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.
3,878
datasets available to search
ShareScore release 0.9.0
Dataset results
3,878 results for “Molecular data”
Fig. 1 in The neglected diversity: Description and molecular characterisation of Trypanosoma haploblephari Yeld and Smit, 2006 from endemic catsharks (Scyliorhinidae) in South Africa, the first trypanosome sequence data from sharks globally
Fig. 1. Map of sampling sites on the south coast of South Africa.
Fig. 1 in New data on Thelohanellus nikolskii Achmerov, 1955 (Myxosporea, Myxobolidae) a parasite of the common carp (Cyprinus carpio, L.): The actinospore stage, intrapiscine tissue preference and molecular sequence
Fig. 1. Thelohanellus nikolskii cysts on the fins of carp fingerlings.
Contrasting Views of the Electric Double Layer in Electrochemical CO2 Reduction: Continuum Models vs Molecular Dynamics (data for figures)
<p>This is the data used to create the figures in the article:</p> <h4>Contrasting Views of the Electric Double Layer in Electrochemical CO<sub>2</sub> Reduction: Continuum Models vs Molecular Dynamics</h4> <div>Evan Johnson and Sophia Haussener</div> <div>The Journal of Physical Chemistry C <strong>2024</strong> <em>128</em> (25), 10450-10464</div> <p>DOI: 10.1021/acs.jpcc.4c03469</p> <p>See the file "Naming conventions" for the file names and column/row meanings. </p>
Data from: Molecular landscapes of glioblastoma cell lines revealed a group of patients that do not benefit from WWOX tumor suppressor expression
<p>Supporting data for the article "Molecular landscapes of glioblastoma cell lines revealed a group of patients that do not benefit from WWOX tumor suppressor expression", published in Frontiers in Neuroscience (DOI: 10.3389/fnins.2023.1260409).</p>
Coarse-Grained and Multi-Dimensional Data-Driven Molecular Generation: A Structure-Based Framework for Selective Inhibitor Design and Optimization
<p><span>Many approaches not only fail to consider the intricate binding pocket interactions, leading to molecules with suboptimal properties and stability, but also struggle with designing selective inhibitors. To address this challenge, we have developed an innovative structure-based three-dimensional molecular generation framework named </span><span>Coarse-grained and Multi-dimensional Data-driven molecular generation (CMD-GEN). This framework bridges three-dimensional ligand-protein complex data with two-dimensional drug-like molecule data by utilizing coarse-grained pharmacophore points sampled from diffusion models, thereby enriching the training data for generative models.</span> <span>Through a hierarchical architecture, it decomposes the generation of three-dimensional molecules within the pocket into sampling of coarse-grained pharmacophore points, generating of chemical structures, and alignment of conformations, avoiding the instability issues associated with inherent in deep generative model-based generation of molecular conformations.<br><br>This project provide the source dataset used to train and evaluate the overall model.<br></span></p>
Data for: Molecular Aharonov-Bohm Interferometers Based on Porphyrin Nanorings
<p>The directories in this dataset contain:</p> <p>./cpn<br>xyz files: Optimized geometries of the c-PN porphyrin nanorings using the GFN1-xTB method in the absence of a magnetic field. <br>txt files: Orbital energies of the c-PN porphyrin nanorings relative to its HOMO-LUMO gap center in the absence of a magnetic field calculated using the GFN1-xTB-M1 method where first number in each line is the magnetic field strength (Tesla) and the remaining numbers are the orbital energies (eV).</p> <p>./cpn_flat<br>xyz files: Optimized geometries of the flattened c-PN porphyrin nanorings using the GFN1-xTB method in the absence of a magnetic field. <br>txt files: Orbital energies of the flattened c-PN porphyrin nanorings relative to its HOMO-LUMO gap center in the absence of a magnetic field calculated using the GFN1-xTB-M1 method where the first number in each line is the magnetic field strength (Tesla) and the remaining numbers are the orbital energies (eV).