cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7581041
title: Replication package for: Negative Nominal Interest Rates and The Bank Lending Channel

description: Package contains replication kit for "Negative Nominal Interest Rates and the Bank Lending Channel", including both replication of the empirical results and the theoretical simulations.
DOI: 10.5281/zenodo.7673949
title: Replication package for: Negative Nominal Interest Rates and The Bank Lending Channel

description: Package contains replication kit for "Negative Nominal Interest Rates and the Bank Lending Channel", including both replication of the empirical results and the theoretical simulations.
DOI: 10.5281/zenodo.8113824
title: Replication package for: Negative Nominal Interest Rates and The Bank Lending Channel

description: Package contains replication kit for "Negative Nominal Interest Rates and the Bank Lending Channel", including both replication of the empirical results and the theoretical simulations.
DOI: 10.18112/openneuro.ds004626.v1.0.1
title: Can we dissociate hypervigilance to social threats from altered perceptual decision-making processes in lonely individuals? An exploration with Drift Diffusion Modelling and EEG event-related potentials.
DOI: 10.5281/zenodo.8220732
title: Clusternets: A deep learning approach to probe clustering dark energy

description: This directory contains all the necessary data, codes, and notebooks to reproduce the results of the paper titled "Clusternets: A deep learning approach to probe clustering dark energy" Directories <strong>Codes</strong>: This directory contains different codes used to generate and post-process the simulation data, including k-evolution, gevolution, Pylians, CLASS, and LATfield2. <strong>Final figures and data</strong>: This directory includes the data for the power spectra, simulation settings and Jupyter notebooks to produce the figures. How to Use Download the files to your local machine. Navigate to the directory where the files are saved. Install the necessary packages Navigate to the "<strong>Final figures and data</strong>" directory and open the Jupyter notebooks in your preferred environment. Run the cells in the notebooks to reproduce the figures. Navigate to the "<strong>Codes</strong>" directory and use the appropriate code to generate and post-process the simulation data. Use the simulation setting files in the "<strong>Final figures and data</strong>" directory to replicate the simulations. Note: Some of the simulations may require high computational resources and may take a significant amount of time to run.<br> <br> If you have any feedback or request feel free to email chgeniamirsbu@gmail.com and farbod.hassani@gmail.com
description: If you have any feedback or request feel free to email chgeniamirsbu@gmail.com and farbod.hassani@gmail.com
DOI: 10.5281/zenodo.8220731
title: Clusternets: A deep learning approach to probe clustering dark energy

description: This directory contains all the necessary data, codes, and notebooks to reproduce the results of the paper titled "Clusternets: A deep learning approach to probe clustering dark energy" Directories <strong>Codes</strong>: This directory contains different codes used to generate and post-process the simulation data, including k-evolution, gevolution, Pylians, CLASS, and LATfield2. <strong>Final figures and data</strong>: This directory includes the data for the power spectra, simulation settings and Jupyter notebooks to produce the figures. How to Use Download the files to your local machine. Navigate to the directory where the files are saved. Install the necessary packages Navigate to the "<strong>Final figures and data</strong>" directory and open the Jupyter notebooks in your preferred environment. Run the cells in the notebooks to reproduce the figures. Navigate to the "<strong>Codes</strong>" directory and use the appropriate code to generate and post-process the simulation data. Use the simulation setting files in the "<strong>Final figures and data</strong>" directory to replicate the simulations. Note: Some of the simulations may require high computational resources and may take a significant amount of time to run.<br> <br> If you have any feedback or request feel free to email chgeniamirsbu@gmail.com and farbod.hassani@gmail.com
description: If you have any feedback or request feel free to email chgeniamirsbu@gmail.com and farbod.hassani@gmail.com
DOI: 10.5281/zenodo.8218858
title: Organic Matrix Effect on the Molecular Light Absorption of Brown Carbon

description: Data for article <em>Organic Matrix Effect on the Molecular Light Absorption of Brown Carbon</em>, including the raw intensity extracted from mass spectrum data, time-serial and categorial for organic molecules, and calculated organic matrix effect for light absorption.
DOI: 10.5281/zenodo.8220442
title: TIE Research Database

