Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,670
datasets available to search
ShareScore release 0.9.0
Dataset results
1,670 results for “FORCE”
Collaborative Assessment of Molecular Geometries and Energies from the Open Force Field
<p>OpenFF Industry Public Dataset optimized at the (1) B3LYP-D3BJ / DZVP; (2) OpenFF-2.0.0; (3) Gaff-2.11-AM1BCC; (3) OPLS4 + default parameters; (4) OPLS4 + custom parameters.</p> <p>For (1) the corresponding tar.gz archive contains (a) sdf files of the final, optimized geometries and (b) json files with molecular properties of the final step of the optimization.</p> <p>For (2-4) the corresponding tar.gz archive contains sdf files of the final, optimized geometries.</p> <p>Note: The optimization carried out with both OPLS4 including default and custom parameters (4) was performed using the ffld_server to include virtual sites.</p>
Pure POPE membrane simulations with the CHARMM-Drude force field at 310K (OpenMM)
<p>MD simulation data of a pure POPE membrane with the CHARMM-Drude force field generated with the OpenMM simulation engine at 310K.</p> <p>Parameters from CHARMM-GUI.</p>
MD Simulation data for a pure DOPC bilayer without salt with AMOEBA force field + OpenMM
<p>MD simulation data for the DOPC bilayer with the AMOEBA-based force field developed by Li (<a href="https://doi.org/10.1080/00268976.2018.1436201">https://doi.org/10.1080/00268976.2018.1436201</a>).</p> <p>The simulation contains 72 DOPC lipids and 2880 water molecules. The trajectory is 201,61 ns long (20161 frames with 10 ps saving frequency).</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 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 12 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>
MD Simulation data for a pure POPE bilayer with AMOEBA force field + OpenMM
<p>MD simulation data for the POPE bilayer with the AMOEBA-based force field developed by Li (<a href="https://doi.org/10.1080/00268976.2018.1436201">https://doi.org/10.1080/00268976.2018.1436201</a>).</p> <p>The simulation contains 72 POPE lipids and 2880 water molecules. The trajectory is 305,94 ns long (30594 frames with 10 ps saving frequency).</p> <p><strong>It has been discovered that (https://github.com/NMRLipids/Databank/issues/2#issuecomment-1357871243) the openmm_combined.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.xtc" which has the correct timestamp. The frame saving frequency in this trajectory is 10 ps. The correction to the timestamp was done via MDAnalysis.</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> </p>
Forcing, cloud feedbacks, cloud masking, and internal variability in the cloud radiative effect satellite record (Data)
<p>README file for ERA5-PRP datasets used in:</p> <p>Raghuraman et al., 2023, Journal of Climate,<br> "Forcing, cloud feedbacks, cloud masking, and internal variability in the cloud radiative effect satellite record"</p> <p>Point of Contact: Shiv Priyam Raghuraman, shivr@alumni.princeton.edu</p> <p>30 ERA5-PRP files:</p> <p>2 files: '2000_2020-allsky_small.nc' and '2000_2020-clearsky_small.nc'</p> <ul> <li>all input quantities varying</li> </ul> <p>2 files: 'clim-allsky-clim-var-all.nc' and 'clim-clearsky-clim-var-all.nc'</p> <ul> <li>all input quantities at climatology</li> </ul> <p>13 files: 'clim-var'</p> <ul> <li>Input quantity X at climatology, rest varying</li> <li>X = clouds, q, fal, skt, t, ghg, or o3. Fluxes computed in clear-sky and all-sky, apart from clouds which will only have all-sky.</li> </ul> <p>13 files: 'clim-except-var'</p> <ul> <li>Input quantity X varying, rest at climatology</li> <li>X = clouds, q, fal, skt, t, ghg, or o3. Fluxes computed in clear-sky and all-sky, apart from clouds which will only have all-sky.</li> </ul> <p>Other datasets:</p> <p>GFDL AM4 AMIP and Control<br> https://doi.org/10.5281/zenodo.4784726</p> <p>CMIP6 Control, Historical, RFMIP<br> Downloaded from ESGF</p>
supplementary to "SnowPappus v1.0, a blowing-snow model for large-scale applications of Crocus snow scheme" , 2D wind forcing
<p>This is a supplementary material to article "SnowPappus v1.0, a blowing-snow model for large-scale applications of Crocus snow scheme" ( unpublished at the publication date of this dataset)</p> <p>It contains the 2D wind forcing for Crocus-SnowPappus simulations on the Grandes Rousses test zone. It was generated using DEVINE wind downscaling method ( Le Toumelin et al., 2022 )</p>
3D-Printed Encapsulation of Thin-Film Transducers for Reliable Force Measurement in Biomedical Applications
<p>Data csv</p>
RIG-I and RNA complex MD simulations in ff19SB+OL3, ff14SB+OL3, OPLS4 and AMOEBA force fields
<p>MD simulation trajectories of RIG-I variant in complex with RNA. 10*100ns in AMOEBA (a single file from OpenMM), 4*500ns in Amber(ff19sb+OL3, ff14sb+Ol3, polarizable water in amberpol), 4*500ns in Desmond (opls4). </p>
Cas12j-RNA simulations in ff19SB+OL3, ff14SB+OL3, OPLS4 and AMOEBA force fields
<p>MD simulations of Cas12j-RNA complex in different force fields. AMOEBA 10*100ns (in a single file), Amber simulations (ff19sb+ff14sb+amberpol) 4*500 ns and Desmond simulations (opls4) 4*500ns. </p>
Ago2-RNA complex simulations in AMOEBA force field
<p>10*100ns simulations of Ago2-RNA complex in the AMOEBA force field. The simulations were conducted using OpenMM. </p>
Data from "Enhanced radiative forcing from aerosol-cloud interactions due to large-scale circulation adjustments" by Guy Dagan, Netta Yeheskel and Andrew I. L. Williams.