</p> <p>./current<br>txt files: The calculated current as a function of the external magnetic field for the specified Fermi transport windows and electronic temperatures where the first number in each line is the magnetic field strength (Tesla) and the second number is the current (micro Amps).</p> <p>./finite_junction<br>xyz files: Optimized geometries of the finite junction model using the GFN1-xTB method in the absence of a magnetic field. <br>txt files: Orbital energies of the finite junction model relative to the HOMO-LUMO gap center of the c-P10 nanoring in the absence of a magnetic field calculated using the GFN1-xTB-M1 method where the first number in each line is the magnetic field strength (Tesla) and the remaining numbers are the orbital energies (eV).</p> <p>./fpn<br>xyz files: Optimized geometries of the f-PN porphyrin nanobelts using the GFN1-xTB method in the absence of a magnetic field. <br>txt files: Orbital energies of the f-PN porphyrin nanobelts relative to its HOMO-LUMO gap center in the absence of a magnetic field calculated using the GFN1-xTB-M1 method where the first number in each line is the magnetic field strength (Tesla) and the remaining numbers are the orbital energies (eV).</p> <p>./transmission<br>txt files: The transmission function calculation results using the NEGF+GFN1-xTB-M1 method for a number of field strengths and biased electron densities where the 1st, 2nd and 3rd numbers in each line are the energy (Hartree) relative to the HOMO-LUMO gap center of the c-P10 nanoring in the absence of a magnetic field, transmission probability, and density of states.</p> <p>./transmission_homo-1<br>txt files: The transmission function calculation results using the NEGF+GFN1-xTB-M1 method for energies around the HOMO-1 and HOMO-2 peaks for a number of field strengths where the 1st, 2nd and 3rd numbers in each line are the energy (Hartree) relative to the HOMO-LUMO gap center of the c-P10 nanoring in the absence of a magnetic field, transmission probability, and density of states.</p>
Processed data supporting the manuscript "Cutting the sap: first molecular phylogeny of twig-girdler longhorn beetles (Coleoptera: Cerambycidae: Lamiinae: Onciderini) suggests shifts in host plant attack behaviors contributed to morphological evolution"
<div><strong>Processed data supporting the manuscript: </strong>Cutting the sap: first molecular phylogeny of twig-girdler longhorn beetles (Coleoptera: Cerambycidae: Lamiinae: Onciderini) suggests shifts in host plant attack behaviors contributed to morphological evolution</div> <div> </div> <div><strong>By:</strong> Diego de S. Souza 1, 2, Rowan L. K. French 3, José O. Silva Júnior 4, Eugenio H. Nearns 5, Luciane Marinoni 4, Ian P. Swift 6, Kelly B. Miller 7, Felix A. H. Sperling 2 & Marcela L. Monné 1</div> <div> </div> <div>1 Department of Entomology, National Museum, Federal University of Rio de Janeiro, Rio de Janeiro, Rio de Janeiro, Brazil.</div> <div>2 Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada.</div> <div>3 Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, Canada.</div> <div>4 Department of Zoology, Federal University of Paraná, Curitiba, Paraná, Brazil.</div> <div>5 National Museum of Natural History, Smithsonian Institution, Washington, DC, USA.</div> <div>6 California State Collection of Arthropods, Sacramento, California, USA.</div> <div>7 Department of Biology and Museum of Southwestern Biology, University of New Mexico, Albuquerque, New Mexico, USA.</div> <div> </div> <div>Corresponding author: Diego de S. Souza, dsouza@fieldmuseum.org. Current affiliation: Field Museum of Natural History, Chicago, Illinois, USA.</div> <div> </div> <div> </div> <div><strong>List of Contents: </strong></div> <div> </div> <div><strong>Onciderini_concat_matrix.phy</strong></div> <div>Concatenated matrix (cox1, Wg and CPS) used for the phylogenetic analyses of Onciderini (Coleoptera: Cerambycidae: Lamiinae: Onciderini). </div> <div> </div> <div><strong>PartitionFinder_AICc_best_scheme.txt</strong></div> <div>Results from PartitionFinder v2.1.1, containing the best partitioning scheme for the concatenated matrix of Onciderini, identified using the corrected Akaike Information Criterion (AICc), with model definitions for use in the phylogenetic analyses.