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”
Chirped Bloch-harmonic oscillations in a parametrically forced optical lattice
<p>Dataset and codes of the publication "Chirped Bloch-harmonic oscillations in a parametrically forced optical lattice" by Usman Ali, Martin Holthaus, and Torsten Meier,<br>published in PHYSICAL REVIEW RESEARCH 5, 043152 (2023)<br>( <a href="https://doi.org/10.1103/PhysRevResearch.5.043152">https://doi.org/10.1103/PhysRevResearch.5.043152</a> )</p>
Hartree potentials and geometries of relaxed on-surface ice clusters in "Structure discovery in Atomic Force Microscopy imaging of ice"
<p>Hartree potentials and geometries of on-surface DFT-relaxed ice clusters used in the paper "Structure discovery in Atomic Force Microscopy imaging of ice".</p><p>The data are saved in a compressed .tar.gz archive. The unpacked archive contains the data for each ice cluster in the .xsf format. The density functional theory (DFT) calculations were done using the Vienna Ab-initio Simulation Package with the optB86b-vdW density functional.</p>
Arctic Climate Response to European Radiative Forcing: A Deep Learning Approach (Example codes)
<p>## Introduction<br>This folder contains example code used in our paper with the title "Arctic Climate Response to European Radiative Forcing: A Deep Learning Approach". These scripts are intended to demonstrate key functionalities and calculations described in the paper.</p> <p>## Files Description</p> <p>1. **DL_Model_test.py**<br> - Description: Loads the trained deep learning algorithms featured in the paper.<br> - Functionality: Demonstrates the use of the model with example data points.</p> <p>2. **SIC_class_contribution.py**<br> - Description: Calculates the class contribution for Sea Ice Concentration (SIC).<br> - Note: The procedure can be adapted for other fields in a similar manner.</p> <p>## Requirements<br>the requred linrary are listed in the code header</p>
CICE JRA55 forcing for gx3
<p>This is just the January 2005 JRA55 forcing for gx3 for github actions.</p>
Anthropogenic forcing has increased the risk of longer-traveling and slower-moving large contiguous heatwaves
<p>Heatwaves are consecutive hot days with devastating impacts on human health and environment. These events may evolve across both space and time, characterizing a spatio-temporal contiguous propagation pattern that has not been fully understood. Here we track the spatiotemporally contiguous heatwaves in both reanalysis datasets and model simulations, and examine their moving patterns (i.e., moving distance, speed, and direction) in different continents and periods. Our findings provide important implications for the adaptation and mitigation of globally connected extreme heatwaves. The tracked contiguous heatwave events are saved as the <strong>PICKLE format</strong>. More detailed information related to the uploaded data and code can be found in “README.pdf”.</p> <p> </p> <p>If you have any questions when using the dataset and code of contiguous heatwaves, please feel free to contact Miss Sijia Wu via <a href="mailto:zhangh573@mail2.sysu.edu.cn">wusj7@mail2.sysu.edu.cn</a>, Dr. Ming Luo via <a href="mailto:luom38@mail.sysu.edu.cn">luom38@mail.sysu.edu.cn</a>. More details on the procedure of producing the contiguous heatwaves dataset and its methods can be found in:</p> <p>Luo, M.<sup> †</sup>*, Wu., S<sup>†</sup>, Lau, G., Pei, T.*, Liu, Z., Wang, X., Ning, G., Chan, T., Yang, Y., Zhang, W.* Anthropogenic forcing has increased the risk of longer-traveling and slower-moving large contiguous heatwaves. Science Advances, 10, eadl1598.</p> <p> </p>
Analysis code in support of "The Interaction Between Climate Forcing and Feedbacks"
<p>Analysis code in support of Manuscript "The Interaction Between Climate Forcing and Feedbacks"</p> <p>Data is already available through DOI: https://doi.org/10.26024/bzne-yf09</p> <p>read_ppe2map.ipynb will create data files from the PPE output (if the files at the DOI do not work), and then the different codes herein are used to create figures in the manuscript.</p>
FoRC-Subtask-I@NSLP2024 Dataset
<p>Training, validation, and test datasets for the first subtask of the shared task "Field of Research Classification" held at the Natural Scientific Language Processing and Research Knowledge Graphs (NSLP 2024) workshop (https://nfdi4ds.github.io/nslp2024/). </p>
Steady threefold Arctic Amplification of externally forced warming masked by natural variability
<p>SourceData.xlsx:</p> <p>Time series of the observed Arctic and global mean temperature, the MME Arctic and global mean temperature, the IPO index, the AM index, and the influences of tropical Pacific variability on the Arctic and global mean temperature.</p> <p> </p> <p>Arctic_Amplificaiton_Historicaltrend.py:</p> <p>Script for analyzing datasets and plotting figures</p> <p> </p>
