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655 results for “constrain”
MCR LTER: Coral Reef: Growth-predation risk trade-offs constrain the local distribution of a thicket-forming staghorn coral to marginal reef habitats; Data for Ladd et al., 2025, Scientific Reports.
This dataset is in support of the manuscript: Growth-predation risk tradeoffs constrain the local distribution of a thicket-forming staghorn coral to marginal reef habitats. These data were collected to 1) document how Acropora pulchra is distributed around the island of Moorea, and 2) to better understand the ecological processes that shape that distribution. Data include 1) results from surveys around the island of Moorea documenting the presence and size distribution of Acropora pulchra thickets, 2) results from an experiment measuring the growth and survivorship of Acropora pulchra fragments in the presence and absence of fish predators at nearshore fringing reef sites and adjacent sites in the mid lagoon (n = 20 sites in total), and 3) ancillary data on nitrogen content and dN15 in the tissue of the macroalgae Turbinaria ornata, sediment accumulation, and corallivore biomass at the experimental sites. All data were collected in 2016 and 2017.
ForestAge-Constrained Eddy-Covariance Gridded NEP Product
<p><strong>Description</strong></p> <p>This repository holds global spatial estimates of the Net Ecosystem Productivity of forests (NEP), circa 2010, for a grid spacing of 0.5° by 0.5º pixel size. Three different approaches were used to create the maps.</p> <ol> <li> <p><strong>Model M1 (Regional Age–NEP Relationships Per Biome)</strong>: This model scales site-level NEP observations to a global gridded field using biome-specific NEP-age curves and site-level anomalies. The random forest model (RF1) is trained on forest age, GPP, temperature, and nitrogen deposition, producing NEP anomalies that reflect site-specific deviations from biome-wide trends. Gridded predictor fields of forest age, GPP, temperature (MAT), and nitrogen deposition are used to create 0.5° by 0.5° NEP grids, with uncertainties estimated using an ensemble of 180 members. The data from Model M1 can be investigated from the ForestAge_EC_NEP_M1_v1.0.nc file.</p> </li> <li> <p><strong>Model M2 (Global Age–NEP Relationship)</strong>: This model uses a random forest algorithm (RF2) to upscale NEP observations but applies a global NEP-age relationship across all sites. It uses the same gridded predictor fields as M1—forest age, GPP, MAT, and nitrogen deposition—but the age–NEP relationship is determined globally. Uncertainty is calculated similarly to M1, using ensembles of model parameters and predictor fields. The data from Model M2 can be investigated from the ForestAge_EC_NEP_M2_v1.0.nc file.</p> </li> <li> <p><strong>Model M3 (Without Age Consideration)</strong>: This model predicts NEP solely based on GPP, MAT, and nitrogen deposition without accounting for forest age. It follows a similar approach to RF3 models from previous work and uses the same gridded predictors and uncertainty estimation methods as M1 and M2. The data from Model M3 can be investigated from the ForestAge_EC_NEP_M3_v1.0.nc file.</p> </li> </ol> <p>The variation across each model's members can assess the uncertainty in each model, which represents uncertainty caused by input variables and the k-fold cross-validation approach. </p> <p>More details about the methodologies behind the three approaches can be found in Ciais, P., Yao, Y. Besnard, S. et al. (2024) (see reference below).</p> <p><strong>Data structure</strong></p> <p>The datasets are stored in <strong>NetCDF format</strong> with a structure consistent across the different models (M1, M2, M3). Each file contains multiple variables representing components of the Net Ecosystem Production (NEP) estimates, such as the mean NEP and its quantiles. The primary variables are:</p> <ul> <li><strong>NEP_MX_mean</strong>: The mean estimate of NEP for each model (M1, M2, M3), with units of grams of carbon per square meter per year (gC m⁻² year⁻¹).</li> <li><strong>NEP_MX_quantiles</strong>: Estimates of NEP at different quantiles, providing uncertainty ranges. The quantiles represented in the data are: [0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75]<br> <div> </div> </li> <li><strong>Members dimension</strong>: Each model includes a <strong>members</strong> dimension, representing several NEP estimates generated using different ensemble members. These members capture uncertainty from input variables such as GPP, temperature, nitrogen deposition, and forest age. The members dimension provides users with multiple realizations of NEP estimates, reflecting the variability these factors introduce.</li> </ul> <p>Coordinates include latitude and longitude with CRS information (EPSG:4326). Missing data values are represented by <strong>-9999</strong>.</p> <p><strong>Citation</strong></p> <p>When using the maps, please cite the dataset, including the version number and the following paper: Ciais, P., Yao, Y. Besnard, S. et al. (2024) The global carbon balance of forests based on flux towers and forest age data, <em>submitted</em>. </p> <p><strong>Version History</strong></p> <ul> <li>1.0 - Initial version, covering 2010</li> </ul>
