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3,878 results for “Molecular data”
FIGURE 7 in From Parataxonomy To Molecular Data: The Case Of Rhagidiidae (Acari) From Belgian Soils
FIGURE 7: Legs III and IV in antiaxial (A, B) and paraxial (C, D) views of Brevipalpia minima (A-B) and Hammenia macrostella (C-D). Insert showing the microsculpture in B and D. Scale bar = 100 µm.
FIGURE 6 in From Parataxonomy To Molecular Data: The Case Of Rhagidiidae (Acari) From Belgian Soils
FIGURE 6: Hammenia macrostella Zacharda, 1980: A – dorsum, B – venter, C – trichobothrium, D – palp, E – chelicera, F – subcapitulum, G – tarsus I in lateral aspect, H – rhagidial organ I (from Zacharda 1980).
Figure 3. Proposed 16S in Phylogenetic relationships of thorny catfishes (Siluriformes: Doradidae) inferred from molecular and morphological data
Figure 3. Proposed 16S rRNA secondary structure model for Doradidae: (A) 5¢ end of the molecule.
Figure 3 in Phylogenetic relationships of thorny catfishes (Siluriformes: Doradidae) inferred from molecular and morphological data
Figure 3. (Continued) Proposed 16S rRNA secondary structure model for Doradidae: (B) 3¢ end.
Supporting data for "Nuclear quantum effects on zeolite proton hopping kinetics explored with machine learning potentials and path integral molecular dynamics"
<p>Supporting data for "<a href="https://www.nature.com/articles/s41467-023-36666-y">Nuclear quantum effects on zeolite proton hopping kinetics explored with machine learning potentials and path integral molecular dynamics</a>" by M. Bocus, R. Goeminne, A. Lamaire, M. Cools-Ceuppens, T. Verstraelen and V. Van Speybroeck, <em>Nature Communications</em>, <strong>2023</strong>, 14, 1008.</p> <p>This dataset contains examples of input files, submission and analysis scripts to train and use a machine learning potential based on the Schnet architecture for the proton hopping reaction in the H-CHA zeolite. The complete DFT training set, obtained by unbiasing the forces printed by CP2K (with PLUMED coupling), is stored as extended xyz files in the folders DFT/A-B/training_data.xyz where A=1-3 and A<B<5. More details on the folder architecture can be found in the README.md file.</p>
Data from: Molecular and cellular processes of deafferentation after plexus injury in human dorsal root ganglia
<p><strong>Dataset of human dorsal root ganglia (DRG) images and deep learning models</strong></p> <p>Sections of six control and six plexus patient DRG were immunostained and imaged using large-field (tile) microscopy. Two staining combinations were investigated: </p> <p>- n<em>eurofilament </em>(NF), <em>fatty acid binding protein 7</em> (FABP7), and <em>clusterin (</em>APOJ)</p> <p>- n<em>eurofilament </em>(NF),<em> ionized calcium-binding adapter molecule 1 (</em>IBA1), and <em>microtubule-associated protein 2</em> (MAP2) </p> <p>NF-positive neurons were segmented with a deep learning method (<em>deepflash2</em>). FABP7 and IBA1 were segmented with a thresholding method.</p> <p>Provided are:</p> <p>- all images and corresponding segmentation masks for NF, FABP7, and IBA1 </p> <p>- images and annotated segmentation masks of NF-positive neurons (3 experts annotated the same 10 images, the estimated ground truth masks were used for training of deep learning models)</p> <p>- the deep learning model ensemble (3) for segmentation of NF-positive neurons</p> <p> </p> <p> </p>
Data presented in "An ultracold molecular beam for testing fundamental physics"
<p>Data presented in "An ultracold molecular beam for testing fundamental physics". The original paper can be found at https://doi.org/10.1088/2058-9565/ac107e. This is the data underlying the simulations and the experimental results.</p>
