cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/grc3jp7b8p.1
title: The Impact of Multicultural Education on Tolerance and Understanding Levels Among Filipino Students.

description: This literature review looks at how multicultural education affects tolerance and understanding among Filipino students. It gathers insights from various studies published from 2010 to 2023, focusing on important themes like the experiences of indigenous learners, the effectiveness of multicultural programs, teacher attitudes, and the importance of cultural integrity. The review highlights both the positive effects of multicultural education and the challenges that still exist, such as lack of support from schools and issues like bullying. It also offers suggestions for future research, including better training for teachers, improved school policies, and encouraging student participation in multicultural activities. This review is intended for educators and policymakers who want to create more inclusive learning environments in the Philippines.
DOI: 10.17632/bz8k2gpcbd.1
title: RTEII: A new high-resolution (0.1° × 0.1°) road transport emission inventory for India

description: This dataset represents the first VOC-speciated national emission inventory for road traffic sector emissions in India. By integrating the measured emission factors (EFs) for 74 speciated volatile organic compounds (VOCs), as well as methane (CH4), carbon monoxide (CO), and carbon dioxide (CO2), with high-resolution activity data, we generated gridded emissions for the Indian region at a spatial resolution of 0.1° × 0.1° for the year 2015. The MOZART species calculations are available in both TXT and NC formats.
DOI: 10.17632/c4frm9w5mv.1
title: A novel method for correlating/predicting the thermodynamic properties of aqueous ions at high T and P

description: Data showing results of calculations described in the research:

1). Excel files (2024ARC_BAC_Isobaric, 2024ARC_BAC_Isochoric, 1981MAR_FRA_Isobaric, 1981MAR_FRA_Isochoric) with calculations of the values of the standard partial molar volumes, their temperature and pressure derivarives, isobaric and isochoric heat capacities for the hypothetical HOH electrolyte from the literature formulations for water ionization - Arcis et al. (2024) and Marshall and Franck (1981) over wide temperature and pressure ranges.

2). G at P=300 MPa (Mathematica files with calculations pKw_300MPa_1981MAR_FRA.nb, pKw_300MPa_2024ARC_BAC.nb), and their copies in the pdf format (pKw_300MPa_1981MAR_FRA.pdf, pKw_300MPa_2024ARC_BAC.pdf) with results of calculating the Gibbs energy, G2(o), of the HOH electrolyte and pKw on the basis two variants of the proposed model, which were parameterized using two literature formulations for water ionization.

3). Excel files (673-1273 K pKw 2024ARC_BAC.xlsx, 673-1273 K pKw 1981MAR_FRA.xlsx) with calculated values of pKw at 673, 873, 1073 and 1273 K over wide range of water densities, and comparison with results of two literature formulations for water ionization (either 2024ARC_BAC or 1981MAR_FRA).
DOI: 10.15468/dl.9u9j9z
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"equals","key":"TAXON_KEY","value":"7019161","matchCase":false},{"type":"equals","key":"OCCURRENCE_STATUS","value":"PRESENT","matchCase":false},{"type":"equals","key":"HAS_COORDINATE","value":"true","matchCase":false},{"type":"equals","key":"HAS_GEOSPATIAL_ISSUE","value":"false","matchCase":false},{"type":"lessThan","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"in","key":"YEAR","values":["2008","2009","2010","2011","2012","2013","2014","2015","2016","2017","2018","2019","2020","2021","2022","2023","2024"],"matchCase":false},{"type":"in","key":"BASIS_OF_RECORD","values":["LIVING_SPECIMEN","HUMAN OBSERVATION","OBSERVATION","OCCURRENCE","MACHINE_OBSERVATION"],"matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024805-241024112534372/datasets/export for details.
DOI: 10.4226/47/5ad57a1d6dbb1
title: Overconfidence and the conjunction fallacy in sports betting behaviour

description: Respondents were asked whether or not they would bet on the outcomes of eight single pairs of NRL matches. Sports betting behaviour, gambling consumption, overconfidence and other demographics were also obtained.
description: Data Processing: Stata/IC 14.2
description: Data was collected by Dedar Abjula, Leigh Grant and Alison Bowling, by means of a Qualtrix survey.
description: Data was collected by Dedar Abjula, Leigh Grant and Alison Bowling, by means of a Qualtrix survey.
DOI: 10.15468/dl.bsageu
title: Occurrence Download

description: A dataset containing 35791 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Carnegiea gigantea (Engelm.) Britton & Rose"
 ]
}

