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669 results for “ATOM”
Atomic resolution X-ray diffraction images of native endothiapepsin.
<p>X-ray diffraction images that were collected at DESY (Hamburg) to a resolution of 0.9 Angstroms from native endothiapepsin. The data were collected using a MAR345 detector at beamline BW7B in June 1999. More details are in the included notes. </p>
Atomic resolution X-ray diffraction images for endothiapepsin complexed with the inhibitor H261.
<p>X-ray diffraction images for a complex of endothiapepsin with the hydroxyethylene renin inhibitor H261 which were collected at DESY (Hamburg) in June 1998 using the beamline BW7B with a Mar image plate detector in two passes. The data extend to a resolution of almost 1.1 Angstroms. More details are given in the accompanying notes. </p>
Atomic resolution X-ray diffraction images for endothiapepsin complexed with a cyclic statine inhibitor.
<p>X-ray diffraction images for endothiapepsin complexed with inhibitor CP-129,541. The data were collected on 29th April 2001. </p>
Algorithms for Reconstruction of Undersampled Atomic Force Microscopy Images Dataset
<p>This deposition contains the results from a simulation of reconstructions of undersampled atomic force microscopy (AFM) images. The reconstructions were obtained using a variety of interpolation and reconstruction methods.</p> <p>The deposition consists of:</p> <ol> <li>An HDF5 database containing the results from simulations of reconstructions of undersampled atomic force microscopy images (reconstruction_goblet_ID_0_of_1.hdf5).</li> <li>The Python script which was used to create the database (reconstruction_goblet.py).</li> <li>Auxillary Python scripts needed to run the simulations (optim_reconstructions.py, it_reconstruction.py, interp_reconstructions.py, gamp_reconstructions.py, and utils.py).</li> <li>MD5 and SHA256 checksums of the database and Python script files (reconstruction_goblet.MD5SUMS, reconstruction_goblet.SHA256SUMS).</li> </ol> <p>The HDF5 database is licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/) . Since the CC BY 4.0 license is not well suited for source code, the Python script is licensed under the BSD 2-Clause license (http://opensource.org/licenses/BSD-2-Clause) .</p> <p><strong>The files are provided as-is with no warranty as detailed in the above mentioned licenses.</strong></p> <p>The simulation results in the database are based on "Atomic Force Microscopy Images of Cell Specimens" and "Atomic Force Microscopy Images of Various Specimens" by Christian Rankl licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). The original images are available at http://dx.doi.org/10.5281/zenodo.17573 and http://dx.doi.org/10.5281/zenodo.60434. The original images are provided as-is without warranty of any kind. Both the original images as well as adapted images are part of the dataset. </p>
Data accompanying publication: "General Chemically Intuitive Atom-Level DFT Descriptors for Machine Learning Approaches to Reaction Condition Prediction"
<p>Embeddings and raw files to complement the paper "General Chemically Intuitive Atom-Level DFT Descriptors for Machine Learning Approaches to Reaction Condition Prediction". The embeddings should be all the data needed for full reproducibility of the results published. The GitHub repo GeneralDFT (https://github.com/moleculebits/GeneralDFT) contains the python scripts required to make use of the data, along with some basic plotting functionalities.</p>
Raw data of "Proximity-Induced Superconductivity in Atomically Precise Nanographene on Ag/Nb(110)"
<p>E.M., R.P., and W.W. designed the experiments. P.Z., S.-X.L., R.H., and SD synthesized the molecule. J.-C.L. performed STM/AFM experiments and analyzed the data. W.W. provided the dilution STM and H.C. assisted the measurement. X.W. and U.A. performed the DFT calculations. J.-C.L. wrote the manuscript with the help of R.P. All authors discussed the results and revised the manuscript.</p>
A Dataset of Atoms of Confusion in the Android Open Source Project
