cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.64xw2r
title: Occurrence Download

description: A dataset containing 103990 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Human Observation, Machine Observation, Material citation, Material sample, Occurrence evidence)",
 "Continent is Africa",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Terpsiphone viridis (Statius Muller, 1776)",
 "Year 2016-2024"
 ]
}

 The dataset includes 103990 records from 39 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041216-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2c5z7j
title: Occurrence Download

description: A dataset containing 1319 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Thalictrum dasycarpum Fisch., C.A.Mey. & Avé-Lall.)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "HasGeospatialIssue is false",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Specimen)"
 ]
 }
 }
 ]
}

 The dataset includes 1319 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041232-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mbyaw6
title: Occurrence Download

description: A dataset listing the 426 species recorded in GBIF matching the query:
{
 "and" : [
 "Continent is Africa",
 "Country is South Africa",
 {
 "or" : [
 "Geometry POLYGON((25.61074 -26.5038,25.59673 -26.51634,25.5913 -26.541,25.59492 -26.55434,25.58633 -26.57011,25.56871 -26.57779,25.55831 -26.57294,25.5565 -26.59961,25.57548 -26.60406,25.57594 -26.61901,25.58588 -26.61295,25.58452 -26.60406,25.59989 -26.60163,25.61345 -26.57536,25.61164 -26.55677,25.60667 -26.541,25.60848 -26.52443,25.61706 -26.50946,25.61074 -26.5038))",
 "Geometry POINT(25.58364 -26.58362)"
 ]
 },
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Animalia"
 ]
}

 The dataset's 426 records were derived from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041217-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.12996917
title: Wallops SuperDARN data in netCDF format (2023-Jul)

description: 2023-Jul Wallops SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 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.12996918
title: Wallops SuperDARN data in netCDF format (2023-Jul)

description: 2023-Jul Wallops SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 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.12996971
title: Wallops SuperDARN data in netCDF format (2023-Aug)

description: 2023-Aug Wallops SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 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.12996972
title: Wallops SuperDARN data in netCDF format (2023-Aug)

description: 2023-Aug Wallops SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 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.12997747
title: Wallops SuperDARN data in netCDF format (2023-Sep)

description: 2023-Sep Wallops SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 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.12997748
title: Wallops SuperDARN data in netCDF format (2023-Sep)

description: 2023-Sep Wallops SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 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.12998088
title: Wallops SuperDARN data in netCDF format (2023-Oct)

description: 2023-Oct Wallops SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 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.12998089
title: Wallops SuperDARN data in netCDF format (2023-Oct)

description: 2023-Oct Wallops SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 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.13122002
title: Wallops SuperDARN data in netCDF format (2023-Nov)

description: 2023-Nov Wallops SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 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.13122003
title: Wallops SuperDARN data in netCDF format (2023-Nov)

description: 2023-Nov Wallops SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 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.w39jnf
title: Occurrence Download

description: A dataset containing 5315 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Thalictrum dioicum L.)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "HasGeospatialIssue is false",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Specimen)"
 ]
 }
 }
 ]
}

 The dataset includes 5315 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041233-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13122671
title: Wallops SuperDARN data in netCDF format (2023-Dec)

description: 2023-Dec Wallops SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 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.13122672
title: Wallops SuperDARN data in netCDF format (2023-Dec)

description: 2023-Dec Wallops SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 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.12799225
title: Data and code for "Experimental Observation of Topological Quantum Criticality" (matfiles, matlab script, and figures)

description: Matlab code "zenodo_topological_quantum_criticality.m" generates Figure2 and 3 in the main script, and the Figure 2 and 3 in the Supplemental material. The generated figures are saved under folder 'figure', and the datasets used for the generation are saved under folder 'matfiles'. 

The raw measurement data (approx. 350 GB) will be shared only upon request. Please contact the Integrated Quantum Optics group (head: C. Silberhorn), Paderborn University.

This is additional information of the publication arXiv:2301.05428 "Experimental Observation of Topological Quantum Criticality"
DOI: 10.15468/dl.m6myfp
title: Occurrence Download

description: A dataset containing 39 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Thailand",
 "DatasetKey is iNaturalist Research-grade Observations",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Alophoixus bres (Lesson, 1831)",
 "Year 2000-2024"
 ]
}

 The dataset includes 39 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041235-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.k7tfqr
title: Occurrence Download

description: A dataset containing 38 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Thailand",
 "DatasetKey is iNaturalist Research-grade Observations",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Alophoixus tephrogenys (Jardine & Selby, 1833)",
 "Year 2000-2024"
 ]
}

 The dataset includes 38 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041234-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zsex2m
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Diptera",
 "TaxonKey is Lepidoptera",
 "TaxonKey is Hymenoptera",
 "Country is Norway",
 "HasCoordinate is true",
 "Year is greater than or equal to 2005",
 "Year is less than or equal to 2024",
 "CoordinateUncertaintyInMeters is less than or equal to 200"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041236-240626123714530/datasets/export for details.
DOI: 10.15468/dl.q5smqh
title: Occurrence Download

description: A dataset containing 3004 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Thalictrum minus subsp. minus)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "HasGeospatialIssue is false",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Specimen)"
 ]
 }
 }
 ]
}

 The dataset includes 3004 records from 77 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041237-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.11950169
title: Millikelvin confocal microscope with free-space access and high-frequency electrical control

description: Raw data and analysis scripts repository of the scientific paper "Millikelvin confocal microscope with free-space access and high-frequencyelectrical control".
DOI: 10.5281/zenodo.11950170
title: Millikelvin confocal microscope with free-space access and high-frequency electrical control

description: Raw data and analysis scripts repository of the scientific paper "Millikelvin confocal microscope with free-space access and high-frequencyelectrical control".
DOI: 10.15468/dl.f4dyb9
title: Occurrence Download

description: A dataset containing 5304 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Thalictrum pubescens Pursh)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "HasGeospatialIssue is false",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Specimen)"
 ]
 }
 }
 ]
}

 The dataset includes 5304 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041240-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.