cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4121/0bb03d8b-d1ac-49bc-8010-a498a981cf3e.v1
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.
DOI: 10.26050/wdcc/ar6.c6spmrme0
title: IPCC DDC: MRI MRI-ESM2.0 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.MRI.MRI-ESM2-0' 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 MRI-ESM2.0 climate model, released in 2017, includes the following components: aerosol: MASINGAR mk2r4 (TL95; 192 x 96 longitude/latitude; 80 levels; top level 0.01 hPa), atmos: MRI-AGCM3.5 (TL159; 320 x 160 longitude/latitude; 80 levels; top level 0.01 hPa), atmosChem: MRI-CCM2.1 (T42; 128 x 64 longitude/latitude; 80 levels; top level 0.01 hPa), land: HAL 1.0, ocean: MRI.COM4.4 (tripolar primarily 0.5 deg latitude/1 deg longitude with meridional refinement down to 0.3 deg within 10 degrees north and south of the equator; 360 x 364 longitude/latitude; 61 levels; top grid cell 0-2 m), ocnBgchem: MRI.COM4.4, seaIce: MRI.COM4.4. The model was run by the Meteorological Research Institute, Tsukuba, Ibaraki 305-0052, Japan (MRI) in native nominal resolutions: aerosol: 250 km, atmos: 100 km, atmosChem: 250 km, land: 100 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.
DOI: 10.26050/wdcc/ar6.c6spmrme0s434
title: IPCC DDC: MRI MRI-ESM2.0 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.MRI.MRI-ESM2-0.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 MRI-ESM2.0 climate model, released in 2017, includes the following components: aerosol: MASINGAR mk2r4 (TL95; 192 x 96 longitude/latitude; 80 levels; top level 0.01 hPa), atmos: MRI-AGCM3.5 (TL159; 320 x 160 longitude/latitude; 80 levels; top level 0.01 hPa), atmosChem: MRI-CCM2.1 (T42; 128 x 64 longitude/latitude; 80 levels; top level 0.01 hPa), land: HAL 1.0, ocean: MRI.COM4.4 (tripolar primarily 0.5 deg latitude/1 deg longitude with meridional refinement down to 0.3 deg within 10 degrees north and south of the equator; 360 x 364 longitude/latitude; 61 levels; top grid cell 0-2 m), ocnBgchem: MRI.COM4.4, seaIce: MRI.COM4.4. The model was run by the Meteorological Research Institute, Tsukuba, Ibaraki 305-0052, Japan (MRI) in native nominal resolutions: aerosol: 250 km, atmos: 100 km, atmosChem: 250 km, land: 100 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.
DOI: 10.26050/wdcc/ar6.c6spmrme0so
title: IPCC DDC: MRI MRI-ESM2.0 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.MRI.MRI-ESM2-0.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 MRI-ESM2.0 climate model, released in 2017, includes the following components: aerosol: MASINGAR mk2r4 (TL95; 192 x 96 longitude/latitude; 80 levels; top level 0.01 hPa), atmos: MRI-AGCM3.5 (TL159; 320 x 160 longitude/latitude; 80 levels; top level 0.01 hPa), atmosChem: MRI-CCM2.1 (T42; 128 x 64 longitude/latitude; 80 levels; top level 0.01 hPa), land: HAL 1.0, ocean: MRI.COM4.4 (tripolar primarily 0.5 deg latitude/1 deg longitude with meridional refinement down to 0.3 deg within 10 degrees north and south of the equator; 360 x 364 longitude/latitude; 61 levels; top grid cell 0-2 m), ocnBgchem: MRI.COM4.4, seaIce: MRI.COM4.4. The model was run by the Meteorological Research Institute, Tsukuba, Ibaraki 305-0052, Japan (MRI) in native nominal resolutions: aerosol: 250 km, atmos: 100 km, atmosChem: 250 km, land: 100 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.
DOI: 10.26050/wdcc/ar6.c6spmrme0s460
title: IPCC DDC: MRI MRI-ESM2.0 model output prepared for CMIP6 ScenarioMIP ssp460