<p>This is a dataset cataloging the presence of Atoms of Confusion in the Android Open Source Project, alongside metrics<br>of object-oriented software development.</p> <p> </p> <h3>Schema</h3> <h4>ac_reports</h4> <p><strong>id </strong>INTEGER The primary key <br>project_name VARCHAR Name of the project/repository in the AOSP<br>line INTEGER NOT NULL Line where the AC was found<br>snippet VARCHAR NOT NULL The excerpt of code that represents the Atom of Confusion<br>class_name VARCHAR Name of the class where the AC was found<br>ac VARCHAR NOT NULL Type of Atom of Confusion<br>path VARCHAR Path to the file in the AOSP superproject<br>"commit" VARCHAR SHA of the commit of the file in the repository<br>loc VARCHAR Number of lines of code in the file</p> <h4>files</h4> <p>path VARCHAR The path of the file</p> <p>loc INTEGER Lines of code</p> <p>project_name VARCHAR Name of the project/repository in the AOSP</p> <h4>projects</h4> <p>name VARCHAR The name of the project/repository in the AOSP</p> <p> </p> <h4>classes</h4> <p>id INTEGER The primary key </p> <p>file_path VARCHAR Path to the file in the AOSP superproject</p> <p>name VARCHAR Name of the class</p> <p>type VARCHAR The type of class (e.g. interface, anonymous...)</p> <p>cbo INTEGER From CK: Coupling between objects, number of dependencies a class has.</p> <p>"cboModified" INTEGER From CK: Coupling between objects, number of dependencies a class has. (References from other classes to this class + references from this class to other classes)</p> <p>fanin INTEGER From CK: number of classes that reference this particular class.</p> <p>fanout INTEGER From CK: the number of classes referenced by this class</p> <p>wmc INTEGER From CK: Weight Method Class | McCabe's complexity | Cyclomatic Complexity</p> <p>dit INTEGER From CK: Depth Inheritance Tree</p> <p>noc INTEGER From CK: Number of Children</p> <p>rfc INTEGER From CK: Response for Class</p> <p>lcom INTEGER From CK: Lack of Cohesion of Methods</p> <p>lcom_normalized DOUBLE From CK: Refers to the LCOM* metric that computes Lack of Cohesion of Methods between 0 and 1.</p> <p>tcc DOUBLE From CK: Tight Class Cohesion</p> <p>lcc DOUBLE From CK: Loose Class Cohesion</p> <p>"totalMethodsQty" INTEGER From CK: Number of methods (constructors are included)</p> <p>"staticMethodsQty" INTEGER From CK: Number of static methods</p> <p>"publicMethodsQty" INTEGER From CK: Number of public methods</p> <p>"privateMethodsQty" INTEGER From CK: Number of private methods</p> <p>"protectedMethodsQty" INTEGER From CK: Number of protected methods</p> <p>"defaultMethodsQty" INTEGER From CK: Number of default methods</p> <p>"visibleMethodsQty" INTEGER From CK: Number of visible methods</p> <p>"abstractMethodsQty" INTEGER From CK: Number of abstract methods</p> <p>"finalMethodsQty" INTEGER From CK: Number of final methods</p> <p>"synchronizedMethodsQty" INTEGER From CK: Number of synchronized methods</p> <p>"totalFieldsQty" INTEGER From CK: Number of fields</p> <p>"staticFieldsQty" INTEGER From CK: Number of static fields</p> <p>"publicFieldsQty" INTEGER From CK: Number of public fields</p> <p>"privateFieldsQty" INTEGER From CK: Number of private fields</p> <p>"protectedFieldsQty" INTEGER From CK: Number of protected fields</p> <p>"defaultFieldsQty" INTEGER From CK: Number of default fields</p> <p>"finalFieldsQty" INTEGER From CK: Number of final fields</p> <p>"synchronizedFieldsQty" INTEGER From CK: Number of synchronized fields</p> <p>nosi INTEGER From CK: Number of static invocations</p> <p>loc INTEGER From CK: Lines of code</p> <p>"returnQty" INTEGER From CK: Number of return statements</p> <p>"loopQty" INTEGER From CK: Number of repetition structures (while, for)</p> <p>"comparisonsQty" INTEGER From CK: Number of comparisons, e.g. ==, !=</p> <p>"tryCatchQty" INTEGER From CK: Number of try...catch statements</p> <p>"parenthesizedExpsQty" INTEGER From CK: Number of expressions inside parenthesis.</p> <p>"stringLiteralsQty" INTEGER From CK: Number of string literals. Repeated strings are counted as well.</p> <p>"numbersQty" INTEGER From CK: Number of numbers, e.g. ints, floats, doubles</p> <p>"assignmentsQty" INTEGER From CK: Number of assignments, usage of the = operator.</p> <p>"mathOperationsQty" INTEGER From CK: Number of math operations.