Data from "Molecular Mapping of DR Tau's Protoplanetary Disk, Envelope, Outflow, and Large-Scale Spiral Arm"
<p>Data associated with Huang et al., 2023, "Molecular Mapping of DR Tau's Protoplanetary Disk, Envelope, Outflow, and Large-Scale Spiral Arm," ApJ, 943, 107</p> <p>The raw data can be obtained by requesting data from program W20BE (PI: J. Huang) from the NOEMA archive. </p> <p><strong>Images</strong></p> <p>images.tar: FITS files of image cubes and moment maps as shown in Figs. 2, 3, 5, 9, and 10 (see also finalimagingscript.py)</p> <p><strong>Spectra</strong></p> <p>spectra.tar: ASCII files of spectra shown in Figs. 1 and 9 (see also finalimagingscript.py) </p> <p><strong>Measurement sets (CASA format)</strong></p> <p>drtau_LINENAME_corrected.ms.tar: Measurement set for a given line, corresponding to drtau_LINENAME_selfcal-contsub.uvt in lines_contsub_uvt.tar </p> <p>drtau_ui_cont_corrected.ms.tar: Continuum measurement set corresponding to the UI baseband, generated from w20be-ui-cont-selfcal.uvt in continuum_uvt.tar </p> <p><strong>UV tables (GILDAS format)</strong></p> <p>LR_uvt.tar: Pipeline-calibrated UV tables of low-resolution data (see w20be-tables-LR.clic)</p> <p>HR_uvt.tar: Pipeline-calibrated UV tables of high-resolution data (see w20be-tables-HR.clic)</p> <p>continuum_uvt.tar: Self-calibrated, spectrally averaged continuum UV tables generated from LR_uvt.tar</p> <p>lines_contsub_uvt.tar: Self-calibrated, continuum-subtracted line UV tables generated from the UV tables in HR_uvt.tar</p> <p><strong>Scripts</strong></p> <p>w20be-tables-LR.clic: CLIC script used to output UV tables of low-resolution data from the pipeline-calibrated data (output contained in LR_uvt.tar)</p> <p>w20be-tables-HR.clic: CLIC script used to output UV tables of high-resolution data from the pipeline-calibrated data (output contained in HR_uvt.tar)</p> <p>uvfitstoms.py: Script to export uvfits files to measurement sets and to correct the metadata in the measurement sets </p> <p>finalimagingscript.py: CASA script used to generate imaging products in images.tar and spectra in spectra.tar </p> <p><strong>Other:</strong></p> <p>calib.tar: Files output by NOEMA pipeline </p> <p> </p>
DATA: molecularly enhanced proof of concept for targeting cocrystals at molecular scale in continuous pharmaceuticals cocrystallization - part 2
<p>This is the cleansed files for CoCryM project carried out at the University of Limerick, Ireland for a MSCA postdoctoral fellowship. </p> <p>Link to PNAS paper <a href="https://www.pnas.org/doi/10.1073/pnas.2114277119">https://www.pnas.org/doi/10.1073/pnas.2114277119</a> </p> <p>(molecularly enhanced proof of concept for targeting cocrystals at molecular scale in continuous pharmaceuticals cocrystallization) </p> <p>Original uploaded files on 10 September 2021: </p> <ol> <li><a href="https://doi.org/10.5281/zenodo.6164838">https://doi.org/10.5281/zenodo.6164838</a></li> <li><a href="https://doi.org/10.5281/zenodo.6189068">https://doi.org/10.5281/zenodo.6189068</a></li> <li><a href="https://doi.org/10.5281/zenodo.6189100">https://doi.org/10.5281/zenodo.6189100</a></li> <li><a href="https://doi.org/10.5281/zenodo.6189715">https://doi.org/10.5281/zenodo.6189715</a></li> <li><a href="https://doi.org/10.5281/zenodo.6189885">https://doi.org/10.5281/zenodo.6189885</a></li> </ol> <p> </p> <p>Inhouse: https://sites.google.com/view/makhansary/publications </p>
ConforMine Molecular Dynamics Data: Conformational Variability, Secondary Structure Propensities and Molecular Dynamics Simulations
