cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.d4mstp
title: Occurrence Download

description: A dataset containing 124866 species occurrences available in GBIF matching the query:
{
 "and" : [
 "MediaType is Image",
 "TaxonKey is one of (Otis tarda, Pagophila eburnea, Oriolus oriolus, Oena capensis, Otus scops, Otus brucei, Passerculus sandwichensis, Passer domesticus domesticus, Passer montanus montanus, Passer ammodendri, Passer hispaniolensis, Oenanthe pleschanka, Oenanthe hispanica, Oenanthe picata, Oenanthe deserti, Otus bakkamoena, Otus scops pulchellus, Otus sunia, Oenanthe chrysopygia, Oenanthe melanoleuca, Onychoprion aleuticus, Passer domesticus indicus, Panurus biarmicus russicus, Passer montanus dilutus, Passer hispaniolensis transcaspicus, Passer domesticus bactrianus, Passer ammodendri nigricans, Parus cinereus, Passerella iliaca unalaschcensis, Paradoxornis heudei polivanovi, Pandion haliaetus haliaetus, Oenanthe oenanthe oenanthe, Passer ammodendri ammodendri, Parus major ferghanensis, Parus major bokharensis, Parus major turkestanicus, Passer zarudnyi, Parus major major, Oriolus kundoo, Oriolus chinensis, Passer cinnamomeus rutilans)"
 ]
}

 The dataset includes 124866 records from 72 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006923-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.u95zwv
title: Occurrence Download

description: A dataset containing 620027 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Assiculus punctatus, Blennodesmus scapularis, Congrogadus subducens, Cypho purpurascens, Haliophis guttatus, Labracinus cyclophthalmus, Labracinus lineatus, Lubbockichthys multisquamatus, Manonichthys alleni, Manonichthys paranox, Manonichthys polynemus, Manonichthys splendens, Ogilbyina novaehollandiae, Ogilbyina queenslandiae, Oxycercichthys veliferus, Pholidochromis cerasina, Pictichromis aurifrons, Pictichromis coralensis, Pictichromis diadema, Pictichromis dinar, Pictichromis paccagnellorum, Pictichromis porphyrea, Pseudochromis aldabraensis, Pseudochromis bitaeniatus, Pseudochromis coccinicauda, Pseudochromis cyanotaenia, Pseudochromis dilectus, Pseudochromis dixurus, Pseudochromis dutoiti, Pseudochromis elongatus, Pseudochromis flammicauda, Pseudochromis flavivertex, Pseudochromis fridmani, Pseudochromis fuligifinis, Pseudochromis fuscus, Pseudochromis jamesi, Pseudochromis luteus, Pseudochromis marshallensis, Pseudochromis olivaceus, Pseudochromis omanensis, Pseudochromis persicus, Pseudochromis perspicillatus, Pseudochromis pesi, Pseudochromis sankeyi, Pseudochromis springeri, Pseudochromis steenei, Pseudochromis tapeinosoma, Pseudochromis tonozukai, Pseudochromis viridis, Pseudochromis wilsoni, Pseudoplesiops rosae, Pseudoplesiops typus, Artedius harringtoni, Trematomus bernacchii, Cymbacephalus beauforti, Caracanthus maculatus, Caracanthus madagascariensis, Caracanthus unipinna, Dendrochirus barberi, Dendrochirus biocellatus, Dendrochirus brachypterus, Dendrochirus zebra, Parapterois heterura, Pteroidichthys amboinensis, Pterois antennata, Pterois lunulata, Pterois miles, Pterois paucispinula, Pterois radiata, Pterois russelii, Pterois sphex, Pterois volitans, Rhinopias aphanes, Rhinopias frondosa, Scorpaena brasiliensis, Scorpaena inermis, Scorpaena miostoma, Scorpaena plumieri, Scorpaena scrofa, Scorpaenodes caribbaeus, Scorpaenodes parvipinnis, Scorpaenodes xyris, Scorpaenopsis cacopsis, Scorpaenopsis diabolus, Sebastapistes coniorta, Sebastapistes cyanostigma, Taenianotus triacanthus, Sebastes chrysomelas, Chaetodontoplus meredithi, Sebastes emphaeus, Sebastes flavidus, Holacanthus limbaughi, Sebastes miniatus, Sebastes mystinus, Sebastes nebulosus, Amblyglyphidodon ternatensis, Amphiprion mccullochi, Chrysiptera arnazae, Sebastes serranoides, Inimicus didactylus)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 620027 records from 457 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006972-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25403/upresearchdata.30016897
title: Artificial intelligence (AI) model responses to tax assessment questions at the university second-year level