</p> <p>"variablesQty" INTEGER From CK: Number of variables</p> <p>"maxNestedBlocksQty" INTEGER From CK: Number of the highest nested block in the class.</p> <p>"anonymousClassesQty" INTEGER From CK: Number of anonymous classes</p> <p>"innerClassesQty" INTEGER From CK: Number of inner classes</p> <p>"lambdasQty" INTEGER From CK: Number of lambdas</p> <p>"uniqueWordsQty" INTEGER From CK: Number of unique words in the source file</p> <p>modifiers INTEGER From CK: Number of modifiers in class/methods (usage of "public", "static", "abstract"...)</p> <p>"logStatementsQty" INTEGER, From CK: Number of log statements.</p> <p> </p> <h4>methods</h4> <p>id_ INTEGER NOT NULL The primary key</p> <p>file_path VARCHAR NOT NULL Path to the file in the AOSP superproject</p> <p>class_id INTEGER NOT NULL ID of the class where this method is</p> <p>name VARCHAR NOT NULL The name of the method</p> <p>constructor BOOLEAN NOT NULL From CK: True if method is a constructor</p> <p>line INTEGER From CK: Line where this method is located</p> <p>cbo INTEGER From CK: Coupling between objects, number of dependencies a class has.</p> <p>"cboModified" INTEGER From CK: Coupling between objects, number of dependencies a class has. (References from other classes to this class + references from this class to other classes)</p> <p>fanin INTEGER From CK: number of classes that reference this particular class.</p> <p>fanout INTEGER From CK: the number of classes referenced by this class</p> <p>wmc INTEGER From CK: Weight Method Class | McCabe's complexity | Cyclomatic Complexity</p> <p>rfc INTEGER From CK: Response for Class</p> <p>loc INTEGER From CK: Lines of code</p> <p>"returnQty" INTEGER From CK: Number of return statements</p> <p>"variablesQty" INTEGER From CK: Number of variables</p> <p>"parametersQty" INTEGER From CK: Number of parameters</p> <p>"methodsInvokedQty" INTEGER From CK: all methods invoked</p> <p>"methodsInvokedLocalQty" INTEGER From CK: methods invoked locally</p> <p>"methodsInvokedIndirectLocalQty" INTEGER From CK: number of indirect local invocations</p> <p>"loopQty" INTEGER From CK: Number of repetition structures (while, for)</p> <p>"comparisonsQty" INTEGER From CK: Number of comparisons, e.g. ==, !=</p> <p>"tryCatchQty" INTEGER From CK: Number of try...catch statements</p> <p>"parenthesizedExpsQty" INTEGER From CK: Number of expressions inside parenthesis.</p> <p>"stringLiteralsQty" INTEGER From CK: Number of string literals. Repeated strings are counted as well.</p> <p>"numbersQty" INTEGER From CK: Number of numbers, e.g. ints, floats, doubles</p> <p>"assignmentsQty" INTEGER From CK: Number of assignments, usage of the = operator.</p> <p>"mathOperationsQty" INTEGER From CK: Number of math operations.</p> <p>"maxNestedBlocksQty" INTEGER From CK: Number of the highest nested block in the class.</p> <p>"anonymousClassesQty" INTEGER From CK: Number of anonymous classes</p> <p>"innerClassesQty" INTEGER From CK: Number of inner classes</p> <p>"lambdasQty" INTEGER From CK: Number of lambdas</p> <p>"uniqueWordsQty" INTEGER From CK: Number of unique words in the source file</p> <p>modifiers INTEGER From CK: Number of modifiers in class/methods (usage of "public", "static", "abstract"...)</p> <p>"logStatementsQty" INTEGER, From CK: Number of log statements.</p> <p>"hasJavaDoc" BOOLEAN From CK: True if method has a JavaDoc</p>
Data for "Influence of variation in grain boundary parameters on the evolution of atomic structure and properties of [111] tilt grain boundaries in aluminum"
<p>This repository contains the raw data of experimental STEM images and of the simulations for the paper "Influence of variation in grain boundary parameters on the evolution of atomic structure and properties of [111] tilt boundaries in aluminum".</p>
Figure data for article "Controlling the dynamics of atomic correlations via the coupling to a dissipative cavity"
<p>The files contain the data depicted in the figures of the article "Controlling the dynamics of atomic correlations via the coupling to a dissipative cavity", Phys. Rev. Lett. <strong>134</strong>, 073604 (2025)</p> <p>The format of the data and to which figure it corresponds is described in the file "read_me_metadata.txt".</p>