Proxy-constrained modeled AMOC from 1450-1780 CE
<p>The Atlantic Meridional Overturning Circulation (AMOC) has profound impacts on the climate of the North Atlantic and the global climate at large. It plays a significant role in redistributing heat and freshwater around the globe. Real-time AMOC measurement began in 2004, providing monthly observation of the stream function. Although this observation exhibits large intra-to-multi-annual variability, the records are too short to make inferences about intra-annual AMOC variability. Here, we present a 10-member ensemble simulated AMOC from a stand-alone ocean model MPI-OM. We nudge the ocean model in the surface to proxy the reconstructed SST of Samakinwa et al., 2021, thereby leading to a proxy-constrained Modeled AMOC for the period 1450 - 1780 CE.</p>
Mechanism for the Uplift of Gongga Shan in the Southeastern Tibetan Plateau Constrained by 3D Magnetotelluric Data
<p>The *.data, *.rho, and *.zip files are associated with a paper titled 'Mechanism for the Uplift of Gongga Shan in the Southeastern Tibetan Plateau Constrained by 3D Magnetotelluric Data' in Geophysical Research Letters published in 2022. On the basis of this data and inversion model, we addressed that the rapid uplift of the Gongga Shan massif likely occurred by the underthrusting of the Yangtze Craton. More details about the electrical resistivity model and its interpretations can be found in our journal paper. </p> <p>All the resulting files from ModEM are included in the 'ModEM_Inversion_Results.zip'. All the figures in the paper and supplementary are included in the 'GRL_All_Figures.zip' and 'Figure_S5_All_Responses.zip'.</p> <p>The resulting model and data output in ModEM format can be found in .rho and .data files. The ModEM is an open-source code package for MT 3D inversion, which is provided by Gary Egbert, Anna Kelbert, and Naser Meqbel and can be found on this website: <a href="https://sites.google.com/site/modularem/download">https://sites.google.com/site/modularem/download</a>. </p> <p>Please note that the 3D resistivity model files in general format includes four columns -- longitude, latitude, depth, and resistivity, the one who wants to plot the model via GMT, MATLAB, Surface, etc., can find these files in 'Gongga_3D_Resistivity_Model_Files.zip'. In this zip, you will find the resistivity model of each horizontal slice of different depths and a file including all the slices. A MATLAB script called 'see_slice.m' is included in the folder which can help to quickly view these resistivity slices.</p>
Optimal neutron-star mass ranges to constrain the equation of state of nuclear matter with electromagnetic and gravitational-wave observations: EOS library
<p>This repository includes a library of equations of state (EOS) and stellar models presented in the publications Weih et al. (2019) (see also the related identifier) and Most et al. (2018). The library includes ~ 3 Million physically plausible EOSs that fulfill a number of astrophysical and nuclear constraints. See the README for more information. </p>
Constraining properties of the next nearby core-collapse supernova with multi-messenger signals: multi-messenger signals
<p>1D FLASH simulations with STIR, for alpha_lambda = 1.23, 1.25, and 1.27. Run with SFHo EOS, M1 with 12 energy groups.</p> <p>For more information on these simulations, see Warren, Couch, O'Connor, & Morozova (arXiv:1912.03328) and Couch, Warren, & O'Connor (2020).</p> <p>Includes the multi-messenger data from the STIR simulations. The filename indicates the turbulent mixing parameter a and progenitor mass m of the simulation. Columns are time [s], shock radius [cm], explosion energy [ergs], electron neutrino mean energy [MeV], electron neutrino rms energy [MeV], electron neutrino luminosity [10^51 ergs/s], electron antineutrino mean energy [MeV], electron antineutrino rms energy [MeV], electron antineutrino luminosity [10^51 ergs/s], x neutrino mean energy [MeV], x neutrino rms energy [MeV], x neutrino luminosity [10^51 ergs/s], gravitational wave frequency from eigenmode analysis of the protoneutron star structure [Hz]. Note that the x neutrino luminosity is for <strong>one</strong> neutrino flavor - to get the total mu/tau neutrino and antineutrino luminosities requires multiplying this number by 4.</p> <p>v1.1 - removed unnecessary duplicate files</p> <p>v1.2 - upload failed. Obsolete.</p> <p>v1.3 - packaging alpha values as separate tar files for easy download.</p>
