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39 results for “Field energies”
Output from the Glacier Energy and Mass Balance (GEMB v1.0) forced with 3-hourly ERA5 fields and gridded to 10km, Greenland and Antarctica 1979-2024
<p>These model output of firn air content (FAC) and surface mass balance (SMB) are from version 1.0 of the open-source Glacier Energy and Mass Balance model. GEMB is a column model of ice sheet and glacier surface-atmospheric energy and mass exchange as well as firn state. GEMB has been integrated into the open-source Ice-Sheet and Sea-level System Model which can be downloaded at https://issm.jpl.nasa.gov/. Here, GEMB is forced with 3-hourly ERA5 output from 1979 through end of 2024. For Greenland and its periphery, the ERA5 surface temperature and downwelling longwave radiation forcing are spatially bias-corrected for each month. All values are adjusted by the difference between the RACMO2.3 and the ERA5 1980-2015 monthly means. The GEMB output is bilinearly interpolated onto a 10km grid, from the native ISSM grid, and the output is given as 5-day output or as monthly.</p>
Data deposit accompanying Accurate Energy Barriers for Catalytic Reaction Pathways: An Automatic Training Protocol for Machine Learning Force Fields
<p>Dataset accompanying the paper: <em>"Accurate Energy Barriers for Catalytic Reaction Pathways: An Automatic Training Protocol for Machine Learning Force Fields"</em>. Contains the training sets curated during active learning as well as .xyz files used for creating the Figures. <br> <br> The paper highlights that the computational efficiency of ML force fields not only results in decreased computational costs for routine catalytic investigations but also facilitates more comprehensive exploration of catalytic pathways.</p> <p><strong>Published in NPJ Computational Materials</strong>: <a href="https://www.nature.com/articles/s41524-023-01124-2">https://www.nature.com/articles/s41524-023-01124-2</a><br> Formerly on Arxiv: <a href="https://arxiv.org/abs/2301.09931">https://arxiv.org/abs/2301.09931</a></p>
THz driven field emission: energy and time-of-flight spectra of ions (DATASET)
<p>We present an experimental and numerical study of ion field evaporation from LaB6 nanotips using single-cycle terahertz (THz) transients and a static bias voltage. Varying the amplitude and phase of the THz pulses and the value of the<br> bias, we explore the THz-induced reshaping of the ions energy and their time-of-flight spectra. These results prove that short THz transient of about 1 ps can induce ionization and emission of ions from LaB6 samples by a field effect: the THz<br> transient acts as an ultra-short electrical pulse. Moreover, comparing numerical and experimental results, we prove that the response time of surface atoms to the THz transient is shorter than 1 ps, corresponding to the vibration times of acoustic phonons<br> in LaB6.</p> <p>In the following dataset, you can find data from THz-APT obtained from LaB6 sample and results of simulation of ions under THz Field done with Lorentz</p>
Supplementary underlying data for "Evaluating parameterization protocols for hydration free energy calculations with the AMOEBA polarizable force field"
<p>This dataset includes additional underlying data for the publication "Evaluating parameterization protocols for hydration free energy calculations with the AMOEBA polarizable force field"</p> <p>Contents:</p> <p>Tukey Honest Significant Difference (HSD) results for solutes 1-47 across all seven parameter sets, as *.txt. These are pairwise comparisons of results between all possible parameter sets. Significant differences are treated as p < 0.05.</p>
dataset for paper "Activation energy for pore opening in lipid membranes under an electric field"
<p>Dataset for the paper "Electropermeabilization of hydroperoxidized lipid membranes".</p> <p>Data was generated from Orbit Mini miniaturized bilayer workstation (Nanion Technologies, Munich, Germany), with an inserted microelectrode cavity array (MECA 4) recording chip (Ionera Technologies, Freiburg, Germany).</p> <p>The data files have format .abf, a standard format for electrophysiological data. <br> It can be read by applications such as for instance</p> <p>- Clampex and ClampFit, from the patch-clamp software suite pCLAMP, <br> - Elements Data Analyzer, associated with the elements data reader software from Elements-IC, </p> <p><br> or imported into Python through the package pyABF 2.3.5.</p> <p>import pyabf // abf=pyabf.ABF(path+"/"+f+"/"+abffile) // data = np.vstack((abf.sweepX, abf.data)) </p> <p>Data is organized in five folders named according to target hydroperoxidation degrees:<br> POPC<br> POPC-OOH 25%<br> POPC-OOH 50%<br> POPC-OOH 75%<br> POPC-OOH 100%</p> <p>Inside each of the five above files data is organized by date, and informed with the actual measured hydroperoxidation degree for a given sample. </p>
Unified Field Equation: Exploring the Interplay of Parashakti, Atma, and Quantum Energy