</div> <div> </div> <div><strong>RAxML_Onciderini_concat_matrix (zip file)</strong></div> <div>- Onciderini_concat_matrix.phy: concatenated matrix (cox1, Wg and CPS) used in the RAxML phylogenetic analyses of Onciderini (Coleoptera: Cerambycidae: Lamiinae: Onciderini).</div> <div>- Partitions_AICc_RAxML.txt: partitioning scheme used in the RAxML analysis as predefined by PartitionFinder v2.1.1 using the corrected Akaike Information Criterion (AICc).</div> <div>- RAxML_bestTree.Onciderini_concat_matrix_ML: best-scoring maximum likelihood tree inferred by RAxML for the concatenated matrix of Onciderini.</div> <div>- RAxML_bipartitions.Onciderini_concat_matrix_final: bipartitions (clades) of the maximum likelihood tree inferred by RAxML with support values estimated from 1,000 pseudoreplicates.</div> <div>- RAxML_bipartitionsBranchLabels.Onciderini_concat_matrix_final: final maximum likelihood tree inferred by RAxML for the concatenated matrix of Onciderini, with labeled branches showing bootstrap support values.</div> <div>- RAxML_bootstrap.Onciderini_concat_matrix_bootstrap: bootstrap trees generated from a non-parametric bootstrap analysis in RAxML based on 1,000 pseudoreplicates.</div> <div>- RAxML_info.Onciderini_concat_matrix_bootstrap: log file containing details of the bootstrap analysis, including the settings and parameters used in the non-parametric bootstrap runs in RAxML.</div> <div>- RAxML_info.Onciderini_concat_matrix_final: log file summarizing the RAxML analysis, including settings and convergence statistics for the final maximum likelihood tree.</div> <div>- RAxML_info.Onciderini_concat_matrix_ML: log file containing details of the maximum likelihood tree search, including the parameters and models applied during the maximum likelihood analysis conducted by RAxML.</div> <div>- RAxML_log.Onciderini_concat_matrix_ML: log file of the maximum likelihood tree search for the concatenated matrix of Onciderini.</div> <div>- RAxML_parsimonyTree.Onciderini_concat_matrix_ML: parsimony starting tree used by RAxML during the maximum likelihood analysis for the concatenated matrix of Onciderini.</div> <div>- RAxML_result.Onciderini_concat_matrix_ML: maximum likelihood tree inferred by RAxML from the concatenated matrix of Onciderini, summarizing the tree topology and likelihood score for the best tree obtained.</div> <div> </div> <div><strong>BI_AICc_Onciderini_concat_matrix (zip file)</strong></div> <div>- BI_AICc_Onciderini_concat_matrix.nex: nexus file containing the concatenated matrix of Onciderini used for Bayesian Inference (BI), including the best-fit model scheme identified by PartitionFinder and MCMC parameters for running the analysis in MrBayes.</div> <div>- BI_AICc_Onciderini_concat_matrix.nex_r1_r2_combined_consensus.tree: consensus tree from two combined independent Bayesian Inference (BI) runs based on the concatenated matrix of Onciderini, after discarding the first 25% of initial generations as burn-in.</div> <div>- BI_AICc_Onciderini_concat_matrix.nex.run1.p: log file containing parameter values and likelihood scores from the first run of the Bayesian Inference (BI) based on the concatenated matrix of Onciderini.</div> <div>- BI_AICc_Onciderini_concat_matrix.nex.run2.p: log file containing parameter values and likelihood scores from the second run of the Bayesian Inference (BI) based on the concatenated matrix of Onciderini.</div> <div> </div> <div><strong>BEAST2_Onciderini_BD_lognormal (zip file)</strong></div> <div>- BEAUTi_Onciderini_BD_lognormal.xml: XML file generated by BEAUTi for running BEAST2, based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and a lognormal distribution.</div> <div>- BEAST2_Onciderini_BD_lognormal_run[1-8].log: log files from eight independent runs of BEAST2, based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and a lognormal distribution.