<pre>This dataset contains all the data used to calculate Conformational Variability (ConVa) and Conformational Propensities as well as to train ConforMine. Each directory one level below this document contains another readme for further explanation on the contained data. The following information can be found in this dataset: </pre> <ul> <li>ConforMine_MD_training_sequences.fasta: FASTA file with the amino acid sequences of all used proteins.</li> <li>simulations (directory): Contains all the raw data derived from the MD simulations.</li> <li>ConforMine_training_MD_dihedrals (directory): Contains .xvg files with the dihedral angles of each amino acid at each step of the MD simulation.</li> <li>ConforMine_training_data_conformational_variability (directory): Contains the Conformational Variability values for all amino acids. Each file contains all ConVa values for a whole protein. The data is provided in .csv and .npy format.</li> <li>ConforMine_training_data_conformational_propensities (directory): Contains the Conformational Propensities values for all amino acids. Each file contains all propensities for a whole protein. The data is provided in .csv and .npy format.</li> </ul>
Resurrection of Tectaria cadieri from T. ingens (Tectariaceae, Pteridophyta) based on morphological and molecular data
<p>We demonstrate that <em>Tectaria</em> <em>cadieri</em> (Tectariaceae) is a distinct species in Vietnam after long being placed under the synonymy of <em>T. ingens</em>. Morphologically, <em>T. cadieri</em> is similar to <em>T</em>. <em>ingens</em>, <em>T. setulosa</em>, <em>T. trichotoma</em> in their large fronds (up to 0.8–2 m long), 3-pinnate laminae, fully free veins, and sori in two rows on ultimate segments. However, <em>T. cadieri</em> differs from these species in the presence of linear, membranaceous stipe scales. The analyses of five plastid regions (<em>atpB</em>, <em>ndhF</em> plus <em>ndhF</em>-<em>trnN</em>, <em>rbcL</em>, <em>rps16</em>-<em>matK</em> plus <em>matK</em>, and <em>trnL</em>-<em>trnF</em>) also show that <em>T. cadieri</em> is neither closely related to <em>T. ingens</em>, <em>T. setulosa</em>, or <em>T. trichotoma</em>. Rather, <em>T. cadieri</em> is closely related to <em>T. multicaudata</em>, a morphologically distinct species with anastomosing veins. Along with a detailed description and illustrations for <em>T. cadieri</em>, we provided morphological comparisons and comments on the relationships between <em>T. cadieri</em> and morphologically similar species.</p>
Supporting data for: Condensed-phase molecular representation to link structure and thermodynamics in molecular dynamics
<p>This repository contains supporting data and code for the paper titled "Condensed-phase molecular representation to link structure and thermodynamics in molecular dynamics" by Bernadette Mohr, Diego van der Mast, and Tristan Bereau.</p>
Supplementary data for "Testing the efficacy of different molecular tools for parasite conservation genetics: a case study using horsehair worms (Phylum Nematomorpha)"
<p>Supplementary data for "Testing the efficacy of different molecular tools for parasite conservation genetics: a case study using horsehair worms (Phylum Nematomorpha)"</p> <p>alignments: alignments used for BEAST ("bayes") and PopArt ("popart"). The "popart" folder also has a traits file per each species.</p> <p>bayesian_plots: TSVs ("tsv") and PDF files ("ogs") generated by BEAST. The "tsv" folder also has the scripts for plotting the results in R.</p> <p>easysfs: scripts, population file and results from the VCF to SFS conversione done by easySFS.</p> <p>fineRADstructure: fineRADstructure input files and output PDF plots ("plots") for <em>C. formosanus</em> ipyrad and Stacks ("stacks") data. </p> <p>logs: logs for ipyrad, ModelTest, PGDspider, PopArt ("popart") and Stacks ("stacks"). The "popart" folder also have the generated networks in a TXT file. The "stacks" folder also has ODS files for calculating the amount of loci per each M/n fixed value.