overlapping wedges of crystallographic structure on lamella in old shell; (c) Iberozospeum sp., (RMNH.MOL. 234,120), Cueva Refugio, Trucios, overview of dense, overlapping, scale-like wedges of localized, crystallographic structure on upper part of the lower lamella; (d) ibid., closeup view of c; (e) Iberozospeum sp., (RMNH.MOL. 234,104), Cueva del Comediante, Santander, upper part of the lamella of chemically treated shell showing dense, overlapping wedges of localized, crystallographic structure; (f) Iberozospeum sp., (RMNH. MOL. 234,141), Cueva a Sul, Oviedo, localized, overlapping wedges of crystallographic structure on lamella of chemically treated shell; (g) Iberozospeum vasconicum, (AJC 1849), Cueva Arrikrutz, overview of dense, localized, crystallographic structure on lower part of the lamella; h, ibid., closeup view of g. — Magnification varies for each perspective, see scale bars; Figs. a–b, g–h) imaged by M. Ruppel, (ret.) Goethe University Frankfurt am Main; Figs. c–f imaged by Dirk Vendermarel, Naturalis Biodiversity Center
FIGURE 15 in X-ray Microanalysis and the Chemical Elemental Composition of Gorgonian and Pennatulacean Axial Structures (Anthozoa, Octocorallia)
FIGURE 15. Line chart of the between-taxa elemental weight percentage comparison of both gorgonians and sea pens
FIGURE 12 in X-ray Microanalysis and the Chemical Elemental Composition of Gorgonian and Pennatulacean Axial Structures (Anthozoa, Octocorallia)
FIGURE 12. Energy dispersive spectrometer (EDS) linescan dataset. (A) EDS linescan site of interest BSE image in a cross-sectional view of a sea pen Stylatula sp. axial skeleton acquired by INCA Energy software using same region as Figure 11, horizontal linescan through the middle of the axis was applied for EDS analysis to examine the distribution of elements along the line. Scale bar = 900 μm. (B) Element linescan result displaying grouped linescans as colored: carbon (red), oxygen (orange), fluorine (yellow), sodium (green), magnesium (light green), aluminum (bright green), silicon (aqua), sulfur (light blue), calcium (blue), strontium (pink), ytterbium (purple), iron (hot pink).
FIGURE 10 in X-ray Microanalysis and the Chemical Elemental Composition of Gorgonian and Pennatulacean Axial Structures (Anthozoa, Octocorallia)
FIGURE 10. Energy dispersive spectrometer (EDS) linescan dataset. (A) EDS linescan site of interest BSE image in a cross-sectional view of a sea pen Virgularia sp. axial skeleton acquired by INCA Energy software using same region as Figure 9, horizontal linescan through the middle of the axis was applied for EDS analysis to examine the distribution of elements along the line. (B) Element linescan result displaying grouped linescans as colored: carbon (purple), oxygen (bright green), fluorine (pink), sodium (blue), magnesium (green), sulfur (teal), chlorine (red), calcium (light green), ytterbium (gold). (C) Sum spectrum of the linescan. Scale bar = 1 mm. (D) Bar chart of the quantitative results from the sum spectrum.
FIGURE 9 in X-ray Microanalysis and the Chemical Elemental Composition of Gorgonian and Pennatulacean Axial Structures (Anthozoa, Octocorallia)
FIGURE 9. Scanning electron microscope (SEM) micrographs of a cross-sectional view of a sea pen Virgularia sp. axial skeleton. (A) In this secondary electron (SE) image a great degree of the radiating patterns well-arranged across the whole axial surface can be observed, such character is also used in the morphological phylogenetic studies of the calcaxonian gorgonians and sea pens. Scale bar = 200 μm. (B) Back-scattered electron (BSE) image. There seemed to be some chemical composition contrast appearing darker in the central portion of the axis, though several between-site area scans were performed, yet no differences in chemical composition can be found (data not shown); however, the between-site changes on weight percentage value of some elements can be detected as shown in the next Figure 10B. Scale bar = 200 μm.
FIGURE 5 in X-ray Microanalysis and the Chemical Elemental Composition of Gorgonian and Pennatulacean Axial Structures (Anthozoa, Octocorallia)
FIGURE 5. Scanning electron microscope (SEM) micrographs of a cross-sectional view of a gorgonian Ellisella sp. axial skeleton. (A) Secondary electron (SE) image. The axis of Ellisella sp. is featured by the concentric growth rings resembling that in trees, also by the slightly radiating patterns on the surface. These characters are useful for comparative morphology studies on calcaxonian gorgonians and sea pens. Scale bar = 200 μm. (B) Back-scattered electron (BSE) image. The compositional change is minute since again no detectable contrast is observed in the image though the topographical contrast is apparent like that in the SE image. Scale bar = 200 μm.
FIGURE 2 in X-ray Microanalysis and the Chemical Elemental Composition of Gorgonian and Pennatulacean Axial Structures (Anthozoa, Octocorallia)
FIGURE 2. Energy dispersive spectrometer (EDS) area scan dataset. (A) EDS site of interest BSE image in a cross-sectional view of a gorgonian Isis hippuris axial skeleton acquired by INCA Energy software using the same region as Figure 1, where spectrum 1 was collected across the full span of the maximum horizontal length of the axial surface and spectrum 2 was collected across the full span of the maximum vertical length of the axial surface. Scale bar = 100 μm. (B) Bar chart of the quantitative results from (C) spectrum 1 for reference in that the analytical results of the two sites are fairly similar as presented in Table 1.