Data used to create figures in the ACP Letters manuscipt "The value of remote marine aerosol measurements for constraining radiative forcing uncertainty" by Regayre et al. (2020)
<p>This dataset was created from perturbed parameter ensembles (PPEs) using the HadGEM-UKCA atmospheric composition climate model. All data needed to reproduce figures in the Regayre et al. (2020) ACP Letters article "The value of remote marine aerosol measurements for constraining radiative forcing uncertainty" are included. Other output from the PPEs can be obtained by contacting the lead author.</p> <p>The following data are included here:</p> <ul> <li>CCN measurement data degraded to match the model-measurement comparison resolution.</li> <li>Unconstrained and constrained CCN<sub>0.2</sub> output from the PPE used to make Figure 1. These compressed files contain 48 .dat files. Each .dat file contains the PPE mean, variance and 95% creidble interval data. Files are named consecutively, containing data from 90<sup>o</sup>S to 90<sup>o</sup>N at 0<sup>o</sup>E, then continuing Eastward. When combined, these files provide data for each latitude/longitude pair at the N48 spatial resolution.</li> <li>A zip file of an netcdf file containing 26-dimensional data for parameter values, used to create the sample of 1 million model variants from our statistical emulators of model output.</li> <li>A zip file containing a folder of files made of one million ones and zeros that indicate the retention/rejection criteria from applying our constraint methodology for various constraint combination scenarios, for each model variant. A value of 1 indicates the model variant was retained. Data in these files is in the same order as the unconstrained sample file of parameter values.</li> <li>Compressed files containing global, annual mean RF<sub>aci</sub> and ERF<sub>aci</sub> values for the unconstrained set of one million model variants. The compressed netcdf files contain RF (ERF), RF<sub>aci</sub> (ERF<sub>aci</sub>) and RF<sub>ari</sub> (ERF<sub>ari</sub>) values.</li> </ul>
Phlorest phylogeny derived from Chang et al. 2015 'Ancestry-constrained phylogenetic analysis supports the Indo-European steppe hypothesis'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Chang W, Cathcart C, Hall D, & Garrett A. 2015. Ancestry-constrained phylogenetic analysis supports the Indo-European steppe hypothesis. Language, 91(1):194-244.</p> </blockquote>
Datasets of synthetic workflows for evaluating a multi-objective and multi-constrained scheduling approach for cyber-physical applications
<p>These datasets of synthetic workflows (task graphs) were generated to evaluate the performance and scalability of a multi-objective and multi-constrained scheduling approach for workflow applications of various structures, sizes, and sensing/actuating requirements in a cyber-physical system (CPS) based on the edge-hub-cloud paradigm. The examined CPS comprised four edge devices (i.e., single-board computers, each attached to an unmanned aerial vehicle (UAV) equipped with sensors/actuators) interacting with a hub device (e.g., a laptop), which in turn communicated with a more computationally capable cloud server. All system devices featured heterogeneous multicore processors with different processing core failure rates and varied sensing/actuating or other specialized capabilities. Our objectives were the minimization of the overall latency, the minimization of the overall energy consumption, and the maximization of the overall reliability of the workflow application in the specific CPS, under deadline, reliability, memory, storage, energy, capability, and task precedence constraints.</p> <p>We generated 25 random task graphs with 10, 20, 30, 40, and 50 nodes (5 task graphs for each size), utilizing the Task Graphs For Free (TGFF) random task graph generator [1],[2]. Additional task parameters (e.g., execution time, power consumption, memory, storage, output data size, capability, reliability threshold) were included post-generation, using appropriate values. More details are provided in README.txt.<br><br>References:<br>[1] R. P. Dick, D. L. Rhodes, and W. Wolf, "TGFF: Task graphs for free," Proceedings of the Sixth International Workshop on Hardware/Software Codesign (CODES/CASHE), 1998, pp. 97-101, doi: 10.1109/HSC.1998.666245.<br>[2] R. P. Dick, D. L. Rhodes, and K. Vallerio, "TGFF," https://robertdick.org/projects/tgff/.</p>
