cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.72atz6
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pseudameira reflexa (Scott T., 1894)"
 ]
}

 The dataset includes 8 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0131131-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ax7frj
title: Occurrence Download

description: A dataset containing 21545 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Streptopelia decaocto (Frivaldszky, 1838)",
 "DatasetKey is Observation.org, Nature data from around the World",
 "Country is one of (Germany, France, Spain, Portugal, Denmark, Austria, Italy, Switzerland, Greece, Poland, Hungary, Slovenia, Sweden, Norway, Slovakia, Montenegro, Croatia, North Macedonia, Serbia, Iceland, Ireland, United Kingdom of Great Britain and Northern Ireland, Albania, Bosnia and Herzegovina, Bulgaria)"
 ]
}

 The dataset includes 21545 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0141261-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.n3sw8q
title: Occurrence Download

description: A dataset containing 6001 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Tectaria incisa Cav."
 ]
}

 The dataset includes 6001 records from 245 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0121393-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jfdbax
title: Occurrence Download

description: A dataset containing 150 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Psammotopa phyllosetosa (Noodt, 1952)"
 ]
}

 The dataset includes 150 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0131107-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.twmwvv
title: Occurrence Download

description: A dataset containing 5048143 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 5048143 records from 2421 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0142169-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ksjf3k
title: Occurrence Download

description: A dataset containing 253124856 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "TaxonKey is Tracheophyta"
 ]
}

 The dataset includes 253124856 records from 3393 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0207897-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nzte33
title: Occurrence Download

description: A dataset containing 1652342 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Azorean Biodiversity Portal"
 ]
}

 The dataset includes 1652342 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008493-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.n3nn9g
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "GbifId" : [
 "is 5938027850"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001885-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18529984
title: Hybrid Simulation Results of Ion Precipitation on Mercury

description: This dataset contains simulation results for ion precipitation on Mercury produced by the Amitis model.
DOI: 10.5281/zenodo.18529985
title: Hybrid Simulation Results of Ion Precipitation on Mercury

description: This dataset contains simulation results for ion precipitation on Mercury produced by the Amitis model.
DOI: 10.15468/dl.7cwew8
title: Occurrence Download

description: A dataset containing 1294 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Crotalus scutulatus scutulatus"
 ]
}

 The dataset includes 1294 records from 42 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001886-260208012135463/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.31290734
title: Risk prediction in IgA nephropathy: from conventional models to machine learning, deep learning, and precision nephrology

description: IgA nephropathy (IgAN) is the most prevalent primary glomerular disease worldwide and a leading cause of end-stage kidney disease (ESKD). Its clinical heterogeneity results in divergent renal outcomes, making early identification of high-risk patients essential. Prognostic models are crucial for stratifying ESKD risk, guiding treatment intensity, optimizing timing of interventions such as immunosuppressive therapy, and informing clinical trial enrollment. Over recent decades, multiple prognostic approaches have emerged, ranging from traditional clinical and histopathological scoring systems to advanced machine learning (ML) and deep learning (DL) models designed to capture complex nonlinear interactions and improve predictive precision. Among them, the International IgA Nephropathy Prediction Tool (IIgAN-PT), endorsed by the 2021 KDIGO guidelines, represents a landmark in globally validated risk assessment and has set the foundation for standardized clinical decision support. However, classical models often rely on static baseline parameters and may not adequately reflect dynamic disease trajectories, limiting their utility in real-time clinical management. To overcome these limitations, ML- and DL-based models increasingly integrate multi-omics data, serial clinical measurements, and digital pathology features, offering enhanced accuracy, dynamic risk tracking, and potential for personalized response prediction. These data-driven approaches are progressively bridging the gap between prognostic research and precision nephrology. This review provides a comprehensive overview of the evolution of IgAN prognostic models, summarizes their strengths and limitations, and discusses considerations for clinical translation. By highlighting emerging trends toward explainable AI, dynamic time-series modeling, and multimodal prognostication, we outline how next-generation prediction tools may enable real-time, AI-driven decision support for individualized IgAN management.
DOI: 10.6084/m9.figshare.31290734.v1
title: Risk prediction in IgA nephropathy: from conventional models to machine learning, deep learning, and precision nephrology

description: IgA nephropathy (IgAN) is the most prevalent primary glomerular disease worldwide and a leading cause of end-stage kidney disease (ESKD). Its clinical heterogeneity results in divergent renal outcomes, making early identification of high-risk patients essential. Prognostic models are crucial for stratifying ESKD risk, guiding treatment intensity, optimizing timing of interventions such as immunosuppressive therapy, and informing clinical trial enrollment. Over recent decades, multiple prognostic approaches have emerged, ranging from traditional clinical and histopathological scoring systems to advanced machine learning (ML) and deep learning (DL) models designed to capture complex nonlinear interactions and improve predictive precision. Among them, the International IgA Nephropathy Prediction Tool (IIgAN-PT), endorsed by the 2021 KDIGO guidelines, represents a landmark in globally validated risk assessment and has set the foundation for standardized clinical decision support. However, classical models often rely on static baseline parameters and may not adequately reflect dynamic disease trajectories, limiting their utility in real-time clinical management. To overcome these limitations, ML- and DL-based models increasingly integrate multi-omics data, serial clinical measurements, and digital pathology features, offering enhanced accuracy, dynamic risk tracking, and potential for personalized response prediction. These data-driven approaches are progressively bridging the gap between prognostic research and precision nephrology. This review provides a comprehensive overview of the evolution of IgAN prognostic models, summarizes their strengths and limitations, and discusses considerations for clinical translation. By highlighting emerging trends toward explainable AI, dynamic time-series modeling, and multimodal prognostication, we outline how next-generation prediction tools may enable real-time, AI-driven decision support for individualized IgAN management.
DOI: 10.15468/dl.wqsu3v
title: Occurrence Download

description: A dataset containing 1216431 records in GBIF matching the query:

