cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/67g56k
title: Lectotypification of three names in the genus Blumea DC. (Asteraceae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Dilsha, Muthalaveettil, Satheesh, George (2023): Lectotypification of three names in the genus Blumea DC. (Asteraceae). Adansonia (3) 45 (11): 161-166, DOI: 10.5252/adansonia2023v45a11ABSTRACTTypification of three names in the genus Blumea DC. (Asteraceae) viz. B. solidaginoides (Poir.) DC., B. floresiana (Sch.Bip. ex Miq.) Boerlage and B. dregeanoides Sch.Bip. ex A.Rich. is discussed and lectotypes are designated.
DOI: 10.15468/dl.fs3kwb
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cenchrus echinatus",
 "Country is Denmark",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033561-250920141307145/datasets/export for details.
DOI: 10.15468/dl.362tbk
title: Occurrence Download

description: A dataset containing 1603 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Geometry POLYGON((-58.81441 -39.77033,-62.95066 -35.68677,-67.03422 -32.60434,-70.38011 -33.4474,-72.30333 -39.19073,-73.48871 -47.67799,-70.63162 -52.62409,-63.82665 -50.4523,-58.81441 -39.77033))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Month is one of (November, December, February, January, October, March)",
 "OccurrenceStatus is Present",
 "TaxonKey is Neoxolmis rubetra",
 "Year 1970-2025"
 ]
}

 The dataset includes 1603 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033559-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8ec799
title: Occurrence Download

description: A dataset containing 18991 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Andrena nigroaenea",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 18991 records from 228 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033557-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17236189
title: Accepted species list of Eurotiales, including a DNA sequence reference database, as curated by the International Commission of Penicillium and Aspergillus (ICPA)

description: Eurotiales is a diverse and speciose order and includes economically important genera like Aspergillus, Penicillium, Paecilomyces and Talaromyces. Historically, species identifications based on morphology are challenging. The publication of accepted species lists and the availability of representative DNA sequences for type strains have contributed greatly towards accurate species identification and facilitated the description of many new species. However, despite current advancements, a proportion of newly described species within these taxonomically challenging genera represent, in fact, existing species, which raises obvious concerns. 

This study thus aimed to further modernise the taxonomy of Eurotiales by addressing key challenges in species identification and classification. Our study objectives were threefold: to review species described after 2023, update the accepted species list, and release a curated DNA sequence dataset to facilitate future species identifications. We conclude that a move to a phylogenetic species concept is necessary but continue to support the inclusion of morphological descriptions and, where possible, associated secondary metabolite, exoenzyme, physiology and ecological data when introducing new species. 

Our list now contains 1393 species classified into four families and 26 genera, with Aspergillus (n=465), Penicillium (n=598) and Talaromyces (n=236) containing the most species. To aid sequence-based identifications and species descriptions under a phylogenetic species concept, we release a curated DNA reference sequence database containing 18837 DNA sequences (3867 ITS, 5277 BenA, 5110 CaM and 4583 RPB2) generated from 5325 strains. Sequences were selected to best cover the infraspecies variation under our current understanding of each species. The sequence database will be kept up to date as new information becomes available. This manuscript presents a major leap towards our goal to facilitate work with Eurotiales, while providing the taxonomic framework to support research excellence related to this important fungal group.

This dataset is curated and kept up to date by the International Commission of Penicillium and Aspergillus (ICPA). If you have questions or suggestions, please get in contact with ICPA members.
DOI: 10.15468/dl.7aarse
title: Occurrence Download

description: A dataset containing 24 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Argentina",
 "GadmGid is ARG.6_1",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Chelonoidis chilensis"
 ]
}

 The dataset includes 24 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033563-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17234763
title: Ovarian tissue transplantation using autologous platelet-rich plasma: a pilot clinical trial
DOI: 10.5281/zenodo.17234764
title: Ovarian tissue transplantation using autologous platelet-rich plasma: a pilot clinical trial
DOI: 10.15468/dl.8k2d83
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pternistis griseostriatus",
 "Country is one of (Brunei Darussalam, Indonesia, Philippines, Timor-Leste, Singapore)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033564-250920141307145/datasets/export for details.
DOI: 10.15468/dl.cd594g
title: Occurrence Download

description: A dataset containing 144 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Colombia",
 "HasCoordinate is true",
 "PublishingOrg is one of (bc709e2f-6eb4-4cbe-a295-e12eed0679f2, cccff716-2694-4209-9f9e-2f7a484465a0, 85be57ed-f187-49c9-b7ff-eaa622e06217, f7f9717e-9e50-4a00-a30f-7b134390a566, 97a3ce10-3e54-11d9-8439-b8a03c50a862, 2a7e3080-28a9-11dd-97cd-b8a03c50a862, 0c0c7309-6a47-4760-9f5c-a48f6d354f75, ad3f9c5f-5021-45a3-a7c4-3e64895f6f79, 1d8b951a-3a02-40ab-aa7a-f37dc0009abf)",
 "TaxonKey is Leptophobia penthica"
 ]
}