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.MRI.MRI-ESM2-0.ssp460' 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 MRI-ESM2.0 climate model, released in 2017, includes the following components: aerosol: MASINGAR mk2r4 (TL95; 192 x 96 longitude/latitude; 80 levels; top level 0.01 hPa), atmos: MRI-AGCM3.5 (TL159; 320 x 160 longitude/latitude; 80 levels; top level 0.01 hPa), atmosChem: MRI-CCM2.1 (T42; 128 x 64 longitude/latitude; 80 levels; top level 0.01 hPa), land: HAL 1.0, ocean: MRI.COM4.4 (tripolar primarily 0.5 deg latitude/1 deg longitude with meridional refinement down to 0.3 deg within 10 degrees north and south of the equator; 360 x 364 longitude/latitude; 61 levels; top grid cell 0-2 m), ocnBgchem: MRI.COM4.4, seaIce: MRI.COM4.4. The model was run by the Meteorological Research Institute, Tsukuba, Ibaraki 305-0052, Japan (MRI) in native nominal resolutions: aerosol: 250 km, atmos: 100 km, atmosChem: 250 km, land: 100 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.
DOI: 10.6084/m9.figshare.23897280
title: Modern pollen data-MT.xlsx

description: A total of 62 moss samples were collected from elevation ranging from 743 m and 3551 m along the Zamo road in southwest China. 54 sites were located in Galongqu Valley and Jinzhuqu Valley, which are tributaries of the Yarlung Tsangpo River. Additionally, there were 8 sampling sites distributed along the main channel of the Yarlung Tsangpo River.The pollen samples were processed by heavy liquid separation with specific gravity of 2.0-2.2. Pollen samples were examined using a Zeiss microscope at magnifications of 400× and 630×. Of these, 61 samples were counted more than 500 grains. As some samples were collected in close proximity to Urticaceae plants, indicating strong human activity in this region, and the sampling period coincided with the flowering season of Urticaceae plants, it greatly influenced the pollen assemblages of these samples with a high proportion of Urticaceae pollen. Therefore, we excluded Urticaceae when calculating the percentages of terrestrial pollen taxa. The table lists the 26 main species types.
DOI: 10.6084/m9.figshare.23897280.v1
title: Modern pollen data-MT.xlsx

description: A total of 62 moss samples were collected from elevation ranging from 743 m and 3551 m along the Zamo road in southwest China. 54 sites were located in Galongqu Valley and Jinzhuqu Valley, which are tributaries of the Yarlung Tsangpo River. Additionally, there were 8 sampling sites distributed along the main channel of the Yarlung Tsangpo River.The pollen samples were processed by heavy liquid separation with specific gravity of 2.0-2.2. Pollen samples were examined using a Zeiss microscope at magnifications of 400× and 630×. Of these, 61 samples were counted more than 500 grains. As some samples were collected in close proximity to Urticaceae plants, indicating strong human activity in this region, and the sampling period coincided with the flowering season of Urticaceae plants, it greatly influenced the pollen assemblages of these samples with a high proportion of Urticaceae pollen. Therefore, we excluded Urticaceae when calculating the percentages of terrestrial pollen taxa. The table lists the 26 main species types.
DOI: 10.57841/casdc.0001752
title: 测试
DOI: 10.5281/zenodo.7963498
title: PineTime heart rate dataset

description: Dataset of heart rate measurements collected from the PineTime wristband, with a gold standard reference. <strong>Contents</strong> The repository contains both the raw and the "merged", clean data. The merged data is much easier to work with and should be used when building machine learning models. The raw data is provided for transparency, reproducibility, and to allow for studies that could use the other data collected from the Equivital device. <code>schedule.md</code> – schedule of the study, indicating the start and end times of each exercise and break. <code>data_raw/</code> – raw data collected from the PineTime wristband and the Equivital device. Each subdirectory corresponds to one participant. The files are in the Feather format. <code>data_merged/</code> – merged data series that can be used for building ML models. The files are in JSON format and follow a nested structure, where each heart rate measurement is associated with a series of acceleration measurements that preceded it. Each file corresponds to one continuous measurement session – there are sometimes multiple sessions per participant due to intermittent hardware failures. <strong>Authors</strong> Monika Kobus – data collection Anna Dąbrowska – data collection, methodological supervision Piotr Sowiński – data collection and processing <strong>Acknowledgements</strong> This work is part of the ASSIST-IoT project that has received funding from the EU’s Horizon 2020 research and innovation programme under grant agreement No 957258. The Central Institute for Labour Protection – National Research Institute provided facilities and equipment for data collection. <strong>License</strong> The dataset is licensed under the Creative Commons Attribution 4.0 International License.
DOI: 10.5281/zenodo.7963564
title: PineTime heart rate dataset