</div> <div>- TreeAnnotator_Onciderini_BD_lognormal_run1-run8_consensus.out: TreeAnnotator output file combining the results of eight BEAST2 runs based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and a lognormal distribution.</div> <div>- TreeAnnotator_Onciderini_BD_lognormal_run1-run8_consensus.tre: consensus tree from eight combined BEAST2 runs, based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and a lognormal distribution, after discarding the first 10% of initial generations as burn-in.</div> <div> </div> <div><strong>BEAST2_Onciderini_BD_exponential (zip file)</strong></div> <div>- BEAUTi_Onciderini_BD_exponential.xml: XML file generated by BEAUTi for running BEAST2, based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and an exponential distribution.</div> <div>- BEAST2_Onciderini_BD_exponential_[1-8].log: log files from eight independent runs of BEAST2, based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and an exponential distribution.</div> <div>- TreeAnnotator_Onciderini_BD_exponential_run1-run8_consensus.out: TreeAnnotator output file combining the results of eight BEAST2 runs based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and an exponential distribution.</div> <div>- TreeAnnotator_Onciderini_BD_exponential_run1-run8_consensus.tre: consensus tree from eight combined BEAST2 runs, based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and an exponential distribution, after discarding the first 10% of initial generations as burn-in.</div> <div> </div> <div><strong>BEAST2_Onciderini_BD_uniform (zip file)</strong></div> <div>- BEAUTi_Onciderini_BD_uniform.xml: XML file generated by BEAUTi for running BEAST2, based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and a uniform distribution.</div> <div>- BEAST2_Onciderini_BD_uniform_[1-8].log: log files from eight independent runs of BEAST2, based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and a uniform distribution.</div> <div>- TreeAnnotator_Onciderini_BD_uniform_run1-run8_consensus.out: TreeAnnotator output file combining the results of eight BEAST2 runs based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and a uniform distribution.</div> <div>- TreeAnnotator_Onciderini_BD_uniform_run1-run8_consensus.tre: consensus tree from eight combined BEAST2 runs, based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and a uniform distribution, after discarding the first 10% of initial generations as burn-in.</div> <div> </div> <div><strong>Comparative_analyses (zip file)</strong></div> <div><strong>RawData (folder):</strong> raw morphometric and girdling data, plus tree that was later pruned for downstream comparative analyses; these data were used as input for the OncidHeadDimorphism-DatasetPREP-FINAL.R data cleaning script. </div> <div>- Onciderini_BD_lognormal_run1-run8_consensus.nwk: newick version of TreeAnnotator_Onciderini_BD_lognormal_run1-run8_consensus.tre (outputted as a newick file by importing the .tre file into FigTree and exporting in newick format).</div> <div>- Measurements_Onciderini_Raw_Final.csv: individual-level raw morphometric data for Onciderini.</div> <div>- Behav_Matrix_2_states_trimmed_2022-12-15.csv: species-level data on girdling status for Onciderini species, with all Lochmaeocles species classified as girdlers (2 behavioral states across Onciderini species). </div> <div>- Matrix_3_states_trimmed_final.csv: species-level data on girdling status for Onciderini species, with all Lochmaeocles species classified as facultative girdlers (3 behavioral states across Onciderini species). </div> <div>- Matrix_2_states_trimmed_1LochGirdler_Final.csv: species-level data on girdling status for Onciderini species, with only one Lochmaeocles species (L. tessellatus) classified as a girdler (2 behavioral states across Onciderini species). </div> <div> </div> <div><strong>ProcessedData (folder): </strong>filtered data and pruned trees outputted by the OncidHeadDimorphism-DatasetPREP-FINAL.R script</div> <div>- oncid_f_36spp_clean.csv: dataset of species means and log-ratios for morphometric traits in females, plus girdling data; only includes species that have girdling data and are