cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.26050/wdcc/ar6.c6hrmohgm
title: IPCC DDC: MOHC HadGEM3-GC31-MM model output prepared for CMIP6 HighResMIP

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.HighResMIP.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.c6hrmohgmc1
title: IPCC DDC: MOHC HadGEM3-GC31-MM model output prepared for CMIP6 HighResMIP control-1950

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.HighResMIP.MOHC.HadGEM3-GC31-MM.control-1950' 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.c6hrmohgmh1
title: IPCC DDC: MOHC HadGEM3-GC31-MM model output prepared for CMIP6 HighResMIP hist-1950

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.HighResMIP.MOHC.HadGEM3-GC31-MM.hist-1950' 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: 10.18112/openneuro.ds004703.v1.1.0
title: PassiveListen
DOI: 10.5281/zenodo.8263458
title: microhierax_caerulescens_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.8263457
title: microhierax_caerulescens_spades_01.fasta.gz

description: .
DOI: 10.6084/m9.figshare.23989332
title: Togo vaccine hesitancy dataset

description: This dataset covers x2 surveys carried out in Togo during the COVID-19 pandemic.
DOI: 10.6084/m9.figshare.23989332.v1
title: Togo vaccine hesitancy dataset

description: This dataset covers x2 surveys carried out in Togo during the COVID-19 pandemic.
DOI: 10.3886/e184610
title: fieldwork analysis data

description: Data for analysis of transition to mixed mode fieldwork.
DOI: 10.3886/e184610v1
title: fieldwork analysis data

description: Data for analysis of transition to mixed mode fieldwork.
DOI: 10.3886/e184610v2
title: fieldwork analysis data

description: Data for analysis of transition to mixed mode fieldwork.
DOI: 10.3886/e184610v3
title: fieldwork analysis data

description: Data for analysis of transition to mixed mode fieldwork.
DOI: 10.5281/zenodo.8256121
title: aaa

description: a bbbbbbbbb
DOI: 10.5281/zenodo.8256122
title: aaa

description: a bbbbbbbbb
DOI: 10.5281/zenodo.8260210
title: aaa

description: a bbbbbbbbb
DOI: 10.5281/zenodo.8260213
title: aaa

description: a bbbbbbbbb
DOI: 10.5281/zenodo.8263444
title: aaa

description: a bbbbbbbbb
DOI: 10.5281/zenodo.8263445
title: aaa

description: a bbbbbbbbb
DOI: 10.5281/zenodo.8256120
title: aaa

description: a bbbbbbbbb
DOI: 10.5281/zenodo.8263459
title: aaa

description: a bbbbbbbbb
DOI: 10.5281/zenodo.8263465
title: Database from 2021-2023. Sustainable development in Polish family businesses.

description: Database developed on the basis of a questionnaire based on the approach of self-identification of the degree of fulfillment of certain aspects of the sustainability dimensions (TLB)
DOI: 10.5281/zenodo.8263464
title: Database from 2021-2023. Sustainable development in Polish family businesses.

description: Database developed on the basis of a questionnaire based on the approach of self-identification of the degree of fulfillment of certain aspects of the sustainability dimensions (TLB)
DOI: 10.57967/hf/1000
title: Emotions_Data
DOI: 10.25452/figshare.plus.22109147
title: Dataset for Deep Imaging Inside Scattering Media Through Virtual Spatiotemporal Wavefront Shaping - Hyperspectral Reflection Matrices of a USAF Target Under 1-Millimeter Mouse Brain Tissue

description: This dataset includes the experimental hyperspectral reflection matrices (complex-valued) of a 1951 USAF resolution target underneath 1 mm of mouse brain tissue. Using scattering matrix tomography (SMT), one can reconstruct the high-resolution USAF target image even with the presence of the strong scattering and aberrations induced by the biological tissue. The SMT reconstruction code and more information can be found on GitHub.
DOI: 10.25452/figshare.plus.22109147.v1
title: Dataset for deep volumetric imaging by scattering matrix tomography - hyperspectral reflection matrices of a USAF target under 1-millimeter mouse brain tissue

