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220 results for “force field”
Pure POPE membrane simulations with the CHARMM-Drude force field (OpenMM 7.5.0)
<p>MD simulation data of a pure POPE membrane with the CHARMM-Drude force field generated with the OpenMM 7.5.0 simulation engine.</p> <p>All the input parameters are available in the *inp file. The initial structures have been obtained from CHARMM-GUI.</p> <p>Total simulation duration is 300 ns (100 ns x 3, continuing from the last frame, combined with the mdconvert). First 50 ns is discarded as equilibration. This data set contains 300 ns data with 3000 frames (saving frequency is 100 ps).</p> <p>In total, 72 POPC lipids in each leaflet (144 in total), 5040 SWM4-NPD water molecules.</p> <p><strong>It has been discovered that (https://github.com/NMRLipids/Databank/issues/2#issuecomment-1357871243) the previously uploaded trajectory did not have the correct timestamp: the timestep between two consecutive simulation frames was not correctly embedded into the trajectory information. Therefore, with the latest version we are uploading the "unwrapped_all_fixed_dt.xtc" which has the correct timestamp. The frame saving frequency in this trajectory is 100 ps. </strong></p> <p><strong>This new update should not invalidate any previous calculations that did not explicitly read the timestamp information from the trajectory.</strong></p> <p><strong>This simulation consists of 3 sub-trajectories, each of which starts from the last frame of the previous one. These trajectories (originally in dcd format) were concatenated and saved in xtc format with MDAnalysis.</strong></p> <p> </p> <p> </p>
Pure POPC Membrane with 350mM CaCl2 simulations using Drude Polarizable Force Field and OpenMM
<p>500 ns MD simulation of pure POPC membrane using Charmm-Drude polarizable force field. The system contains 128 POPC lipids, 41 CaCl2, and 6400 SWM4 water molecules.</p> <p>wrapped.dcd has a frame saving frequency of 100 ps.</p> <p>Before running the Drude simulation, the system has been equilibriated using Charmm36 force field for 200 ns. The last frame of that simulation was used to generate Drude polarizable model. The first 100 ns of the Drude simulation has been discarded from this dataset.</p> <p><strong>It has been discovered that (https://github.com/NMRLipids/Databank/issues/2#issuecomment-1357871243) the wrapped_full.dcd trajectory did not have the correct timestamp: the timestep between two consecutive simulation frames was not correctly embedded into the trajectory information. Therefore, with the latest version we are uploading the "wrapped_full_fixed_dt.xtc" which has the correct timestamp. The frame saving frequency in this trajectory is 10 ps. </strong></p> <p><strong>This new update should not invalidate any previous calculations that did not explicitly read the timestamp information from the trajectory.</strong></p> <p><strong>This simulation consists of 5 sub-trajectories, each of which starts from the last frame of the previous one and runs for 100 ns. These trajectories (originally in dcd format) were concatenated and saved in xtc format with MDAnalysis.</strong></p>
Pure POPC membrane simulations with 1000 mM CaCl2 with the CHARMM-Drude force field (OpenMM)
<p>400 ns MD simulation of pure POPC membrane using Charmm-Drude polarizable force field. The system contains 128 POPC lipids, 114 CaCl2, and 6400 SWM4 water molecules.</p> <p>Before running the Drude simulation, the system has been equilibriated using Charmm36 force field for 200 ns. The last frame of that simulation was used to generate Drude polarizable model. The first 100 ns of the Drude simulation has been discarded from this dataset. Total simulation time is 500 ns, included data is 397.5 ns.</p> <p> </p> <p><strong>It has been discovered that (https://github.com/NMRLipids/Databank/issues/2#issuecomment-1357871243) the previously uploaded trajectory did not have the correct timestamp: the timestep between two consecutive simulation frames was not correctly embedded into the trajectory information. Therefore, with the latest version we are uploading the "wrapped_full.xtc" which has the correct timestamp. The frame saving frequency in this trajectory is 10 ps and there are 39750 frames.</strong></p> <p><strong>This new update should not invalidate any previous calculations that did not explicitly read the timestamp information from the trajectory.</strong></p> <p><strong>This simulation consists of 4 sub-trajectories, each of which starts from the last frame of the previous one. These trajectories (originally in dcd format) were concatenated and saved in xtc format with MDAnalysis.</strong></p> <p><strong>Centering of the trajectories has been done via below MDAnalysis script</strong></p> <p><strong> ...: u = mda.Universe('../step3_charmm2omm.psf', 'step5.dcd')<br> ...: prot = u.select_atoms("resname POPC")<br> ...: ag = u.atoms<br> ...: workflow = (transformations.unwrap(ag),<br> ...: transformations.center_in_box(prot, center='mass'),<br> ...: transformations.wrap(ag, compound='fragments'))<br> ...: u.trajectory.add_transformations(*workflow)</strong></p> <p> </p>
