cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5522/04/23807676
title: Molecular Tully Model Data Sets and Quantics files

description: Datasets contain the input and output files for the Quantics program to run MCTDH, vMCG, DD-vMCG and TSH calculations on the Ibele-Curchod models. There are six directories, two for each model to divide the calculations on the LVC Hamiltonian and the direct dynamics surfaces. These files can be used together with Quantics to generate the data in the paper Gomez et al (PCCP, 2024). The input files are standard ascii files, grouped into directories for the different systems studied. The databases with the points calculated during the direct dynamics simulations are SQLite format with tables for geometries, energies, gradients etc. More details are in the paper. The Quantics program is a mostly Fortran code for running quantum dynamics simulations. It is open source and runs on linux workstations. It is freely available on request to the authors of the paper. For further details of the program see Comp. Phys. Comm., 248:107040–15, 2020.
DOI: 10.5522/04/23807676.v1
title: Molecular Tully Model Data Sets and Quantics files

description: Datasets contain the input and output files for the Quantics program to run MCTDH, vMCG, DD-vMCG and TSH calculations on the Ibele-Curchod models. There are six directories, two for each model to divide the calculations on the LVC Hamiltonian and the direct dynamics surfaces. These files can be used together with Quantics to generate the data in the paper Gomez et al (PCCP, 2024). The input files are standard ascii files, grouped into directories for the different systems studied. The databases with the points calculated during the direct dynamics simulations are SQLite format with tables for geometries, energies, gradients etc. More details are in the paper. The Quantics program is a mostly Fortran code for running quantum dynamics simulations. It is open source and runs on linux workstations. It is freely available on request to the authors of the paper. For further details of the program see Comp. Phys. Comm., 248:107040–15, 2020.
DOI: 10.6084/m9.figshare.24167865
title: Appendix_SLR Journal Paper_Order-picking system Design, Development & Testing

description: This dataset includes a series of tables mentioned in an academic journal article entitled "A state-of-the-art classification and review of parameters that affect the design, control, and operating strategies of order-picking systems" which is published by <i>Operational Research -</i> <i>An International Journal</i> (ORIJ). This dataset is part of the Appendix to the journal mentioned above article.
DOI: 10.6084/m9.figshare.24167865.v1
title: Appendix_SLR Journal Paper_Order-picking system Design, Development & Testing

description: This dataset includes a series of tables mentioned in an academic journal article entitled "A state-of-the-art classification and review of parameters that affect the design, control, and operating strategies of order-picking systems" which is published by <i>Operational Research -</i> <i>An International Journal</i> (ORIJ). This dataset is part of the Appendix to the journal mentioned above article.
DOI: 10.35097/1898
title: Complementary Data for "All-At-Once and Reduced Solvers for Visco-Acoustic Full Waveform Inversion"

description: This package contains data (Inversion results and logfiles produced by the FWI software, config files for reproduction) and python scripts to create the figures shown in "All-At-Once and Reduced Solvers for Visco-Acoustic Full Waveform Inversion" by Christian Rheinbay. Moreover, it contains instructions on how to reproduce the contained data.
description: Tested on Ubuntu 22.04
DOI: 10.34730/716ae5f7e9de42d19d60e65ed9e1658d
title: Gesäuse-Johnsbachtal - Austria, Bulk density

description: Bulk density data of the Gesäuse-Johnsbachtal site
DOI: 10.5281/zenodo.10552404
title: AWI-CM model data used in the paper "Subsurface warming in the Antarctica's Weddell Sea can be avoided by reaching the 2°C warming target"

description: This dataset includes the minimal data necessary to reproduce the findings of Teske et al., in revision. Output of model simulations with the global climate model AWI-CM is provided for one ensemble of five historical simulations (hist1 - hist5) and four climate scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5), including the ensemble of five SSP3-7.0 scenario simulations, as well as information on the model grid. 




historical period
hist1
monthly means for sea ice concentration, sea ice formation, sea ice thickness, surface stress, salinity and potential temperatureyearly means of salinity and potential temperature 


 
hist2
monthly means for sea ice concentration, sea ice formation, surface stress, salinity and potential temperatureyearly means for salinity and potential temperature


 
hist3
monthly means for sea ice concentration, sea ice formation, surface stress, salinity and potential temperatureyearly means for salinity and potential temperature


 
hist4
monthly means for sea ice concentration, sea ice formation, surface stress, salinity and potential temperatureyearly means for salinity and potential temperature


 
hist5
monthly means for sea ice concentration, sea ice formation, surface stress, salinity and potential temperatureyearly means for salinity and potential temperature


