cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.jdxrf7
title: Occurrence Download

description: A dataset containing 4129 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-80.18251 25.85029,-79.91554 25.85029,-79.91554 26.59887,-80.18251 26.59887,-80.18251 25.85029))",
 "HasGeospatialIssue is false",
 "IsSequenced is true",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 4129 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003585-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18566382
title: Expert annotations of the paraphrased medical content - comparison study
DOI: 10.5281/zenodo.17224931
title: Expert annotations of the paraphrased medical content - comparison study
DOI: 10.7910/dvn/4xwqoz
title: Sub-evaluation 3 LATIM (Single rater) Dataset

description: This dataset is part of the following publication:
Pandey PU, Hacihaliloglu I, Dardenne G, et al. Bone in ultrasound (BonUS): a systematic evaluation of bone segmentation methods on a multi-institution dataset.

All data has been anonymized prior to submission.
DOI: 10.15468/dl.nc32hf
title: Occurrence Download

description: A dataset containing 64590 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Human Observation, Material sample, Specimen, Living Specimen, Occurrence evidence)",
 "Continent is North America",
 "Country is Canada",
 "DatasetKey is iNaturalist Research-grade Observations",
 "GadmGid is CAN.1_1",
 "Geometry POLYGON((-114.75751 50.37022,-114.8528 50.29445,-114.50008 48.99007,-112.13235 48.99311,-112.20721 49.09521,-112.84019 49.52059,-113.37787 49.67373,-113.55824 49.80645,-113.80666 50.33733,-114.75751 50.37022))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 64590 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003560-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5065/k9vg-t494
title: PAZ Data Products

description: PAZ instrument data and derived products (levels 1-2).
DOI: 10.5065/t353-c093
title: COSMIC-2 Data Products

description: COSMIC-2 instrument data and derived products (levels 0-3) for all six spacecraft.
DOI: 10.15468/dl.rz73yy
title: Occurrence Download

description: A dataset listing the 4187 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is Portugal",
 "TaxonKey is Gastropoda"
 ]
}

 The dataset's 4187 records were derived from 193 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003564-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18313757
title: Sample Based Deforestation and Degradation Area Assessments for Six Central African Countries (2015-20223)

description: These spreadsheets provide the sample based area estimates for six Central African countries from randomly sampled valldation data assessed through visual interpretation. These were produced by the FAO and partner countries in support of the CAFI study on deforestation drivers. 

All data were processed using the Open Foris suite of monitoring tools, including SEPAL and Collect Earth Online

Over 60,000 random samples were visually interpreted by more than 40 experts from Central Africa and internationally. 

More information is available here: https://cafi.org/project/assessing-deforestation-and-forest-degradation-and-direct-drivers/
DOI: 10.5281/zenodo.18313756
title: Sample Based Deforestation and Degradation Area Assessments for Six Central African Countries (2015-20223)

description: These spreadsheets provide the sample based area estimates for six Central African countries from randomly sampled valldation data assessed through visual interpretation. These were produced by the FAO and partner countries in support of the CAFI study on deforestation drivers. 

All data were processed using the Open Foris suite of monitoring tools, including SEPAL and Collect Earth Online

Over 60,000 random samples were visually interpreted by more than 40 experts from Central Africa and internationally. 

More information is available here: https://cafi.org/project/assessing-deforestation-and-forest-degradation-and-direct-drivers/
DOI: 10.15468/dl.pc8h4s
title: Occurrence Download

description: A dataset containing 1504373 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Aves",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 2010",
 "Year is less than or equal to 2012",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "Country is United Kingdom of Great Britain and Northern Ireland",
 {
 "or" : [
 {
 "not" : {
 "EstablishmentMeans" : [
 "is one of (MANAGED, INTRODUCED)"
 ]
 }
 },
 "EstablishmentMeans is null"
 ]
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 1000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 1504373 records from 99 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003543-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3886/e245015
title: College of Nursing Sciences, Ilorin, 2026/27 Admission/Application Form Is Out Call (08058556110) 08058556110

description: College of Nursing Sciences, Ilorin, 2026/2027 Admission Form/Application Form Is Out Call the Registrar (08058556110) 08058556110 (08058556110) For More Details On How To Apply And Register Online. The Management Of The School Hereby Inform The general Public On The Sales Of The General Nursing Admission Form Into The School Of Nursing, And Also The Sales Of Two (2) Years Post Basic Nursing Program. Contact The Educational Board Admin On (08058556110) For More Details On How To Purchase The Form And Register Online. REQUIREMENT FOR THE THREE YEARS GENERAL NURSING PROGRAMMES1. GENERAL NURSING PROGRAMME Admission Requirements Candidates For Admission Into The General Nursing Program Of The Nursing And Midwifery Council Of Nigeria Must Posses The Senior Secondary School Certificate Or It’s Equivalent With Five Credits To Include English Language And Either Physics, Chemistry, Biology/Health Science At Not More Than Two sittings. Duration Of Training: Three Calendar Years. Registered Nurse (RN) Also POST BASIC NURSING MIDWIFERY PROGRAMME Admission Requirements: The Candidate Must Be a Registered Nurse, Five (O,Level) Credits Not More Two Sittings And Must Also Meet All institutional Requirements. Duration of Training: Two Calendar Year.{08058556110}
DOI: 10.15468/dl.ddj7d5
title: Occurrence Download

description: A dataset containing 5007 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Apuleia leiocarpa",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 }
 ]
}

