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zenodo40/100

Ambient temperature CiPA dynamic hERG protocol dataset recorded on the SyncroPatch 384SE

Abstract  The Comprehensive in vitro Proarrhythmic Assay (CiPA) has promoted use of in silico models of drug effects on cardiac repolarization to improve proarrhythmic risk prediction. These models contain a pharmacodynamic component describing drug binding to hERG channels that required in vitro data for kinetics of block, in addition to potency, to constrain them. To date, development and validation has been undertaken using data from manual patch-clamp. The application of this approach at scale requires the development of a high-throughput, automated patch-clamp (APC) implementation. Here, we present a comprehensive analysis of the implementation of the Milnes, or CiPA dynamic protocol, on an APC platform, including quality control and data analysis. Kinetics and potency of block were assessed for bepridil, cisapride, terfenadine and verapamil with data retention/QC pass rate of 21.8% overall, or as high as 50.4% when only appropriate sweep lengths were considered for drugs with faster kinetics. The variability in IC50 and kinetics between manual and APC was comparable to that seen between sites/platforms in previous APC studies of potency. Whilst the experimental success is less than observed in screens of potency alone, it is still significantly greater than manual patch. With the modifications to protocol design, including sweep length, number of repetitions, and leak correction recommended in this study, this protocol can be applied on APC to acquire data comparable to manual patch clamp. Description High-throughput patch clamp electrophysiology dataset acquired on Nanion SyncroPatch 384PE platform describing the potency and kinetics of drug block of hERG channels stably expressed in CHO cells. The voltage protocols used were 10 or 40 s 0mV steps with a 15s, -80mV inter-pulse intervals, repeated 5 or 10 times in the absence (control) and presence of drug. Drug was added and cells clamped at -80 mV for 4 minutes prior to recording to ensure drug equilibration prior to recording the onset (kinetics) of drug block. This dataset accompanies the paper titled 'High throughput measurement of hERG drug block kinetics using the CiPA dynamic protocol' by Monique J. Windley, Jessica Farr, Clifford TeBay, Jamie I. Vandenberg and Adam P. Hill.  File information Raw and leak corrected files for individual plates run on the SyncroPatch 384PE Folder naming convention: Plate identifier (6 digit date_user initials_plate number)_type of data (leak uncorrected raw / leak corrected). <ul> </ul> Raw data <ul> <li>File naming convention: Plate identifier (6 digit date_user initials_plate number)_well number</li> <li>Each raw data folder also contains a file named QC_plate identifier no. which contains information used for cell/patch and hERG current quality controls including seal resistance, series resistance, capacitance, baseline (average current at -80mV), average (average leak uncorrected current during last 100 ms of 0mV step). QC details are reported for each sweep along with the drug and concentration, with external and 0 M reported for control sweeps.</li> <li>Full SyncroPatch plates were not used for all runs, only applicable wells were included where  partial plates were run (see plates 240719_MW1-3, 170719_MW1-3, 180719_MW1 and  291119_MW1)</li> </ul> Leak corrected data <ul> <li>File naming convention: well number_drug_concentration in M_leak_correction</li> <li>Refer to folder for plate identifier number.</li> <li>Each folder containing leak corrected data also contains a file labelled 'percent block' which contains percentage block measurements for each leak correction file in the same folder as identified by the well number.</li> <li>Only files passing the first pass QC were leak corrected (>125 MOhm seal, <80 MOhm series, 10-200 pA capacitance, >-300 pA baseline, >50 pA hERG current size and <20% change in peak control current)</li> </ul>

opencc-by-4.0Jun 2022View details →

zenodo40/100

Dataset for "Application of dissipative particle dynamics to interfacial systems: Parameterization and scaling"

Dataset for figures of the upcoming article "Application of dissipative particle dynamics to interfacial systems: Parameterization and scaling" submitted to "AIP Advances".

opencc-by-4.0Aug 2022View details →

zenodo40/100

Characterization of the material behavior and identification of effective elastic moduli based on molecular dynamics simulations of coarse-grained silica: dataset