 SELECT gbifid, datasetkey, specieskey, kingdom, phylum, class, "order", family, genus, species, scientificname, decimallatitude, decimallongitude, eventdate, institutioncode, "year" AS obs_year, basisofrecord, coordinateuncertaintyinmeters, issue
 FROM occurrence
 WHERE
 decimallatitude IS NOT NULL
 AND decimallongitude IS NOT NULL
 AND NOT ARRAY_CONTAINS(issue, 'ZERO_COORDINATE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_OUT_OF_RANGE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_INVALID')
 AND NOT ARRAY_CONTAINS(issue, 'COUNTRY_COORDINATE_MISMATCH')
 AND "year" &gt;= 2015
 AND "year" &lt;= 2026
 AND coordinateuncertaintyinmeters IS NOT NULL
 AND coordinateuncertaintyinmeters &gt; 0
 AND coordinateuncertaintyinmeters &lt;= 200.0
 AND decimallatitude &gt;= 37.42857426825602
 AND decimallatitude &lt;= 41.89415215079384
 AND decimallongitude &gt;= -95.90519603780048
 AND decimallongitude &lt;= -86.346182691082
 AND GBIF_Within('POLYGON ((-86.346183 37.428574, -86.346183 41.894152, -95.905196 41.894152, -95.905196 37.428574, -86.346183 37.428574))', decimallatitude, decimallongitude) = TRUE
 

 The dataset's 1216431 records were derived from 105 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001884-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8v8ke4
title: Occurrence Download

description: A dataset containing 338 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Acrachne racemosa"
 ]
}

 The dataset includes 338 records from 41 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001887-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wqgejc
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Acrachne verticillata"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001889-260208012135463/datasets/export for details.
DOI: 10.15468/dl.q8qacr
title: Occurrence Download

description: A dataset containing 97538 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Strix uralensis",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Year is 2002,2025"
 ]
}

 The dataset includes 97538 records from 90 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001879-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18530024
title: Tool Registry for Digital Humanities

description: Weekly snapshot of the Tool Registry for Digital Humanities from Wikidata. Data is being downloaded through a combination of SPARQL queries and API calls and serialised as Turtle and JSON-LD.
DOI: 10.15468/dl.gx2u9a
title: Occurrence Download

description: A dataset containing 2188 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is CAN.1_1",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Viola adunca"
 ]
}

 The dataset includes 2188 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001890-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.eb3353
title: Occurrence Download

description: A dataset containing 517 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Aeluropus lagopoides"
 ]
}

 The dataset includes 517 records from 57 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001892-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.s3ah8e
title: Occurrence Download

description: A dataset containing 679 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Malus asiatica"
 ]
}

 The dataset includes 679 records from 57 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001891-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.z295zc
title: Occurrence Download

description: A dataset containing 3451 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Reithrodontomys microdon, Reithrodontomys mexicanus)"
 ]
}

 The dataset includes 3451 records from 56 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001894-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18529815
title: SuperDARN data in netCDF format (2020-Nov)

description: 2020-Nov SuperDARN radar data in netCDF format. These files were produced using version 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.


The RST is available here: https://github.com/SuperDARN/rst



The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation.
Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications.
A brief statement on how to acknowledge use of SuperDARN data is provided below.




Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication.
A listing of radars and PIs with contact information can be found here:
(SuperDARN Radar Overview)



Recommended form of acknowledgement for the use of SuperDARN data:



'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.7308950
title: SuperDARN data in netCDF format (2020-Nov)

description: 2020-Nov SuperDARN radar data in netCDF format. These files were produced using version 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.


The RST is available here: https://github.com/SuperDARN/rst



The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation.
Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications.
A brief statement on how to acknowledge use of SuperDARN data is provided below.




Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication.
A listing of radars and PIs with contact information can be found here:
(SuperDARN Radar Overview)



Recommended form of acknowledgement for the use of SuperDARN data:



'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.15468/dl.7juumw
title: Occurrence Download

description: A dataset containing 931 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "NetworkKey is 99d66b6c-9087-452f-a9d4-f15f2c2d0e7e",
 "TaxonKey is Chordata"
 ]
 },
 "TaxonKey is one of (Tremarctos ornatus [GBIF Backbone Taxonomy])"
 ]
}

 The dataset includes 931 records from 29 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001895-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hphwbt
title: Occurrence Download

description: A dataset containing 4066 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Aeluropus littoralis"
 ]
}

 The dataset includes 4066 records from 170 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001896-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hfbmcz
title: Occurrence Download

description: A dataset containing 285547539 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Year is 2022"
 ]
}

 The dataset includes 285547539 records from 11358 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001881-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh0163654.10fu
title: SH0163654.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0163655.10fu
title: SH0163655.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0163656.10fu
title: SH0163656.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.