Screener and Enumerator with Force-Field Optimization (SEFFO): algorithm for searching adsorption sites and configurations on 2D materials
<p>See ref:</p> <p> </p> <p>(submission stage)</p>
Energy and forces annotated atomic structures of platinum using first-principles calculations
<p>Dataset for training machine learning potential of platinum.</p> <p>Detailed explanation of data generation is described in the preprint. below</p> <p>Chun, H., Kang, J., Kang, D., Heo, J., Cho, H., Heo, J., … & Han, B. (2022). Tracking the 3d atomic structures during thermal treatment and catalytic activity of individual pt nanoparticles.. https://doi.org/10.21203/rs.3.rs-1441062/v1</p>
Repository: No Magmatic Driving Force for Europan Seafloor Volcanism
<p>Included are the data and the source par files for the manuscript "No Magmatic Driving Force for Seafloor Volcanism" by Green et al. (in press). Par files show input parameters for each model included in the main results. Also included are all generated .dat files, named to match their corresponding par file.</p>
Simulation data for Loop-extruders alter bacterial chromosome topology to direct entropic forces for segregation
<p>Polymer configuration data from simulations of loop-extrusion on a replicating bacterial chromosome, used for the manuscript "Loop-extruders alter bacterial chromosome topology to direct entropic forces for segregation". New version includes data for more specific off-loading, as well as fewer SMCs.</p>
Basal force fluctuations and granular rheology: Linking macroscopic descriptions of granular flows to bed forces with implications for monitoring signals
<p>Herein lies user defined functions, submission scripts, and intitial particle data necessary to build a discrete element solver (using MFiX-DEM version 21.1.1: https://mfix.netl.doe.gov/products/mfix/) and run a monodisperse pure granular flow with 5mm beads in a shear cell configuration. Contained within this dataset are particle velocity and force data for the entire collection of particles ('VELOCITY<em>'* </em>and 'FORCES*.DAT', respectively). This dataset also contains particle-force data recorded at the bottom force plate, writen at 10 kHz. Two MATLAB scripts are included for post processing the force plate data. </p>
Dataset for Can we predict kick force based solely on spatial-temporal variables? Applying long short-term memory model for predicting force values of turning and side kick of taekwon-do athletes
<p>This data set is created for a purpose of publication "<span>Can we predict kick force based solely on spatial-temporal variables? Applying long short-term memory model for predicting force values of turning and side kick of taekwon-do athletes". It contains of dataset of kicks and lstm models for predictions a force of kicks upon IMU data. Detailed description of file names are in readme file. Folders are divided into specific kicks - turning or side kick in sport or traditional versions.</span></p>
MD simulation of POPC bilayer with CHARMM36 force field. 7.5 w/l.
<p>MD simulation of POPC (1-palmitoyl-2-oleoyl-phosphatidylcholine) bilayer with CHARMM36 force field. 7.5 w/l.</p> <p>Dataset contains simulation files including trajectories (.xtc) (original production.xtc, and equilibrated and centered ).</p> <p>System: POPC bilayer in water</p> <p>Number of lipids: 200 (100/leaflet)</p> <p>Number of waters: 1500</p> <p>Simulation time: 500 ns</p> <p>Sampling rate: every 10 ps</p> <p>Simulation engine: GROMACS 2022.4</p> <p>Temperature: 300 K</p>
MD simulation of POPC bilayer with CHARMM36 force field. 12 w/l.
<p>MD simulation of POPC (1-palmitoyl-2-oleoyl-phosphatidylcholine) bilayer with CHARMM36 force field. 12 w/l.</p> <p>Dataset contains simulation files including trajectories (.xtc) (original production.xtc, and equilibrated and centered ).</p> <p>System: POPC bilayer in water</p> <p>Number of lipids: 200 (100/leaflet)</p> <p>Number of waters: 2400</p> <p>Simulation time: 500 ns</p> <p>Sampling rate: every 10 ps</p> <p>Simulation engine: GROMACS 2022.4</p> <p>Temperature: 300 K</p>
MD simulation of DMPC bilayer with CHARMM36 force field. 20 w/l
<p>MD simulation of DMPC bilayer with CHARMM36 force field. 20 w/l.</p> <p>Dataset contains simulation files including trajectories (.xtc) (original production.xtc, and equilibrated and centered centered_pbcmol-20-500ns.xtc).</p> <p>System: DMPC bilayer in water</p> <p>Number of lipids: 200 (100/leaflet)</p> <p>Number of waters: 4000</p> <p>Simulation time: 500 ns</p> <p>Simulation engine: GROMACS 2019.5</p> <p>Temperature: 314 K</p>
MD simulation of DMPC bilayer with CHARMM36 force field. 10 w/l.
