cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.26050/wdcc/ar6.c6spmohgm
title: IPCC DDC: MOHC HadGEM3-GC31-MM model output prepared for CMIP6 ScenarioMIP

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.ScenarioMIP.MOHC.HadGEM3-GC31-MM' 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 HadGEM3-GC3.1-N216ORCA025 climate model, released in 2016, includes the following components: aerosol: UKCA-GLOMAP-mode, atmos: MetUM-HadGEM3-GA7.1 (N216; 432 x 324 longitude/latitude; 85 levels; top level 85 km), land: JULES-HadGEM3-GL7.1, ocean: NEMO-HadGEM3-GO6.0 (eORCA025 tripolar primarily 0.25 deg; 1440 x 1205 longitude/latitude; 75 levels; top grid cell 0-1 m), seaIce: CICE-HadGEM3-GSI8 (eORCA025 tripolar primarily 0.25 deg; 1440 x 1205 longitude/latitude). The model was run by the Met Office Hadley Centre, Fitzroy Road, Exeter, Devon, EX1 3PB, UK (MOHC) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, ocean: 25 km, seaIce: 25 km.
DOI: 10.26050/wdcc/ar6.c6spmou0so
title: IPCC DDC: MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP ssp534-over

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.ScenarioMIP.MOHC.UKESM1-0-LL.ssp534-over' 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 UKESM1.0-N96ORCA1 climate model, released in 2018, includes the following components: aerosol: UKCA-GLOMAP-mode, atmos: MetUM-HadGEM3-GA7.1 (N96; 192 x 144 longitude/latitude; 85 levels; top level 85 km), atmosChem: UKCA-StratTrop, land: JULES-ES-1.0, ocean: NEMO-HadGEM3-GO6.0 (eORCA1 tripolar primarily 1 deg with meridional refinement down to 1/3 degree in the tropics; 360 x 330 longitude/latitude; 75 levels; top grid cell 0-1 m), ocnBgchem: MEDUSA2, seaIce: CICE-HadGEM3-GSI8 (eORCA1 tripolar primarily 1 deg; 360 x 330 longitude/latitude). The model was run by the Met Office Hadley Centre, Fitzroy Road, Exeter, Devon, EX1 3PB, UK (MOHC) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.
DOI: 10.26050/wdcc/ar6.c6spmou0s434
title: IPCC DDC: MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP ssp434

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.ScenarioMIP.MOHC.UKESM1-0-LL.ssp434' 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 UKESM1.0-N96ORCA1 climate model, released in 2018, includes the following components: aerosol: UKCA-GLOMAP-mode, atmos: MetUM-HadGEM3-GA7.1 (N96; 192 x 144 longitude/latitude; 85 levels; top level 85 km), atmosChem: UKCA-StratTrop, land: JULES-ES-1.0, ocean: NEMO-HadGEM3-GO6.0 (eORCA1 tripolar primarily 1 deg with meridional refinement down to 1/3 degree in the tropics; 360 x 330 longitude/latitude; 75 levels; top grid cell 0-1 m), ocnBgchem: MEDUSA2, seaIce: CICE-HadGEM3-GSI8 (eORCA1 tripolar primarily 1 deg; 360 x 330 longitude/latitude). The model was run by the Met Office Hadley Centre, Fitzroy Road, Exeter, Devon, EX1 3PB, UK (MOHC) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.
DOI: 10.6084/m9.figshare.23898078
title: Low-grade inflammation

description: The "Database of patients with CHF and CHD" is presented, 80 patients with CHD who underwent CABG during the current hospitalization (2019-2020 inclusion years) are included.The research protocol adhered to the principles of the Declaration of Helsinki and was approved by the Local Ethics Committee (Protocol № 188 of 18.09.2019). Before any procedure, all patients have signed a form of voluntary informed consent.
DOI: 10.6084/m9.figshare.23898078.v1
title: Low-grade inflammation