in the phylogenetic tree</div> <div>- oncid_m_42spp_clean.csv: dataset of species means and log-ratios for morphometric traits in males, plus girdling data; only includes species that have girdling data and are in the phylogenetic tree</div> <div>- oncid_sd_35spp_clean.csv: dataset of species means for sexual dimorphism in morphometric traits, plus girdling data; only includes species that have girdling data and are in the phylogenetic tree</div> <div>- oncid_girdlingbehav_allingroupspp_clean.csv: full dataset of girdling behavior for 56 Onciderini species that are in the phylogenetic tree; includes separate columns for the three alternative girdling classification schemes</div> <div>- oncid_tree_behavfull_56spp.nwk: pruned phylogenetic tree for the full girdling dataset (56 species)</div> <div>- oncid_tree_f_36spp.nwk: pruned phylogenetic tree for the female morphometric dataset (36 species)</div> <div>- oncid_tree_m_42spp.nwk: pruned phylogenetic tree for the male morphometric dataset (42 species)</div> <div>- oncid_tree_mf_43spp.nwk: pruned phylogenetic tree for all species with morphometric data for males or females; used for the stochastic character map next to the heatmap plot (Fig 4)</div> <div>- oncid_tree_sd_35spp.nwk: pruned phylogenetic tree for the sexual dimorphism dataset (35 species)</div> <div> </div> <div><strong>FittedModels (folder): </strong>fitted models (mvgls, model comparison analyses, OUM models, simmaps) outputted by the OncidHeadDimorphism-Analysis-FINAL.R script </div> <div>- MacroModelFits_logRtraits_f_36spp-2024-10-12.Rdata: summary of model comparison results for Brownian Motion (BM), single-peak Ornstein-Uhlenbeck (OU), multipeak OU (OUM), and multi-rate Brownian motion (BMM) models (univariate and multivariate) fitted to female morphometric data across 100 stochastic character maps of girdling behavior</div> <div>- MacroModelFits_logRtraits_m_42spp-2024-10-12.Rdata: summary of model comparison results for Brownian Motion (BM), single-peak Ornstein-Uhlenbeck (OU), multipeak OU (OUM), and multi-rate Brownian motion (BMM) models (univariate and multivariate) fitted to male morphometric data across 100 stochastic character maps of girdling behavior</div> <div>- MacroModelFits-SDDI-35spp_2024-10-11.Rdata: summary of model comparison results for Brownian Motion (BM), single-peak Ornstein-Uhlenbeck (OU), multipeak OU (OUM), and multi-rate Brownian motion (BMM) models (univariate and multivariate) fitted to sexual dimorphism data across 100 stochastic character maps of girdling behavior</div> <div>- mvgls-results-headsize-mf-2024-10-12.Rdata: fitted mvgls regression models for male and female traits (analyzed separately)</div> <div>- mvgls-results-sddi-2024-10-12.Rdata: fitted mvgls regression models for sexual dimorphism</div> <div>- OUM_headtraits_f_36spp-2024-10-12.Rdata: fitted OUM models and summary statistics for female head traits</div> <div>- OUM_headtraits_m_42spp-2024-10-12.Rdata: fitted OUM models and summary statistics for male head traits</div> <div>- OUM-SDDI-35spp-2024-10-12.Rdata: fitted OUM models and summary statistics for sexual dimorphism in head traits</div> <div>- simmaps_ard_full_2state.RDS: stochastic character maps of girdling behaviour for all 56 species with girdling data, with two behavioral states (girdling or non-girdling) - all Lochmaeocles species are classified as girdlers</div> <div>- simmaps_ard_full_3state.RDS: stochastic character maps of girdling behaviour for all 56 species with girdling data, with three behavioral states (girdling, non-girdling, or facultative girdling)</div> <div>- simmaps_ard_full_1Loch.RDS: stochastic character maps of girdling behaviour for all 56 species with girdling data, with two behavioral states (girdling or non-girdling) - only one Lochmaeocles species (L. tessellatus) is classified as a girdler</div> <div>- simmaps_ard_m.RDS: stochastic character map for the 42 species used in the analyses of male morphometric traits</div> <div>- simmaps_ard_f.RDS: stochastic character map for the 36 species used in the analyses of female morphometric traits</div> <div>- simmaps_ard.RDS: stochastic character map for the 35 species used in the sexual dimorphism analyses; 2 behavioral states.