Adjusted ERA5, COREv2 and JRA-55 products constrained by ocean observations
<p>This dataset contains ERA5, JRA-55 and COREv2 air-sea flux fields that have been adjusted to match ocean heat and salt content change in EN4 and IAP ocean observations. It also contains estimes of meridional heat and freshwater transports, globally and in the Atlantic and Indo-Pacific, based on these adjusted air-sea surface flux fields. Please consult the README for more information on the dataset. <br><br>The net heat flux and net freshwater flux into the ocean have been adjusted using the "Optimal Transformation Method" (OTM), a watermass-based inverse method that uses physics-based constraints to close the observed ocean heat and salt budgets. The formulation of OTM and a model validation is provided at Zika & Sohail (2024). The process of producing these adjusted air-sea fluxes is described in Sohail & Zika (2025).</p>
Data for "Unfolding the structural stability of nanoalloys via symmetry-constrained genetic algorithm and neural network potential"
<p><strong>PtNi_alloy_eam.db</strong> is the dataset (ase.db object) consisting of 55982 intially sampled Pt-Ni alloy structures with EAM energies and forces.</p> <p><strong>PtNi_alloy_dft.db</strong> is the dataset (ase.db object) consisting of the final 6828 resampled Pt-Ni alloy structures with DFT energies and forces calculated by VASP. This is the training set for the NNP, and could be very useful for fitting other machine learning models.</p> <p><strong>PtNi_nanoalloy_vertices_nnp.db</strong> is the dataset (ase.db object) consisting of all the vertices (stable structures) on the convex hulls obtained from NNP-based SCGA runs on 36 Pt-Ni nanoalloy systems. The energies are given by the NNP. Additional information such as mixing energy, motif and symmetry axis are also saved in the dataset and can be queried by the 'data' keyword. An xyz format trajectory of these stable structures is also uploaded.</p> <p>All the input files and scripts for hybrid MC-MD simulations, QBC resampling, DFT calculations, NNP training, NNP-based SCGA runs and convex hull analysis are provided in <strong>inputs_and_scripts.zip</strong>.</p>
Seasonal Terrestrial Water Load Modulation of Seismicity at the Southeastern Margin of the Tibetan Plateau Constrained by GNSS and GRACE Data
<p>Data Set S1. The earthquake catalog is used to decluster aftershocks and background events, and the time range is from July 2004 to July 2021. This data set includes 672585 events in the study area.</p> <p>Data Set S2. Focal mechanism solutions of M ≥ 4 earthquakes at the southeastern margin of the Tibetan Plateau. The data set includes 634 solutions of earthquakes M ≥ 4, and the time range is from 2009 to 2017.</p>
Constraining the properties of dense neutron star cores: The case of the transient low-mass X-ray binary HETE J1900.1-2455
<p>This is a basic reproduction package for the paper "Constraining the properties of dense neutron star cores: The case of the transient low-mass X-ray binary HETE J1900.1-2455" by <a href="https://doi.org/10.1093/mnras/stab2202">N. Degenaar et al. (2021)</a>. It provides reduced data products, simulated data and scripts to allow the reproduction of the work performed in this paper. It also lists software used and data archives containing the public observational data.</p>
Constraining Neutron-Star Matter with Microscopic and Macroscopic Collisions
<p>Data release associated with the preprint "<em>Constraining Neutron-Star Matter with Microscopic and Macroscopic Collisions</em>'' (2021; <a href="https://arxiv.org/abs/2107.06229">arxiv:2107.06229[nucl-th]</a>)</p> <p>Data includes:</p> <p>EOS files:</p> <ol> <li>chiral effective field theory (CEFT) up to 1nsat and extended with speed-of-sound extension (cse)</li> <li>CEFT up to 1.5 nsat and cse</li> <li>CEFT up to 1.5 nsat and extended with piecewise-polytrope</li> <li>CEFT up to 1.0 nsat, cse and enforced a uniform distribution on a radius for 1.4 solar mass neutron star (R14)</li> <li>CEFT up to 1.5 nsat, cse and enforced a uniform distribution on R14</li> </ol> <p>Posterior probability files: details to be found in README.txt<br> <br> Data used in Fig.1 and Fig.2 are included</p>
Data from: Radial stem growth of the clonal shrub Alnus alnobetula at treeline is constrained by summer temperature and winter desiccation and differs in carbon allocation strategy compared to co-occurring Pinus cembra