description: The "Database of patients with CHF and CHD" is presented, 80 patients with CHD who underwent CABG during the current hospitalization (2019-2020 inclusion years) are included.The research protocol adhered to the principles of the Declaration of Helsinki and was approved by the Local Ethics Committee (Protocol № 188 of 18.09.2019). Before any procedure, all patients have signed a form of voluntary informed consent.
DOI: 10.17034/f5242e69-5f69-4267-8652-7cebb26a4cd2
title: Dataset for "Enabling High Throughput Kinetic Experimentation by Using Flow as a Differential Kinetic Technique"

description: Kinetic data is most commonly collected through the generation of time-series data under either batch or flow conditions. Currently, existing methods to generate kinetic data in flow collect integral data (concentration over time) only. Here, we report a method for the rapid and direct collection of differential kinetic data (direct measurement of rate) in flow by performing a series of instantaneous rate measurements on sequential small-scale reactions. This technique decouples the time required to generate a full kinetic profile from the time required for a reaction to reach completion, effectively enabling an approach towards high throughput kinetic experimentation. In addition, comparison of kinetic profiles constructed at different residence times allows the robustness of homogeneously catalysed reactions to be interrogated. This approach makes use of a segmented flow platform which was shown to quantitatively reproduce batch kinetic data. The proline mediated aldol reaction was chosen as a model reaction to perform a high throughput kinetic screen of 216 kinetic profiles in 90 hours, one every 25 minutes or 57 profiles per 24 hours, which would have taken an estimated continuous 3500 hours in batch, a significant increase in experimental throughput matched by an equal reduction in material consumption. File Contents: 1) Python files and jupyter notebooks required for SPKA 2) Excel files (.xlsx and .csv) containing SPKA profiles 3) IR raw data (Mettler Toledo .icir files as well as exported .txt files) 4) Agilent OpenLab HPLC sequence, method, and injector programme files
DOI: 10.18419/darus-3608
title: Replication Data for: Lohrmann, Holm: Optimal motility strategies for self-propelled agents to explore porous media

description: Code, Data and additional Material for "Optimal motility strategies for self-propelled agents to explore porous media". For details, see README.md
DOI: 10.5281/zenodo.8220028
title: Building an Ecological Momentary Assessment Smartphone App for 4- to 10-Year-Old Children: A Pilot Study

description: The file "KIDsRawData_0111.RData" is a R data file, in which the "KIDs_data" dataframe contains EMA survey responses of all participants during the study period. The file "iEMA Post-study Caregiver Survey.xlsx" contains caregivers' responses to post-study survey on app usability and other. The file "Call Log and Reasons for not Participating.xlsx" is a list of call (to caregivers) logs.
description: The data files may be updated.
DOI: 10.5281/zenodo.8220027
title: Building an Ecological Momentary Assessment Smartphone App for 4- to 10-Year-Old Children: A Pilot Study

description: The file "KIDsRawData_0111.RData" is a R data file, in which the "KIDs_data" dataframe contains EMA survey responses of all participants during the study period. The file "iEMA Post-study Caregiver Survey.xlsx" contains caregivers' responses to post-study survey on app usability and other. The file "Call Log and Reasons for not Participating.xlsx" is a list of call (to caregivers) logs.
description: The data files may be updated.
DOI: 10.6084/m9.figshare.23898196
title: Table S3. from The tight attachment achieved by the male discoidal setae is possibly a counter-adaptation to the grease layer on female integument surfaces in green dock beetles

description: Compiled data of pull-off force values in a CSV format used for statistical analyses.
DOI: 10.6084/m9.figshare.23898196.v1
title: Table S3. from The tight attachment achieved by the male discoidal setae is possibly a counter-adaptation to the grease layer on female integument surfaces in green dock beetles

description: Compiled data of pull-off force values in a CSV format used for statistical analyses.
DOI: 10.6084/m9.figshare.23898220
title: Table S4. from The tight attachment achieved by the male discoidal setae is possibly a counter-adaptation to the grease layer on female integument surfaces in green dock beetles

description: Compiled data of pull-off force values in a CSV format used for statistical analyses. Measured values were averaged.
DOI: 10.6084/m9.figshare.23898220.v1
title: Table S4. from The tight attachment achieved by the male discoidal setae is possibly a counter-adaptation to the grease layer on female integument surfaces in green dock beetles