CO2 emission scenario
SSP1-2.6
monthly means for sea ice concentration, sea ice formation, surface stress and potential temperatureyearly means for salinity and potential temperature


 
SSP2-4.5
monthly means for sea ice concentration, sea ice formation and surface stressyearly means for salinity and potential temperature


 
SSP3-7.0_1
monthly means for sea ice concentration, sea ice formation, surface stress and salinityyearly means for salinity and potential temperature


 
SSP3-7.0_2
monthly means for sea ice concentration, sea ice formation, surface stress and salinityyearly means for salinity and potential temperature


 
SSP3-7.0_3
monthly means for sea ice concentration, sea ice formation, surface stress and salinityyearly means for salinity and potential temperature


 
SSP3-7.0_4
monthly means for sea ice concentration, sea ice formation, surface stress and salinityyearly means for salinity and potential temperature


 
SSP3-7.0_5
monthly means for sea ice concentration, sea ice formation, surface stress and salinityyearly means for salinity and potential temperature


 
SSP5-8.5
8-hourly means for 10m-wind monthly means for sea ice concentration, sea ice formation, sea ice thickness, surface stress, salinity and potential temperatureyearly means for salinity and potential temperature




Depending on the scenario and variable in question, the variables are provided for the periods 1850-2014 or 2000-2014 for the historical simulations, and 2015-2100 for the scenario simulations. Monthly sea ice concentrations are provided for the time periods 2000-2014, 2036-2050 and 2086-2100.
DOI: 10.5281/zenodo.10552405
title: AWI-CM model data used in the paper "Subsurface warming in the Antarctica's Weddell Sea can be avoided by reaching the 2°C warming target"

description: This dataset includes the minimal data necessary to reproduce the findings of Teske et al., in revision. Output of model simulations with the global climate model AWI-CM is provided for one ensemble of five historical simulations (hist1 - hist5) and four climate scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5), including the ensemble of five SSP3-7.0 scenario simulations, as well as information on the model grid. 




historical period
hist1
monthly means for sea ice concentration, sea ice formation, sea ice thickness, surface stress, salinity and potential temperatureyearly means of salinity and potential temperature 


 
hist2
monthly means for sea ice concentration, sea ice formation, surface stress, salinity and potential temperatureyearly means for salinity and potential temperature


 
hist3
monthly means for sea ice concentration, sea ice formation, surface stress, salinity and potential temperatureyearly means for salinity and potential temperature


 
hist4
monthly means for sea ice concentration, sea ice formation, surface stress, salinity and potential temperatureyearly means for salinity and potential temperature


 
hist5
monthly means for sea ice concentration, sea ice formation, surface stress, salinity and potential temperatureyearly means for salinity and potential temperature


CO2 emission scenario
SSP1-2.6
monthly means for sea ice concentration, sea ice formation, surface stress and potential temperatureyearly means for salinity and potential temperature


 
SSP2-4.5
monthly means for sea ice concentration, sea ice formation and surface stressyearly means for salinity and potential temperature


 
SSP3-7.0_1
monthly means for sea ice concentration, sea ice formation, surface stress and salinityyearly means for salinity and potential temperature


 
SSP3-7.0_2
monthly means for sea ice concentration, sea ice formation, surface stress and salinityyearly means for salinity and potential temperature


 
SSP3-7.0_3
monthly means for sea ice concentration, sea ice formation, surface stress and salinityyearly means for salinity and potential temperature


 
SSP3-7.0_4
monthly means for sea ice concentration, sea ice formation, surface stress and salinityyearly means for salinity and potential temperature


 
SSP3-7.0_5
monthly means for sea ice concentration, sea ice formation, surface stress and salinityyearly means for salinity and potential temperature


 
SSP5-8.5
8-hourly means for 10m-wind monthly means for sea ice concentration, sea ice formation, sea ice thickness, surface stress, salinity and potential temperatureyearly means for salinity and potential temperature