Bacteriophage Lambda Structure at Atomic Resolution
<p>Bacteriophage Lambda structure at atomic resolution. This structure has been constructed in UCSF Chimera software putting together all the structures that compose bacteriophage lambda using cryoEM reconstructions and pdb structures. By Dr. Victor Padilla-Sanchez, PhD from Washington Metropolitan University. Email: drvictorpadilla@aol.com Website: https://www.drvictorpadillasanchez.com</p>
Dataset of the publication: Atomic Force Microscopy beyond Topography: Chemical Sensing of 2D Material Surfaces through Adhesion Measurements
<p>Dataset of the publication: Atomic Force Microscopy beyond Topography: Chemical Sensing of 2D Material Surfaces through Adhesion Measurements</p> <p>DOI: 10.1021/acsami.3c19254</p> <p><span><span>I. Brotons-Alcázar, Jason. S. Terreblanche, S. Giménez-Santamarina, G. M. Gutiérrez-Finol, K. S. Ryder, A. Forment-Aliaga, E. Coronado, <em>ACS Appl. Mater. Interfaces</em> <strong>2024</strong>, <em>16</em>, 19711.</span> </span></p>
Data Grids for examples in Probe Particle Atomic Force Microscopy simulation program (ppafm)
<p>These files are used for running the examples for [ppafm](https://github.com/Probe-Particle/ppafm/) program.</p> <p>The grids are stored in in [.xsf](http://www.xcrysden.org/doc/XSF.html) and [.cube](https://paulbourke.net/dataformats/cube/) format.</p> <p>The data set compiles both the new examples used in paper [Advancing scanning probe microscopy simulations: A decade of development in probe-particle models](https://www.sciencedirect.com/science/article/pii/S0010465524002649) as well as older examples.</p> <p>Notice that the structure does not exactly reflect the directory structure in the [example folder of ppafm](https://github.com/Probe-Particle/ppafm/tree/main/examples) to prevent possible redudancy, but is instead flatenized and sorted by molecules.</p>
CrysAtom: Distributed Representation of Atoms for Crystal Property Prediction
<div> <div> <p>Application of artificial intelligence (AI) has been ubiquitous in the growth of research in the areas of basic sciences. Frequent use of machine learning (ML) and deep learning (DL) based methodologies by researchers has resulted in significant advancements in the last decade. These techniques led to notable performance enhancements in different tasks such as protein structure prediction, drug-target binding affinity prediction, and molecular property prediction. In material science literature, it is well-known that crystalline materials exhibit topological structures. Such topological structures may be represented as graphs and utilization of graph neural network (GNN) based approaches could help encoding them into an augmented representation space. Primarily, such frameworks adopt supervised learning techniques targeted towards downstream property prediction tasks on the basis of electronic properties (formation energy, bandgap, total energy, etc.) and crystalline structures. Generally, such type of frameworks rely highly on the handcrafted atom feature representations along with the structural representations. In this paper, we propose an unsupervised framework namely, CrysAtom, using untagged crystal data to generate dense vector representation of atoms, which can be utilized in existing GNN-based property predictor models to accurately predict important properties of crystals. Empirical results show that our dense representation embeds chemical properties of atoms and enhance the performance of the baseline property predictor models significantly.</p> </div> </div>
Dataset of the publication: Tailoring spin waves in 2D transition metal phosphorus trichalcogenides via atomic-layer substitution
<p>Dataset of the publication: Tailoring spin waves in 2D transition metal phosphorus trichalcogenides via atomic-layer substitution</p> <p>DOI: 10.1039/d2dt02482a</p> <p>A. M. Ruiz, DL. Esteras, A. Rybakov, J. J. Baldov </p> <p>Dalton Trans., 54, 44, 16816-16823 (2022)</p>
Data for the research article: "Simulations of Energetic Neutral Atom sputtering from Ganymede in preparation for the JUICE mission"
