cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.26050/wdcc/ar6.c6daccce
title: IPCC DDC: CCCma CanESM5 model output prepared for CMIP6 DAMIP

description: Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.DAMIP.CCCma.CanESM5' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The CanESM5 climate model, released in 2019, includes the following components: aerosol: interactive, atmos: CanAM5 (T63L49 native atmosphere, T63 Linear Gaussian Grid; 128 x 64 longitude/latitude; 49 levels; top level 1 hPa), atmosChem: specified oxidants for aerosols, land: CLASS3.6/CTEM1.2, landIce: specified ice sheets, ocean: NEMO3.4.1 (ORCA1 tripolar grid, 1 deg with refinement to 1/3 deg within 20 degrees of the equator; 361 x 290 longitude/latitude; 45 vertical levels; top grid cell 0-6.19 m), ocnBgchem: Canadian Model of Ocean Carbon (CMOC); NPZD ecosystem with OMIP prescribed carbonate chemistry, seaIce: LIM2. The model was run by the Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada, Victoria, BC V8P 5C2, Canada (CCCma) in native nominal resolutions: aerosol: 500 km, atmos: 500 km, atmosChem: 500 km, land: 500 km, landIce: 500 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.
DOI: 10.26050/wdcc/ar6.c6dacccesa
title: IPCC DDC: CCCma CanESM5 model output prepared for CMIP6 DAMIP ssp245-aer

description: Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.DAMIP.CCCma.CanESM5.ssp245-aer' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The CanESM5 climate model, released in 2019, includes the following components: aerosol: interactive, atmos: CanAM5 (T63L49 native atmosphere, T63 Linear Gaussian Grid; 128 x 64 longitude/latitude; 49 levels; top level 1 hPa), atmosChem: specified oxidants for aerosols, land: CLASS3.6/CTEM1.2, landIce: specified ice sheets, ocean: NEMO3.4.1 (ORCA1 tripolar grid, 1 deg with refinement to 1/3 deg within 20 degrees of the equator; 361 x 290 longitude/latitude; 45 vertical levels; top grid cell 0-6.19 m), ocnBgchem: Canadian Model of Ocean Carbon (CMOC); NPZD ecosystem with OMIP prescribed carbonate chemistry, seaIce: LIM2. The model was run by the Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada, Victoria, BC V8P 5C2, Canada (CCCma) in native nominal resolutions: aerosol: 500 km, atmos: 500 km, atmosChem: 500 km, land: 500 km, landIce: 500 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.
DOI: 10.26050/wdcc/ar6.c6daccces2co
title: IPCC DDC: CCCma CanESM5 model output prepared for CMIP6 DAMIP ssp245-covid

description: Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.DAMIP.CCCma.CanESM5.ssp245-covid' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The CanESM5 climate model, released in 2019, includes the following components: aerosol: interactive, atmos: CanAM5 (T63L49 native atmosphere, T63 Linear Gaussian Grid; 128 x 64 longitude/latitude; 49 levels; top level 1 hPa), atmosChem: specified oxidants for aerosols, land: CLASS3.6/CTEM1.2, landIce: specified ice sheets, ocean: NEMO3.4.1 (ORCA1 tripolar grid, 1 deg with refinement to 1/3 deg within 20 degrees of the equator; 361 x 290 longitude/latitude; 45 vertical levels; top grid cell 0-6.19 m), ocnBgchem: Canadian Model of Ocean Carbon (CMOC); NPZD ecosystem with OMIP prescribed carbonate chemistry, seaIce: LIM2. The model was run by the Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada, Victoria, BC V8P 5C2, Canada (CCCma) in native nominal resolutions: aerosol: 500 km, atmos: 500 km, atmosChem: 500 km, land: 500 km, landIce: 500 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.
DOI: 10.25345/c5f47h47z
title: MassIVE MSV000092658 - Dehydrocholesterol is an endogenous suppressor of ferroptosis
title: PRM analysis of lipid extracts derived from HT1080 cells for relative quantification of oxidized glycerophospholipids.
DOI: 10.34740/kaggle/dsv/6306904
title: 2021 World Population (updated daily)
DOI: 10.6084/m9.figshare.23580259
title: A Simple Correction for Misspecification in Trend-Cycle Decompositions with an Application to Estimating <i>r</i>*