Abstract: (from [1]) <blockquote> The addition of fillers can significantly improve the mechanical behavior of polymers. The responsible mechanisms at the molecular level can be well assessed by particle-based simulation techniques, such as molecular dynamics. However, the high computational cost of these simulations prevents the study of macroscopic samples. Continuum-based approaches, particularly micromechanics, offer a more efficient alternative but require precise constitutive models for all constituents, which are usually unavailable at these small length scales. In this contribution, we derive a molecular-dynamics-informed constitutive law by employing a characterization strategy introduced in a previous publication. We choose silicon dioxide (silica) as an exemplary filler material used in polymer composites and perform uniaxial and shear deformation tests with molecular dynamics. The material exhibits elastoplastic behavior with a pronounced anisotropy. Based on the pseudo-experimental data, we calibrate an anisotropic elastic constitutive law and reproduce the material response for small strains accurately.   The study validates the characterization strategy that facilitates the calibration of constitutive laws from molecular dynamics simulations. Furthermore, the obtained material model for coarse-grained silica forms the basis for future continuum-based investigations of polymer nanocomposites. In general, the presented transition from a fine-scale particle model to a coarse and  computationally efficient continuum description adds to the body of knowledge of molecular science as well as the engineering community.   </blockquote> Contact: Maximilian Ries Institute of Applied Mechanics Friedrich-Alexander-Universität Erlangen-Nürnberg Egerlandstr. 5 91058 Erlangen Software: All simulations were performed with LAMMPS [3], version: 29 Oct 2020 / 20201029 Compiled with Compiler: GNU C++ 4.8.5 20150623 (Red Hat 4.8.5-39) with OpenMP not enabled C++ standard: C++11 Active compile time flags: -DLAMMPS_GZIP -DLAMMPS_SMALLBIG Installed packages: CLASS2, KSPACE, MANYBODY, MC, MOLECULE, MPIIO, OPT, VORONOI, USER-INTEL, USER-MISC, USER-MOLFILE, USER-NETCD License: Creative Commons Attribution 4.0 International   Context: Data set supplementing  journal paper: [1] Ries, M.; Bauer, C.; Weber, F.; Steinmann, P. & Pfaller, S., "Characterization of the material behavior and identification of effective elastic moduli based on molecular dynamics simulations of coarse-grained silica", Mathematics and Mechanics of Solids, 2022, 108128652211080. This dataset contains the results presented in [1] and the necessary data to obtain those. Content: The files to reproduce our simulations and their results are structured as follows: <ul> <li>01_potentials tabulated potentials calibrated via iterative Boltzmann inversion in [2] kindly provided by the Müller-Plathe group at Technische Universität Darmstadt <ul> <li>Angle_table angular interactions</li> <li>Bond_table bond interactions</li> <li>Nonbond_table pair interactions</li> </ul> </li> <li>02_sample Lammps data file (molecular style) of the investigated silica sample</li> <li>03_simulations The condensed simulation directories with the naming convention given below are organized in the following subfolders: <ul> <li>01_time-proportional time-proportional simulation data</li> <li>02_time-periodic time-periodic simulation data</li> </ul> </li> </ul> Each simulation directory contains: <ul> <li>lammps input file (*.in) of the specific simulation</li> <li>input.prm: input parameters of the specific simulation (read by the input file)</li> <li>meta.info: meta data of the specific simulation run</li> <li>LAMMPS_out: simulation results (lammps thermo_out) in tabulated form, an overview of columns is given below <ul> <li>thermo_out.Dat: raw output</li> <li>thermo_out_SG.Dat: smoothed output (Savitzky-Golay filter)</li> <li>thermo_out_STD.Dat: standard deviation of raw output</li> </ul> </li> </ul> Naming convention: Silica-[deformation]-[direction]_[deformation function]-[deformation magnitude]_[deformation rate] ●    [deformation]: uniaxial tension (UT), simple shear (SS) ●    [direction]: deformation carried out in X/Y/Z (UT) or XY/XZ/YZ (SS) ●    [deformation function]: time-proportional (strain), time-periodic (strain_ampl) ●    [deformation magnitude]: maximum strain (time-proportional), strain amplitude (time-periodic); unitless ●    [deformation rate]: rate-[strain rate] (only time-proportional): 0.001/ns-0.1/ns Output quantities (columns of *.Dat files): ●    Step: time step ●    Time: time in fs ●    TotEng: total energy in kcal/mol ●    PotEng: potential energy in kcal/mol ●    KinEng: kinetic energy in kcal/mol ●    E_pair: pair energy in kcal/mol ●    E_bond: bond energy in kcal/mol ●    E_angle: angle energy in kcal/mol ●    E_dihed: dihedral energy in kcal/mol ●    Temp: temperature in K ●    Press: hydrostatic pressure in atm ●    Pxx: xx component of pressure tensor in atm ●    Pyy: yy component of pressure tensor in atm ●    Pzz: zz component of pressure tensor in atm ●    Pxy: xy component of pressure tensor in atm ●    Pxz: xz component of pressure tensor in atm ●    Pyz: yz component of pressure tensor in atm ●    Volume: volume of simulation box in (Angstroms)^3 ●    Lx: box length in x direction in Angstroms ●    Ly: box length in y direction in Angstroms ●    Lz: box length in z direction in Angstroms ●    Density: density in g/(cm^3) ●    c_RG: radius of gyration in Angstroms ●    c_RG[1]: squared radius of gyration tensor (xx component) in (Angstroms)^2 ●    c_RG[2]: squared radius of gyration tensor (yy component) in (Angstroms)^2 ●    c_RG[3]: squared radius of gyration tensor (zz component) in (Angstroms)^2 ●    c_RG[4]: squared radius of gyration tensor (xy component) in (Angstroms)^2 ●    c_RG[5]: squared radius of gyration tensor (xz component) in (Angstroms)^2 ●    c_RG[6]: squared radius of gyration tensor (yz component) in (Angstroms)^2 ●    c_bondave[1]: bond energy averaged over all atoms in kcal/mol ●    c_bondave[2]: bond distance averaged over all atoms in  Angstroms ●    c_bondave[3]: squared bond distance averaged over all atoms in (Angstroms)^2 ●    c_angleave[1]: angle energy averaged over all atoms in kcal/mol ●    c_angleave[2]: angle averaged over all atoms degree ●    c_angleave[3]: cosine of angle (unitless) ●    c_angleave[4]: squared cosine of angle (unitless) ●    c_MSD[1]: mean squared displacement x-direction in (Angstroms)^2 ●    c_MSD[2]: mean squared displacement y-direction in (Angstroms)^2 ●    c_MSD[3]: mean squared displacement z-direction in (Angstroms)^2 ●    c_MSD[4]: total mean squared displacement in (Angstroms)^2 ●    c_COM[1]: x coordinate of center of mass in Angstroms ●    c_COM[2]: y coordinate of center of mass in Angstroms ●    c_COM[3]: z coordinate of center of mass in Angstroms ●    v_strain_xx: xx component of engineering strain tensor (unitless)   ●    v_strain_yy: yy component of engineering strain tensor (unitless)    ●    v_strain_zz: zz component of engineering strain tensor (unitless)    ●    v_vMisesequivstress: von Mises equivalent stress in MPa ●    v_Cauchy_xx: xx component of stress tensor in MPa   ●    v_Cauchy_yy: yy component of stress tensor in MPa ●    v_Cauchy_zz: zz component of stress tensor in MPa ●    v_Cauchy_xy: xy component of stress tensor in MPa ●    v_Cauchy_xz: xz component of stress tensor in MPa ●    v_Cauchy_yz: yz component of stress tensor in MPa ●    v_strain_xy: xy component of engineering strain tensor (unitless)   ●    v_strain_xz: xz component of engineering strain tensor (unitless)   ●    v_strain_yz: yz component of engineering strain tensor (unitless)   References: [1] Ries, M.; Bauer, C.; Weber, F.; Steinmann, P. & Pfaller, S., "Characterization of the material behavior and identification of effective elastic moduli based on molecular dynamics simulations of coarse-grained silica", Mathematics and Mechanics of Solids, 2022, 108128652211080. [2] Ghanbari, A.; Ndoro, T. V. M.; Leroy, F.; Rahimi, M.; Böhm, M. C. & Müller-Plathe, F., “Interphase Structure in Silica-Polystyrene Nanocomposites: A Coarse-Grained Molecular Dynamics Study”, Macromolecules, 2012, 45, 572-584. [3] Plimpton, S., “Fast parallel algorithms for short-range molecular dynamics,” Journal of computational physics, 1995, 117, 1-19.