description: Compiled data of pull-off force values in a CSV format used for statistical analyses. Measured values were averaged.
DOI: 10.6084/m9.figshare.23898238
title: Table S2. from The tight attachment achieved by the male discoidal setae is possibly a counter-adaptation to the grease layer on female integument surfaces in green dock beetles

description: Raw data of force values presented in the excel format.
DOI: 10.6084/m9.figshare.23898238.v1
title: Table S2. from The tight attachment achieved by the male discoidal setae is possibly a counter-adaptation to the grease layer on female integument surfaces in green dock beetles

description: Raw data of force values presented in the excel format.
DOI: 10.26050/wdcc/ar6.c6spmxml2
title: IPCC DDC: MPI-M MPIESM1.2-LR model output prepared for CMIP6 ScenarioMIP

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.ScenarioMIP.MPI-M.MPI-ESM1-2-LR' 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 MPI-ESM1.2-LR climate model, released in 2017, includes the following components: aerosol: none, prescribed MACv2-SP, atmos: ECHAM6.3 (spectral T63; 192 x 96 longitude/latitude; 47 levels; top level 0.01 hPa), land: JSBACH3.20, landIce: none/prescribed, ocean: MPIOM1.63 (bipolar GR1.5, approximately 1.5deg; 256 x 220 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the Max Planck Institute for Meteorology, Hamburg 20146, Germany (MPI-M) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, landIce: none, ocean: 250 km, ocnBgchem: 250 km, seaIce: 250 km.
DOI: 10.4121/33df3424-ed9b-49cb-910e-64a6d37aa6b0
title: Data underlying the publication: Packing of inhibitor molecules during area-selective atomic layer deposition studied using random sequential adsorption simulations

description: This dataset includes raw and processed data published in manuscript "Packing of inhibitor molecules during area-selective atomic layer deposition studied using random sequential adsorption simulations". Area-selective atomic layer deposition (ALD) can be achieved by functionalization of the area where no growth is desired with inhibitor molecules. The packing of these inhibitor molecules, in terms of molecule arrangement and surface density, plays a vital role in deactivating the surface by blocking the precursor adsorption. Here, we performed random sequential adsorption (RSA) simulations to investigate the packing of small molecule inhibitors (SMIs) on a surface to predict how effective the SMI blocks precursor adsorption. We demonstrated that RSA simulations provide an insightful and straightforward method for screening SMIs in terms of their potential for area-selective ALD. This Dataset includes all the simulation and experimental data used in the manuscript. In the 'Processed data" folder, the reader can find the Matlab code to run RSA simulations, original figure files and data files. The "Raw data" folder includes all Matlab input and outputs without any editing.
DOI: 10.4121/33df3424-ed9b-49cb-910e-64a6d37aa6b0.v1
title: Data underlying the publication: Packing of inhibitor molecules during area-selective atomic layer deposition studied using random sequential adsorption simulations

description: This dataset includes raw and processed data published in manuscript "Packing of inhibitor molecules during area-selective atomic layer deposition studied using random sequential adsorption simulations". Area-selective atomic layer deposition (ALD) can be achieved by functionalization of the area where no growth is desired with inhibitor molecules. The packing of these inhibitor molecules, in terms of molecule arrangement and surface density, plays a vital role in deactivating the surface by blocking the precursor adsorption. Here, we performed random sequential adsorption (RSA) simulations to investigate the packing of small molecule inhibitors (SMIs) on a surface to predict how effective the SMI blocks precursor adsorption. We demonstrated that RSA simulations provide an insightful and straightforward method for screening SMIs in terms of their potential for area-selective ALD. This Dataset includes all the simulation and experimental data used in the manuscript. In the 'Processed data" folder, the reader can find the Matlab code to run RSA simulations, original figure files and data files. The "Raw data" folder includes all Matlab input and outputs without any editing.
DOI: 10.5281/zenodo.8220024
title: Dataset of Commit Classification via Diff-Code GCN based on System Dependency Graph