</p> <p>snapclust: STR files used with R for snapclust. Scripts included.</p> <p>stairway_plot: input (blueprint files) and outputs for Stairway Plot 2 analyses. The <em>C. formosanus</em> folder ("chordodes") also has scripts for R plotting.</p> <p>vcfs: VCF and HDF5 files used in this study. Also scripts for filtering/converting data and plotting the PCA with ipyrad (activate python first!) for <em>C. formosanus</em>.</p> <p>"acutogordius" = <em>A. taiwanensis</em><br> "chordodes" = <em>C. formosanus</em><br> "gordius" = <em>G. chiashanus</em></p>
Input data for Reversible Unwrapping Algorithm for Constant-Pressure Molecular Dynamics Simulations
<p>As described in the main text, here is the input data used for simulation, as well as analysis directories. The archive was generated in my project folder with "tar --exclude=*trr --exclude=pbctools --exclude=qtwrap --exclude=old* --exclude=*npz --exclude=*pdf --exclude=*png --exclude=*ppm --exclude=*dcd* --exclude=*xtc --exclude=*slurm* --exclude=core* --exclude=*sh --exclude=*xvg --exclude=*out --exclude=*git* --exclude=*edr --exclude=*log --dereference -zcvf kulke-$(date +"%F").tar.gz data figures scripts Simulations". Big data and trajectory files were excluded to keep the archive size small. The archive includes all necessary files to reproduce the simulations, analysis and figures for the publication.</p>
Fig. 6 in Relationships of Henicopidae (Chilopoda: Lithobiomorpha): New molecular data, classification and biogeography
Fig. 6. Distributions of Anopsobiinae and Zygethobiini.
Data for "Covalent functionalization of CdSe quantum dot films with molecular [FeFe] hydrogenase mimics for light-driven hydrogen evolution"
<p>This dataset contains UV/Vis absorption and photoluminescence spectra, AFM and SEM data, FT-IR, XPS, and XRF characterization, and data on photocatalytic hydrogen evolution.</p>
Main text figure data and scripts for "Simulating optical linear absorption for mesoscale molecular aggregates: an adaptive hierarchy of pure states approach"
<p>(as README.txt):</p> <p>Main text figure data and scripts for “Simulating optical linear absorption for mesoscale molecular aggregates: an adaptive hierarchy of pure states approach”, by Tarun Gera, Lipeng Chen, Alex Eisfeld, Jeffrey R. Reimers, Elliot J. Taffet and Doran I. G. B. Raccah.</p> <p>Each directory is dedicated to a particular figure published in the paper. In each directory there are sub-directories which contains the data plotted in each panel. Each data file is a 2-D list in the format of (x,y) for each plot. There are python scripts (Fig_X.py) in each directory to plot the data.</p> <p>Table of contents:</p> <p>Figure_2:</p> <p> - 4_site_edge_contri.npy: Calculated edge sites contribution to the total absorption spectrum for a 4-site chain system v/s energy. <br> - 4_site_inner_contri.npy: Calculated inner sites contribution to the total absorption spectrum for a 4-site chain system v/s energy. <br> - 4_site_total_spectra.npy: Calculated total absorption spectrum for a 4-site chain system v/s energy. </p> <p><br> Figure_3:</p> <p>Panel A:<br> <br> - Mean_Error_Edge.npy: Mean error for the edge case v/s number of trajectories.<br> - Mean_Error_Inner.npy: Mean error for the inner case v/s number of trajectories.<br> - Mean_Error_SS.npy: Mean error for a single site initial condition v/s number of trajectories.<br> - Mean_Error_GD.npy: Mean error for a 4-site chain system with Gaussian distributed site energies v/s number of trajectories.