opencc-by-4.0Aug 2022View details →

zenodo40/100

Datasets for 'Label-free imaging of 3D pluripotent stem cell differentiation dynamics on chip'

Here we publish the datasets associated with our publication ‘Label-free imaging of 3D pluripotent stem cell differentiation dynamics on chip’. 3D cultures of human induced pluripotent stem cells (hiPSCs) were imaged while undergoing definitive endoderm (DE) differentiation. Data were acquired either on the live 3D cultures at various time points during the 3 days DE differentiation, or after fixation and immunostaining, with the 3D cultures being fixed at regular 24 h intervals during differentiation, as to form a timeline. Images were recorded with a standard confocal microscope using a xy-resolution of 0.25 μm/px and a z-resolution of 1 μm/plane.

opencc-by-4.0Aug 2022View details →

zenodo40/100

Applying a generic and fast coarse-grained molecular dynamics model to extensively study the mechanical behavior of polymer nanocomposites: supplementary information and dataset

Abstract: (from [1]) <blockquote> The addition of nano-sized filler particles enhances the mechanical performance of polymers. The resulting properties of the polymer nanocomposite depend on a complex interplay of influence factors such as material pairing, filler size, and content as well as filler-matrix adhesion. As a complement to experimental studies, numerical methods, such as molecular dynamics (MD), facilitate an isolated examination of the individual factors in order to understand their interaction better. However, particle-based simulations are, in general, computationally very expensive, rendering a thorough investigation of nanocomposites’ mechanical behavior both expensive and time-consuming. Therefore, this paper presents a fast coarse-grained MD model for a generic nanoparticle-reinforced thermoplastic. First, we examine the matrix and filler phase individually, which exhibit isotropic elasto-viscoplastic and anisotropic elastic behavior, respectively. Based on this, we demonstrate that the effect of filler size, filler content, and filler-matrix adhesion on the stiffness and strength of the nanocomposite corresponds very well with experimental findings in the literature. Consequently, the presented computationally efficient MD model enables the analysis of a generic polymer nanocomposite. In addition to the obtained insights into the mechanical behavior, the material characterization provides the basis for a future continuum mechanical description, which bridges the gap to the engineering scale.  </blockquote>   Contact: Maximilian Ries Institute of Applied Mechanics Friedrich-Alexander-Universität Erlangen-Nürnberg Egerlandstr. 5 91058 Erlangen Software: All MD simulations were performed with LAMMPS [2], version: 29 Oct 2020 / 20201029 Compiled with Compiler: GNU C++ 4.8.5 20150623 (Red Hat 4.8.5-39) with OpenMP not enabled C++ standard: C++11 Active compile time flags: -DLAMMPS_GZIP -DLAMMPS_SMALLBIG Installed packages: CLASS2, KSPACE, MANYBODY, MC, MOLECULE, MPIIO, OPT, VORONOI, USER-INTEL, USER-MISC, USER-MOLFILE, USER-NETCD Polymer and polymer composite samples generated with self-avoiding random-walk algorithm [3] Post-processing Matlab R2019b Evaluation of polymer entanglements with Z1-Algorithm [4]   License: Creative Commons Attribution 4.0 International   Context: Data set supplementing  journal paper: [1] M. Ries, J. Seibert, P. Steinmann, S. Pfaller. “Applying a generic and fast coarse-grained molecular dynamics model to extensively study the mechanical behavior of polymer nanocomposites”, Express Polymer Letters, 2022, 16. This dataset contains the results presented in [1] and the necessary data to obtain those as well as supplementary information. Content: supplementary material: supplementary_information.pdf data:     folder names vary depending on the context, explained in the following:   01_matrix <ul> <li> 01_equilibration sample equilibration to different temperatures nomenclature: equil_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>[-<batch_ID>] <ul> <li> chains: 200 </li> <li> chain_atoms: 200 </li> <li> initial_box_length: 100 </li> <li> SARW_distance: 0.9 </li> <li> SARW_angle: 50 </li> <li> final_temperature: 0.1-1.0 </li> <li> batch_ID: 2-5  </li> </ul> </li> <li> 02_temperature_dependence uniaxial tension simulations to identify temperature dependence nomenclature: 01_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature> <ul> <li> chains: 200 </li> <li> chain_atoms: 200 </li> <li> initial_box_length: 100 </li> <li> SARW_distance: 