Optimized OPEP Force Field for Simulation of Crowded Protein Solutions
<p>Initial structures (in the PDB format) and LBMD trajectories (in the GROMACS XTC format) of crowded protein solutions simulated using the OPEPv7 force field and presented in the article entitled <em>Optimized OPEP Force Field for Simulation of Crowded Protein Solutions </em>(<a href="https://doi.org/10.1021/acs.jpcb.3c00253">https://doi.org/10.1021/acs.jpcb.3c00253</a>).</p>
charmm2gmx: An Automated Method to Port the CHARMM Additive Force Field to GROMACS
<p>Validation dataset for the paper "charmm2gmx: An Automated Method to Port the CHARMM Additive Force Field to GROMACS". The dataset includes molecular dynamics input and output files, as well as scripts for running the simulations and analyzing the results, used for validating the ported CHARMM parameters.</p> <p>CHARMM is one of the most widely used biomolecular force fields. Although developed in close connection with a dedicated molecular simulation engine of the same name, it is also usable with other codes. GROMACS is a well-established, highly optimized and multi-purpose software for molecular dynamics, versatile enough to accommodate many different force field potential functions and the associated algorithms. Due to conceptional differences related to software design and the large amount of numeric data inherent to residue topologies and parameter sets, conversion from one software format to another is not straightforward. Here, we present an automated and validated means to port the CHARMM force field to a format read by the GROMACS engine, harmonizing the different capabilities of the two codes in a self-documenting and reproducible way, with a bare minimum of user interaction required. Being based entirely on the upstream data files, the presented approach does not involve any hard-wired/boilerplate code, in contrast with previous attempts to solve the same problem. The heuristic approach used for perceiving local internal geometry is directly applicable for analogous transformations of other force fields.</p> <p> </p>
Suppression force-fields and diffuse competition: Competition de-escalation is an evolutionarily stable strategy
<p><span>Competition theory is founded on the premise that individuals benefit from harming their competitors, which helps them secure resources and prevent inhibition by neighbours. When multiple individuals compete, however, competition has complex indirect effects that reverberate through competitive neighbourhoods. The consequences of such "diffuse" competition are poorly understood. For example, competitive effects may dilute as they propagate through a neighbourhood, weakening benefits of neighbour suppression. Another possibility is that competitive effects may rebound on strong competitors, as their inhibitory effects on their neighbours benefit other competitors in the community. Diffuse competition is unintuitive in part because we lack a clear conceptual framework for understanding how individual interactions manifest in communities of multiple competitors. Here, I use mathematical and agent-based models to illustrate that diffuse interactions—as opposed to direct pairwise interactions—are likely the dominant mode of interaction among multiple competitors. Consequently, competitive effects may regularly rebound, incurring fitness costs under certain conditions, especially when kin-kin interactions are common. These models provide a powerful framework for investigating competitive ability and its evolution and produce clear predictions in ecologically realistic scenarios.</span></p>
Suppression force-fields and diffuse competition: Competition de-escalation is an evolutionarily stable strategy
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Underlying data for "The role of electrostatics in enzymes: do biomolecular force fields reflect protein electric fields?"