<p>MD simulation of DMPC bilayer with CHARMM36 force field. 10 w/l.</p> <p>Dataset contains simulation files including trajectories (.xtc) (original production.xtc, and equilibrated and centered centered_pbcmol-50-500ns.xtc).</p> <p>System: DMPC bilayer in water</p> <p>Number of lipids: 200 (100/leaflet)</p> <p>Number of waters: 2000</p> <p>Simulation time: 500 ns</p> <p>Simulation engine: GROMACS 2019.5</p> <p>Temperature: 314 K</p>
MD simulation of DMPC bilayer with CHARMM36 force field. Full hydration
<p>MD simulation of DMPC bilayer with CHARMM36 force field. Full hydration</p> <p>Dataset contains simulation files including trajectories (.xtc) (original production.xtc, and equilibrated and centered ).</p> <p>System: DMPC bilayer in water</p> <p>Number of lipids: 200 (100/leaflet)</p> <p>Number of waters: 10000</p> <p>Simulation time: 500 ns</p> <p>Sampling rate: every 10 ps</p> <p>Simulation engine: GROMACS 2019.5</p> <p>Temperature: 314 K</p>
Forced changes in the Pacific Walker circulation over the past millennium
<p><strong>Data & code repository for <em>Forced changes in the Pacific Walker circulation over the past millennium</em></strong></p> <p>This repository contains scripts and auxiliary data necessary for recreating the figures in the paper <em>Forced changes in the Pacific Walker circulation over the past millennium</em> (https://doi.org/10.1038/s41586-023-06447-0) [published in <em>Nature</em> October 2023; available online August 2023].</p> <p>The code is tested with R version 4.1.2. </p> <p><em>Note that the only difference between this and the previous version is a slight update to the reconstruction filenames</em></p> <p><em><strong>Repository Structure</strong></em></p> <ul> <li>`Falster_PWCreconstructions`: zipped folder contains our ensemble reconstruction of ΔSLP (1200-2000 CE) <ul> <li> `Falster2023_PWC_reconstruction.csv`: a csv with one column per ensemble member. Column headers state the reconstruction method, and gridded SLP product used to train the reconstruction (200 ensemble members per unique combination of reconstruction method and training index).</li> <li> `Falster2023_PWC_reconstruction.xlsx`: Excel workbook, with one tab per unique combination of reconstruction method and training index (200 ensemble members each).</li> <li>`Falster2023_PWC_reconstruction_full_ensemble_median_and_95pct_range.csv`: a csv with the median and 95% range (2.5th and 97.5th percentiles) of the full 4800-member ΔSLP reconstruction ensemble. Includes raw summary values, and summary values with a five-year running mean. For robust analysis of ΔSLP, we strongly recommend working with the full ensemble rather than these summary values. </li> </ul> </li> <li>`Falster_PWCreconstruction_code`: zipped folder contains scripts and auxiliary data necessary to generate figures in the main text <ul> <li>`scripts`: contains R scripts necessary to generate figures in the main text <ul> <li>`Figure1`: folder containing scripts to perform analysis and generate Fig. 1 in the main text</li> <li>`Figure2`: folder containing scripts to perform analysis and generate Fig. 2 in the main text</li> <li>`Figure3`: folder containing scripts to perform analysis and generate Fig. 3 in the main text</li> <li>`Figure4`: folder containing scripts to perform analysis and generate Fig. 4 in the main text</li> <li>`Figure5`: folder containing scripts to perform analysis and generate Fig. 5 in the main text</li> <li>`Figure6`: folder containing scripts to perform analysis and generate Fig. 6 in the main text</li> </ul> </li> <li>`data`: contains all auxiliary data necessary for the analysis and visualisation, including: <ul> <li>reconstructions in .rds format</li> <li>shapefiles for mapping</li> <li>processed ΔSLP and Nino 3.4 RSST timeseries from the CESM1 LME and PMIP3/4 models</li> </ul> </li> </ul> </li> </ul> <p><strong>How to cite this repository</strong></p> <p>If using code or data from this repository, please cite the original publication, available from https://www.nature.com/articles/s41586-023-06447-0. </p> <p>This repository can be cited with https://doi.org/10.5281/zenodo.7742760.</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.