<p>Data for the research article: "Simulations of Energetic Neutral Atom sputtering from Ganymede in preparation for the JUICE mission"</p>
Activity cliffs with dual-atom replacements and single-atom analogs
<p>From the ChEMBL database, 852 activity cliffs (ACs) with dual-atom replacements were extracted which were formed by compounds with high-confidence activity data. Each AC captured an at least 10-fold difference in compound potency. For a subset of these ACs, analogs with corresponding single-atom replacements were identified. The dual-atom ACs and available single-atom replacement analogs were provided (SMILES representation and ChEMBL compound ID). For each AC compound and analog, targets from ChEMBL are reported (with UniProt ID). The target shared by all associated compounds represents the primary AC target </p>
High quality protein residues: Top2018 all-atom-filtered residues
<p>Introduction<br> --------------------------------------------------------------------------------<br> This directory contains files from the Top2018 dataset by the Richardson Lab at Duke University.</p> <p>These are high-quality residues from high-quality, low redundancy protein chains in the PDB.</p> <p>This dataset is quality-filtered on all atoms in the residue. For the mainchain-only filtered set, see https://doi.org/10.5281/zenodo.4626149</p> <p>The accompanying publication is:<br> Williams, C. J., Richardson, D. C., & Richardson, J. S. (2021). The importance of residue‐level filtering, and the Top2018 best‐parts dataset of high‐quality protein residues. Protein Science. http://doi.org/10.1002/pro.4239</p> <p>Usage recommendations<br> --------------------------------------------------------------------------------<br> Protein residues that fail the filtering criteria described below have been removed from the files. As a result, these files can be considered pre-filtered and will return only results for residues of good model quality with supporting experimental data. All protein atoms have been considered in filtering; these files should be usable for any protein question. If your work is strictly limited to mainchain atoms (plus CB), there is a separate version that has been filtered on only mainchain atoms.</p> <p>The Top2018 contains several different levels of homology clustering (30%, 50%, 70%, 90%) to ensure nonredundant datasets. The 70% homology level is a reliable default. These chains are listed in top2018_chains_hom70_fullfiltered_60pct_complete.txt and found in top2018_pdbs_full_filtered_hom70.tar.gz</p> <p>Files are organized in subdirectories based on the first two letters of their PDB ids. The included python script sample_file_loop.py may aid in accessing the directory structure.</p> <p>Files already contain hydrogens added by Reduce. NQH flips have been performed to ensure that these are the best versions of these structures.</p> <p>top2018_metadata_full_filtered.csv contains information on release date, resolution, and validation scores for each file.</p> <p>top2018_passrates_fullll_filtered.csv contains information on how many protein residues from the original chain passed the quality filters.</p> <p><br> Homology sets:<br> --------------------------------------------------------------------------------<br> Using sequence homology clusters provided by the RCSB PDB, for each homology cluster, the best chain was selected for inclusion in the dataset. This ensures minimal sequence/structural redundancy.</p> <p>The Top2018 is available at several different levels of homology clustering, which may be appropriate to different uses. Lists of the included chains at each homology level are included in this distribution.</p> <p>Lower homology numbers mean less redundancy, but fewer total chains in the dataset.</p> <p>For general use, ***we recommend the 70% homology set*** as a good balance between inclusivity and variety. This list is given in the file top2018_chains_hom70_fullfiltered_60pct_complete.txt</p> <p><br> Usage caveats:<br> --------------------------------------------------------------------------------<br> These files are incomplete. They are single chains from structures that may have had multiple chains. Residues that fail the filtering criteria have been removed. Programs with strong requirements for completeness or uninterrupted chains should be used with care. Chain completeness and fragmentation statistics are available in top2018_passrates_full_filted.csv and in USER records at the end on each .pdb file.