Linked collectors and determiners for: Internal secretory structures and chemical compounds in leaves of Conyza bonariensis (L.) Cronquist and Solidago chilensis Meyen (Asteraceae, Astereae).
Natural history specimen data linked to collectors and determiners held within, "Internal secretory structures and chemical compounds in leaves of Conyza bonariensis (L.) Cronquist and Solidago chilensis Meyen (Asteraceae, Astereae)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/e646c99d-532a-46e5-a025-46cdcaf3808d">https://bionomia.net/dataset/e646c99d-532a-46e5-a025-46cdcaf3808d</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/e646c99d-532a-46e5-a025-46cdcaf3808d">https://gbif.org/dataset/e646c99d-532a-46e5-a025-46cdcaf3808d</a>. Formatted as a Frictionless Data package.
FIG. 3 in Internal secretory structures and chemical compounds in leaves of Conyza bonariensis (L.) Cronquist and Solidago chilensis Meyen (Asteraceae, Astereae)
FIG. 3. — Cavities location in cross section (CS) view: A, Conyza bonariensis (L.) Cronquist, CS of secondary vein showing vascular bundle without fibres, next to phloem a cavity with lipophilic subtances in epithelial cells surrounded by several layers of attached parenchyma cells (false duct) and parenchyma sheath.; B-D, Solidago chilensis Meyen: B, CS of secondary vein showing fibres on adaxial and abaxial sides, and on phloem side absence of cavity but presence of attached parenchyma cells and parenchyma sheath; C, D, leaf blade CS showing adaxial and abaxial cavities; C, cavities located on different vascular bundles; D, cavities on both sides of one vascular bundle. Abbreviations: ABE, abaxial epidermis; ADE, adaxial epidermis; Apc, attached parenchyma cells; Co, collenchyma; Ec, epithelial cells; Fb, fibres; MES, mesophyll; Ph, phloem; Ps, parenchyma sheath; Xy, xylem. Scale bars: 100 µm.
Chemical-genetic interrogation of RNA polymerase mutants reveals structure-function relationships and physiological tradeoffs
Open the record for dataset details and reuse information.
Data from: Chemical structure predicts the effect of plant-derived low-molecular weight compounds on soil microbiome structure and pathogen suppression
<p>1. Plant-derived low molecular weight compounds play a crucial role in shaping soil microbiome functionality. While various compounds have been demonstrated to affect soil microbes, most data are case-specific and do not provide generalizable predictions on their effects. Here we show that the chemical structural affiliation of low molecular weight compounds typically secreted by plant roots – sugars, amino acids, organic acids and phenolic acids – can predictably affect microbiome diversity, composition and functioning in terms of plant disease suppression.</p> <p>2. We amended soil with single or mixtures of representative compounds, mimicking carbon deposition by plants. We then assessed how different classes of compounds, or their combinations, affected microbiome composition and the protection of tomato plants from the soil-borne Ralstonia solanacearum bacterial pathogen.</p> <p>3. We found that chemical class predicted well the changes in microbiome composition and diversity. Organic and amino acids generally decreased the microbiome diversity compared to sugars and phenolic acids. These changes were also linked to disease incidence, with amino acids and nitrogen-containing compound mixtures inducing more severe disease symptoms connected with a reduction in bacterial community diversity.</p> <p>4. Together, our results demonstrate that low molecular weight compounds can predictably steer rhizosphere microbiome functioning providing guidelines to engineer microbiomes based on root exudation patterns by specific plant cultivars or crop regimes.</p>
Structural Dynamics of an Excited Donor-Acceptor Complex from Ultrafast Polarized Infrared Spectroscopy, Molecular Dynamics Simulations, and Quantum Chemical Calculations
<p>The files contains all the data that are shown in the figures of the article:</p> <p>Rumble, C.; Vauthey, E. Structural Dynamics of an Excited Donor-Acceptor Complex from Ultrafast Polarized Infrared Spectroscopy, Molecular Dynamics Simulations, and Quantum Chemical Calculations. Phys. Chem. Chem. Phys. 21 (2019). 10.1039/C9CP00795D</p>
Supplementary Data for: Benchmark of density functional theory in the prediction of chemical shielding anisotropies for anisotropic NMR based structural elucidation
<p>Additional Data for the research paper titled: Benchmark of density functional theory in the prediction of chemical shielding anisotropies for anisotropic NMR based structural elucidation.</p> <p>Anisotropy Benchmark for Carbon NS372:</p> <ul> <li>Chemical Shielding Tensor for the molecules in the NS372 test set for carbon (XLSX)</li> <li>Coordinate files for the molcules of the NS372 test set that contained carbon (in NS372-Carbon-COORD-Files.zip)</li> </ul> <p>DFT Benchmark for RCSA for Natural Products:</p> <ul> <li>Chemical Shielding Tensor used for the RCSA analysis of 6 Natural Products (CSV)</li> <li>Turbomole Input and Ouput files for the DFT calculation of the natural products (in RAW_DATA_for_RCSA_Analysis.zip)</li> <li>ConArch+ Input and Ouput files for the RCSA analysis using (in RAW_DATA_for_RCSA_Analysis.zip)</li> <li>Coordinate files used for the RCSA analysis (in RAW_DATA_for_RCSA_Analysis.zip)</li> </ul> <p> </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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.