description: The future of our urban mobility systems depends on numerous framework conditions, including technical, political, regulatory, social, cultural, and economic parameters. These conditions are city-specific and serve as the building blocks of what we define here as the Transportation Innovation Ecosystem (TIE) of a city. However, many cities do not have a thorough understanding of their TIEs. This limited understanding reduces the ability of cities to define, adopt, and implement policies that aim at promoting innovative solution in light of the mobility challengers they face. For this purpose a methodology was developed that made it possible to map the ecosystems of innovation in urban transportation, as well as the strengths and weaknesses of the systems. The study funded by the EIT Urban Mobility, a consortium established by the European Institute of Innovation and Technology (EIT), of the EU. The methodology developed in the project was applied to three cities: Munich, Budapest and Prague. The project was carried out in collaboration with local research teams and senior local government officials in the three cities. Mapping the ecosystems of transportation innovation in the three cities included collecting data through local experts. The macro analysis presented in the database details a list of anchors and processes that characterize the ecosystem of innovative transportation in the three inspected cities.
DOI: 10.5281/zenodo.8220441
title: TIE Research Database

description: The future of our urban mobility systems depends on numerous framework conditions, including technical, political, regulatory, social, cultural, and economic parameters. These conditions are city-specific and serve as the building blocks of what we define here as the Transportation Innovation Ecosystem (TIE) of a city. However, many cities do not have a thorough understanding of their TIEs. This limited understanding reduces the ability of cities to define, adopt, and implement policies that aim at promoting innovative solution in light of the mobility challengers they face. For this purpose a methodology was developed that made it possible to map the ecosystems of innovation in urban transportation, as well as the strengths and weaknesses of the systems. The study funded by the EIT Urban Mobility, a consortium established by the European Institute of Innovation and Technology (EIT), of the EU. The methodology developed in the project was applied to three cities: Munich, Budapest and Prague. The project was carried out in collaboration with local research teams and senior local government officials in the three cities. Mapping the ecosystems of transportation innovation in the three cities included collecting data through local experts. The macro analysis presented in the database details a list of anchors and processes that characterize the ecosystem of innovative transportation in the three inspected cities.
DOI: 10.5281/zenodo.3948088
title: Mean sea level fields used within the GTSMip simulations

description: <em>TotalSeaLevel_MapsSROCC_rcp85_Perc50_zero1986to2005dflow_extrap.nc </em>provides mean sea level fields using as reference period. Sea level fields are computed from the sum of different contributors, including dynamic changes, thermal expansion, changes in gravitational fields, and contribution from glaciers and ice sheets. The different contributions are computed and combined using the probabilistic model described in Le Bars (2018). For the period 1950-2016, we use products based on observations for the Antarctic and Greenland ice sheets (Mouginot et al., 2019; Rignot et al., 2019), the glaciers (Marzeion et al., 2015), thermal expansion between 0 and 2000 m depth (Levitus et al., 2012), and climate-driven water storage (Humphrey & Gudmundsson, 2019). The ice sheets are assumed to be in equilibrium before 1979 for Antarctica and 1972 for Greenland because no data are available before these dates. For the period 2016-2050 we use sea-level rise projections based on the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC) for the RCP8.5 scenario (Church et al., 2013), very similar to the SSP585 scenario used by the models as above. The redistribution of water in the ocean due to wind changes and local steric effects is taken from the CMIP5 models (i.e. ‘zos’ field for the entire period). The fingerprints for the ice sheets, glaciers and land water storage are from the AR5 assessment, and include the gravitational, rotational and Earth elastic response. For the dynamics of the Antarctic contribution we use the re-evaluation presented in the IPCC’s Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC) (Oppenheimer et al., 2019). Additionally, we add the glacial isostatic adjustment from the ICE-6G model (Peltier et al., 2015) but do not consider other processes of vertical land motion, such as subsidence or tectonics. The uncertainty in mean sea level is removed by selecting the median of the sea level observations and projections distributions. Note that at the time the GTSM simulation were carried out the SLR projections based on CMIP6 were not yet available. To serve as input to GTSM, the files are converted from a water level to a pressure. <em>ERAInterim_average_msl_neg_19491215_19510101.nc </em>provides a vertical reference based on the mean sea-level pressure field (MSLP) over 1986–2005 as calculated with GTSMv3.0 forced by ERA-Interim. This corrections is used to make the definition of MSL in GTSM more consistent with the vertical reference used in the SLR field .
description: {"references": ["Muis S, Irazoqui Apecechea M, Dullaart J, de Lima Rego J, Madsen KS, Su J, Yan K and Verlaan M (2020) A High-Resolution Global Dataset of Extreme Sea Levels, Tides, and Storm Surges, Including Future Projections. Front. Mar. Sci. 7:263. doi: 10.3389/fmars.2020.00263"]}
DOI: 10.5281/zenodo.3948087
title: Mean sea level fields used within the GTSMip simulations