0.9 </li> <li> SARW_angle: 50 </li> <li> final_temperature: 0.1-1.0 </li> </ul> </li> <li> 03_directional_dependence uniaxial tension simulations to prove isotropy in Y and Z direction; X direction in 04_rate_dependence nomenclature: 03_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>-rate_<strain_rate>-<batchID> <ul> <li> chains: 200 </li> <li> chain_atoms: 200 </li> <li> initial_box_length: 100 </li> <li> SARW_distance: 0.9 </li> <li> SARW_angle: 50 </li> <li> final_temperature: 0.3 </li> <li> strain_rate: 5E-5 </li> <li> batchID: 1-5 </li> </ul> </li> <li> 04_rate_dependence uniaxial tension simulations to identify strain rate dependence nomenclature: 03_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>-rate_<strain_rate>[-<batchID>] <ul> <li> chains: 200 </li> <li> chain_atoms: 200 </li> <li> initial_box_length: 100 </li> <li> SARW_distance: 0.9 </li> <li> SARW_angle: 50 </li> <li> final_temperature: 0.3 </li> <li> strain_rate: 5E-4, 5E-5, 5E-6 </li> <li> batchID: 1-5 </li> </ul> </li> <li> 05_cyclic_loading sinusoidal uniaxial deformation nomenclature: 05_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>-rate_<strain_rate>-sin_<strain_amplitude> <ul> <li> chains: 200 </li> <li> chain_atoms: 200 </li> <li> initial_box_length: 100 </li> <li> SARW_distance: 0.9 </li> <li> SARW_angle: 50 </li> <li> final_temperature: 0.3 </li> <li> strain_rate: 5E-4 </li> <li> strain_amplitude: 0.01, 0.05, 0.15, 0.2 </li> </ul> </li> <li> 06_relaxation relaxation subsequent to time-proportional deformation nomenclature: 07_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>-rate_<strain_rate>-sin_<strain_amplitude>_relax <ul> <li> chains: 200 </li> <li> chain_atoms: 200 </li> <li> initial_box_length: 100 </li> <li> SARW_distance: 0.9 </li> <li> SARW_angle: 50 </li> <li> final_temperature: 0.3 </li> <li> strain_rate: 5E-4 </li> <li> strain_amplitude: 0.01, 0.05, 0.15, 0.2 </li> </ul> </li> <li> 07_simple_shear time-proportional simple shear deformation with different strain rates nomenclature: SS_P2VPSi-rate_<strain_rate>-<batchID> <ul> <li> strain_rate: 5E-4, 5E-5, 5E-6 </li> <li> batchID: 1-5 </li> </ul> </li> <li> 08_large_deformation uniaxial deformation up to 100% strain nomenclature: 02_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperatur>-strain_<max_strain> <ul> <li> chains: 200 </li> <li> chain_atoms: 200 </li> <li> initial_box_length: 100 </li> <li> SARW_distance: 0.9 </li> <li> SARW_angle: 50 </li> <li> final_temperature: 0.3 </li> <li> max_strain: 1 </li> </ul> </li> </ul> 02_filler <ul> <li> 01_Silica_equilibration sample equilibration </li> <li> 02_time_proportional time-proportional uniaxial and simple shear tests nomenclature: Silica_BV-<loadcase>_<direction>-strain_<max_strain>-rate_<strain_rate> <ul> <li> loadcase: uniaxial tension (UT), simple shear (SS) </li> <li> max_strain: 0.1 </li> <li> direction: X, Y, Z (UT); XY, XZ, YZ (SS) </li> <li> strain_rate: 5E-4, 5E-5, 5E-6 </li> </ul> </li> <li> 03_time_periodic time-periodic uniaxial and simple shear tests nomenclature: Silica_BV-<loadcase>_<direction>_sin-ampl_<strain_amplitude>-rate_<max_strain_rate> <ul> <li> loadcase: uniaxial tension (UT), simple shear (SS) </li> <li> direction: X, Y, Z (UT); XY, XZ, YZ (SS) </li> <li> strain_amplitude: 0.025 </li> </ul> </li> </ul> 03_composite <ul> <li> 01_equilibration sample equilibration nomenclature: equil_P2VPSi-rNP_<filler_radius>-nNP_<filler_number>-<batchID> <ul> <li> filler_radius: 2.5-10.0 </li> <li> filler_number: 1-160 (depending on filler_radius) </li> <li> batchID: 1-5 </li> </ul> </li> <li> 02_uniaxial-tension uniaxial tension simulations nomenclature: UT_P2VPSi-rNP_<filler_radius>-nNP_<filler_number>-<batchID> <ul> <li> filler_radius: 2.5-10.0 </li> <li> filler_number: 1-160 (depending on filler_radius) </li> <li> batchID: 1-5 </li> </ul> </li> <li> 03_filler-maxtrix-adhesion equilibration and uniaxial deformation of samples with mid and weak filler-matrix adhesion (for strong adhesion see 01_equilibration and 02_uniaxial-tension nomenclature: see above </li> <li> 04_IP_equilibration equilibration of samples to evaluate the microstructure for neat polymer and composites with filler radius 2.5-7.5 nomenclature: P2VPSi-<chains>x<chain_atoms>_rNP_<filler_radius>-nNP_<filler_number>_pos_<filler_pos>-<batchID> <ul> <li> chains: 200 </li> <li> chain_atoms: 200 </li> <li> filler_radius: 0 (neat), 2.5, 5.0, 7.5 </li> <li> filler_number: 0 (neat), 1 </li> <li> batchID: 1-20 </li> </ul> </li> </ul>       Each simulation directory contains: <ul> <li> lammps