</div> <div> </div> <div><strong>Rscripts (folder): </strong>R scripts used to process data and run phylogenetic comparative analyses of head size and girdling behavior.</div> <div>- OncidHeadDimorphism-DatasetPREP-FINAL.R: R script used to filter data and prune trees from the RawData folder for downstream phylogenetic comparative analyses; outputs of this script are in the ProcessedData folder.</div> <div>- OncidHeadDimorphism-Analysis-FINAL.R: R script used to analyze data in the ProcessedData folder to answer questions about the origin and evolution of girdling behavior and the relationship between girdling and head size or head size sexual dimorphism; fitted models outputted by this script are in the FittedModels folder</div> <div>- OncidHeadDimorphism-Plots-FINAL.R: R script used to generate plots for the manuscript<br><br></div>
Diffraction data for Zajdel et al Turning Molecular Springs into Nano-Shock Absorbers ACS Appl. Mater. Interfaces 2022, 14, 26699−26713
<p>Datasets required to reproduce:</p> <p>Individual files</p> <p>Fig. 1,- XRDs of materials collected on DiscoverD, CuKa</p> <p>(BT-1.zip)</p> <p>Fig. 4a. - neutron powder diffraction of ZIF-8 + 2D2O/1H2O mix collected at the BT-1 diffractometer of the NIST Center of Neutron Research at 30C. For plot the data were normalized to a common scale in counts/h</p> <p>Fig. 4c. Fullprof PCR file and VESTA model with the volmetric data *.pgrid</p> <p>Fig. 4d Single detector file Z8221010.bt1 and raw pressure output + reduced data (Zenodo_30C_drops_Fig4d.DAT).</p> <p>Fig. 4b VSANS (110) integration results Up and Down (VSANS.zip)</p>
Data from: Molecular physiology of chemical defenses in a poison frog
Poison frogs sequester small molecule lipophilic alkaloids from their diet of leaf litter arthropods for use as chemical defenses against predation. Although the dietary acquisition of chemical defenses in poison frogs is well-documented, the physiological mechanisms of alkaloid sequestration has not been investigated. Here, we used RNA sequencing and proteomics to determine how alkaloids impact mRNA or protein abundance in the Little Devil Frog (Oophaga sylvatica) and compared wild caught chemically defended frogs to laboratory frogs raised on an alkaloid-free diet. To understand how poison frogs move alkaloids from their diet to their skin granular glands, we focused on measuring gene expression in the intestines, skin, and liver. Across these tissues, we found many differentially expressed transcripts involved in small molecule transport and metabolism, as well as sodium channels and other ion pumps. We then used proteomic approaches to quantify plasma proteins, where we found several protein abundance differences between wild and laboratory frogs, including the amphibian neurotoxin binding protein saxiphilin. Finally, because many blood proteins are synthesized in the liver, we used thermal proteome profiling as an untargeted screen for soluble proteins that bind the alkaloid decahydroquinoline. Using this approach, we identified several candidate proteins that interact with this alkaloid, including saxiphilin. These transcript and protein abundance patterns suggest the presence of alkaloids influences frog physiology and that small molecule transport proteins may be involved in toxin bioaccumulation in dendrobatid poison frogs.
Supplementary data for the manuscript: Image2SMILES: Transformer-based Molecular Optical Recognition Engine