description: <em>TotalSeaLevel_MapsSROCC_rcp85_Perc50_zero1986to2005dflow_extrap.nc </em>provides mean sea level fields using as reference period. Sea level fields are computed from the sum of different contributors, including dynamic changes, thermal expansion, changes in gravitational fields, and contribution from glaciers and ice sheets. The different contributions are computed and combined using the probabilistic model described in Le Bars (2018). For the period 1950-2016, we use products based on observations for the Antarctic and Greenland ice sheets (Mouginot et al., 2019; Rignot et al., 2019), the glaciers (Marzeion et al., 2015), thermal expansion between 0 and 2000 m depth (Levitus et al., 2012), and climate-driven water storage (Humphrey & Gudmundsson, 2019). The ice sheets are assumed to be in equilibrium before 1979 for Antarctica and 1972 for Greenland because no data are available before these dates. For the period 2016-2050 we use sea-level rise projections based on the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC) for the RCP8.5 scenario (Church et al., 2013), very similar to the SSP585 scenario used by the models as above. The redistribution of water in the ocean due to wind changes and local steric effects is taken from the CMIP5 models (i.e. ‘zos’ field for the entire period). The fingerprints for the ice sheets, glaciers and land water storage are from the AR5 assessment, and include the gravitational, rotational and Earth elastic response. For the dynamics of the Antarctic contribution we use the re-evaluation presented in the IPCC’s Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC) (Oppenheimer et al., 2019). Additionally, we add the glacial isostatic adjustment from the ICE-6G model (Peltier et al., 2015) but do not consider other processes of vertical land motion, such as subsidence or tectonics. The uncertainty in mean sea level is removed by selecting the median of the sea level observations and projections distributions. Note that at the time the GTSM simulation were carried out the SLR projections based on CMIP6 were not yet available. To serve as input to GTSM, the files are converted from a water level to a pressure. <em>ERAInterim_average_msl_neg_19491215_19510101.nc </em>provides a vertical reference based on the mean sea-level pressure field (MSLP) over 1986–2005 as calculated with GTSMv3.0 forced by ERA-Interim. This corrections is used to make the definition of MSL in GTSM more consistent with the vertical reference used in the SLR field .
description: {"references": ["Muis S, Irazoqui Apecechea M, Dullaart J, de Lima Rego J, Madsen KS, Su J, Yan K and Verlaan M (2020) A High-Resolution Global Dataset of Extreme Sea Levels, Tides, and Storm Surges, Including Future Projections. Front. Mar. Sci. 7:263. doi: 10.3389/fmars.2020.00263"]}
DOI: 10.6084/m9.figshare.23866962.v6
title: Untitled Item

description: Regridding Global CMIP6 GCMs dataset(0.5 degree)Historical dataset: 7 CMIP6 GCMs (1850-2014, ACCESS-CM2 to GFDL-ESM4)Future dataset: 7 CMIP6 GCMs and 4 SSP scenarios (2015-2100)Composition of dataset: Average temperature, Maximum temperature, Minimum temperature, Windspeed, Relative humidity, Solar radiationIf you are using our codes and dataset, please use the following references:Song, Y.H., Chung, E.S., Shahid, S., Kim, Y.J., Kim, D.K. 2023. Development of global monthly dataset of CMIP6 climate variables for estimating evapotranspiration. Scientific Data.<br>
DOI: 10.6084/m9.figshare.23653110.v1
title: Higher vegetation sensitivity to meteorological drought in autumn than spring across European biomes

description: The Spearman correlation between a vegeataion indicator and SPEI at each half-month step (24 time series for a pixel). This is done for seven indicator (NDVI, NIRv, PPI, LAI, FPAR, SIF, and FluxSat GPP) and 24 time scales (1 to 21 months). The multiple linear regression is done inthe same data combination, including SM, VPD, Rn, and aCO2. The above is at 0.1 degree resolution, and the whole study area is shown in GOTIFF land cover map at 0.05 degree resolution.
DOI: 10.5281/zenodo.8220849
title: Parameters for the solvated KcsA-K+-LAB-TEA, when both LAB-TEA and K+-ion placed inside the channel cavity

description: Parameters for the solvated KcsA-K<sup>+</sup>-LAB-TEA. These KcsA-K<sup>+</sup>-LAB-TEA systems were solvated by truncated octahedron TIP3PBOX, using the leap program in AMBER20. Both LAB-TEA and K<sup>+</sup>-ion are placed inside the channel cavity.
DOI: 10.5281/zenodo.8220848
title: Parameters for the solvated KcsA-K+-LAB-TEA, when both LAB-TEA and K+-ion placed inside the channel cavity