 The dataset includes 35791 records from 43 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024804-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hm8xdh
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"equals","key":"TAXON_KEY","value":"7019163","matchCase":false},{"type":"equals","key":"OCCURRENCE_STATUS","value":"PRESENT","matchCase":false},{"type":"equals","key":"HAS_COORDINATE","value":"true","matchCase":false},{"type":"equals","key":"HAS_GEOSPATIAL_ISSUE","value":"false","matchCase":false},{"type":"lessThan","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"in","key":"YEAR","values":["2008","2009","2010","2011","2012","2013","2014","2015","2016","2017","2018","2019","2020","2021","2022","2023","2024"],"matchCase":false},{"type":"in","key":"BASIS_OF_RECORD","values":["LIVING_SPECIMEN","HUMAN OBSERVATION","OBSERVATION","OCCURRENCE","MACHINE_OBSERVATION"],"matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024806-241024112534372/datasets/export for details.
DOI: 10.15468/dl.3b6ejx
title: Occurrence Download

description: A dataset containing 162 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Myriophyllum oguraense Miki"
 ]
}

 The dataset includes 162 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024807-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xup5hp
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"equals","key":"TAXON_KEY","value":"7019165","matchCase":false},{"type":"equals","key":"OCCURRENCE_STATUS","value":"PRESENT","matchCase":false},{"type":"equals","key":"HAS_COORDINATE","value":"true","matchCase":false},{"type":"equals","key":"HAS_GEOSPATIAL_ISSUE","value":"false","matchCase":false},{"type":"lessThan","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"in","key":"YEAR","values":["2008","2009","2010","2011","2012","2013","2014","2015","2016","2017","2018","2019","2020","2021","2022","2023","2024"],"matchCase":false},{"type":"in","key":"BASIS_OF_RECORD","values":["LIVING_SPECIMEN","HUMAN OBSERVATION","OBSERVATION","OCCURRENCE","MACHINE_OBSERVATION"],"matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024808-241024112534372/datasets/export for details.
DOI: 10.15468/dl.qsqyja
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"equals","key":"TAXON_KEY","value":"7019166","matchCase":false},{"type":"equals","key":"OCCURRENCE_STATUS","value":"PRESENT","matchCase":false},{"type":"equals","key":"HAS_COORDINATE","value":"true","matchCase":false},{"type":"equals","key":"HAS_GEOSPATIAL_ISSUE","value":"false","matchCase":false},{"type":"lessThan","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"in","key":"YEAR","values":["2008","2009","2010","2011","2012","2013","2014","2015","2016","2017","2018","2019","2020","2021","2022","2023","2024"],"matchCase":false},{"type":"in","key":"BASIS_OF_RECORD","values":["LIVING_SPECIMEN","HUMAN OBSERVATION","OBSERVATION","OCCURRENCE","MACHINE_OBSERVATION"],"matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024809-241024112534372/datasets/export for details.
DOI: 10.25918/data.186
title: SNP genotype data of 158 diverse Indian mustard lines