</p> <p>All header information from the original structure has been preserved. This includes information about chains and residues no longer present in the file.</p> <p>All ligands and waters associated with the chain have been preserved without filtering. Robust ligand filtering is beyond the scope of this dataset. Trust the ligands at your own discretion.</p> <p><br> Filtering criteria: Chain level<br> --------------------------------------------------------------------------------<br> Chain is protein<br> Released on or before Dec 31, 2018<br> Resolution < 2.0<br> MolProbity Score < 2.0<br> <3% residues have cbeta deviations<br> <2% residues have covalent bond length outliers<br> <2% residues have covalent bond geometry outliers</p> <p>Using sequence homology clusters provided by the RCSB PDB, for each homology cluster, the chain with the best (lowest) average of Resolution and MolProbity Score was selected.</p> <p><br> Filtering criteria: Residue level<br> --------------------------------------------------------------------------------<br> Even excellent structures usually contain some poorly-resolved regions. Residue-level filtering helps avoid including these regions in otherwise high-quality data</p> <p>All atoms in a residue:<br> Bfactor <= 40<br> Real-space correlation coefficient (rscc) >= 0.7<br> 2Fo-Fc map value >= 1.2</p> <p>Additionally, residues are not allowed to have:<br> Covalent geometry outliers<br> Steric overlaps or "clashes", as per Probe<br> Alternate conformations</p> <p><br> Chain Completeness criteria<br> --------------------------------------------------------------------------------<br> Chains which lost >40% of their residues during filtering were dropped from this dataset. All chains present here are at least 60% complete.</p> <p>Filtering documentation<br> --------------------------------------------------------------------------------<br> Each file documents its pruned and included residues with USER records. These include self-documenting USER DOC lines as follow:<br> USER DOC Lines marked with USER DEL list residues pruned by<br> USER DOC quality filtering.<br> USER DOC Format is chain:resseq:icode:reason_for_pruning<br> USER DOC Reasons for pruning are abbreviated as 1-letter codes: bcmgoa<br> USER DOC b=bfactor, c=real space correlation, m=2Fo-Fc mapvalue<br> USER DOC g=geometry outlier, o=steric overlap, a=alternate conformations<br> USER DOC Lines marked USER INC list the uninterrupted fragments of structure<br> USER DOC still included after pruning by quality filtering<br> USER DOC Format is chain1:resseq1:icode1:chain2:resseq2:icode2:fragment_length<br> USER DOC where 1 is the first and 2 the last residue of the fragment<br> USER DOC Line marked with USER PCT gives statistics for structure completeness</p> <p>Version history<br> --------------------------------------------------------------------------------<br> Version 1.0 10.5281/zenodo.5115233 Jul 19, 2021<br> Initial version</p> <p>Version 2.0<br> Removed ~6000 additional residues due to mainchain atom clashes<br> Set case of filenames to unambiguous standard: all lowercase except L</p>
All-atom Molecular Dynamics Simulations of Meiosis 1-associated protein (M1AP) to Investagate the Impact of Known Missense Mutations Associated with Male Infertility through Non-obstructive Azoospermia