description: This study analyses the accuracy of closed-source AI models in responding to second-year taxation assessments at UP. The research question guiding the study was: How accurately can AI models answer taxation assessments from the University of Pretoria and align with official memoranda? To address this, three objectives were pursued: (1) to measure the alignment between AI-generated responses and model answers, (2) to compare the performance of different AI models, and (3) to assess the models’ mastery of the RBT cognitive dimensions. Adopting a pragmatic philosophy, the study employed a mixed-methods design. Quantitative accuracy was tested by benchmarking AI responses against official memoranda, while qualitative analysis coded responses into RBT categories. Data were drawn from the 2024 TXA201 exam and supplementary exam and analysed using a standardised coding framework and predetermined prompts. The findings demonstrate that AI models excel at lower-order cognitive tasks such as remembering, understanding, and applying, but their performance declines with higher-order tasks requiring analysis, evaluation, and creation. The study concludes that AI holds potential as a supplementary tool in taxation education, yet it cannot replace human reasoning, judgment, and contextual interpretation.<br>
DOI: 10.25403/upresearchdata.30016897.v1
title: Artificial intelligence (AI) model responses to tax assessment questions at the university second-year level

description: This study analyses the accuracy of closed-source AI models in responding to second-year taxation assessments at UP. The research question guiding the study was: How accurately can AI models answer taxation assessments from the University of Pretoria and align with official memoranda? To address this, three objectives were pursued: (1) to measure the alignment between AI-generated responses and model answers, (2) to compare the performance of different AI models, and (3) to assess the models’ mastery of the RBT cognitive dimensions. Adopting a pragmatic philosophy, the study employed a mixed-methods design. Quantitative accuracy was tested by benchmarking AI responses against official memoranda, while qualitative analysis coded responses into RBT categories. Data were drawn from the 2024 TXA201 exam and supplementary exam and analysed using a standardised coding framework and predetermined prompts. The findings demonstrate that AI models excel at lower-order cognitive tasks such as remembering, understanding, and applying, but their performance declines with higher-order tasks requiring analysis, evaluation, and creation. The study concludes that AI holds potential as a supplementary tool in taxation education, yet it cannot replace human reasoning, judgment, and contextual interpretation.<br>
DOI: 10.15468/dl.vmprat
title: Occurrence Download

description: A dataset containing 36189 records in GBIF matching the query:
SELECT
 kingdom,
 kingdomkey,
 phylum,
 phylumkey,
 class,
 classkey,
 "order",
 orderkey,
 family,
 familykey,
 genus,
 genuskey,
 species,
 specieskey,
 "year",
 IF(ISNULL(kingdomkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY kingdomkey, "year")) kingdomcount,
 IF(ISNULL(phylumkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY phylumkey, "year")) phylumcount,
 IF(ISNULL(classkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY classkey, "year")) classcount,
 IF(ISNULL(orderkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY orderkey, "year")) ordercount,
 IF(ISNULL(familykey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY familykey, "year")) familycount,
 IF(ISNULL(genuskey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY genuskey, "year")) genuscount,
 COUNT(*) occurrences,
 MIN(GBIF_TEMPORALUNCERTAINTY(eventdate, eventtime)) mintemporaluncertainty,
 MIN(COALESCE(coordinateuncertaintyinmeters, 1000)) mincoordinateuncertaintyinmeters
FROM
 occurrence
WHERE
 occurrence.countrycode = 'UG'
 AND occurrence.occurrencestatus = 'PRESENT'
 AND (occurrence."year" &gt;= 2001 AND occurrence."year" &lt;= 2025)
 AND occurrence.hasgeospatialissues = FALSE
 AND NOT GBIF_STRINGARRAYCONTAINS(occurrence.issue, 'TAXON_MATCH_FUZZY', TRUE)
 AND (occurrence.distancefromcentroidinmeters &gt;= 2000.0 OR occurrence.distancefromcentroidinmeters IS NULL)
 AND NOT occurrence.basisofrecord IN ('FOSSIL_SPECIMEN', 'LIVING_SPECIMEN')
 AND (occurrence.specieskey IS NOT NULL AND occurrence."year" IS NOT NULL)
GROUP BY
 occurrence.kingdom,
 occurrence.kingdomkey,
 occurrence.phylum,
 occurrence.phylumkey,
 occurrence.class,
 occurrence.classkey,
 occurrence."order",
 occurrence.orderkey,
 occurrence.family,
 occurrence.familykey,
 occurrence.genus,
 occurrence.genuskey,
 occurrence.species,
 occurrence.specieskey,
 occurrence."year"

