cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7648406
title: SuperDARN Grid data in netCDF format (2013-Aug)

description: 2013-Aug 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.7648407
title: SuperDARN Grid data in netCDF format (2013-Aug)

description: 2013-Aug 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.7648456
title: SuperDARN Grid data in netCDF format (2013-Jul)

description: 2013-Jul 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.7648375
title: SuperDARN Grid data in netCDF format (2013-Sep)

description: 2013-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.7648374
title: SuperDARN Grid data in netCDF format (2013-Sep)

description: 2013-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: 10.25582/data.2021-07.2273315/1950633
title: Data for EMSL Project 50686 from July 2021
DOI: 10.5281/zenodo.7648287
title: SuperDARN Grid data in netCDF format (2013-Nov)

description: 2013-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.7648288
title: SuperDARN Grid data in netCDF format (2013-Nov)

description: 2013-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.7648323
title: SuperDARN Grid data in netCDF format (2013-Oct)

description: 2013-Oct 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.7648324
title: SuperDARN Grid data in netCDF format (2013-Oct)

description: 2013-Oct 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.7648247
title: SuperDARN Grid data in netCDF format (2013-Dec)

description: 2013-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.7648248
title: SuperDARN Grid data in netCDF format (2013-Dec)

description: 2013-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: 10.25582/data.2021-07.2275849/1950634
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2267927/1950635
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2269505/1950636
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2273586/1950637
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2274030/1950638
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2270249/1950639
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2276501/1950640
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2268675/1950641
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2274537/1950642
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2276354/1950643
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2267842/1950644
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2269970/1950645
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2275846/1950646
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2268907/1950647
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2273057/1950648
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2269035/1950649
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2273309/1950650
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2274300/1950651
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2275561/1950652
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2269138/1950653
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2274662/1950654
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2270536/1950655
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2269042/1950656
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2275724/1950657
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2273810/1950658
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2267313/1950659
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2269125/1950660
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2275314/1950661
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2268005/1950662
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2267651/1950663
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2270338/1950664
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2273643/1950665
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2269422/1950666
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2276033/1950667
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2269637/1950668
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2269163/1950669
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2275175/1950670
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2275191/1950671
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2268192/1950672
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2269149/1950673
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2273959/1950674
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2267618/1950675
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2274588/1950676
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2270557/1950677
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2269107/1950678
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2268348/1950679
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2266918/1950680
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2267853/1950681
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2270684/1950682
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2273675/1950683
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2269673/1950684
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2269070/1950685
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2273299/1950686
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2267481/1950687
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2269875/1950688
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2275719/1950689
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2275380/1950690
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2274288/1950691
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2269288/1950692
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2269591/1950693
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2274016/1950694
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2270521/1950695
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2275854/1950696
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2269652/1950697
title: Data for EMSL Project 50686 from July 2021
DOI: 10.24431/rw1k7d4
title: Studies of reproductive maturity among age cohorts of Pacific Herring (Clupea pallasii) in Prince William Sound, Alaska, 2017-2019, EVOS Herring Program

description: These data are part of the Herring Research and Monitoring Program of the Exxon Valdez Oil Spill Trustee Council, which is a multi-faceted study to determine why herring populations in Prince William Sound (PWS) remain depressed since the early 1990s. The information contained in this dataset includes various measurements on the body weight and size; sex; age; gonad maturity including gonad weight, and gonadosomatic and hjort indices; histological information of the ovary including number of post-ovulatory follicles, number of post-ovulatory eggs, inflammation, gonadal macrophage aggregates, follicular atresia, and hyalinized vessel walls; and annual scale growth measurements of adult Pacific Herring (Clupea pallasii, hereafter herring) collected throughout Prince William Sound, Alaska between 2017-2019.
DOI: 10.25582/data.2021-07.2269215/1950698
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2275737/1950699
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2269856/1950700
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2267382/1950701
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2269389/1950702
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2274294/1950703
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2267326/1950704
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2276044/1950705
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2267665/1950706
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2270689/1950707
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2270021/1950708
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2270035/1950709
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2275772/1950710
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2268121/1950711
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2269726/1950712
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2274761/1950713
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2267994/1950714
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2275712/1950715
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2267106/1950716
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2274843/1950717
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2269495/1950718
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2270402/1950719
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2275382/1950720
title: Data for EMSL Project 50686 from July 2021