<p>This is the data presented in "Enhanced radiative forcing from aerosol-cloud interactions due to large-scale circulation adjustments" by Guy Dagan, Netta Yeheskel and Andrew I. L. Williams.</p> <p> </p> <p>Please read the README file for explanations about the data.</p> <p> </p>
FAFMIP forcing datasets used in MOM5 1-degree simulations
<p>FAFMIP surface flux perturbations datasets (see http://www.fafmip.org/; <a href="http://www.geosci-model-dev.net/9/3993/2016">Gregory <em>et al.</em>, 2016</a>) interpolated on the MOM5 1-degree grid. Data are climatological monthly means in the netCDF format. The data variables in these files are dimensioned (longitude,latitude,time) in Fortran order, (time,latitude,longitude) in CDL. The time dimension has size 12, for months from January to December.</p>
Bio-Optic bio-shortwave model code, initial conditions, river and boundary forcing for "Estimating the seaonal impact of optically significant water constituents on surface heating rates in the Western Baltic Sea" paper.
<p>Bio-Optic bio-shortwave model code, initial conditions, river and boundary forcing as well as selected model output used for analysis and producing figures in the paper "Estimating the seaonal impact of optically significant water constituents on surface heating rates in the Western Baltic Sea". Contact Bronwyn Cahill if you have questions at: bronwyn.cahill@io-warnemuende.de</p>
Nanoscale chemical characterization of secondary protein structure of F-Actin using mid-infrared photoinduced force microscopy (PiF-IR)
<p>Raw data for manuscript for special issue in Spectrochimica Acta related to ECSBM 2022</p> <p> </p>
Data of Forced-response characterization of PBF-LB/AlSi10Mg particle dampers with thin and flat cavities
<p>Powder Bed Fusion (PBF) enables the production of complex geometries which offer the opportunity to manufacture lightweight, stiffness-optimised or integrally designed components. Although these properties are usually advantageous for the performance in many applications, they pose disadvantages under vibration as they lead to low damped components. These are prone to high vibration amplitudes which result in higher sound radiation and a reduced lifetime. Particle damping can counteract these disadvantages. By including cavities during the design process, unmelted powder remains inside the component after its production. This powder dissipates energy under vibration by inelastic impacts and friction in particle-–particle or particle-–wall-interactions, increasing the damping characteristics of the component. In this work, additively manufactured AlSi10Mg specimens with cavities are investigated with respect to their damping characteristics by experimental modal analysis. The focus of the investigation is on thin and flat cavities that can be easily integrated into components without adapting the external geometry. The damping characteristics in dependence on excitation amplitude and mode are quantified. The extent to which settling effects of the powder during shaking influence the damping is analysed. The vibration of the specimens is forced by an electrodynamic shaker and their response is measured contactlessly via Scanning Laser Doppler Vibrometry (SLDV). A damping effect of up to 564% depending on the mode, excitation amplitude and specimen can be achieved. In addition, a significant settling effect of the powder which hampers the damping effect is identified by CT scans and modal analysis.<br> The data of the CT scans as well as the data of the modal analysis is uploaded here. </p>
Data from: Evolution of Large Aβ16-22 Aggregates at Atomic Details and Potential of Mean force Associated to Peptide Unbinding and Fragmentation Events
<p>This data accompanies the paper entitled <em>Evolution of Large Aβ16-22 Aggregates at Atomic Details and Potential of Mean force Associated to Peptide Unbinding and Fragmentation Events</em></p> <p>The zip archive contains the results of molecular dynamics simulations of the 2 systems investigated in the paper: the first one with 139 <em>Aβ16-22 </em><em>peptides, the second one with 106 peptides.</em><em> </em>Each system has been simulated at 300 K. Starting configurations of the peptides are provided for all the systems in GRO Gromos87 format. Trajectories with the positions of the peptides every 100 ps are provided for all the systems in XTC gromacs format. For system 1 we also provide XTC trajectories for all the replicas of the REST2 simulation.</p>
Estimation of best-fitting force, moment tensor, and depth for the 2022 Hunga-Tonga submarine volcanic eruption: Misfit plots in model parameter space
<p>Supporting documents for the correction of the publication <em>Multi‐Event Explosive Seismic Source for the 2022 Mw 6.3 Hunga Tonga Submarine Volcanic Eruption, </em>published in The Seismic Record (<a href="https://doi.org/10.1785/0320220027">https://doi.org/10.1785/0320220027</a>).</p>
Zenodo Collection: Estimation of best-fitting force, moment tensor, and depth for the 2022 Hunga-Tonga submarine volcanic eruption
<p>This Zenodo collection contains figures and results from grid searches used to estimate point source parameters (force or moment tensor) for the main seismic subevent of the 2022 Hunga-Tonga submarine volcanic eruption. The collection includes misfit maps and waveform fits for the best-fitting force or moment tensor, as well as MTUQ weight files and a zipped version of the MTUQ code. These results were obtained using various software tools, including MTUQ, Axisem, Instaseis, Syngine, Obspy, GMT, and PyGMT. The collection was prepared for a manuscript in review for Geophysical Journal International.</p>
Data Supplement for "Wettability Gradient-Driven Droplets with an Applied External Force"
<p>Data set and python code to recreate the figures of "Wettability Gradient-Driven Droplets with an Applied External Force".</p>
Supporting Data for "Does a Machine-Learned Potential Perform Better Than an Optimally Tuned Traditional Force Field? A Case Study on Fluorohydrins"
<p>Supporting Data for "Does a Machine-Learned Potential Perform Better Than an Optimally Tuned Traditional Force Field? A Case Study on Fluorohydrins"</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.