 The dataset includes 144 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033566-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ndab8a
title: Occurrence Download

description: A dataset listing the 5778 species recorded in GBIF matching the query:
{
 "and" : [
 "GadmGid is NLD.8.1_1",
 "OccurrenceStatus is Present"
 ]
}

 The dataset's 5778 records were derived from 62 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033543-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.an3php
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cenchrus echinatus",
 "Country is Greece",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033568-250920141307145/datasets/export for details.
DOI: 10.15468/dl.zwgew8
title: Occurrence Download

description: A dataset containing 12 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cenchrus echinatus",
 "Country is El Salvador",
 "HasCoordinate is true"
 ]
}

 The dataset includes 12 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033567-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zfmztw
title: Occurrence Download

description: A dataset containing 4258 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is ARG.6_1",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Vultur gryphus"
 ]
}

 The dataset includes 4258 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033570-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25919/n1wt-z253
title: Heat hazard data for the National Climate Risk Assessment

description: Heat hazard regional change summaries for the National Climate Risk Assessment (NCRA). The heat hazards have been summarised for boundaries relating to the assessment of freshwater and terrestrial natural environments.

Australian Climate Service (ACS) heat hazard datasets (https://github.com/AusClimateService/hazards-heat)
1. TXx - the hottest day of the year
2. TXm - annual mean daily maximum temperature
3. HWF - heatwave frequency

Each hazard dataset includes an ensemble of climate models for four global warming levels (GWLs), 1.2, 1.5, 2.0 and 3.0 degrees Celsius, above a pre-industrial mean for 1850 to 1900.

Either the absolute change (difference to GWL1.2) or the relative change (difference to GWL1.2 divided by GWL1.2) was calculated for each of the higher GWLs compared to GWL1.2.

Regions of interest boundaries
1. Australia
2. NCRA regions
3. Aggregate Ecological Groups (AEGs)
4. Combined NCRA regions and AEGs
5. Level 2 drainage basins from the Australian Hydrological Geospatial Fabric (AHGF)
6. Spatially combined AHGF Level 2 drainage basins and hazard dataset grid cells which intersect with perennial drainage lines from the AHGF Network Streams 
7. Spatially combined AHGF Level 2 drainage basins and hazard dataset grid cells which intersect with ephemeral drainage lines from AHGF Network Streams

For each hazard/GWL-change combination, a set of statistics (the mean, 10th percentile, 50th percentile, 90th percentile and standard deviation) was calculated for each climate model in the ensemble over the regions of interest areas. From these, the median of each statistic from all the models in the ensemble was calculated. 

Collection data files
The outputs are tabular data in comma-separated value (csv) files with one file per hazard/GWL-change combination. The columns are ID and region name that link to the boundary datasets for visualising in GIS software, and the medians of the statistics. Rows are the individual region of interest polygons.
DOI: 10.15468/dl.3nt825
title: Occurrence Download

description: A dataset containing 38 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cenchrus echinatus",
 "Country is Honduras",
 "HasCoordinate is true"
 ]
}

 The dataset includes 38 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033569-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25919/da4s-j545
title: Drought and aridity hazard data for the National Climate Risk Assessment

description: Drought and aridity hazard regional change summaries for the National Climate Risk Assessment (NCRA). The drought and aridity hazards have been summarised for boundaries relating to assessments of freshwater and terrestrial natural environments.

Australian Climate Service (ACS) drought and aridity hazard datasets (https://github.com/AusClimateService/hazards-drought)
1. AI - Aridity index
2. SPI3 - Standardised precipitation index 3

Each hazard dataset includes an ensemble of climate models for four global warming levels (GWLs), 1.2, 1.5, 2.0 and 3.0 degrees Celsius, above a pre-industrial mean for 1850 to 1900.

Either the absolute change (difference to GWL1.2) or the relative change (difference to GWL1.2 divided by GWL1.2) was calculated for each of the higher GWLs compared to GWL1.2.