description: This dataset includes the experimental hyperspectral reflection matrices (complex-valued) of a 1951 USAF resolution target underneath 1 mm of mouse brain tissue. Using scattering matrix tomography (SMT), one can reconstruct the high-resolution USAF target image even with the presence of the strong scattering and aberrations induced by the biological tissue. The SMT reconstruction code and more information can be found on GitHub.
DOI: 10.25452/figshare.plus.22114424
title: Dataset for Deep Imaging Inside Scattering Media Through Virtual Spatiotemporal Wavefront Shaping - Hyperspectral Reflection Matrices of Dense Colloid Nanoparticle Sample

description: This dataset includes the experimental hyperspectral reflection matrices (complex-valued) more than 1 millimeter deep inside a dense TiO2 nanoparticle colloid, with which one can reconstruct high-resolution deep volumetric images of the sample using scattering matrix tomography (SMT). The SMT reconstruction code and more information can be found on GitHub. <br>
DOI: 10.25452/figshare.plus.22114424.v1
title: Dataset for deep volumetric imaging by scattering matrix tomography - hyperspectral reflection matrices of a dense colloid

description: This dataset includes the experimental hyperspectral reflection matrices (complex-valued) more than 1 millimeter deep inside a dense TiO2 nanoparticle colloid, with which one can reconstruct high-resolution deep volumetric images of the sample using scattering matrix tomography (SMT). The SMT reconstruction code and more information can be found on GitHub. <br>
DOI: 10.5281/zenodo.8251977
title: Brugge, Spanjaardstraat: inventaris leervondsten

description: Inventarislijst van de leervondsten uit de vindplaats Spanjaardstraat te Brugge - 1996, opgemaakt tijdens de verwerking van het vondstenmateriaal. Moens J., Poulain M., Huyghe J., Hillewaert B. & Ervynck A. 2023:<em> Een bijzonder laatmiddeleeuws ensemble van leerresten uit een beerput langs de Spanjaardstraat in Brugge</em>, Onderzoeksrapporten agentschap Onroerend Erfgoed 296, Brussel. https://doi.org/10.55465/ILCN8960
DOI: 10.5281/zenodo.8251978
title: Brugge, Spanjaardstraat: inventaris leervondsten

description: Inventarislijst van de leervondsten uit de vindplaats Spanjaardstraat te Brugge - 1996, opgemaakt tijdens de verwerking van het vondstenmateriaal. Moens J., Poulain M., Huyghe J., Hillewaert B. & Ervynck A. 2023:<em> Een bijzonder laatmiddeleeuws ensemble van leerresten uit een beerput langs de Spanjaardstraat in Brugge</em>, Onderzoeksrapporten agentschap Onroerend Erfgoed 296, Brussel. https://doi.org/10.55465/ILCN8960
DOI: 10.5066/p9llrsw1
title: Digital Database of Microfossil samples from Southwestern Coastal Oregon

description: A geodatabase containing shapefiles representing the geologic units present within the study area, as well as the locations where sampling ocurred, field photography conducted by John Armentrout ocurred, and satellite imagery providing overhead views of key points of John Armentrout's study.
DOI: 10.48366/r606158
title: A Catalog of OpenAI's GPT series

description: The comparison showcases the GPT series of Large Language Models from GPT-1 to the latest, at the time of creation of this comparison, GPT-4.
DOI: 10.6084/m9.figshare.23989077
title: The poleward naturalization of intra-continental alien plants

description: This is the data and code for the paper *The poleward naturalization of intra-continental alien plants*, which is accpeted in Science Advances.<b>Data:</b>wf_dat.csv is the data of native sp x region occurence (i.e. species A is native in region a). Species names were already cleaned with TPL, so that it can be matched with glonaf_dat.csv.glonaf_dat.csv is the data of naturalized alien x region occurrence. Note that this include both intra- and inter-continental aliens.ppp.csv is the GDP per caipta of each regiongeoentities_simple include the shape file. The entt_ID can be matched with the entity_ID in the other data.entity_ID_all.csv is all regions in the four continents of the study. This is used for plotting only.<br><b>Code, results and reports:</b>The <b>intra_flow_analyses </b>is the main analyses. The .Rmd file is the code (R markdown). The .html is the report that containing both results and code. So, I suggest first checking the .html file.The intra_flow_analyses_sp_level is the analyses at species level, i.e. the supplementary method 1 in the paper.I have also saved the results to the rdata.zip file so that one can check the results without re-running e.g. the models.** PLEASE unzip the zip files before using.<br>
DOI: 10.6084/m9.figshare.23989077.v1
title: The poleward naturalization of intra-continental alien plants