 The dataset includes 5007 records from 188 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003586-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.m28fmz
title: Occurrence Download

description: A dataset containing 19 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Leporinus microphysus",
 "DecimalLatitude is not null",
 "DecimalLongitude is not null"
 ]
}

 The dataset includes 19 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003587-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.f535m7
title: Occurrence Download

description: A dataset containing 659 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Baccaurea ramiflora",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 }
 ]
}

 The dataset includes 659 records from 37 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003589-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3886/e245015v1
title: College of Nursing Sciences, Ilorin, 2026/27 Admission/Application Form Is Out Call (08058556110) 08058556110

description: College of Nursing Sciences, Ilorin, 2026/2027 Admission Form/Application Form Is Out Call the Registrar (08058556110) 08058556110 (08058556110) For More Details On How To Apply And Register Online. The Management Of The School Hereby Inform The general Public On The Sales Of The General Nursing Admission Form Into The School Of Nursing, And Also The Sales Of Two (2) Years Post Basic Nursing Program. Contact The Educational Board Admin On (08058556110) For More Details On How To Purchase The Form And Register Online. REQUIREMENT FOR THE THREE YEARS GENERAL NURSING PROGRAMMES1. GENERAL NURSING PROGRAMME Admission Requirements Candidates For Admission Into The General Nursing Program Of The Nursing And Midwifery Council Of Nigeria Must Posses The Senior Secondary School Certificate Or It’s Equivalent With Five Credits To Include English Language And Either Physics, Chemistry, Biology/Health Science At Not More Than Two sittings. Duration Of Training: Three Calendar Years. Registered Nurse (RN) Also POST BASIC NURSING MIDWIFERY PROGRAMME Admission Requirements: The Candidate Must Be a Registered Nurse, Five (O,Level) Credits Not More Two Sittings And Must Also Meet All institutional Requirements. Duration of Training: Two Calendar Year.{08058556110}
DOI: 10.5281/zenodo.18563974
title: SuperDARN data in netCDF format (2017-Dec)

description: 2017-Dec 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.6213187
title: SuperDARN data in netCDF format (2017-Dec)

description: 2017-Dec 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.ph6uhh
title: Occurrence Download

description: A dataset containing 6335850 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Spain",
 "Year is 2021",
 "HasCoordinate is TRUE"
 ]
}

 The dataset includes 6335850 records from 247 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003567-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh0168007.10fu
title: SH0168007.10FU

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: 10.5285/1957166d-7523-44f4-b279-aa5314163237
title: Integrated Hydrological Units of the United Kingdom: Hydrometric Areas with Coastline

description: This dataset is part of Integrated Hydrometric Units (IHU) of the UK. Hydrometric Areas are used to organise river flow measurement and hydrometric data collection in the UK. Hydrometric Areas are either integral river catchments having one or more outlets to the sea or tidal estuary, or they may include several contiguous river catchments having topographical similarity but separate tidal outlets. In mainland Britain they are numbered from 1 to 97 in clockwise order around the coast commencing in north east Scotland. The larger islands and groups of islands are numbered from 100-108. Ireland has a unified numbering system from 1 to 40 commencing with the River Foyle catchment and circulating clockwise; not all Irish Hydrometric Areas, however, have an outlet to the coast. Only those Hydrometric Areas covering Great Britain and Northern Ireland are included in this dataset. The boundaries between hydrometric areas correspond to catchment boundaries as digitally-derived from CEH Integrated Hydrological Digital Terrain Model (IHDTM) using a catchment definition program. It should be noticed that the Northern Ireland data are clipped to its political boundary so not every Hydrometric Area in this region is completely represented. 

The naming and numbering convention for the hydrometric areas in Great Britain was originally defined by the Inland Water Survey Committee (and first published in the Surface Water Year-Book of Great Britain 1936-37). For Northern Ireland the system was developed by a multi-agency working group in the 1970s (and first published in Surface Water: United Kingdom 1971-73. Note that full citations of those two publications are provided as additional information source. 

This dataset represent the same entities as the IHU Hydrometric Areas of the UK without Coastline, however, the outer boundaries of the units follow coastline published by the Ordnance Survey (Meridian 2), rather than the boundaries of the CEH Integrated Hydrological Digital Terrain Model. 
description: The hydrometric areas are based on watershed derived from the Integrated Hydrological Digital Terrain Model (IHDTM) elevation grid using an in-house catchment definition program. In Great Britain the coastline corresponds to the OS Meridian2 coastline. In Northern Ireland the boundary and coastline was derived from OS Panorama data in times when Ordnance Survey covered Northern Ireland. All boundaries have been processed to ensure they are topologically correct; there are no gaps or overlaps so every point in the country is allocated to one and only one Hydrometric Area
DOI: 10.15156/bio/sh0168008.10fu
title: SH0168008.10FU

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/sh0168009.10fu
title: SH0168009.10FU

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/sh0168010.10fu
title: SH0168010.10FU

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/sh0168011.10fu
title: SH0168011.10FU

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/sh0168012.10fu
title: SH0168012.10FU

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/sh0168013.10fu
title: SH0168013.10FU

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.