<p>This research examines the convergence of ancient philosophical concepts, such as Parashakti (the supreme feminine energy) and Atma (the eternal self), with modern quantum energy theories. It introduces a Unified Field Equation that integrates metaphysical ideas like total energy (E), vacuum energy (V), potential energy (P), and consciousness (Atma) into a scientific framework. The study explores the potential applications of this model in quantum physics, cosmology, and metaphysical philosophy, seeking to bridge spiritual notions of cosmic energy with contemporary science.</p>
Data files of 'Large spatial extension of the zero-energy Yu-Shiba-Rusinov state in magnetic field'
<p>Data files of 'Large spatial extension of the zero-energy Yu-Shiba-Rusinov state in magnetic field'</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>
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>
Dataset for the publication "Photoelectron energy peaks shift against the radiation pressure in strong field ionization"
<p>The data used to create the figures in the publication "Photoelectron energy peaks shift against the radiation pressure in strong field ionization".</p>
Field Survey of Wireless M-Bus Encryption for Energy Metering Applications in Residential Buildings
<p>This is the pseudonymized data of the paper "Field Survey of Wireless M-Bus Encryption for Energy Metering Applications in Residential Buildings" by Hiller v. Gärtringen et al. 2024.</p> <p>Each entry represents a unique wireless M-Bus device that was captured during our field study.</p> <p>Manufacturers and serial numbers are mapped to new identifiers.<br>Payload was removed.</p> <p>The meaning of the columns in the data set are:</p> <table> <tbody> <tr> <td><strong>name</strong></td> <td><strong>type and manifestations</strong></td> <td><strong>description</strong></td> </tr> <tr> <td>id</td> <td>integer</td> <td> <p>Unique for each wireless transmitting device.<br>Counting up from 1 to n of devices.</p> </td> </tr> <tr> <td>manufacturer</td> <td> <p>enumeration</p> <ul> <li>MAN1 - MAN16</li> </ul> </td> <td>Pseudonymized manufacturer identifier.</td> </tr> <tr> <td>device type</td> <td> <p>enumeration</p> <ul> <li>heat cost allocator</li> <li>heat meter</li> <li>temperature or humidity sensor</li> <li>warm water meter</li> <li>water meter</li> <li>radio control device</li> <li>smoke detector</li> <li>unknown type</li> </ul> </td> <td>Device types are described in EN 13757-7 Table 13</td> </tr> <tr> <td>number of telegrams</td> <td>integer</td> <td>Number of telegrams received from the device.</td> </tr> <tr> <td>has DLL Encryption</td> <td>boolean</td> <td>Indicating, if the device uses DLL encryption.</td> </tr> <tr> <td>AES mode</td> <td> <p>enumeration</p> <ul> <li>not encrypted (mode 0)</li> <li>AES-CBC static key (mode 5)</li> <li>AES-CBC dynamic key (mode 7)</li> <li>AES-CCM (mode 10)</li> </ul> </td> <td>Indicates the AES encryption mode.</td> </tr> <tr> <td>detected in 2022</td> <td>boolean</td> <td> <p>Indicates if the device was detected in the given year.<br>If detected in 2022 and 2023, both are 1.</p> </td> </tr> <tr> <td>detected in 2023</td> <td>boolean</td> <td> <p>Indicates if the device was detected in the given year.<br>If detected in 2022 and 2023, both are 1.</p> </td> </tr> <tr> <td>interpretable</td> <td>boolean</td> <td> <p>Indicates whether we identified the message as interpretable.<br>For a detailed description, see the paper.</p> </td> </tr> </tbody> </table> <p> </p>
Free Energy-based Refinement of DNA Force Field via Modification of Multiple Non-bonding Energy Terms
<p>Relevant files of the benchmark DNA simulations.</p> <p>*.xtc --> truncated OPESE trajectory file.</p> <p>*.tar --> compressed initial structures.</p> <p>*.mdp --> GROMACS molecular dynamics parameter file.</p> <p>*.dat --> PLUMED input file for OPESE simulation.</p> <p> </p>
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>
Field Application of a High-Power Density Electromagnetic Energy Harvester to Power Wireless Sensors in Transportation Infrastructures
<p>Traffic-induced vibration of transportation infrastructures is a reliable source of kinetic energy, which can be harvested to power conventional monitoring sensors and peripherals installed on bridges, thereby reducing some dependence on non-renewable energy. The highway statistics shows that the average daily vehicles miles travelled in the US is more than 5 billion. This is a massive source of kinetic energy that lies unused in the national transportation network. This study focuses on the design and field testing of a high-power density electromagnetic energy harvester (EMEH) to convert such a kinetic energy into electrical energy for powering ubiquitous sensors installed on transportation infrastructures. The principal investigators have been investigating the design of the EMEH using analytical and finite element simulations, as well as, its laboratory prototype fabrication and testing in the first phase. The proposed EMEH utilizes the innovative concept of creating planar array of large number of small permanent magnets through certain optimization criteria to achieve strong and focused magnetic field in a particular orientation. The proposed EMEH has a compact design, such that it can be integrated into the power circuit of wireless sensor nodes (WSNs) and installed at suitable part of a transportation infrastructure without elaborate wiring. It is capable of continuously charging the rechargeable battery of a WSN, thereby extending