<p>This dataset contains code, data, trajectories, and figures used in the article "The role of electrostatics in enzymes: do biomolecular force fields reflect protein electric fields?".</p> <p> </p> <p>Contents:</p> <p>code/* - Code used to calculate electric fields from simulation trajectories with either polarizable or additive force fields</p> <p>data/* - Electric fields calculated for the CypA WT cis, WT trans, R55A cis, and R55A trans systems, with AMOEBA, Amber, or Charmm force fields. Each subfolder also includes a set of structural coordinates extracted at 2.5 ns intervals from the first simulation trajectory and used to calculate ONETEP DFT electric fields.</p> <p>figures/* - Underlying data and scripts used to create all figures and movies used in the article.</p> <p>trajectories/* - Simulation trajectories of the CypA WT cis, WT trans, R55A cis, and R55A trans systems</p> <p> </p> <p>Where appropriate, README files include instructions for regenerating data used in the article, and details of the Python packages and other software used to generate data are available in Dependencies.txt</p>
Molecular geometries and energies from quantum mechanical calculations and small molecule force field evaluations.
<p>Force fields are used in a wide variety of contexts for classical molecular simulation, including studies on protein-ligand binding, membrane permeation, and thermophysical property prediction.<br> The quality of these studies relies on the quality of the force fields used to represent the systems. <br> Focusing on small molecules of fewer than 50 heavy atoms, this data compares nine force fields: GAFF, GAFF2, MMFF94, MMFF94S, OPLS3e, SMIRNOFF99Frosst, and the Open Force Field Parsley, versions 1.0, 1.1, and 1.2.<br> On a dataset comprising 22,675 molecular structures of 3,271 molecules, we analyzed force field-optimized geometries and conformer energies compared to reference quantum mechanical (QM) data.<br> <br> The data was created using scripts of the <a href="https://github.com/MobleyLab/benchmarkff/commit/fa45247aa9867f504c02eb6f62d8459a94a0a936">benchmarkff github repository</a>.</p> <p>A corresponding manuscript is submitted, a preprint is available on ChemRxiv:<br> <a href="https://doi.org/10.26434/chemrxiv.12551867.v2 ">Lim, Victoria T.; Hahn, David F.; Tresadern, Gary; Bayly, Christopher I.; Mobley, David (2020): Benchmark Assessment of Molecular Geometries and Energies from Small Molecule Force Fields. ChemRxiv. Preprint</a></p> <p>Read below or the file README.md for further information and description of the content:</p> <pre><code class="language-markdown"># README Version: 04 Nov 2020 For Python scripts that are NOT found in these directories, please check the [BenchmarkFF Github repo](https://github.com/MobleyLab/benchmarkff/tree/master/tools). ## Procedure 1. Prep OPLS3e file for analysis: standardize format by OpenEye in case of differences and convert from kJ/mol to kcal/mol. ``` cd prep python convert_extension.py -i opls3e_minimized.sd -o opls3e.sdf ``` 2. Remove mols that couldn't parameterize by ALL FFs. ``` python get_by_tag.py -i opls3e.sdf -s "SMILES QCArchive" -list trim3.txt -o trim3_full_opls3e.sdf ``` 3. Run analysis. ``` conda activate parsley # calc ddE, RMSD, and TFD distributions python compare_ffs.py -i match.in -t 'SMILES QCArchive' --plot > metrics.out # match_minima, only in 01_analysis_all and 02_analysis_all_smaller_cutoff python match_minima.py -i match.in --plot --cutoff 1.0 --readpickle # look at specific subsets, only in 01_analysis_all python color_by_moiety.py -i match.in -p metrics.pickle -s N-N.dat azetidine.dat octahydrotetracene.dat -o scatter_tfd_3_ # look at outliers,only in 01_analysis_all and 02_analysis_all_smaller_cutoff python tailed_parameters.py -i refdata_trim_overlap_full_openff_unconstrained-1.2.0.sdf -f <offxml file> --metric 'TFD' --cutoff 0.12 --tag "TFD to trim_overlap_full_qcarchive.sdf" --tag_smiles "SMILES QCArchive" > output_tfd.dat ``` ## Brief description of contents * High level: ``` . ├── 00_prep │ ├── convert_extension.py │ ├── opls3e_minimized.sd OPLS3e minimized structures from Schrodinger Maestro │ ├── opls3e.sdf standardized through OpenEye tools │ ├── opt_openff*.sdf OpenFF minimized conformations ├── 01_analysis_all compare all ffs (qm, GAFF(2), MMFF94(S), Smirnoff, OpenFF-X.X, OPLS3e) ├── 02_analysis_all_smaller_cutoff compare all ffs (qm, GAFF(2), MMFF94(S), Smirnoff, OpenFF-X.X, OPLS3e) with a smaller cutoff of .3 for match_minima ├── 03_analysis_latest_ffs compare only the latest versions of ffs (qm, GAFF2, MMFF94S, OpenFF-1.2, OPLS3e) ├── 04_analysis_openff_only compare only OpenFF ffs (qm, Smirnoff, OpenFF-X.X) └── README.md ``` * Inside an output directory: ``` YY_analysis_* various output files of above mentioned scripts, some are listed and described below: ├── bar*.png parameter coverage bar plots ├── ddE.dat relative energies data ├── fig_density_*.png scatter plots of ddE vs (RMSD or TFD) for each force field ├── match.in input file for compare_ffs.py ├── metrics.out output file for compare_ffs.py ├── metrics.pickle pickle file for compare_ffs.py -- you can read this into compare_ffs instead of rerunning the full analysis ├── refdata_*.sdf output SDF files with stored RMSD / TFD scores with reference to QM for each structure ├── relene_*.dat relative energies of matched conformers ├── ridge_dde.png compared energies plot ├── ridge_rmsd.svg compared rmsds plot ├── ridge_tfd.svg compared tfds plot ├── fig_scatter_*.png scatter plots of ddE vs (RMSD or TFD). these are noisy; I don't use these ├── trim3_*.sdf input SDF files for compare_ffs.py listed in match.in file ├── violin*.* violin plot showing ddE distributions ``` </code></pre>
Pure POPC membrane simulations using Amber Lipid 14 Force Field
<p>Pure POPC membrane simulations using the Amber Lipid 14 force field.</p> <pre>@article{dickson2014lipid14, title={Lipid14: the amber lipid force field}, author={Dickson, Callum J and Madej, Benjamin D and Skjevik, {\AA}ge A and Betz, Robin M and Teigen, Knut and Gould, Ian R and Walker, Ross C}, journal={Journal of chemical theory and computation}, volume={10}, number={2}, pages={865--879}, year={2014}, publisher={ACS Publications} }</pre> <p>The trajectories are centered such that the center of mass of the lipid tails are at the origin. <strong>Please check the imaging again to make sure that there are no problems. </strong></p> <p><strong>The trajectories do not contain water molecules.</strong> </p> <p>Simulation Details:</p> <p>Lipids : 72 POPC lipids, 36 per leaflet</p> <p>Water: 9560 TIP3P water molecules (<strong>water coordinates are not saved</strong>)</p> <p>Temperature: 303 K</p> <p>Pressure: 1 bar</p> <p>Thermostat: Langevin</p> <p>Barostat: Berendsen</p> <p>Pressure coupling: Semi-isotropic</p> <p>Trajectory Length: 100 ns (after 100 ns pre-equilibration)</p> <p>Saving frequency: 100 ps</p> <p>Further details are available at the 04_Run.in file</p> <p>All trajectories started from the same structure but equilibriated for 100 ns independently (using 03_Hold.in)</p>
SAXS calculations for Refinement of 𝛼-synuclein ensembles against SAXS data: Comparison of force fields and methods
<p>SAXS curves calculated from MD simulations used as input to reweighting as described in preprint Refinement of 𝛼-synuclein ensembles against SAXS data: Comparison of force fields and methods</p>
DLPC@ 323K, CHARMM36UA force field
<p>Files required for a simulation of a DLPC bilayer in LAMMPS (<strong>lammps</strong>.sandia.gov/). Force field is taken from http://pubs.acs.org/doi/abs/10.1021/jp410344g</p> <p>This data is used in the project "Matching lipid force fields with NMR data", see: http://nmrlipids.blogspot.fi </p>
Underlying data for "Evaluating parameterization protocols for hydration free energy calculations with the AMOEBA polarizable force field"