<p><strong>Data are documented in the following article:</strong></p> <p>Oberhuber W., G Wieser, F. Bernich, A. Gruber (2022) Radial stem growth of the clonal shrub <em>Alnus alnobetula</em> at treeline is constrained by summer temperature and winter desiccation and differs in carbon allocation strategy compared to co-occurring <em>Pinus cembra</em>. Forests 2022, 13, 440. doi: 10.3390/f13030440.</p> <p> </p> <p><strong>Summary:</strong></p> <p>Global change is affecting species areal distribution in many regions. A better understanding of how land-use change and climate warming affects shrub growth is essential for improved predictions of forest dynamics at the alpine treeline. Evaluation of radial stem growth of the clonal shrub <em>Alnus alnobetula</em> (= <em>Alnus viridis</em>) and the co-occurring tree species Swiss stone pine (<em>Pinus cembra</em>) within an alpine treeline ecotone revealed that mean ring width of nitrogen fixing <em>A. alnobetula</em> was about four times lower compared to <em>P. cembra</em>. Our findings are based on ring width data from <em>A. alnobetula</em> and <em>P. cembra</em> stems sampled at the alpine treeline ecotone on Mt. Patscherkofel (47°12’N, 11°27’E, Central European Alps, Austria, elevation range 2050 to 2190 m asl). Ring width time series include 86 radii from 51 stems of <em>A. alnobetula</em> (stems had mean age of 18±7 yrs) and 24 radii from 16 stems of <em>P. cembra </em>(18±4 yrs). We explain our findings by different carbon allocation strategies, i.e., preference of “vertical” stem growth in late successional <em>P. cembra</em> vs. favoring “horizontal” spread in the pioneer shrub<em> A. alnobetula.</em> By favouring clonal propagation over individual stem growth <em>A. alnobetula</em> is able to quickly spread at the alpine treeline ecotone.</p>
Dataset for "Gross primary productivity of four European ecosystems constrained by joint CO2 and COS flux measurements"
<p>Data of measurements and model output of the publication "Gross primary productivity of four European ecosystems constrained by joint CO<sub>2</sub> and COS flux measurements".</p> <p>Data consists of micrometeorological data, COS and CO<sub>2</sub> flux measurements for 4 sites including filters for the fluxes.</p> <p>The sites include: a managed temperate mountain grassland in Austria (18.06.-21.08.2015), a Mediterranean savanna ecosystem in Spain(29.04.-24.05.2016)), a Temperate beach forest in Denmark(07.06.-03.07.2016) and an agricultural soy bean field in Italy(03.07.-01.08.2017).</p> <p>Version 2: param2950** are now correct (were filled with the same values for all field sites) </p> <p>For additional information please contact: <a href="mailto:Georg.Wohlfahrt@uibk.ac.at">Georg.Wohlfahrt@uibk.ac.at</a></p>
Constraining scalar-tensor theories by neutron star-balck hole gravitational wave events
<p>This data release corresponds to the paper "Constraining scalar-tensor theories by neutron star-balck hole gravitational wave events" (<a href="https://arxiv.org/abs/2105.13644">arXiv:2105.13644</a>). In this paper, we consider three specific models of scalar-tensor theories, including the Brans-Dicke theory (BD), the theory with scalarization phenomena proposed by Damour and Esposito-Far\`{e}se (DEF), and Screened Modified Gravity (SMG). From all 4 possible NSBH events so far, we use two of them to place the constraints. The other two are excluded in this work due to the possible unphysical deviations. Four equations of state (EoSs), <em>sly</em>, <em>alf2</em>, <em>H4</em> and <em>mpa1</em>, are used to derive the scalar charges of neutron stars for BD and DEF. The constraints are obtained by performing the full Bayesian inference with the help of the open source software <a href="https://git.ligo.org/lscsoft/bilby">Bilby</a>.</p> <p>This dataset contains all posterior samples of the runs discussed in the paper. The models and EoSs can be read form the filenames for the runs of BD and DEF. The files of "<em>*_half_dipole.json</em>" correspond to the runs for constraining the dipole radiation without considering specific model parameters. All files are JSON format which is the default output format of <a href="https://git.ligo.org/lscsoft/bilby">Bilby</a>. They are human readable and also can be processed or visualized by <a href="https://git.ligo.org/lscsoft/bilby">Bilby</a> or <a href="https://git.ligo.org/lscsoft/pesummary">PESummary</a> conveniently.</p>
Evaluating the predictive character of the method of Constrained Geometries Simulate External Force with Density Functional Theory.