description: This is the data and code for the paper *The poleward naturalization of intra-continental alien plants*, which is accpeted in Science Advances.<b>Data:</b>wf_dat.csv is the data of native sp x region occurence (i.e. species A is native in region a). Species names were already cleaned with TPL, so that it can be matched with glonaf_dat.csv.glonaf_dat.csv is the data of naturalized alien x region occurrence. Note that this include both intra- and inter-continental aliens.ppp.csv is the GDP per caipta of each regiongeoentities_simple include the shape file. The entt_ID can be matched with the entity_ID in the other data.entity_ID_all.csv is all regions in the four continents of the study. This is used for plotting only.<br><b>Code, results and reports:</b>The <b>intra_flow_analyses </b>is the main analyses. The .Rmd file is the code (R markdown). The .html is the report that containing both results and code. So, I suggest first checking the .html file.The intra_flow_analyses_sp_level is the analyses at species level, i.e. the supplementary method 1 in the paper.I have also saved the results to the rdata.zip file so that one can check the results without re-running e.g. the models.** PLEASE unzip the zip files before using.<br>
DOI: 10.5281/zenodo.8263514
title: Kinetics assessment of the homogeneously catalyzed hydroformylation of ethylene on a Rh-catalyst

description: Supplementary Information. Section S1, description of dependence of rate and equilibrium coefficients on selected standard state, reaction entropies of reaction steps at different standard states, and derivation of rate equations; Section S2, experimental conditions of data sets selected for model regression and model validation; Section S3, calculation of enthalpies and entropies of solvation explained in more detail and obtained values; and Section S4, calculation of entropies of coordination explained in detail and resulting values
DOI: 10.5281/zenodo.8263513
title: Kinetics assessment of the homogeneously catalyzed hydroformylation of ethylene on a Rh-catalyst

description: Supplementary Information. Section S1, description of dependence of rate and equilibrium coefficients on selected standard state, reaction entropies of reaction steps at different standard states, and derivation of rate equations; Section S2, experimental conditions of data sets selected for model regression and model validation; Section S3, calculation of enthalpies and entropies of solvation explained in more detail and obtained values; and Section S4, calculation of entropies of coordination explained in detail and resulting values
DOI: 10.48420/23989143
title: XPS and HAXPES of compressed MXene aerogels

description: X-ray photoelectron spectroscopy and Hard X-Ray Photoelectron Spectroscopy are used to derive a semi-quantitative estimate of Ti3C2Tz MXene terminating group stoichiometry and sample purity.For details of the experiment and uploaded data, please see the attached .pdf.
DOI: 10.48420/23989143.v1
title: XPS and HAXPES of compressed MXene aerogels

description: X-ray photoelectron spectroscopy and Hard X-Ray Photoelectron Spectroscopy are used to derive a semi-quantitative estimate of Ti3C2Tz MXene terminating group stoichiometry and sample purity.For details of the experiment and uploaded data, please see the attached .pdf.
DOI: 10.5281/zenodo.5105815
title: Index of Supplementary Files from "Next2You: Robust Copresence Detection Based on Channel State Information"