<p>This dataset includes underlying data for the publication "Evaluating<br /> parameterization protocols for hydration free energy calculations with the<br /> AMOEBA polarizable force field"</p> <p>Contents:<br /> Modified valence parameters for the Poltype software (valence.py). This can be<br /> substituted for the existing valence.py module packaged with Poltype to make the<br /> parameter assignment changes detailed in the article supplementary information. </p> <p>Results files for each parameter set (*.txt). Each consists of a 4 x 47 array of<br /> numbers. Rows correspond to entries for each sequential ligand. The first column<br /> in each row is the experimental hydration free energy. The following three rows<br /> are computational hydration free energy predictions from three independent<br /> repeat simulations.</p> <p>Script for analysis of results files (analyse_hfe.py). Short script to produce<br /> descriptive statistics for packaged datasets. Expects input files in the syntax<br /> of *.txt (i.e. 4 x 47 arrays)</p>
POPC lipid membrane, 303K, Charmm36 force field, simulation files and 200 ns trajectory for openMM simulation engine v7
<p>POPC lipid membrane, 303K, Charmm36 force field, simulation files and 200 ns trajectory for for openMM simulation engine v7</p> <p>The starting structure was obtained from CHARMM-GUI Membrane Builder v1.7 (http://www.charmm-gui.org/) online tool. [1]</p> <p>All runs were performed with openMM simulation engine v7 and CHARMM36 additive force field parameters obtained from CHARMM-GUI input files [1]. Conditions: T=303, 128 POPC molecules, 5120 tip3p waters, 200ns trajectory (preceded with equilibration).</p> <p>Note that the provided trajectories are in Gromacs XTC format, whereas NAMD DCD format was generated by openMM. This required trajectory conversion using Gromacs package (v5.1.2) with binary topology file from https://doi.org/10.5281/zenodo.153944 </p> <p>These data were originally obtained for the nmrlipids.blospot.fi project.</p> <p>Find more details at nmrlipids.blospot.fi and https://github.com/NMRLipids/nmrlipids.blogspot.fi</p> <p>[1] CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field, J. Lee et al.<strong>,</strong> JCTC,<strong> </strong>DOI: 10.1021/acs.jctc.5b00935</p>
POPC lipid membrane, 303K, Charmm36 force field, simulation files and 200 ns trajectory for Gromacs MD simulation engine v5.1.2
<p>POPC lipid membrane, 303K, Charmm36 force field, simulation files and 200 ns trajectory for Gromacs MD simulation engine v5.1.2</p> <p>The starting structure was obtained from CHARMM-GUI Membrane Builder v1.7 (http://www.charmm-gui.org/) online tool. [1]</p> <p>All runs were performed with Gromacs 5.1.2 software package and CHARMM36 additive force field parameters obtained from CHARMM-GUI input files [1]. Conditions: T=303, 128 POPC molecules, 5120 tip3p waters, 200ns trajectory (preceded with equilibration)</p> <p>These data were originally obtained for the nmrlipids.blospot.fi project.</p> <p>Find more details at nmrlipids.blospot.fi and https://github.com/NMRLipids/nmrlipids.blogspot.fi</p> <p>[1] CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field, J. Lee et al.<strong>,</strong> JCTC,<strong> </strong>DOI: 10.1021/acs.jctc.5b00935</p>
POPC/Cholesterol (50:50) lipid membrane, 303K, Charmm36 force field from charmm-gui, simulation files and 200 ns trajectory for Gromacs MD simulation engine v5.1.2
<p>The starting structure was obtained from CHARMM-GUI Membrane Builder v1.7 (http://www.charmm-gui.org/) online tool. [1]</p> <p>All runs were performed with Gromacs 5.1.2 software package and CHARMM36 additive force field parameters obtained from CHARMM-GUI input files [1]. Conditions: T=303, 80 POPC and 80 Cholesterol molecules, 7200 tip3p waters, 200ns trajectory (preceded with equilibration)</p> <p>These data were originally obtained for the nmrlipids.blospot.fi project.</p> <p>Find more details at nmrlipids.blospot.fi and https://github.com/NMRLipids/nmrlipids.blogspot.fi</p> <p>[1] CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field, J. Lee et al.<strong>,</strong> JCTC,<strong> </strong>DOI: 10.1021/acs.jctc.5b00935</p>