description: We propose a simple correction for misspecification in trend-cycle decompositions when the stochastic trend is assumed to be a random walk process but the estimated trend displays some serial correlation in first differences. Possible sources of misspecification that would otherwise be hard to detect and correct for include a small amount of measurement error, omitted variables, or minor approximation errors in model dynamics when estimating trend. Our proposed correction is conducted via application of a univariate Beveridge-Nelson decomposition to the preliminary estimated trend and we show with Monte Carlo analysis that our approach can work as well as if the original model used to estimate trend were correctly specified. We demonstrate the empirical relevance of the correction in an application to estimating <i>r</i>* as the trend of a risk-free short-term real interest rate. We find that our corrected estimate of <i>r</i>* is considerably smoother than the preliminary estimate from a multivariate Beveridge-Nelson decomposition based on a vector error correction model, consistent with the presence of at least a small amount of measurement error in some of the variables included in the multivariate model.
DOI: 10.6084/m9.figshare.23580259.v3
title: A Simple Correction for Misspecification in Trend-Cycle Decompositions with an Application to Estimating <i>r</i>*

description: We propose a simple correction for misspecification in trend-cycle decompositions when the stochastic trend is assumed to be a random walk process but the estimated trend displays some serial correlation in first differences. Possible sources of misspecification that would otherwise be hard to detect and correct for include a small amount of measurement error, omitted variables, or minor approximation errors in model dynamics when estimating trend. Our proposed correction is conducted via application of a univariate Beveridge-Nelson decomposition to the preliminary estimated trend and we show with Monte Carlo analysis that our approach can work as well as if the original model used to estimate trend were correctly specified. We demonstrate the empirical relevance of the correction in an application to estimating <i>r</i>* as the trend of a risk-free short-term real interest rate. We find that our corrected estimate of <i>r</i>* is considerably smoother than the preliminary estimate from a multivariate Beveridge-Nelson decomposition based on a vector error correction model, consistent with the presence of at least a small amount of measurement error in some of the variables included in the multivariate model.
DOI: 10.6084/m9.figshare.23580259.v2
title: A Simple Correction for Misspecification in Trend-Cycle Decompositions with an Application to Estimating <i>r</i>*

description: We propose a simple correction for misspecification in trend-cycle decompositions when the stochastic trend is assumed to be a random walk process but the estimated trend displays some serial correlation in first differences. Possible sources of misspecification that would otherwise be hard to detect and correct for include a small amount of measurement error, omitted variables, or minor approximation errors in model dynamics when estimating trend. Our proposed correction is conducted via application of a univariate Beveridge-Nelson decomposition to the preliminary estimated trend and we show with Monte Carlo analysis that our approach can work as well as if the original model used to estimate trend were correctly specified. We demonstrate the empirical relevance of the correction in an application to estimating <i>r</i>* as the trend of a risk-free short-term real interest rate. We find that our corrected estimate of <i>r</i>* is considerably smoother than the preliminary estimate from a multivariate Beveridge-Nelson decomposition based on a vector error correction model, consistent with the presence of at least a small amount of measurement error in some of the variables included in the multivariate model.
DOI: 10.34740/kaggle/dsv/6306910
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.18419/darus-3252/1
title: vel_sweep_s_200_dir_pos.mat

description: :unav
DOI: 10.18419/darus-3252/2
title: vel_sweep_s_550_dir_pos.mat

description: :unav
DOI: 10.18419/darus-3252/3
title: vel_sweep_s_150_dir_neg.mat

description: :unav
DOI: 10.18419/darus-3252/4
title: ImpulseResponse_Hammer.mat

description: :unav
DOI: 10.18419/darus-3252/5
title: vel_sweep_s_400_dir_pos.mat

description: :unav
DOI: 10.18419/darus-3252/6
title: vel_sweep_s_600_dir_pos.mat

description: :unav
DOI: 10.18419/darus-3252/7
title: vel_sweep_s_550_dir_neg.mat

description: :unav
DOI: 10.18419/darus-3252/8
title: control_parameters.txt

description: :unav
DOI: 10.18419/darus-3252/9
title: vel_sweep_s_0_dir_pos.mat

description: :unav
DOI: 10.18419/darus-3252/10
title: vel_sweep_s_50_dir_pos.mat

description: :unav
DOI: 10.18419/darus-3252/11
title: vel_sweep_s_200_dir_neg.mat

description: :unav
DOI: 10.18419/darus-3252/12
title: vel_sweep_s_700_dir_neg.mat

description: :unav
DOI: 10.18419/darus-3252/13
title: vel_sweep_s_250_dir_pos.mat

description: :unav
DOI: 10.18419/darus-3252/14
title: vel_sweep_s_250_dir_neg.mat

description: :unav
DOI: 10.18419/darus-3252/15
title: vel_sweep_s_100_dir_neg.mat

description: :unav
DOI: 10.18419/darus-3252/16
title: vel_sweep_s_450_dir_pos.mat

description: :unav
DOI: 10.18419/darus-3252/17
title: vel_sweep_s_600_dir_neg.mat

