cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.bx2va5
title: Occurrence Download

description: A dataset containing 7552 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Rheum rhabarbarum L.",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Human Observation, Living Specimen, Occurrence evidence)"
 ]
}

 The dataset includes 7552 records from 67 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004603-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xh5wua
title: Occurrence Download

description: A dataset containing 32236 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Invertebrata Collection of Institute of Systematics and Evolution of Animals",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 32236 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004604-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.22033/esgf/cmip6.6659
title: MPI-M MPI-ESM1.2-LR model output prepared for CMIP6 RFMIP piClim-4xCO2

description: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets: These data include all datasets published for 'CMIP6.RFMIP.MPI-M.MPI-ESM1-2-LR.piClim-4xCO2' 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. 

Project: 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. - Project website: https://pcmdi.llnl.gov/CMIP6.
DOI: 10.22033/esgf/cmip6.6662
title: MPI-M MPI-ESM1.2-LR model output prepared for CMIP6 RFMIP piClim-control

description: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets: These data include all datasets published for 'CMIP6.RFMIP.MPI-M.MPI-ESM1-2-LR.piClim-control' 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. 

Project: 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. - Project website: https://pcmdi.llnl.gov/CMIP6.
DOI: 10.22033/esgf/cmip6.6663
title: MPI-M MPI-ESM1.2-LR model output prepared for CMIP6 RFMIP piClim-ghg

description: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets: These data include all datasets published for 'CMIP6.RFMIP.MPI-M.MPI-ESM1-2-LR.piClim-ghg' 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. 

Project: 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. - Project website: https://pcmdi.llnl.gov/CMIP6.
DOI: 10.22033/esgf/cmip6.6668
title: MPI-M MPI-ESM1.2-LR model output prepared for CMIP6 RFMIP piClim-lu

description: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets: These data include all datasets published for 'CMIP6.RFMIP.MPI-M.MPI-ESM1-2-LR.piClim-lu' 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. 

Project: 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. - Project website: https://pcmdi.llnl.gov/CMIP6.
DOI: 10.6084/m9.figshare.28830278.v14
title: Reconstructed Zurich Air Pollution Data (2015–2019) with Lag Structure for Urinary Metal Toxicokinetic Analysis

description: This dataset contains reconstructed daily air pollution data from the Zurich NABEL monitoring station (“Kaserne”) between 2015 and 2019, including NO₂, SO₂, CO, PM₁₀, PM₂.₅, and derived PM₁₀–₂.₅ values. A few missing values for all pollutants except PM₂.₅ were imputed using linear interpolation, while missing PM₂.₅ values were reconstructed using an XGBoost-based machine learning model trained on the remaining pollutants. All imputed values are flagged accordingly.To enable toxicokinetic modeling of airborne metal exposure, the dataset includes:14-day average concentrations for each pollutant preceding the urine test27 lagged weekly averages (Lag –1 to –26), aligned with the dates of Chelate-Evoked Metal Excretion Testing (CEMET) conducted at a clinical center in Pfäffikon SZ, SwitzerlandThe lag structure was designed to explore the delay between environmental exposure and urinary excretion of heavy metals. These lag times are still largely unknown, which may explain why many environmental impact studies fail to detect associations between pollutant exposure and metal biomarkers. This dataset provides a novel foundation for investigating these temporal dynamics.These lags may reflect a combination of biological and environmental delay mechanisms. For example, the respiratory uptake and translocation of fine dust particles (e.g., PM₂.₅) can involve delayed pulmonary clearance and subsequent redistribution, leading to gradual urinary excretion of associated metals. In parallel, pollutants may also leach into the groundwater or accumulate in the food chain, resulting in delayed indirect exposure and biomarker response. The inclusion of a time-resolved lag structure in this dataset enables the exploration of such toxicokinetic patterns across multiple temporal scales.In addition to the structured numerical data, the dataset includes three supplementary Excel files containing image-based Spearman correlation heatmaps. These visualize associations between Ca-EDTA–DMPS–provoked urinary metal concentrations and lagged air pollutant levels:Organized into <b>full-year</b>, <b>winter (November–April)</b>, and <b>summer (May–October)</b> subsetsEach sheet within these files corresponds to one air pollutant (e.g., PM₁₀, NO₂), with correlations shown across 27 weekly lag intervalsThe heatmaps highlight temporal correlation patterns and are intended as visual tools to support exploratory toxicokinetic interpretation and hypothesis generation
DOI: 10.22033/esgf/cmip6.6670
title: MPI-M MPI-ESM1.2-LR model output prepared for CMIP6 RFMIP piClim-spAer-anthro

description: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets: These data include all datasets published for 'CMIP6.RFMIP.MPI-M.MPI-ESM1-2-LR.piClim-spAer-anthro' 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. 