description: Glucosinolates (GSLs) are sulphur- and nitrogen-containing secondary metabolites implicated in the fitness of Brassicaceae and appreciated for their pungency and health-conferring properties. In Indian mustard (Brassica juncea L.), GSL content and composition are seed-quality-determining traits affecting its economic value. Depending on the end use, i.e., condiment or oil, different GSL levels constitute breeding targets. The genetic control of GSL accumulation in Indian mustard, however, is poorly understood, and current knowledge of GSL biosynthesis and regulation is largely based on Arabidopsis thaliana. A genome-wide association study was carried out to dissect the genetic architecture of total GSL content and the content of two major GSLs, sinigrin and gluconapin, in a diverse panel of 158 Indian mustard lines, which broadly grouped into a South Asia cluster and outside-South-Asia cluster. Using 14,125 single-nucleotide polymorphisms (SNPs) as genotyping input, seven distinct significant associations were discovered for total GSL content, eight associations for sinigrin content and 19 for gluconapin. Close homologues of known GSL structural and regulatory genes were identified as candidate genes in proximity to peak SNPs. Our results provide a comprehensive map of the genetic control of GLS biosynthesis in Indian mustard, including priority targets for further investigation and molecular marker development.
DOI: 10.15468/dl.f9bjwm
title: Occurrence Download

description: A dataset containing 151 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Ireland",
 "TaxonKey is Lasius flavus (Fabricius, 1782)"
 ]
}

 The dataset includes 151 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024810-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kgcv4b
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"equals","key":"TAXON_KEY","value":"7019167","matchCase":false},{"type":"equals","key":"OCCURRENCE_STATUS","value":"PRESENT","matchCase":false},{"type":"equals","key":"HAS_COORDINATE","value":"true","matchCase":false},{"type":"equals","key":"HAS_GEOSPATIAL_ISSUE","value":"false","matchCase":false},{"type":"lessThan","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"in","key":"YEAR","values":["2008","2009","2010","2011","2012","2013","2014","2015","2016","2017","2018","2019","2020","2021","2022","2023","2024"],"matchCase":false},{"type":"in","key":"BASIS_OF_RECORD","values":["LIVING_SPECIMEN","HUMAN OBSERVATION","OBSERVATION","OCCURRENCE","MACHINE_OBSERVATION"],"matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024812-241024112534372/datasets/export for details.
DOI: 10.25918/data.193
title: Heron Island 2016 - dissolved DMS, CH4, CO2, O2 concentrations and environmental data

description: This is a full dataset collected on Heron Island in February 2016. It contains dissolved DMS, CH4, CO2, O2 concentrations as well as environmental data collected simultaneously.
DOI: 10.15468/dl.zqe8qc
title: Occurrence Download

description: A dataset containing 17640 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Myriophyllum sibiricum Kom."
 ]
}

 The dataset includes 17640 records from 259 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024811-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.426aym
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"equals","key":"TAXON_KEY","value":"7019169","matchCase":false},{"type":"equals","key":"OCCURRENCE_STATUS","value":"PRESENT","matchCase":false},{"type":"equals","key":"HAS_COORDINATE","value":"true","matchCase":false},{"type":"equals","key":"HAS_GEOSPATIAL_ISSUE","value":"false","matchCase":false},{"type":"lessThan","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"in","key":"YEAR","values":["2008","2009","2010","2011","2012","2013","2014","2015","2016","2017","2018","2019","2020","2021","2022","2023","2024"],"matchCase":false},{"type":"in","key":"BASIS_OF_RECORD","values":["LIVING_SPECIMEN","HUMAN OBSERVATION","OBSERVATION","OCCURRENCE","MACHINE_OBSERVATION"],"matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024814-241024112534372/datasets/export for details.
DOI: 10.15468/dl.pqeefz
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"equals","key":"TAXON_KEY","value":"7019170","matchCase":false},{"type":"equals","key":"OCCURRENCE_STATUS","value":"PRESENT","matchCase":false},{"type":"equals","key":"HAS_COORDINATE","value":"true","matchCase":false},{"type":"equals","key":"HAS_GEOSPATIAL_ISSUE","value":"false","matchCase":false},{"type":"lessThan","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"in","key":"YEAR","values":["2008","2009","2010","2011","2012","2013","2014","2015","2016","2017","2018","2019","2020","2021","2022","2023","2024"],"matchCase":false},{"type":"in","key":"BASIS_OF_RECORD","values":["LIVING_SPECIMEN","HUMAN OBSERVATION","OBSERVATION","OCCURRENCE","MACHINE_OBSERVATION"],"matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024815-241024112534372/datasets/export for details.
DOI: 10.5281/zenodo.14041473
title: List of 403 Keywords Related to Visual Impairment, Ocular Diseases and Eye Conditions
DOI: 10.5281/zenodo.14041474
title: List of 403 Keywords Related to Visual Impairment, Ocular Diseases and Eye Conditions
DOI: 10.5281/zenodo.14040877
title: CMIP6-based local-scale climate scenarios for impact assessment in Great Britain.