<p>This is the supplementary data for the manuscript: <a href="https://chemrxiv.org/engage/chemrxiv/article-details/60c758c6469df4169bf45744">Image2SMILES: Transformer-based Molecular Optical Recognition Engine</a></p> <p>It contains pairs of image-string, generated from 1M SMILES strings. These strings were randomly chosen from PubChem database.<br> It was prepared using the code, published at <a href="https://github.com/syntelly/img2smiles_generator/">https://github.com/syntelly/img2smiles_generator/</a></p> <p>To unpack do:<br> <em>tar xvf subset_1M.tar.xz && tar xvf subset_1M_dump.tar.gz && rm subset_1M_dump.tar.gz</em></p> <p>You'll get the following data:</p> <ul> <li>subset_1M.smi - list of 1M source SMILES</li> <li>subset_1M_dump - directory with images </li> <li>subset_1M_result.csv - list of pairs FGSMILES - pathcode, first 3 chars of pathcode are corresponding subdirs in subset_1M_dump</li> <li>subset_1M_fails.csv - list of failed molecules from subset_1M.smi</li> <li>subset_1M_grpcounter.lst - list of counted groups, used in this generation</li> </ul> <p>You can generate your own data using <a href="https://github.com/syntelly/img2smiles_generator/">https://github.com/syntelly/img2smiles_generator/</a> </p>
Supplementary Data for "Molecular dynamics simulations provide structural insight into binding of cyclic dinucleotides to human STING protein"
<p>Supplementary Data for "Molecular dynamics simulations provide structural insight into binding of cyclic dinucleotides to human STING protein", Journal of Biomolecular Structure and Dynamics, 2021, 10.1080/07391102.2021.1942213</p> <p>A random selection of 10 representative structures from each MSM state of STING/CDN complexes is provided in .pdb file format. The selected MSM representatives are aligned and available as PyMOL session files.</p>
Arundinella tengchongensis (Poaceae), a name at new rank and newly combined based on morphological and molecular data
To clarify the taxonomic status of Arundinella setosa var. tengchongensis and its relationships with A. setosa var. setosa and other related species, molecular phylogenetic analyses based on nuclear ITS and two plastid DNA (matK and trnL-F) sequences, and morphological comparisons using one-way ANOVA were carried out. Our results reveal that A. setosa var. tengchongensis is recovered within a different clade and separated from the type variety. Morphologically, A. setosa var. tengchongensis can be easily distinguished from A. setosa var. setosa and other varieties by its smaller spikelets, shorter awns, and bearded nodes. Thus, A. setosa var. tengchongensis is here elevated to the species rank and newly combined as A. tengchongensis.
Raw data for "X-ray studies bridge the molecular and macro length scales during the emergence of CoO assemblies"
<p><strong>Raw data for „X-ray studies bridge the molecular and macro length scales during the emergence of CoO assemblies“</strong></p> <p><strong>TEM and SEM</strong></p> <ul> <li>SEM image of CoO assemblies after 90 min</li> <li>TEM images of CoO assemblies after 5, 10, 15, 20, 40, 60 and 90 min</li> <li>HR-TEM images of CoO assemblies after 90 min</li> <li>ED patterns of CoO assemblies after 10, 15 and 20 min</li> </ul> <p><strong>HERFD-XANES</strong></p> <p>In situ spectra for the CoO synthesis at 160°C were recorded at beamline ID26 at the European Synchrotron Radiation Facility (ESRF), Grenoble, France. Using a Si (111) double crystal monochromator, the incident energy was varied from 7.70 to 7.78 keV. The emitted fluorescence was measured with an emission spectrometer in Rowland geometry with five Si (531) analyzer crystals aligned at the Bragg angle of 77°. HERFD-XANES spectra were recorded in continuous scan mode every 80 s with average energy steps of 0.05 eV. The data set consists of one file containing a sequential set of energy scans, representing the pre- and main edge regions of the HERFD-XANES spectra.</p> <p>Important column identifiers:</p> <ul> <li>incident energy: “arr_hdh_ene”</li> <li>incident intensity: “I02”</li> <li>fluorescence intensity: “apd”</li> </ul> <p><strong>X-ray total scattering</strong></p> <p>Data was taken at beamline P21.1 of PETRA III at Deutsches Elektronen-Synchrotron (DESY), Hamburg, Germany. Diffraction patterns were recorded every 10 s at an X-ray energy of 102.92 keV (λ = 0.121 Å) with an XRD1621 detector (Perkin Elmer Inc., USA) with a pixel size of 200 × 200 μm² and a sample-to-detector distance of 0.411 m. The dataset consist of</p> <ul> <li>in situ data set of CoO nanoassembly synthesis at 160°C</li> <li>in situ data set of CoO nanoassembly synthesis at 140°C</li> <li>ex situ samples prepared at 160 °C at different times</li> <li>commercial reference compounds in powder and 0.1 M BnOH solution</li> </ul> <p>data format: 2D scattering patterns in .tif format</p> <p>The total scattering data is split into individual archives for the above subsets.