description: Parameters for the solvated KcsA-K<sup>+</sup>-LAB-TEA. These KcsA-K<sup>+</sup>-LAB-TEA systems were solvated by truncated octahedron TIP3PBOX, using the leap program in AMBER20. Both LAB-TEA and K<sup>+</sup>-ion are placed inside the channel cavity.
DOI: 10.5281/zenodo.8220840
title: Parameters for the solvated KcsA-LAB-TEA, when the LAB-TEA is placed inside the channel cavity without K+-ion

description: Parameters for the solvated KcsA-LAB-TEA. These KcsA-LAB-TEA systems were solvated by truncated octahedron TIP3PBOX, using the leap program in AMBER20. The LAB-TEA is placed inside the channel cavity. The simulation is performed without K<sup>+</sup>-ion.
DOI: 10.5281/zenodo.8220839
title: Parameters for the solvated KcsA-LAB-TEA, when the LAB-TEA is placed inside the channel cavity without K+-ion

description: Parameters for the solvated KcsA-LAB-TEA. These KcsA-LAB-TEA systems were solvated by truncated octahedron TIP3PBOX, using the leap program in AMBER20. The LAB-TEA is placed inside the channel cavity. The simulation is performed without K<sup>+</sup>-ion.
DOI: 10.5281/zenodo.8220825
title: Parameters for the solvated KcsA-K+-LAB-TEA, when LAB-TEA place in cytoplasmic region and K+-ion inside the cavity

description: Parameters for the solvated KcsA-K<sup>+</sup>-LAB-TEA. These KcsA-K<sup>+</sup>-LAB-TEA systems were solvated by truncated octahedron TIP3PBOX, using the leap program in AMBER20. The LAB-TEA is placed in the cytoplasmic region and K<sup>+</sup>-ion inside the channel cavity.
DOI: 10.5281/zenodo.8220824
title: Parameters for the solvated KcsA-K+-LAB-TEA, when LAB-TEA place in cytoplasmic region and K+-ion inside the cavity

description: Parameters for the solvated KcsA-K<sup>+</sup>-LAB-TEA. These KcsA-K<sup>+</sup>-LAB-TEA systems were solvated by truncated octahedron TIP3PBOX, using the leap program in AMBER20. The LAB-TEA is placed in the cytoplasmic region and K<sup>+</sup>-ion inside the channel cavity.
DOI: 10.5281/zenodo.8220817
title: Optimization output files

description: Output file containing input data and the results of optimization of LAB-TEA at the B3LYP/6-31+G(d) level of theory using Gaussian 09 program package. The resulting optimized structure is then used as the starting geometry for generating parameters for the LAB-TEA molecule.
DOI: 10.5281/zenodo.8220816
title: Optimization output files

description: Output file containing input data and the results of optimization of LAB-TEA at the B3LYP/6-31+G(d) level of theory using Gaussian 09 program package. The resulting optimized structure is then used as the starting geometry for generating parameters for the LAB-TEA molecule.
DOI: 10.5281/zenodo.8220807
title: frcmod files for Z- and E-LAB-TEA

description: The undesirable torsional changes of the LAB moiety are restricted by modifying the torsional parameters for the same as that of standard azobenzene, as reported by Böckmann et al. (Böckmann, M.; Peter, C.; Site, L. D.; Doltsinis, N. L.; Kremer, K.; Marx, D. Atomistic Force Field for Azobenzene Compounds Adapted for QM/MM Simulations with Applications to Liquids and Liquid Crystals. <em>J. Chem. Theory Comput.</em> <strong>2007</strong>, <em>3</em>, 1789−1802. DOI: 10.1021/ct7000733.)
DOI: 10.5281/zenodo.8220808
title: frcmod files for Z- and E-LAB-TEA

description: The undesirable torsional changes of the LAB moiety are restricted by modifying the torsional parameters for the same as that of standard azobenzene, as reported by Böckmann et al. (Böckmann, M.; Peter, C.; Site, L. D.; Doltsinis, N. L.; Kremer, K.; Marx, D. Atomistic Force Field for Azobenzene Compounds Adapted for QM/MM Simulations with Applications to Liquids and Liquid Crystals. <em>J. Chem. Theory Comput.</em> <strong>2007</strong>, <em>3</em>, 1789−1802. DOI: 10.1021/ct7000733.)
DOI: 10.34740/kaggle/ds/3598467
title: Borderlands
DOI: 10.5281/zenodo.8220792
title: Input files for SAPT0 calculation in PSI4