 The dataset's 36189 records were derived from 249 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006960-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8283ky
title: Occurrence Download

description: A dataset listing the 34 species recorded in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((26.49275 -27.16318,26.98978 -27.16318,26.98978 -26.72876,26.49275 -26.72876,26.49275 -27.16318))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Squamata"
 ]
}

 The dataset's 34 records were derived from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006980-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.30138520
title: Figure2_data

description: Data for figure 2
DOI: 10.6084/m9.figshare.30138520.v1
title: Figure2_data

description: Data for figure 2
DOI: 10.15468/dl.eazvh4
title: Occurrence Download

description: A dataset containing 1513 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Galanthus elwesii",
 "Country is Sweden",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1513 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007020-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6m9g8u
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Galanthus elwesii",
 "Country is Ireland",
 "HasCoordinate is true"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007017-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qxsc46
title: Occurrence Download

description: A dataset containing 18326 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Abies grandis",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 18326 records from 288 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007018-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cdr3cv
title: Occurrence Download

description: A dataset containing 1786 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Galanthus elwesii",
 "Country is Netherlands",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1786 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007019-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16984591
title: Visibile radiation-driven photomineralization and photoproduction of dissolved organic matter in a large estuary: Implications for coastal ocean biogeochemical cycles

description: This is the supplemental data for the manuscript, titled "Visibile radiation-driven photomineralization and photoproduction of dissolved organic matter in a large estuary: Implications for coastal ocean biogeochemical cycles", submitted to Journal of Geophysical Research - Oceans.
DOI: 10.5281/zenodo.17133706
title: Visibile radiation-driven photomineralization and photoproduction of dissolved organic matter in a large estuary: Implications for coastal ocean biogeochemical cycles

description: This is the supplemental data for the manuscript, titled "Visibile radiation-driven photomineralization and photoproduction of dissolved organic matter in a large estuary: Implications for coastal ocean biogeochemical cycles", submitted to Journal of Geophysical Research - Oceans.
DOI: 10.5281/zenodo.17135582
title: Random forest code for downscaling county-level livestock statistics to 1 km resolution using environmental factors

description: We introduce a random forest–based framework designed to downscale county-level livestock census data to a fine spatial resolution of 1 km by integrating key environmental factors (e.g., climate, topography, and vegetation indices). This approach addresses the spatial mismatch between administrative statistics and ecological processes, thereby providing more accurate and ecologically meaningful estimates of livestock distribution.

 

We provide code for relevance analysis to screen environmental variables as well as code for the random forest model. The county-level livestock census data for the Qinghai-Tibet Plateau from 2000 and 2020 used to build the random forest model, along with the corresponding environmental factor tables, are also provided.
DOI: 10.5281/zenodo.17107042
title: ECMWF daily maximum air temperature exceedance of 27°C - Kent (UK), 2022

description: Demonstration Case Name



Multi-hazards in the Southeast UK region





Dataset Title



ECMWF daily maximum air temperature exceedance of 27°C - Kent (UK), 2022





Dataset Description



This dataset consists of:



ECMWF_Daily_AirTmax_Kent2022_1km.tif: a multi-layer image where each layer represents the ECMWF Daily Maximum Air Temperature at 2m for each date in 2022 for the wider Kent region in Southeast UK.