input file (*.in) of the specific simulation </li> <li> data file (*.data) containing the initial sample configuration </li> <li> input.prm: input parameters of the specific simulation (read by the input file) </li> <li> meta.info: meta data of the specific simulation run </li> <li> LAMMPS_out: simulation results (lammps thermo_out) in tabulated form, an overview of columns is given below <ul> <li> thermo_out.Dat: raw output  </li> <li> thermo_out_SG.Dat: smoothed output (Savitzky-Golay filter) </li> <li> thermo_out_STD.Dat: standard deviation of raw output </li> </ul> </li> </ul>   Output quantities (columns of *.Dat files): Please note that the normalized Lennard-Jones unit set is used, so all quantities are normalized to fundamental mass, length, energy, time and the Boltzmann constant. Thus all entries are unitless [1]. <ul> <li> Step: time step  </li> <li> Time: time  </li> <li> TotEng: total energy  </li> <li> PotEng: potential energy </li> <li> KinEng: kinetic energy  </li> <li> E_pair: pair energy  </li> <li> E_bond: bond energy  </li> <li> E_angle: angle energy  </li> <li> E_dihed: dihedral energy  </li> <li> Temp: temperature </li> <li> Press: hydrostatic pressure </li> <li> Pxx: xx component of pressure tensor  </li> <li> Pyy: yy component of pressure tensor  </li> <li> Pzz: zz component of pressure tensor  </li> <li> Pxy: xy component of pressure tensor </li> <li> Pxz: xz component of pressure tensor </li> <li> Pyz: yz component of pressure tensor </li> <li> Volume: volume of simulation box  </li> <li> Lx: box length in x direction   </li> <li> Ly: box length in y direction   </li> <li> Lz: box length in z direction   </li> <li> Density: density   </li> <li> c_RG: radius of gyration scalar  </li> <li> c_RG[1]: squared radius of gyration tensor (xx component)   </li> <li> c_RG[2]: squared radius of gyration tensor (yy component)   </li> <li> c_RG[3]: squared radius of gyration tensor (zz component)   </li> <li> c_RG[4]: squared radius of gyration tensor (xy component)   </li> <li> c_RG[5]: squared radius of gyration tensor (xz component)   </li> <li> c_RG[6]: squared radius of gyration tensor (yz component)   </li> <li> c_bondave[1]: bond energy averaged over all atoms   </li> <li> c_bondave[2]: bond distance averaged over all atoms   </li> <li> c_bondave[3]: squared bond distance averaged over all atoms   </li> <li> c_angleave[1]: angle energy averaged over all atoms   </li> <li> c_angleave[2]: angle averaged over all atoms degree </li> <li> c_angleave[3]: cosine of angle  </li> <li> c_angleave[4]: squared cosine of angle  </li> <li> c_MSD[1]: mean squared displacement x-direction   </li> <li> c_MSD[2]: mean squared displacement y-direction   </li> <li> c_MSD[3]: mean squared displacement z-direction   </li> <li> c_MSD[4]: total mean squared displacement   </li> <li> c_COM[1]: x coordinate of center of mass   </li> <li> c_COM[2]: y coordinate of center of mass   </li> <li> c_COM[3]: z coordinate of center of mass   </li> <li> v_strain_xx: xx component of engineering strain tensor    </li> <li> v_strain_yy: yy component of engineering strain tensor     </li> <li> v_strain_zz: zz component of engineering strain tensor     </li> <li> v_vMisesequivstress: von Mises equivalent stress  </li> <li> v_Cauchy_xx: xx component of stress tensor   </li> <li> v_Cauchy_yy: yy component of stress tensor </li> <li> v_Cauchy_zz: zz component of stress tensor </li> <li> v_Cauchy_xy: xy component of stress tensor  </li> <li> v_Cauchy_xz: xz component of stress tensor  </li> <li> v_Cauchy_yz: yz component of stress tensor  </li> <li> v_strain_xy: xy component of engineering strain tensor    </li> <li> v_strain_xz: xz component of engineering strain tensor    </li> <li> v_strain_yz: yz component of engineering strain tensor    </li> </ul>   References: [1] M. Ries, J. Seibert, P. Steinmann, S. Pfaller. “Applying a generic and fast coarse-grained molecular dynamics model to extensively study the mechanical behavior of polymer nanocomposites”, Express Polymer Letters, 2022, 16. [2] S. Plimpton, “Fast parallel algorithms for short-range molecular dynamics,” Journal of computational physics, 1995, 117, 1-19. [3] A. P. Thompson et al., “LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales,” Computer Physics Communications, vol. 271, p. 108171, 2022. [4] M. Ries, V. Dötschel, J. Seibert, S. Pfaller. “A self-avoiding random walk algorithm (SARW) for generic thermoplastic polymers and nanocomposites”, Zenodo, 2022. <a href="https://doi.org/10.5281/zenodo.6245699">https://doi.org/10.5281/zenodo.6245699</a>