<p>## Abstract</p> <p>from [1]:</p> <p>Mechanochemistry is a fast-developing field of interdisciplinary research with a growing number of applications. Therefore, many theoretical methods have been developed to quickly predict the outcome of mechanically induced reactions. Constrained geometries simulate External Force (CoGEF) is one of the earlier methods in this field. It is easily implemented and can be conducted with most DFT codes. However, recently, we observed totally different predictions for model systems of epoxy resins in different conformations and with different density functionals. To better understand the conformational and functional dependence in typical CoGEF calculations we present a systematic evaluation of the CoGEF method for different model systems covering homolytic and heterolytic bond cleavage reactions, electrocyclic ring opening reactions and scission of non-covalent interactions in hydrogen-bond complexes. From our calculations we observe that many mechanochemical descriptors strongly depend on the functional used, however, a systematic trend exists for the relative maximum Force. In general, we observe that the CoGEF procedure is forcing the system to high energetic regions on the molecular potential energy profiles, which can lead to unexpected and uncorrelated predictions of mechanochemical reactions. This is questioning the true predictive character of the method.</p> <p> </p> <p>## Contact</p> <p>Christian R. Wick</p> <p>Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Faculty of Science, Department of Physics, PULS Group, Interdisciplinary Center for Nanostructured Films (IZNF), Cauerstrasse 3, 91058, Germany</p> <p> </p> <p>## License</p> <p>Creative Commons Attribution 4.0 International</p> <p> </p> <p>## Context</p> <p>Dataset to paper [1]</p> <p> </p> <p>## Contents</p> <ul> <li>All COGEF trajectories in xyz format.</li> <li>All CoGEF distances and DFT Energies in csv format.</li> </ul> <p>The following DFT levels of theory were investigated:</p> <ul> <li>B3LYP/6-31G(d)</li> <li>B3LYP-D3BJ/def2-SVP</li> <li>BP86-D3/def2-SVP</li> <li>PBE1PBE/def2-SVP</li> <li>M06-D3/def2-SVP</li> </ul> <p> </p> <p>## Folder structure</p> <ul> <li>- compound_X : data set for compound number X (numbering corresponds to the numbering scheme in [1]) <ul> <li>the xyz trajectories follow the following naming convention: "DFT_method"_"unrestricted/restricted".xyz</li> <li>the csv files follow the naming convention: "DFT_method"_"unrestricted/restricted".xyz.csv</li> </ul> </li> </ul> <p>## Software</p> <p>### COGEFF calculations: COGEF.py v1.8.0</p> <p>Zenodo release:</p> <p>https://doi.org/10.5281/zenodo.7079733</p> <p>### DFT calculations:</p> <p>Gaussian 16 Rev B [2]</p> <p> </p> <p>## Funding</p> <p>This research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 377472739/GRK 2423/1-2019 FRASCAL.</p> <p><br> ## References</p> <p>[1] C. R. Wick, E. Topraksal, D. M. Smith, A.-S. Smith, "Evaluating the predictive character of the method of Constrained Geometries Simulate External Force with Density Functional Theory.", Forces in Mechanics, 9, 100143; doi:10.1016/j.finmec.2022.100143</p> <p>[2] Frisch, M. J.; Trucks, G. W.; Schlegel, H. B.; Scuseria, G. E.; Robb, M. A.; Cheeseman, J. R.; Scalmani, G.; Barone, V.; Petersson, G. A.; Nakatsuji, H.; et al. Gaussian 16 Rev. B.01, 2016.</p>
DL-RMD: A geophysically constrained electromagnetic resistivity model database for deep learning applications (Dataset)
<p>Deep learning algorithms have shown incredible potential in many applications. The success of these data-hungry methods is largely associated with the availability of large-scale data sets, as millions of observations are often required to achieve acceptable performance levels. Recently, there has been an increased interest in applying deep learning methods to geophysical applications where electromagnetic methods are used to map the subsurface geology by observing variations in the electrical resistivity of the subsurface materials. To date, there are no standardized datasets for electromagnetic methods, which hinders the progress, evaluation, benchmarking, and evolution of deep learning algorithms due to data inconsistency. Therefore, we present a large-scale electrical resistivity model database of a wide variety of geologically plausible and geophysically resolvable subsurface structures for the commonly deployed ground-based and airborne electromagnetic systems. The presented database can potentially be used to build surrogate models of well-known processes and aid in labour intensive tasks. The geophysically constrained property of this database will not only achieve enhanced performance and improved generalization but, more importantly, it will incorporate consistency and credibility in deep learning models. We urge the geophysical community interested in deep learning for electromagnetic methods to utilize the presented database.</p>