</p> <p>Panel B: </p> <p> - Scaled_error_SS.npy: Mean error for a single site initial condition normalized by the square-root of one v/s number of trajectories.<br> - Scaled_error_PS.npy: Mean error for a pair site initial condition normalized by the square-root of two v/s number of trajectories.<br> - Scaled_error_AS.npy: Mean error for an all site initial condition normalized by the square-root of four v/s number of trajectories.</p> <p>Figure_4: </p> <p>Panel_A:</p> <p> - List_Error.npy: Calculated mean error for a 4-site chain for a set of auxiliary error bounds.</p> <p>Panel_B:</p> <p> - Cw_4S_HOPS.npy: Absorption spectrum for a 4-site chain calculated using dyadic HOPS v/s energy.<br> - Cw_4S_DadHOPS.npy: Absorption spectrum for a 4-site chain calculated using DadHOPS v/s energy.</p> <p>Panel_C: </p> <p> - Cw_12S_DadHOPS.npy: Absorption spectrum for a 12-site chain calculated using DadHOPS without including state adaptivity v/s energy.<br> - Cw_12S_DadHOPS_SA.npy: Absorption spectrum for a 12-site chain calculated using DadHOPS with state adaptivity v/s energy.</p> <p>Panel_D:</p> <p> - Aux_states_DadHOPS.npy: Number of auxiliary states required to run a DadHOPS calculation for each N-pigment system.<br> - Aux_states_HOPS.npy: Number of auxiliary states required to run a dyadic HOPS calculation for each N-pigment system.<br> - N_states_DadHOPS.npy: Number of site states required to run a DadHOPS calculation for each N-pigment system.<br> - N_states_HOPS.npy: Number of site states required to run a dyadic HOPS calculation for each N-pigment system.<br> </p> <p>Figure_5:<br> <br> Panel_C: </p> <p> - PSI_Cw_HEOM.npy: PSI absorption spectrum calculated using HEOM v/s energy.<br> - PSI_Cw_HOPS.npy: PSI absorption spectrum calculated using dyadic HOPS v/s energy.</p> <p>Panel_D:</p> <p> - PSI_Error_Random.npy: Calculated mean error, where clusters of 4 were assigned randomly v/s number of trajectories.<br> - PSI_Error_Coupling.npy: Calculated mean error, where clusters of 4 were assigned based on electronic coupling values v/s number of trajectories.</p> <p><br> Figure_6:</p> <p>Panel_A:</p> <p> - PBI_Exp_data_dil.npy: Experimental data for a dilute solution of PBI v/s energy.<br> - PBI_Cw_DadHOPS_300.npy: Calculated spectrum for a PBI monomer with the spread in static disorder of value 300 cm^{-1} v/s energy.<br> - PBI_Cw_DadHOPS_400.npy:: Calculated spectrum for a PBI monomer with the spread in static disorder of value 400 cm^{-1} v/s energy.</p> <p>Panel_B:</p> <p> - PBI_Exp_data_conc.npy: Experimental data for a concentrated solution of PBI v/s energy.<br> - PBI_trimer_Cw_DadHOPS.npy: Calculated spectrum for a PBI trimer using DadHOPS v/s energy.</p> <p>Panel_C: </p> <p> - Cw_PBI_monomer.npy: Calculated spectrum for a PBI monomer using DadHOPS v/s energy.<br> - Cw_PBI_dimer.npy: Calculated spectrum for a PBI dimer using DadHOPS v/s energy.<br> - Cw_PBI_trimer.npy: Calculated spectrum for a PBI trimer using DadHOPS v/s energy.<br> - Cw_PBI_heptamer.npy: Calculated spectrum for a PBI heptamer using DadHOPS v/s energy.<br> - Cw_PBI_1000mer.npy: Calculated spectrum for a PBI 1000mer using DadHOPS v/s energy.</p> <p>Panel_D:</p> <p> - peak_00_position.npy: relative position of the 00 peak for different number of pigments.<br> - peak_00_position_1000.npy: relative position of the 0,0 peak for a system with 1000 pigments. (Single value file)<br> - peak_I_ratio.npy: ratio of intensities of peak 0,1 w.r.t peak 0,0 for different number of pigments.<br> - peak_I_ratio_1000.npy: ratio of intensities of peak 0,1 w.r.t peak 0,0 for a system with 1000 pigments. (Single value file)</p> <p><br> Figure_7:</p> <p> - PBI_N_states_DadHOPS.npy: Number of states required to run a DadHOPS calculation for each N-PBI molecules system. <br> - PBI_Aux_states_HOPS.npy: Number of auxiliary states required to run a dyadic HOPS calculation for each N-PBI molecules system. <br> - PBI_Aux_states_DadHOPS.npy: Number of auxiliary states required to run a DadHOPS calculation for each N-PBI molecules system. </p> <p>The packaged scripts may be run with Python 3.10 and the associated versions of the os, numpy, and matplotlib packages. <br> </p>