description: :unav
DOI: 10.18419/darus-3252/18
title: vel_sweep_s_100_dir_pos.mat

description: :unav
DOI: 10.18419/darus-3252/19
title: vel_sweep_s_400_dir_neg.mat

description: :unav
DOI: 10.18419/darus-3252/20
title: vel_sweep_s_350_dir_pos.mat

description: :unav
DOI: 10.18419/darus-3252/21
title: vel_sweep_s_150_dir_pos.mat

description: :unav
DOI: 10.18419/darus-3252/22
title: vel_sweep_s_300_dir_pos.mat

description: :unav
DOI: 10.18419/darus-3252/23
title: vel_sweep_s_500_dir_neg.mat

description: :unav
DOI: 10.18419/darus-3252/24
title: vel_sweep_s_450_dir_neg.mat

description: :unav
DOI: 10.18419/darus-3252/25
title: vel_sweep_s_300_dir_neg.mat

description: :unav
DOI: 10.18419/darus-3252/26
title: vel_sweep_s_350_dir_neg.mat

description: :unav
DOI: 10.18419/darus-3252/27
title: vel_sweep_s_500_dir_pos.mat

description: :unav
DOI: 10.18419/darus-3252/28
title: vel_sweep_s_650_dir_neg.mat

description: :unav
DOI: 10.18419/darus-3252/29
title: 7PP_PPC_v700.mat

description: :unav
DOI: 10.18419/darus-3252/30
title: parameters.txt

description: :unav
DOI: 10.18419/darus-3252/31
title: 7PP_PPC_v300.mat

description: :unav
DOI: 10.18419/darus-3252/32
title: dist_steps_PPC.mat

description: :unav
DOI: 10.18419/darus-3252/33
title: parameters.txt

description: :unav
DOI: 10.18419/darus-3252/34
title: dist_milling_PPC.mat

description: :unav
DOI: 10.18419/darus-3252/35
title: dist_milling_PPI.mat

description: :unav
DOI: 10.18419/darus-3252/36
title: parameters.txt

description: :unav
DOI: 10.18419/darus-3252/37
title: control_parameters.txt

description: :unav
DOI: 10.18419/darus-3252/38
title: PRBS_PPC_s_700.mat

description: :unav
DOI: 10.18419/darus-3252/39
title: PRBS_RSMC_s_350.mat

description: :unav
DOI: 10.18419/darus-3252/40
title: PRBS_PPC_s_350.mat

description: :unav
DOI: 10.18419/darus-3252/41
title: PRBS_PPI_s_350.mat

description: :unav
DOI: 10.18419/darus-3252/42
title: parameters.txt

description: :unav
DOI: 10.18419/darus-3252/43
title: PRBS_PPI_s_700.mat

description: :unav
DOI: 10.18419/darus-3252/44
title: PRBS_RSMC_s_0.mat

description: :unav
DOI: 10.18419/darus-3252/45
title: PRBS_PPC_s_0.mat

description: :unav
DOI: 10.5517/ccdc.csd.cc2g6ggm
title: CCDC 2270330: Experimental Crystal Structure Determination

description: Related Article: Patricia Rose H. Ayson, Seth N. Brown|2023|Chem.Commun.|59|9618|doi:10.1039/D3CC02943C
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2g6ghn
title: CCDC 2270331: Experimental Crystal Structure Determination

description: Related Article: Patricia Rose H. Ayson, Seth N. Brown|2023|Chem.Commun.|59|9618|doi:10.1039/D3CC02943C
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2g6gjp
title: CCDC 2270332: Experimental Crystal Structure Determination

description: Related Article: Patricia Rose H. Ayson, Seth N. Brown|2023|Chem.Commun.|59|9618|doi:10.1039/D3CC02943C
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2g6gkq
title: CCDC 2270333: Experimental Crystal Structure Determination

description: Related Article: Patricia Rose H. Ayson, Seth N. Brown|2023|Chem.Commun.|59|9618|doi:10.1039/D3CC02943C
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2g6glr
title: CCDC 2270334: Experimental Crystal Structure Determination

description: Related Article: Patricia Rose H. Ayson, Seth N. Brown|2023|Chem.Commun.|59|9618|doi:10.1039/D3CC02943C
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fyzp2
title: CCDC 2263145: Experimental Crystal Structure Determination

description: Related Article: Pawan Kumar, Pankaj Kalita, María A. Palacios, Vierandra Kumar, Joydev Acharya, Enrique Colacio, Vadapalli Chandrasekhar|2023|Dalton Trans.|52|10594|doi:10.1039/D3DT01489D
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.18419/darus-3252/46
title: PRBS_PPI_s_0.mat