description: Dataset of heart rate measurements collected from the PineTime wristband, with a gold standard reference. <strong>Contents</strong> The repository contains both the raw and the "merged", clean data. The merged data is much easier to work with and should be used when building machine learning models. The raw data is provided for transparency, reproducibility, and to allow for studies that could use the other data collected from the Equivital device. <code>schedule.md</code> – schedule of the study, indicating the start and end times of each exercise and break. <code>data_raw/</code> – raw data collected from the PineTime wristband and the Equivital device. Each subdirectory corresponds to one participant. The files are in the Feather format. <code>data_merged/</code> – merged data series that can be used for building ML models. The files are in JSON format and follow a nested structure, where each heart rate measurement is associated with a series of acceleration measurements that preceded it. Each file corresponds to one continuous measurement session – there are sometimes multiple sessions per participant due to intermittent hardware failures. <strong>Citation</strong> If you use this data in research works, please cite the following paper: Sowiński, P., Rachwał, K., Danilenka, A., Bogacka, K., Kobus, M., Dąbrowska, A., Paszkiewicz, A., et al. (2023). Frugal Heart Rate Correction Method for Scalable Health and Safety Monitoring in Construction Sites. <em>Sensors</em>, <em>23</em>(14), 6464. MDPI AG. Retrieved from http://dx.doi.org/10.3390/s23146464 BibTeX: <pre><code>@article{sowinski2023frugal, title={Frugal Heart Rate Correction Method for Scalable Health and Safety Monitoring in Construction Sites}, author={Sowi{\'n}ski, Piotr and Rachwa{\l}, Kajetan and Danilenka, Anastasiya and Bogacka, Karolina and Kobus, Monika and D{\k{a}}browska, Anna and Paszkiewicz, Andrzej and Bolanowski, Marek and Ganzha, Maria and Paprzycki, Marcin}, journal={Sensors}, volume={23}, number={14}, pages={6464}, year={2023}, publisher={MDPI}, url = {https://www.mdpi.com/1424-8220/23/14/6464}, doi = {10.3390/s23146464} }</code></pre> <strong>Authors</strong> Monika Kobus – data collection Anna Dąbrowska – data collection, methodological supervision Piotr Sowiński – data collection and processing <strong>Acknowledgements</strong> This work is part of the ASSIST-IoT project that has received funding from the EU’s Horizon 2020 research and innovation programme under grant agreement No 957258. The Central Institute for Labour Protection – National Research Institute provided facilities and equipment for data collection. <strong>License</strong> The dataset is licensed under the Creative Commons Attribution 4.0 International License.
DOI: 10.5281/zenodo.8220127
title: PineTime heart rate dataset

description: Dataset of heart rate measurements collected from the PineTime wristband, with a gold standard reference. <strong>Contents</strong> The repository contains both the raw and the "merged", clean data. The merged data is much easier to work with and should be used when building machine learning models. The raw data is provided for transparency, reproducibility, and to allow for studies that could use the other data collected from the Equivital device. <code>schedule.md</code> – schedule of the study, indicating the start and end times of each exercise and break. <code>data_raw/</code> – raw data collected from the PineTime wristband and the Equivital device. Each subdirectory corresponds to one participant. The files are in the Feather format. <code>data_merged/</code> – merged data series that can be used for building ML models. The files are in JSON format and follow a nested structure, where each heart rate measurement is associated with a series of acceleration measurements that preceded it. Each file corresponds to one continuous measurement session – there are sometimes multiple sessions per participant due to intermittent hardware failures. <strong>Citation</strong> If you use this data in research works, please cite the following paper: Sowiński, P., Rachwał, K., Danilenka, A., Bogacka, K., Kobus, M., Dąbrowska, A., Paszkiewicz, A., et al. (2023). Frugal Heart Rate Correction Method for Scalable Health and Safety Monitoring in Construction Sites. <em>Sensors</em>, <em>23</em>(14), 6464. MDPI AG. Retrieved from http://dx.doi.org/10.3390/s23146464 BibTeX: <pre><code>@article{sowinski2023frugal, title={Frugal Heart Rate Correction Method for Scalable Health and Safety Monitoring in Construction Sites}, author={Sowi{\'n}ski, Piotr and Rachwa{\l}, Kajetan and Danilenka, Anastasiya and Bogacka, Karolina and Kobus, Monika and D{\k{a}}browska, Anna and Paszkiewicz, Andrzej and Bolanowski, Marek and Ganzha, Maria and Paprzycki, Marcin}, journal={Sensors}, volume={23}, number={14}, pages={6464}, year={2023}, publisher={MDPI}, url = {https://www.mdpi.com/1424-8220/23/14/6464}, doi = {10.3390/s23146464} }</code></pre> <strong>Authors</strong> Monika Kobus – data collection Anna Dąbrowska – data collection, methodological supervision Piotr Sowiński – data collection and processing <strong>Acknowledgements</strong> This work is part of the ASSIST-IoT project that has received funding from the EU’s Horizon 2020 research and innovation programme under grant agreement No 957258. The Central Institute for Labour Protection – National Research Institute provided facilities and equipment for data collection. <strong>License</strong> The dataset is licensed under the Creative Commons Attribution 4.0 International License.