opencc-by-4.0Sep 2022View details →

zenodo40/100

Dataset: Global range dynamics of the Bearded Vulture (Gypaetus barbatus) from the Last Glacial Maxima to climate change scenarios

This dataset consists of Bearded Vulture Gypaetus barbatus occurrence points which were used to develop a distribution model to study its suitable habitat of this species. Using these data, we modelled the current distribution of Bearded Vulture throughout its entire range and projected the Last Glacial Maxima (LGM), Mid-Holocene (MH) and future distribution under 2070s climate change scenarios. We compiled these data from the entire distribution range in Asia, Europe and Africa using different sources: freely accessible online resources including, eBird and GBIF repositories, published reports and grey literature and occurrence data collected by the authors in the field, mostly in Nepal.

opencc-by-4.0Oct 2022View details →

zenodo40/100

Neural Puppeteer: Keypoint-Based Neural Rendering of Dynamic Shapes (Dataset)

This data entry contains the synthetic data from the <a href="https://urs-waldmann.github.io/NePu/docs/nepu_paper.pdf">Neural Puppeteer ACCV 2022 paper</a>. This data entry contains a public release of the synthetic data from the Neural Puppeteer paper. The data contains synthetic cow, giraffe, human and pigeon data. We provide depth maps, RGB images, silhouettes and 3D keypoints. We also provide the camera parameters of the 24 camera views (56 in case of pigeons). The data was rendered in Blender by Ole Johannsen.

opencc-by-4.0Oct 2022View details →

zenodo40/100

Dataset: Blue carbon dynamics across a salt marsh-seagrass ecotone in a cool-temperate South African estuary

This is a dataset of organic carbon, nitrogen, and phosphorus content from the Olifants estuary. The study was designed to investigate drivers of variability at different spatial scales and across the salt marsh-seagrass ecotone. Samples were collected in March 2023 from three selected sites (upper, middle, and lower) in the estuary. Each site featured three transects, extending from the salt marsh vegetation of mixed species through the Zostera capensis seagrass meadows towards the water. Sediment cores were taken to a depth of 50 cm, but only the top 0-5 cm section was analyzed. Carbon and nitrogen content were measured using an Elementar Vario EL Cube Elemental CHNS Analyzer, while phosphorus was determined by ICP at Central Analytical Facilities  (Stellenbosch University).   <table> <tbody> <tr> <td> Sampling site </td> <td> GPS coordinates </td> </tr> <tr> <td> Upper </td> <td> 31°39'45.27"S, 18°11'42.40"E </td> </tr> <tr> <td> Middle </td> <td> 31°40'56.46"S, 18°12'3.72"E </td> </tr> <tr> <td> Lower </td> <td> 31°41'39.85"S, 18°11'15.95"E </td> </tr> </tbody> </table>   File description CHNS_Dataset_SM&INT.xlsx: Data for intermediate sample measurements including percent organic carbon content and and percent nitrogen content for all sites ICP_Data_INT.xlsx: Data for Phosphorus content and other related measurements for the upper site.