description: Climate change impact assessments require local-scale climate scenarios. The climate change projections from Global Climate Models (GCMs) are difficult to use at local scale due to their coarse spatial and temporal resolution. It is important to have climate change scenarios based on GCMs climate projections GCMs ensembles, e.g. CMIP6, downscaled to local scale to account for their inherent uncertainty, and to generate a sufficient large number of realisations to account for inter-annual climate variability and low frequency but high impact extreme climatic events. A dataset of future climate change scenarios was therefore generated at 26 representative sites across the UK based on the latest CMIP6 multi-model ensemble downscaled to local-scale by using a stochastic weather generator LARS-WG 7.0. The data set provides 1,000 years of daily weather at each selected site for a baseline (1985-2015), and very near- (2030) and near-future (2050) climate change scenarios, based on five GCMs and two emission scenarios (Shared Socioeconomic Pathways - SSPs viz. SSP2-4.5 and SSP5-8.5). A total of 15 GCMs from the CMIP6 ensemble were integrated in LARS-WG 7.0. LARS-WG downscales future climate projections from the GCMs and incorporates changes at local scale in the mean climate, climatic variability, and extreme events by modifying the statistical distributions of the weather variables at each site. Based on the performance of the GCMs over northern Europe and their climate sensitivity, a subset of five GCMs was selected, viz.; ACCESS-ESM1-5, CNRM-CM6-1, HadGEM3-GC31-LL, MPI-ESM1-2-LR and MRI-ESM2-0. The selected GCMs are evenly distributed among the full set of 15 GCMs. The use of a subset of GCMs substantially reduces computational time, while allowing assessment of uncertainties in impact studies related to uncertain future climate projections arising from GCMs. The 1000 years of realisations of daily weather for the baseline as well as future climate change scenarios are helpful for estimating seasonality and inter-annual variation, and for detecting short, low frequency but high impact extreme climatic signals, such as heat waves, floods and drought events. The dataset can be used as an input to climate change impact models in various fields, including, land and water resources, agriculture and food production, ecology and epidemiology, and human health and welfare. Researchers, breeders, farm and programme managers, social and public sector leaders, and policymakers may benefit from this new dataset when undertaking impact assessment of climate change and decision support for mitigation and adaptation.
DOI: 10.5281/zenodo.10556985
title: CMIP6-based local-scale climate scenarios for impact assessment in Great Britain.

description: Climate change impact assessments require local-scale climate scenarios. The climate change projections from Global Climate Models (GCMs) are difficult to use at local scale due to their coarse spatial and temporal resolution. It is important to have climate change scenarios based on GCMs climate projections GCMs ensembles, e.g. CMIP6, downscaled to local scale to account for their inherent uncertainty, and to generate a sufficient large number of realisations to account for inter-annual climate variability and low frequency but high impact extreme climatic events. A dataset of future climate change scenarios was therefore generated at 26 representative sites across the UK based on the latest CMIP6 multi-model ensemble downscaled to local-scale by using a stochastic weather generator LARS-WG 7.0. The data set provides 1,000 years of daily weather at each selected site for a baseline (1985-2015), and very near- (2030) and near-future (2050) climate change scenarios, based on five GCMs and two emission scenarios (Shared Socioeconomic Pathways - SSPs viz. SSP2-4.5 and SSP5-8.5). A total of 15 GCMs from the CMIP6 ensemble were integrated in LARS-WG 7.0. LARS-WG downscales future climate projections from the GCMs and incorporates changes at local scale in the mean climate, climatic variability, and extreme events by modifying the statistical distributions of the weather variables at each site. Based on the performance of the GCMs over northern Europe and their climate sensitivity, a subset of five GCMs was selected, viz.; ACCESS-ESM1-5, CNRM-CM6-1, HadGEM3-GC31-LL, MPI-ESM1-2-LR and MRI-ESM2-0. The selected GCMs are evenly distributed among the full set of 15 GCMs. The use of a subset of GCMs substantially reduces computational time, while allowing assessment of uncertainties in impact studies related to uncertain future climate projections arising from GCMs. The 1000 years of realisations of daily weather for the baseline as well as future climate change scenarios are helpful for estimating seasonality and inter-annual variation, and for detecting short, low frequency but high impact extreme climatic signals, such as heat waves, floods and drought events. The dataset can be used as an input to climate change impact models in various fields, including, land and water resources, agriculture and food production, ecology and epidemiology, and human health and welfare. Researchers, breeders, farm and programme managers, social and public sector leaders, and policymakers may benefit from this new dataset when undertaking impact assessment of climate change and decision support for mitigation and adaptation.
DOI: 10.5281/zenodo.10556986
title: CMIP6-based local-scale climate scenarios for impact assessment in Great Britain.