Regions of interest boundaries
1. Australia
2. NCRA regions
3. Aggregate Ecological Groups (AEGs)
4. Combined NCRA regions and AEGs
5. Level 2 drainage basins from the Australian Hydrological Geospatial Fabric (AHGF)
6. Spatially combined AHGF Level 2 drainage basins and hazard dataset grid cells which intersect with perennial drainage lines from the AHGF Network Streams 
7. Spatially combined AHGF Level 2 drainage basins and hazard dataset grid cells which intersect with ephemeral drainage lines from AHGF Network Streams

For each hazard/GWL-change combination, a set of statistics (the mean, 10th percentile, 50th percentile, 90th percentile and standard deviation) was calculated for each climate model in the ensemble over the regions of interest areas. From these, the median of each statistic from all the models in the ensemble was calculated. 

Collection data files
The outputs are tabular data in comma-separated value (csv) files with one file per hazard/GWL-change combination. The columns are ID and region name that link to the regions of interest boundary datasets for visualising in GIS software, and the medians of the statistics. Rows are the individual region of interest polygons.
DOI: 10.25919/ypgd-bc70
title: Flood hazard maximum 1-day runoff data for the National Climate Risk Assessment

description: Flood hazard maximum 1-day runoff regional change summaries for the National Climate Risk Assessment (NCRA). The flood hazard has been summarised for boundaries relating to the assessment of freshwater natural environments.

Australian Climate Service (ACS) flood hazard datasets (https://github.com/AusClimateService/hazard-flood)
1. Max1day_runoff - Median annual maximum 1-day runoff

Each hazard dataset includes an ensemble of climate models for four global warming levels (GWLs), 1.2, 1.5, 2.0 and 3.0 degrees Celsius, above a pre-industrial mean for 1850 to 1900.

The relative change (difference to GWL1.2 divided by GWL1.2) was calculated for each of the higher GWLs compared to GWL1.2.

Regions of interest boundaries
1. Level 2 drainage basins from the Australian Hydrological Geospatial Fabric (AHGF)

For each hazard/GWL-change combination, a set of statistics (the mean, 10th percentile, 50th percentile, 90th percentile and standard deviation) was calculated for each climate model in the ensemble over the regions of interest areas. From these, the median of each statistic from all the models in the ensemble was calculated. 

Collection data files
The outputs are tabular data in comma-separated value (csv) files with one file per hazard/GWL-change combination. The columns are ID and region name that link to the boundary datasets for visualising in GIS software, and the medians of the statistics. Rows are the individual region of interest polygons.
DOI: 10.25919/30p4-ys29
title: FFDI bushfire hazard data for the National Climate Risk Assessment

description: FFDI bushfire hazard regional change summaries for the National Climate Risk Assessment (NCRA). The FFDI hazard has been summarised for boundaries relating to the assessment of terrestrial natural environments.

Australian Climate Service (ACS) fire hazard datasets (https://github.com/AusClimateService/hazard_fire)
1. FFDI - Forest Fire Danger index, a measure of fire weather, modelled as a function of wind speed, temperature, humidity, and past and recent rainfall
The FFDI hazard is represented as the number of days in a year where the index exceeds 50 (FFDI50), and where the index exceeds 75 (FFDI75).

Each hazard dataset includes an ensemble of climate models for four global warming levels (GWLs), 1.2, 1.5, 2.0 and 3.0 degrees Celsius, above a pre-industrial mean for 1850 to 1900.

The absolute change (difference to GWL1.2) was calculated for each of the higher GWLs compared to GWL1.2. 

Regions of interest boundaries
1. Australia
2. NCRA regions
3. Aggregate Ecological Groups (AEGs)
4. Combined NCRA regions and AEGs

For each hazard/GWL-change combination, a set of statistics (the mean, 10th percentile, 50th percentile, 90th percentile and standard deviation) was calculated for each climate model in the ensemble over the regions of interest areas. From these, the median of each statistic from all the models in the ensemble was calculated. 

Collection data files
The outputs are tabular data in comma-separated value (csv) files with one file per hazard/GWL-change combination. The columns are ID and region name that link to the boundary datasets for visualising in GIS software, and the medians of the statistics. Rows are the individual region of interest polygons.
DOI: 10.25919/rem4-pa26
title: Fisheries and aquaculture data for the National Climate Risk Assessment

description: What is the data?
Projections of marine climate hazards, and marine exploitable animal biomass across Australia's Exclusive Economic Zone and regional areas. 

Hazard data were supplied by the Australian Climate Service (ACS) from data stored on the National Computational Infrastructure (NCI) as part of Project ia39. 

Gridded exploitable animal biomass projection data for the Australian EEZ are from the Fisheries and Marine Ecosystem Model Intercomparison Project (FishMIP, Blanchard and Novaglio 2024). 

Polygons for marine regions were used in this study to derive regional summaries for hazards, cumulative impact scores and animal biomass projections.