POPC/Cholesterol (70:30) lipid membrane, 303K, Charmm36 force field, simulation files and 200 ns trajectory for Gromacs MD simulation engine v5.1.2
<p>The starting structure was obtained from CHARMM-GUI Membrane Builder v1.7 (http://www.charmm-gui.org/) online tool. [1]</p> <p>All runs were performed with Gromacs 5.1.2 software package and CHARMM36 additive force field parameters obtained from CHARMM-GUI input files [1]. Conditions: T=303, 128 POPC molecules, 5120 tip3p waters, 200ns trajectory (preceded with equilibration)</p> <p>These data were originally obtained for the nmrlipids.blospot.fi project.</p> <p>Find more details at nmrlipids.blospot.fi and https://github.com/NMRLipids/nmrlipids.blogspot.fi</p> <p>[1] CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field, J. Lee et al.<strong>,</strong> JCTC,<strong> </strong>DOI: 10.1021/acs.jctc.5b00935</p>
POPC/Cholesterol (70:30) lipid membrane, 303K, Charmm36 force field, simulation files and 100 ns trajectory for openMM simulation engine v7
<p>The starting structure was obtained from CHARMM-GUI Membrane Builder v1.7 (http://www.charmm-gui.org/) online tool. [1]</p> <p>All runs were performed with openMM simulation engine v7 and CHARMM36 additive force field parameters obtained from CHARMM-GUI input files [1]. Conditions: T=303, 84 POPC and 36 Cholesterol molecules, 4800 tip3p waters, 100ns trajectory (preceded with equilibration).</p> <p>These data were originally obtained for the nmrlipids.blospot.fi project.</p> <p>Find more details at nmrlipids.blospot.fi and https://github.com/NMRLipids/nmrlipids.blogspot.fi</p> <p>[1] CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field, J. Lee et al.<strong>,</strong> JCTC,<strong> </strong>DOI: 10.1021/acs.jctc.5b00935</p>
POPC/Cholesterol (50:50) lipid membrane, 303K, Charmm36 force field from charmm-gui, simulation files and 100 ns trajectory for openMM simulation engine v7
<p>The starting structure was obtained from CHARMM-GUI Membrane Builder v1.7 (http://www.charmm-gui.org/) online tool. [1]</p> <p>All runs were performed with openMM simulation engine v7 and CHARMM36 additive force field parameters obtained from CHARMM-GUI input files [1]. Conditions: T=303, 80 POPC and 80 Cholesterol molecules, 7200 tip3p waters, 100ns trajectory (preceded with equilibration)</p> <p>These data were originally obtained for the nmrlipids.blospot.fi project.</p> <p>Find more details at nmrlipids.blospot.fi and https://github.com/NMRLipids/nmrlipids.blogspot.fi</p> <p>[1] CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field, J. Lee et al.<strong>,</strong> JCTC,<strong> </strong>DOI: 10.1021/acs.jctc.5b00935</p>
POPC/Cholesterol (70:30) lipid membrane, 303K, Charmm36 force field through the use of Gromacs input files, simulation files and 100 ns trajectory for openMM simulation engine v7
<p>The starting structure was obtained from CHARMM-GUI Membrane Builder v1.7 (http://www.charmm-gui.org/) online tool. [1]</p> <p>All runs were performed with openMM simulation engine v7 and CHARMM36 additive force field parameters obtained from CHARMM-GUI input files [1]. Specifically, Gromacs file format provided by [1] was specifically used for this simulation.</p> <p>Conditions: T=303, 84 POPC and 36 Cholesterol molecules, 4800 tip3p waters, 100ns trajectory (preceded with equilibration).</p> <p>These data were originally obtained for the nmrlipids.blospot.fi project.</p> <p>Find more details at nmrlipids.blospot.fi and https://github.com/NMRLipids/nmrlipids.blogspot.fi</p> <p>[1] CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field, J. Lee et al.<strong>,</strong> JCTC,<strong> </strong>DOI: 10.1021/acs.jctc.5b00935</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.