Depending on the scenario and variable in question, the variables are provided for the periods 1850-2014 or 2000-2014 for the historical simulations, and 2015-2100 for the scenario simulations. Monthly sea ice concentrations are provided for the time periods 2000-2014, 2036-2050 and 2086-2100.
DOI: 10.5281/zenodo.10556105
title: Training dataset for "Principal Component Analysis Enables Design of Deep Learning Potential Precisely Capturing LLZO Phase Transitions"
DOI: 10.5281/zenodo.10556106
title: Training dataset for "Principal Component Analysis Enables Design of Deep Learning Potential Precisely Capturing LLZO Phase Transitions"
DOI: 10.5281/zenodo.10556131
title: Macrorhamphosodes_platycheilus_EPLATE_53_H03_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.10556132
title: Macrorhamphosodes_platycheilus_EPLATE_53_H03_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.10527800
title: Anisotropic characteristics of unfolding dynamics and mechanical stability of tetrameric parallel G-quadruplex

description: This dataset include the data and input files used for the Steered MD (SMD) simulations and standard MD simulations done in the article (see the data availability section of the article). Note that the starting structure PDBs are in the Supporting Information of the article.

plumed_input.tar.gz: It has the plumed input files for all SMD simulations in this study, from which you can find the definition of the anchors, and the pulling setups. Note that the plumed input files for SMD with SPC/E water have suffix "spce".

top_gro.tar.gz: The topology and ".gro" files used for SMD simulations in Gromacs. Note that for the SMD systems with the same number of G-quartets but different pulling anchors (1-6/1-7/1-8), they share the same ".top" and ".gro" file, the difference of pulling anchors are defined in plumed files. Also note that the files used for SMD with SPC/E water have the suffix "spce".

distance_force.tar.gz: It includes the "COLVAR" files for each SMD simulation replicate (in total 45 runs). The SMD simulations with SPC/E water have suffix "spce". The header of these files are: "time x1 eRMSD_start RMSD_start pulling.force2 pulling.work pulling.bias". Here are more explanations of each column in these files:



time: simulation time (in picosecond)

x1: distance between the anchors (anchors are set in the corresponding plumed file, in nanometer)

eRMSD_start: eRMSD value to the starting structure (see: https://www.plumed.org/doc-v2.8/user-doc/html/_e_r_m_s_d.html)

RMSD_start: RMSD value to the starting structure

pulling.force2: The instantaneous value of the squared force due to this bias potential (in [kJ/(nm*mol)]^2, convert it to pN/mol, should do sqrt, and then *1.661)

work: The total work performed changing this restraint

bias: The instantaneous value of the bias potential


input.tar.gz: Contains the input files used for:



Minimization and equilibration (necessary before both standard MD and SMD)

Standard MD simulations

SMD simulations
DOI: 10.5281/zenodo.10527801
title: Anisotropic characteristics of unfolding dynamics and mechanical stability of tetrameric parallel G-quadruplex

description: This dataset include the data and input files used for the Steered MD (SMD) simulations and standard MD simulations done in the article (see the data availability section of the article). Note that the starting structure PDBs are in the Supporting Information of the article.

plumed_input.tar.gz: It has the plumed input files for all SMD simulations in this study, from which you can find the definition of the anchors, and the pulling setups. Note that the plumed input files for SMD with SPC/E water have suffix "spce".

top_gro.tar.gz: The topology and ".gro" files used for SMD simulations in Gromacs. Note that for the SMD systems with the same number of G-quartets but different pulling anchors (1-6/1-7/1-8), they share the same ".top" and ".gro" file, the difference of pulling anchors are defined in plumed files. Also note that the files used for SMD with SPC/E water have the suffix "spce".

distance_force.tar.gz: It includes the "COLVAR" files for each SMD simulation replicate (in total 45 runs). The SMD simulations with SPC/E water have suffix "spce". The header of these files are: "time x1 eRMSD_start RMSD_start pulling.force2 pulling.work pulling.bias". Here are more explanations of each column in these files:



time: simulation time (in picosecond)

x1: distance between the anchors (anchors are set in the corresponding plumed file, in nanometer)

eRMSD_start: eRMSD value to the starting structure (see: https://www.plumed.org/doc-v2.8/user-doc/html/_e_r_m_s_d.html)