description: Commit Classification via Diff-Code GCN based on System Dependency Graph The dataset is based on Lobna Ghadhab et al. [1]. Levin et al.[2]'s dataset, and we extract all commits with pure java codes of two versions. In the dataset, evert commit folder have two sub-folder called before and after, they contains two version of codes. we extracted it by pydriller. The dataset have 1213 commits with two version java codes,and it contains three categories: (1) The first category is Corrective, which involves rectifying errors and faults identified during software usage. (2)The second category is Perfective, which entails enhancing software quality attributes, such as performance, maintainability, and usability. (3) Lastly, is Adaptive, which encompasses adapting the software to new environments (e.g., software or hardware) or introducing new functionalities. The dataset have 450 labels of Corrective. 441 for Perfective the rest for Adaptive. [1]L. Ghadhab, I. Jenhani, M. W. Mkaouer, and M.Ben Messaoud, ”Augmenting commit classification by using fine-grained source code changes and a pretrained deep neural language model,” Information and Software Technology, vol. 135, p. 106566, 2021/07/01/2021. [2]S. Levin and A. Yehudai, ”Using Temporal and Semantic Developer-Level Information to Predict Main tenance Activity Profiles,” in 2016 IEEE International Conference on Software Maintenance and Evolution (ICSME), 2016.
DOI: 10.5281/zenodo.8220023
title: Dataset of Commit Classification via Diff-Code GCN based on System Dependency Graph

description: Commit Classification via Diff-Code GCN based on System Dependency Graph The dataset is based on Lobna Ghadhab et al. [1]. Levin et al.[2]'s dataset, and we extract all commits with pure java codes of two versions. In the dataset, evert commit folder have two sub-folder called before and after, they contains two version of codes. we extracted it by pydriller. The dataset have 1213 commits with two version java codes,and it contains three categories: (1) The first category is Corrective, which involves rectifying errors and faults identified during software usage. (2)The second category is Perfective, which entails enhancing software quality attributes, such as performance, maintainability, and usability. (3) Lastly, is Adaptive, which encompasses adapting the software to new environments (e.g., software or hardware) or introducing new functionalities. The dataset have 450 labels of Corrective. 441 for Perfective the rest for Adaptive. [1]L. Ghadhab, I. Jenhani, M. W. Mkaouer, and M.Ben Messaoud, ”Augmenting commit classification by using fine-grained source code changes and a pretrained deep neural language model,” Information and Software Technology, vol. 135, p. 106566, 2021/07/01/2021. [2]S. Levin and A. Yehudai, ”Using Temporal and Semantic Developer-Level Information to Predict Main tenance Activity Profiles,” in 2016 IEEE International Conference on Software Maintenance and Evolution (ICSME), 2016.
DOI: 10.4121/0bb03d8b-d1ac-49bc-8010-a498a981cf3e
title: Data underlying the publication: Computational Investigation of Precursor Blocking during Area-Selective Atomic Layer Deposition Using Aniline as a Small-Molecule Inhibitor

description: This dataset includes raw and processed data published in manuscript "Computational Investigation of Precursor Blocking during Area- Selective Atomic Layer Deposition Using Aniline as a Small-Molecule Inhibitor". The study focuses on area-selective atomic layer deposition (ASD) using small-molecule inhibitors (SMIs) to achieve selective growth on specific surfaces. Aniline (C6H5NH2) was identified as an effective SMI for ASD of TiN, enabling selective growth on SiO2 while inhibiting growth on Ru and Co non-growth areas. We used density functional theory (DFT) and random sequential adsorption (RSA) simulations to understand how aniline achieves this inhibition. The DFT calculations confirmed that aniline selectively adsorbs on Ru and Co non-growth areas, while its adsorption on the SiO2 growth area is limited to physisorption. The RSA simulations showed that having two stable adsorption configurations of aniline allows for a high surface inhibitor coverage on both Co and Ru surfaces. Overall, the study provides insights into the mechanism behind area-selective atomic layer deposition using aniline as a small-molecule inhibitor, which can be crucial for developing industry-compatible approaches for selective growth in microfabrication processes. This dataset includes the originals of the published figures as well as raw DFT and RSA data to generate those images and plots.