Data for manuscript "Adaptive Ensemble Refinement of Protein Structures in High Resolution Electron Microscopy Density Maps with Radical Augmented Molecular Dynamics Flexible Fitting"
<p>The tar file contains the input files for RADICAL augmented MDFF implementation (R-MDFF) for two protein systems, Adenylate Kinase (ADK) and Carbon Monoxide Dehydrogenase (CODH). These examples demonstrate the implementation of R-MDFF using RADICAL-Cybertools to flexibly fit biomolecules in cryo-EM density maps with on-the-fly decision making.</p> <p>All molecular simulations were performed using CUDA enabled NAMD 2.14 installed on OLCF Summit HPC resource. The CHARMM36 force field parameters were used for the proteins. Synthetic density maps were prepared at 1.8, 3 and 5 Å for ADK and 1.8 and 3 Å for CODH using VMD 1.9.3 software installed on OLCF Summit HPC resource. During the analysis stage, the cross correlation coefficients between density maps and atomic model were computed using VMD 1.9.3 on Summit HPC as part of the R-MDFF workflow.</p> <p>The source code is publicly available on GitHub: <a href="https://github.com/radical-collaboration/MDFF-EnTK">https://github.com/radical-collaboration/MDFF-EnTK </a></p> <p>The preprint of this research is submitted on bioRxiv, doi: <a href="https://doi.org/10.1101/2021.12.07.471672">https://doi.org/10.1101/2021.12.07.471672 </a></p> <p>To obtain maximum compression of the data, the tar command used to generate this tarball was:</p> <pre><code class="language-bash">GZIP=-9 tar --exclude='last.pdb' --exclude='*last_from_prev_iter.pdb' --exclude='*old' --exclude='*log' --exclude='*coor' --exclude='*vel' --exclude='*xsc' --exclude='*dcd' --exclude='lastframepdbs_fix' --exclude='*out' --exclude='*sl' --exclude='*rs' --exclude='*prof' --exclude='*err' --exclude='*dx' --exclude='*grid.pdb' --exclude='*txt' -cvzf rmdffv2.tar.gz rmdff-zenodo/</code></pre> <p> </p>
Machine-guided path sampling to discover mechanisms of molecular self-organization (Training and validation data)
<p>Training and validation data for the Nature Computational Science manuscript "Machine-guided path sampling to discover mechanisms of molecular self-organization"</p>
Molecular and 13C isotopic composition of diacids and related compounds in wintertime PM2.5 at three sites over Northeast Asia – Data set
<p>To investigate the spatial changes in atmospheric organic aerosols (OA) loading and composition over the northeast Asian region, fine aerosols (PM<sub>2.5</sub>) were collected at three sites: Tianjin (TJ), North China and in Padori (PD) and Daejeon (DJ), South Korea, during winter 2019. We studied the molecular distributions and compound-specific carbon isotopic composition (δ<sup>13</sup>C) of dicarboxylic acids (diacids), oxocarboxylic acids (oxoacids) and α-dicarbonyls as well as the carbonaceous and inorganic ionic components in two sets of selected samples, representing a clean and a polluted period, during the campaign. Based on the molecular distributions and δ<sup>13</sup>C of diacids and related compounds and mass ratios and linear relations of selected species, we discuss the origins of OA and the influence of long-range transported air masses on their loading and composition over Northeast Asia.</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.