ECMWF_Daily_AirTmax_exceeding_27C_binary_mask_Kent2022_1km.tif: a multi-layer image where each layer represents where the ECMWF Daily Maximum Air Temperature at 2m exceeds 27°C, UK Met Office threshold for heatwave conditions, for each date in 2022 for the wider Kent region in Southeast UK.

ECMWF_Daily_AirTmax_exceeding_27C_mask_applied_Kent2022.tif: a multi-layer image where each layer represents the ECMWF Daily Maximum Air Temperature at 2m values after application of the binary mask for each date in 2022 for the wider Kent region in Southeast UK.


Data was generated using code developed within the Google Earth Engine online platform and output in GeoTIFF formats for interoperability and comparative analysis within GIS.





Key Methodologies



Calculation of pixel threshold exceedance of 27°C for each day on the 2022 daily air temperature variable (air_temperature_2m_max) multi-date image stack, coded in Google Earth Engine online platform. Output in raster format.





Temporal Domain



Daily: 1st January 2022 to 31st December 2022.





Spatial Domain



Geographic extent covering the region of Kent, Southeast UK.

Projected Coordinate System: British National Grid (EPSG:27700)

Spatial Extent Bounding Box: Upper Left (182000N, 509000E), Lower Right (95000N, 641000E)

Spatial Resolution: 1km x 1km (resampled from original 11km data in GEE)





Key Indicators



Daily binary image stack indicating where pixels exceed the temperature threshold of 27°C, temperature threshold indicated by the UK Met Office to signify a heatwave in Kent.





Data Format



GeoTIFF (*.tif)





Source Data



Primary source: ECMWF Climate Reanalysis ERA5-Land Daily Aggregated dataset (variable: ‘air_temperature_2m_max’), accessed through the Google Earth Engine online platform (GEE) made available to by the Copernicus Climate Data Store (https://cds.climate.copernicus.eu/) under CC-BY-4.0 licence (https://creativecommons.org/licenses/by/4.0/).





Accessibility



10.5281/zenodo.17130997





Stakeholder Relevance



Urban planning departmentsEmergency response organizationsInfrastructure development teamsDisaster risk assessment analysts





Limitations/Assumptions



Original spatial resolution of data 11km (as available in GEE).





Additional Information



Technical specifications: GeoTIFF format require use of a multi-band image visualisation package

Binary pixel values: 0 representing no threshold exceedance, 1 representing threshold exceedance

Non binary mask images: pixel values represent the ECMWF Daily Maximum Air Temperature at 2m within that pixel.





Contact Information



kmcm@bgs.ac.uk
DOI: 10.5281/zenodo.17130997
title: ECMWF daily maximum air temperature exceedance of 27°C - Kent (UK), 2022

description: Demonstration Case Name



Multi-hazards in the Southeast UK region





Dataset Title



ECMWF daily maximum air temperature exceedance of 27°C - Kent (UK), 2022





Dataset Description



This dataset consists of:



ECMWF_Daily_AirTmax_Kent2022_1km.tif: a multi-layer image where each layer represents the ECMWF Daily Maximum Air Temperature at 2m for each date in 2022 for the wider Kent region in Southeast UK.

ECMWF_Daily_AirTmax_exceeding_27C_binary_mask_Kent2022_1km.tif: a multi-layer image where each layer represents where the ECMWF Daily Maximum Air Temperature at 2m exceeds 27°C, UK Met Office threshold for heatwave conditions, for each date in 2022 for the wider Kent region in Southeast UK.

ECMWF_Daily_AirTmax_exceeding_27C_mask_applied_Kent2022.tif: a multi-layer image where each layer represents the ECMWF Daily Maximum Air Temperature at 2m values after application of the binary mask for each date in 2022 for the wider Kent region in Southeast UK.


Data was generated using code developed within the Google Earth Engine online platform and output in GeoTIFF formats for interoperability and comparative analysis within GIS.





Key Methodologies



Calculation of pixel threshold exceedance of 27°C for each day on the 2022 daily air temperature variable (air_temperature_2m_max) multi-date image stack, coded in Google Earth Engine online platform. Output in raster format.





Temporal Domain



Daily: 1st January 2022 to 31st December 2022.





Spatial Domain



Geographic extent covering the region of Kent, Southeast UK.

