cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7821802
title: SuperDARN Grid data in netCDF format (2021-Dec)

description: 2021-Dec SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, 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/rstThe 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)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'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.7821803
title: SuperDARN Grid data in netCDF format (2021-Dec)

description: 2021-Dec SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, 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/rstThe 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)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'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.7745258
title: Wallops SuperDARN data in netCDF format (2023-Feb)

description: 2023-Feb 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/rstThe 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)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'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.7745259
title: Wallops SuperDARN data in netCDF format (2023-Feb)

description: 2023-Feb 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/rstThe 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)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'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.7309758
title: SuperDARN data in netCDF format (2020-Feb)

description: 2020-Feb 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) <strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong> '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.7821882
title: SuperDARN Grid data in netCDF format (2021-Nov)

description: 2021-Nov SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, 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/rstThe 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)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'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.7821883
title: SuperDARN Grid data in netCDF format (2021-Nov)

description: 2021-Nov SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, 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/rstThe 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)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'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.7794644
title: Wallops SuperDARN data in netCDF format (2023-Mar)

description: 2023-Mar 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/rstThe 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)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'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.7794645
title: Wallops SuperDARN data in netCDF format (2023-Mar)

description: 2023-Mar 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/rstThe 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)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'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.7822023
title: SuperDARN Grid data in netCDF format (2021-Sep)

description: 2021-Sep SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, 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/rstThe 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)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'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.7822024
title: SuperDARN Grid data in netCDF format (2021-Sep)

description: 2021-Sep SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, 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/rstThe 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)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'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: 10.6084/m9.figshare.22637428.v1
title: HG007 Ultima

description: HG007 Ultima VCF 40x coverage
DOI: 10.6084/m9.figshare.22637428
title: HG007 Ultima

description: HG007 Ultima VCF 40x coverage
DOI: 10.5281/zenodo.7829479
title: Genome sequences for 2031 Saccharomyces cerevisiae genome assemblies

description: Genome sequences for 2031 Saccharomyces cerevisiae genome assemblies
DOI: 10.18430/m38sbn
title: X-ray diffraction data for the crystal Structure of Dephospho-CoA kinase from Klebsiella aerogenes (P21 Form 1) (8SBN)
DOI: 10.5281/zenodo.7829478
title: Genome sequences for 2031 Saccharomyces cerevisiae genome assemblies

description: Genome sequences for 2031 Saccharomyces cerevisiae genome assemblies
DOI: 10.18430/m38sbo
title: X-ray diffraction data for the crystal Structure of Dephospho-CoA kinase from Klebsiella aerogenes (P21 Form 2) (8SBO)
DOI: 10.18430/m38sa7
title: X-ray diffraction data for the crystal Structure of Cystathionine beta lyase from Klebsiella aerogenes (C2 form) (8SA7)
DOI: 10.18430/m38sa8
title: X-ray diffraction data for the crystal Structure of Cystathionine beta lyase from Klebsiella aerogenes, Covalently bound and free PLP (I2 form) (8SA8)
DOI: 10.18430/m38sac
title: X-ray diffraction data for the crystal Structure of Cystathionine beta lyase from Klebsiella aerogenes (P21212 form) (8SAC)
DOI: 10.18430/m38sab
title: X-ray diffraction data for the crystal Structure of Cystathionine beta lyase from Klebsiella aerogenes, PLP adduct with Alanine (C2 form) (8SAB)
DOI: 10.25318/9810012701-fra
title: État matrimonial, groupe d'âge et genre : Canada, provinces et territoires, régions métropolitaines de recensement, agglomérations de recensement et subdivisions de recensement

description: Données sur l’état matrimonial, le groupe d’âge et le genre de la population de 15 ans et plus, Canada, provinces et territoires, régions métropolitaines de recensement, agglomérations de recensement, subdivisions de recensement, Recensement de 2021.
DOI: 10.18430/m38sae
title: X-ray diffraction data for the crystal Structure of Cystathionine beta lyase from Klebsiella aerogenes, PLP and Hepes bound (C2 form) (8SAE)
DOI: 10.18430/m38sad
title: X-ray diffraction data for the crystal Structure of Cystathionine beta lyase from Klebsiella aerogenes, PLP/Malonate complex (C2 form) (8SAD)
DOI: 10.18430/m38sa9
title: X-ray diffraction data for the crystal Structure of Cystathionine beta lyase from Klebsiella aerogenes, PLP-Oxamate Adduct (C2 form) (8SA9)
DOI: 10.25318/9810013101-eng
title: Marital status, age group and gender: Canada, provinces and territories and federal electoral districts (2013 Representation Order)

description: Data on marital status, age group and gender for the population aged 15 and over, Canada, provinces and territories, federal electoral districts, 2021 Census.
DOI: 10.25318/9810013101-fra
title: État matrimonial, groupe d'âge et genre : Canada, provinces et territoires et circonscriptions électorales fédérales (Ordonnance de représentation de 2013)

description: Données sur l’état matrimonial, le groupe d’âge et le genre de la population de 15 ans et plus, Canada, provinces et territoires, circonscriptions électorales fédérales, Recensement de 2021.
DOI: 10.5281/zenodo.7829679
title: Global daily 0.5 deg coarse mode aerosol optical depth (cAOD) from 2001 to 2021

description: Coarse mode aerosol optical depth (cAOD) is critical for understanding the impact of coarse mode particles on climate, such as dust. Currently, the limited data length and high uncertainty of satellite products impair the applicability of cAOD for climate research. Here, we propose a spatiotemporal co-action deep learning model (SCAM) for the retrieval of global land cAOD (500 nm) from 2001 to 2021 at daily temporal resolution and 0.5° spatial resolution. The cAOD products are uploaded in Geotiff format, stretched from -89.5° to 89.5° latitude and from -179.5° to 179.5° longitude. On certain days the products might be unavailable, due to the missing MODIS satellite data used for calculating cAOD.
DOI: 10.5281/zenodo.7829678
title: Global daily 0.5 deg coarse mode aerosol optical depth (cAOD) from 2001 to 2021

description: Coarse mode aerosol optical depth (cAOD) is critical for understanding the impact of coarse mode particles on climate, such as dust. Currently, the limited data length and high uncertainty of satellite products impair the applicability of cAOD for climate research. Here, we propose a spatiotemporal co-action deep learning model (SCAM) for the retrieval of global land cAOD (500 nm) from 2001 to 2021 at daily temporal resolution and 0.5° spatial resolution. The cAOD products are uploaded in Geotiff format, stretched from -89.5° to 89.5° latitude and from -179.5° to 179.5° longitude. On certain days the products might be unavailable, due to the missing MODIS satellite data used for calculating cAOD.
DOI: 10.15156/bio/sh335108.07fu
title: SH335108.07FU

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/sh335109.07fu
title: SH335109.07FU

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/sh335110.07fu
title: SH335110.07FU

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.