<p>Protein structure of meiosis 1-associated protein (M1AP) was modelled by using GalaxyWeb (from Seok Lab). We used this model to investigate the impact of variants (i.e., S50P, R266Q, P389L, G317R, and L430P) in M1AP which were recently associated with non-obstructive azoospermia (NOA). NOA is a male infertility-related condition causing absence of sperm in the seminal fluid due to meiosis failure. We aimed to elucidate the pathogenicity mechanisms of these five missense NOA-related mutations on M1AP by performing molecular modeling and molecular dynamics (MD) simulations. This dataset includes the results of 1000 ns MD simulations (two repeats, each 500 ns) for each of the mutant and wild-type systems.</p> <p>Systems were prepared in Visual Molecular Dynamics (VMD 1.9.3) by placing them in a TIP3P water box with approximately 20 Å thickness from the protein surface and neutralizing the system charge with 0.15 M KCl. Of note, only protein parts were kept for the submission to reduce the size of files. Nanoscale Molecular Dynamics (NAMD 2.13-CUDA) was used to perform MD simulations with CHARMM36m force field. For pressure and temperature controls, Nosé-Hoover Langevin barostat and Langevin thermostat were used. ShakeH algorithm of NAMD was applied for water molecule constraints. 12 Å cut-off distance was used for van der Waals interactions. Switching function starts at 10 Å and reaches zero at 14 Å. Integration time-step was 2 fs. To compute the long-range Coulomb interactions, the particle-mash Ewald method was used. NPT ensemble was applied for whole simulations. Two step minimization & equilibration procedure was performed: (1) 5,000-step minimization and 1 ns equilibrium with constraints on the protein; (2) 5,000-step minimization and 1 ns equilibrium without the constraints on the protein. All related configuration files for wild-type system were also included to the dataset. Production simulations were run twice along 500 ns by using different random seeds to assign the velocities from Boltzmann distribution (total simulation time for each system was 1000 ns, which are given as 500 ns repeat 1, and 500 ns repeat 2). The production simulations were supplied in the dataset. "out" and "log" files were used for energy analysis.</p> <p>For all analysis scripts, see https://github.com/ugerlevik/M1AP_analysis.</p>
Quantum stochastic resonance of individual Fe atoms. Open data sets.
<p>Data sets for publication:</p> <p><strong>Quantum Stochastic Resonance of individual Fe atoms</strong><br> Max Hänze, Gregory McMurtrie, Susanne Baumann, Luigi Malavolti, Susan N. Coppersmith, Sebastian Loth</p>
High quality protein residues: Top2018 all-atom-filtered residues - mmCIF
<p>Introduction<br> --------------------------------------------------------------------------------<br> This directory contains files from the Top2018 dataset by the Richardson Lab at Duke University.</p> <p>These are high-quality residues from high-quality, low redundancy protein chains in the PDB.</p> <p>This dataset is quality-filtered on all atoms in the residue.</p> <p>The accompanying publication is:<br> Williams, C. J., Richardson, D. C., & Richardson, J. S. (2021). The importance of residue-level filtering, and the Top2018 best-parts dataset of high‐quality protein residues. Protein Science. http://doi.org/10.1002/pro.4239</p> <p>Usage recommendations<br> --------------------------------------------------------------------------------<br> Protein residues that fail the filtering criteria described below have been removed from the files. As a result, these files can be considered pre-filtered and will return only results for residues of good model quality with supporting experimental data. All protein atoms have been considered in filtering; these files should be usable for any protein question. If your work is strictly limited to mainchain atoms (plus CB), there is a separate version that has been filtered on only mainchain atoms.</p> <p>The Top2018 contains several different levels of homology clustering (30%, 50%, 70%, 90%) to ensure nonredundant datasets. The 70% homology level is a reliable default. These chains are listed in top2018_chains_hom70_fullfiltered_60pct_complete.txt and found in top2018_pdbs_full_filtered_hom70.tar.gz</p> <p>Files are organized in subdirectories based on the first two letters of their PDB ids. The included python script sample_file_loop.py may aid in accessing the directory structure.</p> <p>Files already contain hydrogens added by Reduce. NQH flips have been performed to ensure that these are the best versions of these structures.