Understanding Electron Transfer Reactions using Constrained Density Functional Theory: Complications due to Surface Interactions
<p>For reproducing the results presented in "<strong>Hashemi, A., Peljo, P., & Laasonen, K. (2022). Understanding Electron Transfer Reactions using Constrained Density Functional Theory: Complications due to Surface Interactions</strong>", this database provides the input files and CDFT-AIMD trajectory information. Please refer to the publication if you wish to use these data.</p> <p>---------------------------------------**************************************************************************-------------------------------------------------</p> <p><em>This study was financed by the Horizon 2020 Framework Programme CompBat with project number 875565. We also thank CSC-IT Center for Science Ltd. and Aalto Science-IT project for generous grants of computer time.</em><br> -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>The content of a directory is shown in a tree-like format:</strong><br> ├── 1DMDQ<br> │ ├── 1_md<br> │ │ ├── dft-common-params.inc<br> │ │ ├── dmdq-md-pos-1.xyz<br> │ │ ├── md.inp<br> │ │ ├── pos.xyz<br> │ │ ├── submit.sh<br> │ │ └── subsys.inc<br> │ ├── 2_cdftaimd<br> │ │ ├── state_a<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ ├── state_b<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ └── subsys.inc<br> │ │ └── state_c<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── cdft_md.bash<br> │ │ ├── cdft_md.inp<br> │ │ ├── dft-common-params.inc<br> │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ └── subsys.inc<br> │ └── 3_cdft_wH2O_sccs<br> │ ├── state_a<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ ├── state_b<br> │ │ ├── b_to_a.tar.gz<br> │ │ └── b_to_c.tar.gz<br> │ └── state_c<br> │ ├── framePrint.py<br> │ ├── input_files<br> │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── dft-common-params.inc<br> │ │ ├── energy_cdft.inp<br> │ │ ├── energy_mixed_cdft.inp<br> │ │ └── subsys.inc<br> │ └── README<br> ├── 2MeVi<br> │ ├── 1_md<br> │ │ ├── dft-common-params.inc<br> │ │ ├── md.inp<br> │ │ ├── mevi-md-pos-1.xyz<br> │ │ ├── pos.xyz<br> │ │ ├── submit.sh<br> │ │ └── subsys.inc<br> │ ├── 2_cdftaimd<br> │ │ ├── state_a<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ └── subsys.inc<br> │ │ ├── state_b<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ └── state_c<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── cdft_md.bash<br> │ │ ├── cdft_md.inp<br> │ │ ├── dft-common-params.inc<br> │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ ├── frame.xyz<br> │ │ └── subsys.inc<br> │ └── 3_cdft_wH2O_sccs<br> │ ├── state_a<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ ├── state_b<br> │ │ ├── b_to_a<br> │ │ │ ├── framePrint.py<br> │ │ │ ├── input_files<br> │ │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ │ ├── dft-common-params.inc<br> │ │ │ │ ├── energy_cdft.inp<br> │ │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ │ └── subsys.inc<br> │ │ │ └── README<br> │ │ └── b_to_c<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ └── state_c<br> │ ├── framePrint.py<br> │ ├── input_files<br> │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── dft-common-params.inc<br> │ │ ├── energy_cdft.inp<br> │ │ ├── energy_mixed_cdft.inp<br> │ │ └── subsys.inc<br> │ └── README<br> ├── 3OHVi<br> │ ├── 1_md<br> │ │ ├── dft-common-params.inc<br> │ │ ├── md.inp<br> │ │ ├── ohvi-md-pos-1.xyz<br> │ │ ├── pos.xyz<br> │ │ ├── submit.sh<br> │ │ └── subsys.inc<br> │ ├── 2_cdftaimd<br> │ │ ├── state_a<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ ├── state_b<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ └── state_c<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── cdft_md.bash<br> │ │ ├── cdft_md.inp<br> │ │ ├── dft-common-params.inc<br> │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ ├── frame.xyz<br> │ │ └── subsys.inc<br> │ └── 3_cdft_wH2O_sccs<br> │ ├── state_a<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ ├── state_b<br> │ │ ├── b_to_a<br> │ │ │ ├── framePrint.py<br> │ │ │ ├── input_files<br> │ │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ │ ├── dft-common-params.inc<br> │ │ │ │ ├── energy_cdft.inp<br> │ │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ │ └── subsys.inc<br> │ │ │ └── README<br> │ │ ├── b_to_a.tar.gz<br> │ │ ├── b_to_c<br> │ │ │ ├── framePrint.py<br> │ │ │ ├── input_files<br> │ │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ │ ├── dft-common-params.inc<br> │ │ │ │ ├── energy_cdft.inp<br> │ │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ │ └── subsys.inc<br> │ │ │ └── README<br> │ │ └── b_to_c.tar.gz<br> │ └── state_c<br> │ ├── framePrint.py<br> │ ├── input_files<br> │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── dft-common-params.inc<br> │ │ ├── energy_cdft.inp<br> │ │ ├── energy_mixed_cdft.inp<br> │ │ └── subsys.inc<br> │ └── README<br> ├── 4dBR5<br> │ ├── 1_md<br> │ │ ├── dft-common-params.inc<br> │ │ ├── dmdq-md-pos-1.xyz<br> │ │ ├── md.inp<br> │ │ ├── pos.xyz<br> │ │ ├── submit.sh<br> │ │ └── subsys.inc<br> │ ├── 2_cdftaimd<br> │ │ ├── state_a<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ ├── state_b<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ └── state_c<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── cdft_md.bash<br> │ │ ├── cdft_md.inp<br> │ │ ├── dft-common-params.inc<br> │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ ├── frame.xyz<br> │ │ └── subsys.inc<br> │ └── 3_cdft_wH2O_sccs<br> │ ├── state_a<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ ├── state_b<br> │ │ ├── b_to_a.tar.gz<br> │ │ └── b_to_c.tar.gz<br> │ └── state_c<br> │ ├── framePrint.py<br> │ ├── input_files<br> │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── dft-common-params.inc<br> │ │ ├── energy_cdft.inp<br> │ │ ├── energy_mixed_cdft.inp<br> │ │ └── subsys.inc<br> │ └── README<br> ├── 52HNQ<br> │ ├── 1_md<br> │ │ ├── dft-common-params.inc<br> │ │ ├── hnq-md-pos-1.xyz<br> │ │ ├── md.inp<br> │ │ ├── pos.xyz<br> │ │ ├── submit.sh<br> │ │ └── subsys.inc<br> │ ├── 2_cdftaimd<br> │ │ ├── state_a<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ ├── state_b<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ └── state_c<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── cdft_md.bash<br> │ │ ├── cdft_md.inp<br> │ │ ├── dft-common-params.inc<br> │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ ├── frame.xyz<br> │ │ └── subsys.inc<br> │ └── 3_cdft_wH2O_sccs<br> │ ├── state_a<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ ├── state_b<br> │ │ ├── b_to_a.tar.gz<br> │ │ └── b_to_c.tar.gz<br> │ └── state_c<br> │ ├── framePrint.py<br> │ ├── input_files<br> │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── dft-common-params.inc<br> │ │ ├── energy_cdft.inp<br> │ │ ├── energy_mixed_cdft.inp<br> │ │ └── subsys.inc<br> │ └── README<br> └── 6_n_H2O_effect_mevi<br> ├── 08h2o<br> │ ├── framePrint.py<br> │ ├── README<br> │ ├── state_a.tar.gz<br> │ └── state_b.tar.gz<br> ├── 10h2o<br> │ ├── framePrint.py<br> │ ├── README<br> │ ├── state_a.tar.gz<br> │ └── state_b.tar.gz<br> ├── 20h2o<br> │ ├── framePrint.py<br> │ ├── README<br> │ ├── state_a.tar.gz<br> │ └── state_b.tar.gz<br> ├── 40h2o<br> │ ├── framePrint.py<br> │ ├── README<br> │ ├── state_a.tar.gz<br> │ └── state_b.tar.gz<br> ├── 97h2o<br> │ ├── framePrint.py<br> │ ├── README<br> │ ├── state_a.tar.gz<br> │ └── state_b.tar.gz<br> └── fig3.png</p> <p>74 directories, 301 files<br> -------------------------------------------------------<br> There are 6 directories: 1DMDQ, 2MeVi, 3OHVi, 4dBR5, 52HNQ, 6_n_H2O_effect_mevi. Except for "6_n_H2O_effect_mevi", we see 3 subdirectories named 1_md, 2_cdftaimd, and 3_cdft_wH2O_sccs. The input files and AIMD trajectories can be found in 1_md. While 2_cdftaimd contains the CDFT-AIMD input files and trajectories. To reproduce snapshots and input files of 3_cdft_wH2O_sccs, follow the README files in the subdirectories.</p> <p>The directory "6_n_H2O_effect_mevi" contains the number of water effects (Figure 3 of the publication). Users are guided by README files once again. </p>
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