the lifespan of the monitoring system, almost, indefinitely. For the next phase of this research, the principal investigators propose the development and field implementation of a larger scale and more compact version of the EMEH with a minimum of 500 mW output power to be installed on selected transportation infrastructures for the evaluation of its energy harvesting efficiency and capability to derive different types of monitoring sensors and peripherals. Three different highway bridges with different fundamental frequencies, ideally between 2Hz to 8Hz, will be selected for the field testing of the EMEH. An acceleration sensor will be used to record the traffic-induced vibration of each bridge during a normal daily traffic that after signal processing is used to measure the fundamental frequency of that bridge. The dynamic characteristics of the proposed EMEH (i.e. tip mass and spring stiffness) will be modified to put it into a resonant condition with the bridge by matching their natural frequencies. The output power will be monitored and used to continuously charge a rechargeable battery powering a wireless sensor. The focus is on the feasibility of the proposed EMEH to power sensors that are used to regularly monitor the structural integrity of materials and components of highway bridges such as acceleration and temperature sensors.</p>
MCNP Results for Nanocomposite Shielding in High-Energy Proton Fields
<p>Monte Carlo radiation transport results for various physics schemes (neutron + proton, neutron + proton + delta ray, and neutron + proton + delta ray + light recoil ions) and nanocomposite structural models (bulk homogenous material, hollow carbon cylinders suspended in polymer matrix, and carbon spheres in nanotube structure suspended in polymer matrix) of a polymer-carbon-nanotube nanocomposite shielding material in high-energy proton beams of 63 MeV and 105 MeV. </p>
Natural lignin modulators improve bagasse saccharification of sugarcane and energy cane in field trials
<p>The burgeoning cellulosic ethanol industry necessitates advancements in enzymatic saccharification, effective pretreatments for lignin removal, and the cultivation of crops more amenable to saccharification. Studies have demonstrated that natural inhibitors of lignin biosynthesis can enhance the saccharification of lignocellulose, even in tissues generated several months post-treatment. In this study, we applied daidzin (a competitive inhibitor of coniferaldehyde dehydrogenase), piperonylic acid (a <i>quasi</i>-irreversible inhibitor of cinnamate 4-hydroxylase), and methylenedioxy cinnamic acid (a competitive inhibitor of 4-coenzyme A ligase) to 60-day-old crops of two conventional Brazilian sugarcane cultivars and two energy cane clones, bred specifically for enhanced biomass production. The resultant biomasses were evaluated for lignin content and enzymatic saccharification efficiency without additional lignin-removal pretreatments. The treatments amplified the production of fermentable sugars in both the sugarcane cultivars and energy cane clones. The most successful results softened the most recalcitrant lignocellulose to the level of the least recalcitrant of the biomasses tested. Interestingly, the softest material became even more susceptible to saccharification.</p>
Feasibility Study to Test Clinical Efficacy of Low Energy AC Magnetic Field to Treat Major Depression
ClinicalTrials.gov study NCT01683019. IPD Sharing: Not stated. Countries: 2. Publications: 1.
Underlying data for "Evaluation of solvation free energies for small molecules with the AMOEBA polarizable force field"
<p>This dataset provides the parameters and results generated for the study "Evaluation of solvation free energies for small molecules with the AMOEBA polarizable force field"</p> <p>Contents of dataset:<br /> PARAMETERS.tar.gz<br /> A gzipped and tar'd directory with the parameters of all solutes sorted by solvent for AMOEBA and GAFF force field. This parameters directory is distributed to 4 different subdirectories: a)chloroform (21 solutes) b)toluene (21 solutes) c)dmso (6 solutes) d)acetonitrile (6 solutes). Each solvent includes two set of parameters for each solute (AMOEBA and GAFF).</p> <p>RESULTS.tar.gz<br /> A gzipped and tar'd directory with the solvation free energy results of all solutes in kcal/mol, with AMOEBA and GAFF force fields, for each solvent, in text format. A single set of conditions for a single solute is one row in the text files. The first column represents the experimental data, the remaining columns (2, 3 and 4) correspond to the three repeat simulations.</p>
Turbulent Kinetic Energy Production in a Far-Field River Plume under Upwelling-Favorable Winds
<p>Data for submitted paper "Turbulent Kinetic Energy Production in a Far-Field River Plume under Upwelling-Favorable Winds"</p>
Information Nudges, Subsidies, and Crowding Out of Attention: Field Evidence from Energy Efficiency Investments
<p>This package contains the survey data, programs and instructions to replicate manuscript "Information Nudges, Subsidies, and Crowding Out of Attention: Field Evidence from Energy Efficiency Investments" by Matthias Rodemeier & Andreas Löschel forthcoming at JEEA.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.