</p> <p>top2018_metadata_full_filtered.csv contains information on release date, resolution, and validation scores for each file.</p> <p>top2018_passrates_full_filtered.csv contains information on how many protein residues from the original chain passed the quality filters.</p> <p><br> Homology sets:<br> --------------------------------------------------------------------------------<br> Using sequence homology clusters provided by the RCSB PDB, for each homology cluster, the best chain was selected for inclusion in the dataset. This ensures minimal sequence/structural redundancy.</p> <p>The Top2018 is available at several different levels of homology clustering, which may be appropriate to different uses. Lists of the included chains at each homology level are included in this distribution.</p> <p>Lower homology numbers mean less redundancy, but fewer total chains in the dataset.</p> <p>For general use, ***we recommend the 70% homology set*** as a good balance between inclusivity and variety. This list is given in the file top2018_chains_hom70_fullfiltered_60pct_complete.txt</p> <p><br> Usage caveats:<br> --------------------------------------------------------------------------------<br> These files are incomplete. They are single chains from structures that may have had multiple chains. Residues that fail the filtering criteria have been removed. Programs with strong requirements for completeness or uninterrupted chains should be used with care. Chain completeness and fragmentation statistics are available in top2018_passrates_full_filtered.csv and as _top2018.percent_passrate in the .cif file.</p> <p>All ligands and waters associated with the chain have been preserved without filtering. Robust ligand filtering is beyond the scope of this dataset. Trust the ligands at your own discretion.</p> <p><br> Filtering criteria: Chain level<br> --------------------------------------------------------------------------------<br> Chain is protein<br> Released on or before Dec 31, 2018<br> Resolution < 2.0<br> MolProbity Score < 2.0<br> <3% residues have cbeta deviations<br> <2% residues have covalent bond length outliers<br> <2% residues have covalent bond geometry outliers</p> <p>Using sequence homology clusters provided by the RCSB PDB, for each homology cluster, the chain with the best (lowest) average of Resolution and MolProbity Score was selected.</p> <p><br> Filtering criteria: Residue level<br> --------------------------------------------------------------------------------<br> Even excellent structures usually contain some poorly-resolved regions. Residue-level filtering helps avoid including these regions in otherwise high-quality data</p> <p>All atoms in a residue:<br> Bfactor <= 40<br> Real-space correlation coefficient (rscc) >= 0.7<br> 2Fo-Fc map value >= 1.2</p> <p>Additionally, residues are not allowed to have:<br> Covalent geometry outliers<br> Steric overlaps or "clashes", as per Probe<br> Alternate conformations</p> <p><br> Chain Completeness criteria<br> --------------------------------------------------------------------------------<br> Chains which lost >40% of their residues during filtering were dropped from this dataset. All chains present here are at least 60% complete.</p> <p>Filtering documentation<br> --------------------------------------------------------------------------------<br> Each file documents its pruned residues and included segments in a cif data block named data_top2018_dataset. This block can be found at the end of the file.</p> <p>In the _top2018_deleted_residue loop, causes of pruning are documented. If a residues was removed due to failing the B-factor filter, a "b" will appear in the appropriate column. Otherwise, a "." will appear. Other filtering criteria are treated similarly with the following codes:<br> b - B-factor<br> c - RSCC<br> m - map value<br> g - geometry outliers<br> o - steric overlaps<br> a - alternate conformations</p> <p>Version history<br> --------------------------------------------------------------------------------<br> Version 0.9<br> Initial upload to establish DOI</p> <p>Version 1.0<br> Initial full upload</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.