description: Climate change impact assessments require local-scale climate scenarios. The climate change projections from Global Climate Models (GCMs) are difficult to use at local scale due to their coarse spatial and temporal resolution. It is important to have climate change scenarios based on GCMs climate projections GCMs ensembles, e.g. CMIP6, downscaled to local scale to account for their inherent uncertainty, and to generate a sufficient large number of realisations to account for inter-annual climate variability and low frequency but high impact extreme climatic events. A dataset of future climate change scenarios was therefore generated at 26 representative sites across the UK based on the latest CMIP6 multi-model ensemble downscaled to local-scale by using a stochastic weather generator LARS-WG 7.0. The data set provides 1,000 years of daily weather at each selected site for a baseline (1985-2015), and very near- (2030) and near-future (2050) climate change scenarios, based on five GCMs and two emission scenarios (Shared Socioeconomic Pathways - SSPs viz. SSP2-4.5 and SSP5-8.5). A total of 15 GCMs from the CMIP6 ensemble were integrated in LARS-WG 7.0. LARS-WG downscales future climate projections from the GCMs and incorporates changes at local scale in the mean climate, climatic variability, and extreme events by modifying the statistical distributions of the weather variables at each site. Based on the performance of the GCMs over northern Europe and their climate sensitivity, a subset of five GCMs was selected, viz.; ACCESS-ESM1-5, CNRM-CM6-1, HadGEM3-GC31-LL, MPI-ESM1-2-LR and MRI-ESM2-0. The selected GCMs are evenly distributed among the full set of 15 GCMs. The use of a subset of GCMs substantially reduces computational time, while allowing assessment of uncertainties in impact studies related to uncertain future climate projections arising from GCMs. The 1000 years of realisations of daily weather for the baseline as well as future climate change scenarios are helpful for estimating seasonality and inter-annual variation, and for detecting short, low frequency but high impact extreme climatic signals, such as heat waves, floods and drought events. The dataset can be used as an input to climate change impact models in various fields, including, land and water resources, agriculture and food production, ecology and epidemiology, and human health and welfare. Researchers, breeders, farm and programme managers, social and public sector leaders, and policymakers may benefit from this new dataset when undertaking impact assessment of climate change and decision support for mitigation and adaptation.
DOI: 10.5281/zenodo.10609248
title: Mapping of STEAM practices - anonymised dataset

description: This dataset is the result of the initial dissemination of a survey submitted to STEAM practitioners, aiming at gaining insight on their on-going STEAM practices, which have informed the project's mapping of STEAM practices (Deliverable 4.2).
DOI: 10.5281/zenodo.14041365
title: Metagenomics reveals sex differences in murine fecal microbiota profile induced by chronic alcohol consumption
DOI: 10.5281/zenodo.14041366
title: Metagenomics reveals sex differences in murine fecal microbiota profile induced by chronic alcohol consumption