Project: 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. - Project website: https://pcmdi.llnl.gov/CMIP6.
DOI: 10.15468/dl.dvd69v
title: Occurrence Download

description: A dataset containing 2576 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "TaxonKey is Dianthus chinensis L."
 ]
}

 The dataset includes 2576 records from 54 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004602-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zajrwa
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Potentilla coriandrifolia G.Don",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0321067-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rtzxgi
title: Occurrence Download

description: A dataset containing 1647242805 species occurrences available in GBIF matching the query:
{ }

 The dataset includes 1647242805 records from 18535 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000909-171219132708484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.s5w4et
title: Occurrence Download

description: A dataset containing 22 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Mitrephora calcarea Diels ex Weeras. & R.M.K.Saunders"
 ]
}

 The dataset includes 22 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001188-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6zxa6r
title: Occurrence Download

description: A dataset containing 178 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Asarum caucasicum (Duch.) N.Busch"
 ]
}

 The dataset includes 178 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009949-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/sj5m75pj22.1
title: MPMEs2

description: The database is an integral part of the article "DIGITAL STRATEGY AND ITS MEDIATING ROLE BETWEEN DIGITALIZATION, INNOVATION, AND FINANCIAL PERFORMANCE," submitted to the REGEPE journal in June 2025. The database includes the following variables:
basic digitalization (DIG_A)
advanced digitalization (DIG_B)
digital strategy (EST)
innovation (INOV)
financial performance (RF)
The research sample consists of 703 MSMEs operating in the 27 states of the Federative Republic of Brazil. The largest portion of the companies surveyed is located in the states of São Paulo, Belo Horizonte, Rio Grande do Sul, and Santa Catarina, which together account for 83% of the companies participating in the survey. The sampling was non-probabilistic due to the impossibility of applying the randomization process due to the lack of a registry of Brazilian MSMEs. However, this type of sampling has the advantages of greater control over sample composition, efficiency due to direct selection, and greater flexibility in adapting the sample to the specific needs of the research (Etikan, Musa & Alkassim, 2016). A quantitative survey-type questionnaire was administered to managers or owners of the MSMEs to obtain data on their technological capacity, innovation capacity, performance, and use of advanced technologies. The questionnaire consists of structured questions based on a five-point Likert scale. Data collection was carried out online using a link provided by FAEDPYME from February to May 2022. The items that make up the questionnaire were proposed by FAEDPYME and endorsed by research groups in Brazil, with minor adjustments made, mainly in relation to the equivalence of the National Registry of Economic Activities (CNAE 2.0) in Brazil and Europe.
DOI: 10.17632/b4fgwh6n9m
title: Dataset for: Awareness of Autism Spectrum Disorder among Public Primary School Teachers in Iraq

description: Autism is one of the most common neurodevelopmental conditions worldwide, impacting roughly 1 in 36 children, as per the latest estimates from the Centers for Disease Control and Prevention (CDC). Autism is associated with lifelong challenges, leading to significant social, educational, and psychological impacts for both the children and their families. Yet, In Iraq, there exists a critical lack of research on this condition to the extent that no official estimate of its prevalence has been reported. This gap severely hinders the ability to provide adequate healthcare and support systems for these children and their families.
To address this gap, the International Branch of Tehran University for Medical Sciences and Al-Subtain Academy for Autism and Neurodevelopmental Disorders have conducted nine studies as a part of a research collaboration titled “Epidemiological, Clinical, and Psychosocial Aspects of Iraqi Children with Autism Spectrum Disorder”.
In recognition of the crucial role of primary school teachers in identifying children with ASD, referring them for appropriate evaluation, and suggesting treatments by interacting with parents, we explored primary school teachers’ awareness of autism and found that most teachers had a limited understanding of the condition. 
Interestingly, teachers were less likely to identify communication problems as possible symptoms compared to repetitive/restrictive behaviors. The misconceptions that dietary restrictions and mineral chelation are viable treatment options, and that complete cure is possible were also prevalent. Educational workshops were associated with higher awareness and should be conducted more often to primary school teachers of all backgrounds, taking into account the gaps identified in this investigation.