RMSD_start: RMSD value to the starting structure

pulling.force2: The instantaneous value of the squared force due to this bias potential (in [kJ/(nm*mol)]^2, convert it to pN/mol, should do sqrt, and then *1.661)

work: The total work performed changing this restraint

bias: The instantaneous value of the bias potential


input.tar.gz: Contains the input files used for:



Minimization and equilibration (necessary before both standard MD and SMD)

Standard MD simulations

SMD simulations
DOI: 10.5281/zenodo.10556127
title: YUTO MMS Dataset, Sequence C (Calibration files, Groundtruth, GPS, IMU, Timestamp)
DOI: 10.5281/zenodo.10556128
title: YUTO MMS Dataset, Sequence C (Calibration files, Groundtruth, GPS, IMU, Timestamp)
DOI: 10.14284/654
title: Measurements for a 360 day climate manipulation experiment on replicate macrofaunal mesocosms collected on the cruise JR18006 in the Western Barents Sea and at Rothera research station in 2019

description: Measurements were collected during the 360 day lab-based experiment carried out on replicate macrofaunal individuals of Astarte crenata, Ctenodiscus crispatus, Cistenides hyperborea from the Western Barents Sea and Aequiyoldia eightsi from Rothera Point, Antarctic Peninsula. Macrofauna were collected on cruise JR18006 in the Western Barents Sea and by hand during scuba dives in the cove adjacent to Rothera research station. Experimental measurements include carbonate chemistry indices measured in water samples; carbon dioxide concentration levels; water temperature, salinity and pH during experiment; benthic invertebrate organic matter content, growth, tissue:shell wet weight, oxygen consumption rates, excretion rates, total protein concentrations and glucose concentrations.
DOI: 10.6084/m9.figshare.23613651.v1
title: Data_Stochastic Simulation of Extrem Surge Extremes along the United States Coastlines Using Max-Stable Process

description: This database includes data developed from the spatial-temporal modeling of storm surge extremes (annual maxima) using the max-stable process, which is as follows.
<br>
(1) Parameters of the modeled/ fitted Generalized Extrem Values distribution at grided (gauged and ungahttps://assets.grammarly.com/emoji/v1/1f454.svguged) locations along the contiguous United States (CONUS) coastlines.
(2) Storm surge magnitudes at different return periods (i.e., 10-year, 50-year, and 100-year) at the grided location along the CONUS coastlines. 
(3) Stochastic simulation of synthetic time series of AM surges at the grided locations along the CONUS coastlines.
DOI: 10.5281/zenodo.10556143
title: Characterization of digital oligomers by NMR and GPC
DOI: 10.5281/zenodo.10556144
title: Characterization of digital oligomers by NMR and GPC
DOI: 10.6084/m9.figshare.23613651.v2
title: Data_Stochastic Simulation of Storm Surge Extremes along the United States Coastlines Using Max-Stable Process

description: This database includes data developed from the spatial-temporal modeling of storm surge extremes (annual maxima) using the max-stable process, which is as follows.<br>(1) Parameters of the modeled/ fitted Generalized Extreme Values distribution at grided (gauged and ungahttps://assets.grammarly.com/emoji/v1/1f454.svguged) locations along the contiguous United States (CONUS) coastlines.(2) Storm surge magnitudes at different return periods (i.e., 10-year, 50-year, and 100-year) at the grided location along the CONUS coastlines.(3) Stochastic simulation of synthetic time series of AM surges at the grided locations along the CONUS coastlines.<b>Please cite the paper </b>Rashid, M.M., Moftakhari, H. & Moradkhani, H. Stochastic simulation of storm surge extremes along the contiguous United States coastlines using the max-stable process. <i>Commun Earth Environ</i> <b>5</b>, 39 (2024). https://doi.org/10.1038/s43247-024-01206-z
DOI: 10.6084/m9.figshare.23613651
title: Data: Stochastic Simulation of Storm Surge Extremes along the United States Coastlines Using the Max-Stable Process