Projected Coordinate System: British National Grid (EPSG:27700)

Spatial Extent Bounding Box: Upper Left (182000N, 509000E), Lower Right (95000N, 641000E)

Spatial Resolution: 1km x 1km (resampled from original 11km data in GEE)





Key Indicators



Daily binary image stack indicating where pixels exceed the temperature threshold of 27°C, temperature threshold indicated by the UK Met Office to signify a heatwave in Kent.





Data Format



GeoTIFF (*.tif)





Source Data



Primary source: ECMWF Climate Reanalysis ERA5-Land Daily Aggregated dataset (variable: ‘air_temperature_2m_max’), accessed through the Google Earth Engine online platform (GEE) made available to by the Copernicus Climate Data Store (https://cds.climate.copernicus.eu/) under CC-BY-4.0 licence (https://creativecommons.org/licenses/by/4.0/).





Accessibility



10.5281/zenodo.17130997





Stakeholder Relevance



Urban planning departmentsEmergency response organizationsInfrastructure development teamsDisaster risk assessment analysts





Limitations/Assumptions



Original spatial resolution of data 11km (as available in GEE).





Additional Information



Technical specifications: GeoTIFF format require use of a multi-band image visualisation package

Binary pixel values: 0 representing no threshold exceedance, 1 representing threshold exceedance

Non binary mask images: pixel values represent the ECMWF Daily Maximum Air Temperature at 2m within that pixel.





Contact Information



kmcm@bgs.ac.uk
DOI: 10.15468/dl.2b8m3x
title: Occurrence Download

description: A dataset containing 19027 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Ecuador",
 "TaxonKey is Tangara chilensis"
 ]
}

 The dataset includes 19027 records from 38 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007022-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17107311
title: ECMWF daily maximum precipitation under 0.1mm - Kent (UK), 2022

description: Demonstration Case Name



Multi-hazards in the Southeast UK region





Dataset Title



ECMWF daily maximum precipitation under 0.1mm - Kent (UK), 2022





Dataset Description



This dataset consists of a multi-layer image, where each layer represents the individual date within 2022 and pixel values for each date representing where the ECMWF daily maximum precipitation is under the threshold of 0.1mm, the threshold indicative of drought conditions.

Data was generated using code developed within the Goolge Earth Engine online platform and output in GeoTIFF and ENVI format for interoperabilty and comparative analysis within GIS.





Key Methodologies



Calculation where pixels are under a threshold of 0.1mm for each date in the 2022 daily precipitation variable (total_precipitation_max) multi-date image stack, coded in Google Earth Engine online platform. Output in raster format.





Temporal Domain



Daily: 1st January 2022 to 31st December 2022.





Spatial Domain



Geographic extent covering the region of Kent, Southeast UK.

Projected Coordinate System: British National Grid (EPSG:27700)

Spatial Extent Bounding Box: Upper Left (182000N, 509000E), Lower Right (95000N, 641000E)

Spatial Resolution: 1km x 1km (resampled from original 11km data in GEE)





Key Indicators



Daily binary image stack indicating where pixels fail to reach the precipitation threshold of 0.1mm, threshold indicative of drought conditions.





Data Format



GeoTIFF (*.tif)





Source Data



Primary source: ECMWF Climate Reanalysis ERA5-Land Daily Aggregated dataset (variable: ‘total_precipitation_max’), accessed through the Google Earth Engine online platform (GEE) made available to by the Copernicus Climate Data Store (https://cds.climate.copernicus.eu/) under CC-BY-4.0 licence (https://creativecommons.org/licenses/by/4.0/).





Accessibility



10.5281/zenodo.17130868





Stakeholder Relevance



Urban planning departmentsEmergency response organizationsInfrastructure development teamsDisaster risk assessment analysts





Limitations/Assumptions



Original spatial resolution of data 11km (as available in GEE).





Additional Information



Technical specifications: GeoTIFF formats require use of a multi-band image visualisation package

Binary pixel values: 0 representing above threshold, 1 representing beneath threshold.

Non binary mask images: pixel values represent the ECMWF Daily Maximum Precipitation (in mm) within that pixel.





Contact Information



kmcm@bgs.ac.uk