Why was it produced?
The fisheries and aquaculture data presented here is part of the National Climate Risk Assessment (NCRA) and supported the assessment of Australian primary industries (Darbyshire et al., 2025). Atmospheric projection data used in Darbyshire et al., 2025. can be found in https://data.csiro.au/collection/64751.

References:
Blanchard, J.L., Novaglio, C., 2024. Climate change risks to marine ecosystems and fisheries – Projections to 2100 from the Fisheries and Marine Ecosystem Model Intercomparison Project, FAO Fisheries and Aquaculture Technical Paper, No. 707. Rome, FAO. https://doi.org/10.4060/cd1379en

Darbyshire R, Blamey L, Bolt A, Brodie S, Buhagiar L, Bustamante RH, Greenwood S, Holzworth D, Hopper M, Huth N, Keogh T, Mayberry D, Nidumolu U, Paroz A, Rich J, Rosauer D, Roxburgh S, Thomas I and van Herwaarden A (2025) Technical Report: Primary Industries. A technical report prepared for the Australian Climate Service as part of the National Climate Risk Assessment. CSIRO, Australia. https://doi.org/10.25919/g1b2-fj19
DOI: 10.25919/8tqq-0007
title: Agricultural climate metrics for the National Climate Risk Assessment

description: Agricultural climate metrics were derived for the National Climate Risk Assessment.

This data collection describes the agricultural climate metrics derived from projections of climate variables across Australia from 8 CMIP6 global climate models, as well as a historical baseline from the Bureau of Meteorology’s Atmospheric high-Resolution Reanalysis for Australia (BARRA-R2) data. Agricultural climate metrics focus on climate conditions that are important to Australian agricultural productivity, for example the annual number of heat stress days for livestock or total growing season rainfall for grain cropping. The metrics are evaluated for a historical baseline (2001-2020) and global warming levels (GWLs) 1.5, 2.0 and 3.0 degrees Celsius above pre-industrial levels. Data are provided in netCDF format, and each grid location gives the minimum, median, and maximum metric values across the ensemble of global climate models climate models for each GWL, as well as the mean of the historical period. Projection data are provided for values (e.g. total rainfall in mm) as well as change from the mean historical baseline (e.g. % change in rainfall from historical baseline). 

More information can be found in the technical report¹. 

1. Darbyshire R, Blamey L, Bolt A, Brodie S, Buhagiar L, Bustamante RH, Greenwood S, Holzworth D, Hopper M, Huth N, Keogh T, Mayberry D, Nidumolu U, Paroz A, Rich J, Rosauer D, Roxburgh S, Thomas I and van Herwaarden A (2025). Primary Industries Technical Report. A technical report prepared for the Australian Climate Service as part of the National Climate Risk Assessment. CSIRO, Australia. https://doi.org/10.25919/g1b2-fj19.
DOI: 10.25919/cjf0-cm26
title: Marine natural environments data for the National Climate Risk Assessment

description: What is the data?
Projections of marine climate hazards, cumulative impact scores and marine animal biomass across Australia's Exclusive Economic Zone (EEZ) and regional areas. 
Hazard data were supplied by the Australian Climate Service (ACS) from data stored on the National Computational Infrastructure (NCI) as part of Project ia39. 
Gridded cumulative impact scores for the Australian EEZ have been produced by CSIRO following an approach previously developed by Hayes et al (2021). 
Gridded animal biomass projection data for the Australian EEZ are from the Fisheries and Marine Ecosystem Model Intercomparison Project (FishMIP, Blanchard and Novaglio 2024). 
Polygons for marine regions used in this study to derive regional summaries for hazards, cumulative impact scores and animal biomass projections.

Why was it produced?
The data presented here was prepared for the National Climate Risk Assessment (NCRA) and supported the assessment of Australia's marine natural environments.

References
Blanchard, J.L., Novaglio, C., 2024. Climate change risks to marine ecosystems and fisheries – Projections to 2100 from the Fisheries and Marine Ecosystem Model Intercomparison Project, FAO Fisheries and Aquaculture Technical Paper, No. 707. Rome, FAO.
Hayes, K.R., Dunstan, P., Woolley, S., Barrett, N., Howe, S.A., Samson, C.R., Bowling, R., Ryan, M.P., Foster, S., Monk, J., Peel, D., Hosack, G.R., Francis, S.O., 2021. Designing a Targeted Monitoring Program to Support Evidence Based Management of Australian Marine Parks: A Pilot on the South-East Marine Parks Network. Report to Parks Australia and the National Environmental Science Program, Marine Biodiversity Hub. Parks Australia, University of Tasmanian and CSIRO, Hobart, Australia