</p> <p><strong>SAXS and PXRD</strong></p> <p><strong>data set “laboratory_SAXS_data”:</strong></p> <p>Data were recorded with the laboratory molybdenum anode microfocus X-ray setup at LMU, Munich. SAXS data are multiple successive 20 min exposures. Transmission data are 10 s exposures with the beamstop removed.</p> <p>data format: .tif detector images recorded with a Dectris Pilatus 300K.<br> <em>X-ray wavelength:</em> 0.71075 Angstrom<br> Calibrant for sample-to-detector distance, beam center and detector tilt: Silver behenate (AgBeh)</p> <p><strong>data set “synchrotron_SAXS_data”:</strong></p> <p>Data were recorded at beamline P03 at PETRA III, DESY, Hamburg. Data are recorded separately at a long and a short sample-to-detector distance. At the long distance, the exposure time is 5x0.1s. At the short distance, the exposure time is 1x0.5s.</p> <p>data format: .cbf detector images recorded with a Dectris Pilatus 1M.<br> X-ray wavelength: 0.961 Angstrom<br> Calibrant for sample-to-detector distance, beam center and detector tilt: Silver behenate (AgBeh) </p> <p><strong>data set “laboratory_PXRD_data”:</strong></p> <p>Data were recorded with the laboratory molybdenum anode microfocus X-ray setup at LMU, Munich. The overlap for stitching is 10 px in both horizontal and vertical direction.</p> <p>The recording sequence is<br> 01 02 07<br> 03 04 08<br> 05 06 09<br> 10 11 12<br> 13 14 15<br> with 3 images at each position, to take the median. The exposure time per image is 1200 s.</p> <p>For LaB6 the sequence is<br> 01 02<br> 03 04<br> 05 06<br> 07 08<br> 09 10<br> with 5 images at each position, to take the median. The exposure time per image is 300 s.</p> <p>data format: .tif detector images recorded with a Dectris Pilatus 100K.<br> X-ray wavelength: 0.71 Angstrom<br> Calibrant for sample-to-detector distance, beam center and detector tilt: Lanthanum hexaboride NIST SRM 660c (LaB6)</p>
Quantum coherent spin-electric control in a molecular nanomagnet at clock transitions. Open data set
<p>Data supporting the related publication.</p>
Fig. 4 in Two new species of the genus Mystilus Distant (Hemiptera: Miridae: Mirinae) from Vietnam, with discussion on morphological variation based on molecular data, and a revised key for Mystilus species
Fig. 4. Host plant, Gigantochloa sp.
Figure 5 in First record of Liposcelis entomophila (Enderlein) (Psocodea: Liposcelididae) from Sri Lanka based on morphological and molecular data
Figure 5. The barcoding gap analysis of COI and ITS2 for L. entomophila.
Figure 1 in First record of Liposcelis entomophila (Enderlein) (Psocodea: Liposcelididae) from Sri Lanka based on morphological and molecular data
Figure 1. Habitats of the samples from the wooden box in Sri Lanka.
FIGURE 5 in From Parataxonomy To Molecular Data: The Case Of Rhagidiidae (Acari) From Belgian Soils
FIGURE 5: Rhagidial organs I in dorsal (A, D) and lateral (B, E) views and rhagidial organs II in latero-dorsal view (C, F) of Brevipalpia minima (A-C) and Hammenia macrostella (D-F). Insert of famulus in C. Scale bar = 100 µm.
FIGURE 1 in From Parataxonomy To Molecular Data: The Case Of Rhagidiidae (Acari) From Belgian Soils
FIGURE 1: Dorsal aspect of different soil Rhagidiidae. A – Brevipalpia minima Zacharda, 1980; B – Coccorhagidia clavifrons (Canestrini, 1886); C – Hammenia macrostella Zacharda, 1980; D – Parallelorhagidia evansi (Strandtmann and Prasse, 1976); E – Crassocheles virgo Zacharda, 1980 (from Zacharda 1980).
FIGURE 4 in From Parataxonomy To Molecular Data: The Case Of Rhagidiidae (Acari) From Belgian Soils
FIGURE 4: Brevipalpia minima Zacharda, 1980: A – dorsum, B – venter, C – trichobothrium, D – palp, E – tarsus I in lateral aspect, F – chelicera, G – subcapitulum, H – rhagidial organ I (from Zacharda 1980).
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.