opencc-by-4.0May 2024View details →

zenodo40/100

Dataset for "Self-heating effects and switching dynamics in graphene multiterminal Josephson junctions"

Dataset containing raw measurement data and simulation results for the manuscript "Self-heating effects and switching dynamics in graphene multiterminal Josephson junctions".

opencc-by-4.0Jun 2024View details →

zenodo40/100

Dataset for High-throughput combinatorial analysis of the spatiotemporal dynamics of nanoscale lithium metal plating

This is a dataset for the manuscript High-throughput combinatorial analysis of the spatiotemporal dynamics of nanoscale lithium metal plating. This mansucript is currently under peer-review in ACS Nano.

opencc-by-4.0Apr 2024View details →

zenodo40/100

Dataset: Alpha Blue Capital US Small-Mid Cap Dynamic ETF (ABCS) Stock Performance

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

opencc-zeroJun 2024View details →

zenodo40/100

Dataset: BrandywineGLOBAL Dynamic U.S. Large Cap Value ETF (DVAL) Stock Performance

opencc-zeroJun 2024View details →

zenodo40/100

Dataset: iShares GSCI Commodity Dynamic Roll Strategy ETF (COMT) Stock Performance

opencc-zeroJun 2024View details →

zenodo40/100

Dataset: Calamos Long/Short Equity & Dynamic Income Term Trust (CPZ) Stock Performance

opencc-zeroJun 2024View details →

zenodo40/100

Dataset: Calamos Global Dynamic Income Fund (CHW) Stock Performance

opencc-zeroJun 2024View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record

Find research datasets worth reusing

Dataset results

Ambient temperature CiPA dynamic hERG protocol dataset recorded on the SyncroPatch 384SE

Dataset for "Application of dissipative particle dynamics to interfacial systems: Parameterization and scaling"

Characterization of the material behavior and identification of effective elastic moduli based on molecular dynamics simulations of coarse-grained silica: dataset

Datasets for 'Label-free imaging of 3D pluripotent stem cell differentiation dynamics on chip'

Applying a generic and fast coarse-grained molecular dynamics model to extensively study the mechanical behavior of polymer nanocomposites: supplementary information and dataset

Dataset: Global range dynamics of the Bearded Vulture (Gypaetus barbatus) from the Last Glacial Maxima to climate change scenarios

Neural Puppeteer: Keypoint-Based Neural Rendering of Dynamic Shapes (Dataset)

Dataset: Blue carbon dynamics across a salt marsh-seagrass ecotone in a cool-temperate South African estuary

Dataset for "Self-heating effects and switching dynamics in graphene multiterminal Josephson junctions"

Dataset for High-throughput combinatorial analysis of the spatiotemporal dynamics of nanoscale lithium metal plating

Dataset: Alpha Blue Capital US Small-Mid Cap Dynamic ETF (ABCS) Stock Performance

Dataset: Grid Dynamics Holdings, Inc. (GDYN) Stock Performance

Dataset: First Trust Dorsey Wright Dynamic Focus 5 ETF (FVC) Stock Performance

Dataset: IDX Dynamic Innovation ETF (DYNI) Stock Performance

Dataset: SGI Dynamic Tactical ETF (DYTA) Stock Performance

Dataset: IDX Dynamic Fixed Income ETF (DYFI) Stock Performance

Dataset: BrandywineGLOBAL Dynamic U.S. Large Cap Value ETF (DVAL) Stock Performance

Dataset: iShares GSCI Commodity Dynamic Roll Strategy ETF (COMT) Stock Performance

Dataset: Calamos Long/Short Equity & Dynamic Income Term Trust (CPZ) Stock Performance

Dataset: Calamos Global Dynamic Income Fund (CHW) Stock Performance

Understand access before you commit

Allen Brain Atlas

Annotated Behaviour and Observability Dataset (ABODe)

DANDI Archive for NWB datasets

International Brain Laboratory public data

OpenNeuro

Your academic CV and NIH biosketch