description: :unav
DOI: 10.18419/darus-3252/47
title: PRBS_RSMC_s_700.mat

description: :unav
DOI: 10.18419/darus-3252/48
title: parameters.txt

description: :unav
DOI: 10.18419/darus-3252/49
title: dist_PRBS_PPC_s_0.mat

description: :unav
DOI: 10.18419/darus-3252/50
title: dist_PRBS_RSMC_s_0.mat

description: :unav
DOI: 10.18419/darus-3252/51
title: dist_PRBS_RSMC_s_350.mat

description: :unav
DOI: 10.18419/darus-3252/52
title: dist_PRBS_PPI_s_350.mat

description: :unav
DOI: 10.18419/darus-3252/53
title: dist_PRBS_RSMC_s_700.mat

description: :unav
DOI: 10.18419/darus-3252/54
title: dist_PRBS_PPI_s_0.mat

description: :unav
DOI: 10.18419/darus-3252/55
title: dist_PRBS_PPI_s_700.mat

description: :unav
DOI: 10.18419/darus-3252/56
title: dist_PRBS_PPC_s_350.mat

description: :unav
DOI: 10.18419/darus-3252/57
title: dist_PRBS_PPC_s_700.mat

description: :unav
DOI: 10.18419/darus-3252/58
title: parameters.txt

description: :unav
DOI: 10.18419/darus-3252/59
title: parameters.txt

description: :unav
DOI: 10.18419/darus-3252/60
title: dist_steps_PPI.mat

description: :unav
DOI: 10.18419/darus-3252/61
title: 7PP_PPI_v300.mat

description: :unav
DOI: 10.18419/darus-3252/62
title: 7PP_PPI_v700.mat

description: :unav
DOI: 10.18419/darus-3252/63
title: 7PP_RSMC_v700.mat

description: :unav
DOI: 10.18419/darus-3252/64
title: 7PP_RSMC_v300.mat

description: :unav
DOI: 10.18419/darus-3252/65
title: dist_steps_RSMC.mat

description: :unav
DOI: 10.18419/darus-3252/66
title: dist_milling_RSMC.mat

description: :unav
DOI: 10.18419/darus-3252
title: Replication Data for: Robust µ parameterization with low tuning complexity of cascaded control for feed drives

description: Experimental dataset for model identification and validation of position controllers on a ball screw test bench. This dataset belongs to the Open Access publication "Robust µ parameterization with low tuning complexity of cascaded control for feed drives" (doi: 10.1016/j.conengprac.2023.105607). A detailed description of the setup can be found in the publication. Velocity controller: PI velocity controller with Kp = 175, Ki = 11. Position controllers: P position controller with Kp = 53. Nominal sliding mode controller (nSMC or PPC) based on norminal PT2I model with lambda = [-250 -250 -250]. Robustly-tuned sliding mode controller (rSMC or RSMC) tuned via mu-synthesis with k = 610.4625, p = [51883 188.5168 1]. Feedforward: Please refer to control_parameters.txt in this dataset for more detailed information. The dataset contains the following folders (Please refer to corresponding parameters.txt for more details): (Notice: For identification, velocity control is active, position control is inactive.) Identification/ImpulseResponse measurement for identification with impulse hammer to estimate the disturbance response of PI velocity control loop (with constant velocity reference vRef = 8mm/s) Identification/VelocitySweeps measurements for identification with velocity sweeps to estimate the PT2I approximation of PI velocity control loop (with constant velocity offset + sweeps as reference) (Notice: For validation, velocity control and position control are both active.) Validation/FrequencyDomain/disturbance_force_PRBS measurements for validation with PRBS disturbance forces generated by linear direct drive (velocity offset 8mm/s, PRBS disturbance forces) Validation/FrequencyDomain/tracking_pos_PRBS measurements for validation with PRBS position reference (velocity offset 8mm/s + PRBS position reference) Validation/TimeDomain/disturbance_milling measurements for validation with milling forces (velocity offset 8mm/s) Validation/TimeDomain/disturbance_steps measurements for validation with steps-shaped disturbance forces (velocity offset 8mm/s) Validation/TimeDomain/tracking_7PhaseProfile measurements for validation with 7 phase profile as reference (maximum velocity: 300mm/s and 700mm/s) Notice: To analyze the disturbance response (while disturbance forces acting directly on the working table), we used an impulse hammer to generate disturbance forces for identification, and the linear direct drive for validation.
DOI: 10.18419/darus-3680/1
title: run_in_parallel.py

description: :unav
DOI: 10.18419/darus-3680/2
title: parameters_attach.py

description: :unav