description: This database includes data developed from the spatial-temporal modeling of storm surge extremes (annual maxima) using the max-stable process, which is as follows.(1) Parameters of the modeled/ fitted Generalized Extreme Values distribution at grided (gauged and ungauged) locations along the contiguous United States (CONUS) coastlines.(2) Storm surge magnitudes at different return periods (i.e., 10-year, 50-year, and 100-year) at the grided location along the CONUS coastlines.(3) Stochastic simulation of synthetic time series of AM surges at the grided locations along the CONUS coastlines.<b>Please cite the paper</b>Rashid, M.M., Moftakhari, H. & Moradkhani, H. Stochastic simulation of storm surge extremes along the contiguous United States coastlines using the max-stable process. <i>Commun Earth Environ</i> <b>5</b>, 39 (2024). https://doi.org/10.1038/s43247-024-01206-z
DOI: 10.6084/m9.figshare.23613651.v3
title: Data_Stochastic Simulation of Storm Surge Extremes along the United States Coastlines Using the Max-Stable Process

description: This database includes data developed from the spatial-temporal modeling of storm surge extremes (annual maxima) using the max-stable process, which is as follows.<br>(1) Parameters of the modeled/ fitted Generalized Extreme Values distribution at grided (gauged and ungahttps://assets.grammarly.com/emoji/v1/1f454.svguged) locations along the contiguous United States (CONUS) coastlines.(2) Storm surge magnitudes at different return periods (i.e., 10-year, 50-year, and 100-year) at the grided location along the CONUS coastlines.(3) Stochastic simulation of synthetic time series of AM surges at the grided locations along the CONUS coastlines.<b>Please cite the paper </b>Rashid, M.M., Moftakhari, H. & Moradkhani, H. Stochastic simulation of storm surge extremes along the contiguous United States coastlines using the max-stable process. <i>Commun Earth Environ</i> <b>5</b>, 39 (2024). https://doi.org/10.1038/s43247-024-01206-z
DOI: 10.5281/zenodo.10556187
title: THE ACT OF PROHIBITION AND ITS COMPONENTS.
DOI: 10.5281/zenodo.10556188
title: THE ACT OF PROHIBITION AND ITS COMPONENTS.
DOI: 10.6084/m9.figshare.22317091
title: Chen_et_al_SpikeTrains_BetaTimes.mat

description: Spike trains (samples at 20 KHz) of units recorded in the lateral hypothalamus (LH), the medial prefrontal cortex (mPFC) and ventral tegmental area (VTA, a subset of putative dopamine cells) in behaving mice concurrently with beta oscillations (15-30 Hz) in the lateral hypothalamus (for LH and mPFC units) or in the VTA (for VTA units). Times of beta oscillations (at 20 KHz) for each unit: first and third columns represent beginning and end of each beta epoch, respectively, the second column is the time of the beta peak of the maximal amplitude.
DOI: 10.5281/zenodo.10556201
title: RAQAMLASHTIRISH JARAYONIDA AXBOROT XAVFSIZLIGA TAHDID VA UNING NAMOYON BO'LISHI
DOI: 10.5281/zenodo.10556202
title: RAQAMLASHTIRISH JARAYONIDA AXBOROT XAVFSIZLIGA TAHDID VA UNING NAMOYON BO'LISHI
DOI: 10.5285/0ec16a65-abdf-2822-e063-6c86abc06533
title: ATOMIX shear probes benchmark data: a dissipation profile from Haro Strait, British Columbia, Canada obtained with a vertical microstructure profiler in October 2016.

description: This is a profile collected using a tethered free-fall vertical microstructure profiler (VMP-250IR, Rockland Scientific, Canada), prepared as a benchmark shear-probe data example for the Scientific Committee on Oceanographic Research (SCOR) Working Group #160, "Analyzing ocean turbulence observations to quantify mixing" (ATOMIX). The profile was collected on 19 October 2016 from the R.V. Strickland in Haro Strait for a demonstration class. The bottom depth at this location is approximately 80 metres. The profile was processed in two ways, resulting in two files. One was processed using a speed equal to the rate-of-change of pressure, and the other was processed using a constant speed of 0.75 metres per second (m/s). The dissipation rate was measured using two airfoil shear probes. This dataset has been processed and formatted in accordance with ATOMIX guidelines and recommendations. The provided files are organised in four hierarchical groups including L1_converted: continuous time series of data converted into physical units, L2_cleaned: cleaned time series used for spectral analysis, L3_spectra: wavenumber spectra, and L4_dissipation: dissipation rate estimates. For more detailed information, please refer to the comments within the data file.