description: This record serves as an index to the other dataset releases that are part of the paper "Next2You: Robust Copresence Detection Based on Channel State Information" by Mikhail Fomichev, Luis F. Abanto-Leon, Max Stiegler, Alejandro Molina, Jakob Link, and Matthias Hollick in ACM Transactions on Internet of Things, Volume 3, Issue 2. 2022. We have chosen to split the dataset into several parts to meet Zenodo size requirements and make it easier to find specific pieces of data. In total, the following datasets exist: <strong>Raw data</strong>: This dataset contains raw channel state information (CSI) data in terms of CSI magnitude and phase values. The data is collected in the following environments: <em>office</em>, <em>urban apartment</em>, <em>rural house</em>, <em>moving</em> and <em>parked</em> cars, as well as additional setups in these environments such as <em>heterogeneous</em> devices (i.e., Nexus 6P and Raspberry Pi), different <em>frame types</em> to extract CSI data from (i.e., beacon), and varying transmission <em>power</em> of devices sending frames from which CSI is extracted. The data collection in additional setups was performed in the office environment. These raw CSI data can be used to repeat our own experiment or to develop new context-based copresence detection schemes. To collect the raw CSI data, we used the following Android app and scripts on the Raspberry Pi 3 Model B+ (both include Nexmon patches to enable CSI extraction). <strong>Results Data</strong>: The results dataset contains the evaluation results (e.g., computed error rates and AUCs for different cases, Right for the Right Reasons trained models, etc.). The codebase to generate these results can be found in the source code repository.
DOI: 10.5281/zenodo.5105814
title: Index of Supplementary Files from "Next2You: Robust Copresence Detection Based on Channel State Information"

description: This record serves as an index to the other dataset releases that are part of the paper "Next2You: Robust Copresence Detection Based on Channel State Information" by Mikhail Fomichev, Luis F. Abanto-Leon, Max Stiegler, Alejandro Molina, Jakob Link, and Matthias Hollick in ACM Transactions on Internet of Things, Volume 3, Issue 2. 2022. We have chosen to split the dataset into several parts to meet Zenodo size requirements and make it easier to find specific pieces of data. In total, the following datasets exist: <strong>Raw data</strong>: This dataset contains raw channel state information (CSI) data in terms of CSI magnitude and phase values. The data is collected in the following environments: <em>office</em>, <em>urban apartment</em>, <em>rural house</em>, <em>moving</em> and <em>parked</em> cars, as well as additional setups in these environments such as <em>heterogeneous</em> devices (i.e., Nexus 6P and Raspberry Pi), different <em>frame types</em> to extract CSI data from (i.e., beacon), and varying transmission <em>power</em> of devices sending frames from which CSI is extracted. The data collection in additional setups was performed in the office environment. These raw CSI data can be used to repeat our own experiment or to develop new context-based copresence detection schemes. To collect the raw CSI data, we used the following Android app and scripts on the Raspberry Pi 3 Model B+ (both include Nexmon patches to enable CSI extraction). <strong>Results Data</strong>: The results dataset contains the evaluation results (e.g., computed error rates and AUCs for different cases, Right for the Right Reasons trained models, etc.). The codebase to generate these results can be found in the source code repository.
DOI: 10.5281/zenodo.5592335
title: Raw data from "Next2You: Robust Copresence Detection Based on Channel State Information"

description: This deposit contains the raw channel state information (CSI) data from the paper "Next2You: Robust Copresence Detection Based on Channel State Information" by Mikhail Fomichev, Luis F. Abanto-Leon, Max Stiegler, Alejandro Molina, Jakob Link, and Matthias Hollick in ACM Transactions on Internet of Things, Volume 3, Issue 2. 2022. See the index of all related datasets for more details on the paper, and see the included README for details on this dataset.
DOI: 10.5281/zenodo.5592334
title: Raw data from "Next2You: Robust Copresence Detection Based on Channel State Information"

description: This deposit contains the raw channel state information (CSI) data from the paper "Next2You: Robust Copresence Detection Based on Channel State Information" by Mikhail Fomichev, Luis F. Abanto-Leon, Max Stiegler, Alejandro Molina, Jakob Link, and Matthias Hollick in ACM Transactions on Internet of Things, Volume 3, Issue 2. 2022. See the index of all related datasets for more details on the paper, and see the included README for details on this dataset.
DOI: 10.48366/r606160
title: A Catalog of OpenAI's GPT series

description: The comparison showcases the GPT series of Large Language Models from GPT-1 to the latest, at the time of creation of this comparison, GPT-4.