description: Input files for the Lower order symmetry adapted-perturbation theory calculation (SAPT0) calculation on LAB-TEA - Amino acid systems. The calculation is performed in the quantum chemistry package PSI4.
DOI: 10.5281/zenodo.8220791
title: Input files for SAPT0 calculation in PSI4

description: Input files for the Lower order symmetry adapted-perturbation theory calculation (SAPT0) calculation on LAB-TEA - Amino acid systems. The calculation is performed in the quantum chemistry package PSI4.
DOI: 10.5281/zenodo.8220766
title: Input files for the simulation of KcsA-K+-LAB-TEA, when both LAB-TEA and K+-ion are placed inside the channel cavity.

description: Input files for the simulation of KcsA-K<sup>+</sup>-LAB-TEA, when both LAB-TEA and K+-ion are placed inside the channel cavity. The inputs include,<br> 1. Input files for minimization (*min.in)<br> 2. Input files for heating (*h1.in, *h2.in, and *h3.in)<br> 3. Input files for equilibration (*eq*.in) and production runs (*prd*.in)<br> 4. Input files for the first trajectories start with file name, 1-lab-tea-k<sup>+</sup>*<br> 5. Input files for the remaining different trajectories start with the file name, lab-tea-k<sup>+</sup>*
DOI: 10.5281/zenodo.8220765
title: Input files for the simulation of KcsA-K+-LAB-TEA, when both LAB-TEA and K+-ion are placed inside the channel cavity.

description: Input files for the simulation of KcsA-K<sup>+</sup>-LAB-TEA, when both LAB-TEA and K+-ion are placed inside the channel cavity. The inputs include,<br> 1. Input files for minimization (*min.in)<br> 2. Input files for heating (*h1.in, *h2.in, and *h3.in)<br> 3. Input files for equilibration (*eq*.in) and production runs (*prd*.in)<br> 4. Input files for the first trajectories start with file name, 1-lab-tea-k<sup>+</sup>*<br> 5. Input files for the remaining different trajectories start with the file name, lab-tea-k<sup>+</sup>*
DOI: 10.5281/zenodo.8220746
title: Input files for the simulation of KcsA-LAB-TEA, when LAB-TEA is placed in the channel cavity with no K+-ion

description: Input files for the simulation of KcsA-LAB-TEA, when LAB-TEA is placed in the channel cavity. The simulation is performed without K<sup>+</sup>-ion. The inputs include,<br> 1. Input files for minimization (*min.in)<br> 2. Input files for heating (*h1.in, *h2.in, and *h3.in)<br> 3. Input files for equilibration (*eq*.in) and production runs (*prd*.in)<br> 4. Input files for the first trajectories start with file name, 1-lab-tea*<br> 5. Input files for the remaining different trajectories start with file name, lab-tea*
DOI: 10.5281/zenodo.8220747
title: Input files for the simulation of KcsA-LAB-TEA, when LAB-TEA is placed in the channel cavity with no K+-ion

description: Input files for the simulation of KcsA-LAB-TEA, when LAB-TEA is placed in the channel cavity. The simulation is performed without K<sup>+</sup>-ion. The inputs include,<br> 1. Input files for minimization (*min.in)<br> 2. Input files for heating (*h1.in, *h2.in, and *h3.in)<br> 3. Input files for equilibration (*eq*.in) and production runs (*prd*.in)<br> 4. Input files for the first trajectories start with file name, 1-lab-tea*<br> 5. Input files for the remaining different trajectories start with file name, lab-tea*
DOI: 10.5281/zenodo.8220726
title: Input files for the simulation of KcsA-K+-LAB-TEA, when LAB-TEA is placed in the cytoplasmic region with K+-ion in the channel cavity

description: Input files for the simulation of KcsA-K<sup>+</sup>-LAB-TEA, when LAB-TEA is placed in the cytoplasmic region with K<sup>+</sup>-ion in the channel cavity. The inputs include,<br> 1. Input files for minimization (*min.in)<br> 2. Input files for heating (*h1.in, *h2.in, and *h3.in)<br> 3. Input files for equilibration (*eq*.in) and production runs (*prd*.in)<br> 4. Input files for the first trajectories start with file name, 1-lab-tea-k-entra*<br> 5. Input files for the remaining different trajectories start with file name, lab-tea-k-entra*<br> 6. Input files for the trajectories with restraint weight on K<sup>+</sup>-ion starting with file name, lab-tea-entra-k-restraint*