cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7652292
title: SuperDARN Grid data in netCDF format (2002-Jul)

description: 2002-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.7652301
title: SuperDARN Grid data in netCDF format (2002-Jun)

description: 2002-Jun 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.7652302
title: SuperDARN Grid data in netCDF format (2002-Jun)

description: 2002-Jun 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.7652317
title: SuperDARN Grid data in netCDF format (2002-May)

description: 2002-May 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.7652318
title: SuperDARN Grid data in netCDF format (2002-May)

description: 2002-May 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.7652325
title: SuperDARN Grid data in netCDF format (2002-Apr)

description: 2002-Apr 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.7652326
title: SuperDARN Grid data in netCDF format (2002-Apr)

description: 2002-Apr 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.7652339
title: SuperDARN Grid data in netCDF format (2002-Mar)

description: 2002-Mar 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.7652340
title: SuperDARN Grid data in netCDF format (2002-Mar)

description: 2002-Mar 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.7652362
title: SuperDARN Grid data in netCDF format (2002-Jan)

description: 2002-Jan 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.7652363
title: SuperDARN Grid data in netCDF format (2002-Jan)

description: 2002-Jan 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.7652350
title: SuperDARN Grid data in netCDF format (2002-Feb)

description: 2002-Feb 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.7652351
title: SuperDARN Grid data in netCDF format (2002-Feb)

description: 2002-Feb 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.22180219
title: Primer information.docx

description: The seven primer combinations used for amplifying the complete mitogenome of<em> N. hainanensis</em>
DOI: 10.6084/m9.figshare.22180219.v2
title: Primer information.docx

description: The seven primer combinations used for amplifying the complete mitogenome of<em> N. hainanensis</em>
DOI: 10.17632/2yp53y989g.1
title: RISK FACTORS FOR NOT RETURN TO PREVIOUS LEVEL OF FUNCTION IN PATIENTS 65 YEARS OR OLDER ONE-YEAR AFTER SURGERY FOR HIP FRACTURE: A PROSPECTIVE OBERVATIONAL COHORT STUDY

description: Data Bank of hip fracture patients treated surgically. Measured in this group the pre-fracture level of function and measured this same level one year after surgical treatment.
DOI: 10.17632/bsszk2txmw.1
title: Study of Hospital Acquired hypervirulent Klebsiella pneumoniae from Pediatric patients and its Relation to Carbapenem and Extended-Spectrum beta-lactamase Resistance.

description: Study of Hospital Acquired hypervirulent Klebsiella pneumoniae from Pediatric patients and its Relation to Carbapenem and Extended-Spectrum beta-lactamase Resistance.
DOI: 10.17632/7gfh9fyv27.1
title: GPT Nursing Samples

description: Sample output from ChatGPT.  Shows how ChatGPT can make first drafts quickly and save students from having to cut and paste.  Allows more time for faculty and students to discuss care plans instead of spending time grading based on grammer and formatting issues.
DOI: 10.6084/m9.figshare.15090717.v2
title: Annual Direct Economic Burden and Influencing Factors of Dry Eye Diseases in Central China

description: To study the direct economic burden of dry eye diseases (DED) on Chinese residents and analyze the influencing factors of the direct economic burden of patients with DED. Two hundred and twenty-one Chinese adults with DED who underwent treatment in Wuhan Aier Hankou Eye Hospital were enrolled in this health economics research from January 2018 to August 2018 and followed for at least 1 year. Examination, pharmacological therapy, and nonpharmacological therapy costs were collected to calculate the annual direct economic burden of DED on patients through the outpatient medical record system. Annual direct economic burden caused by DED on each patient was $465.54 ± 303.08. The direct economic burden of female patients in the 40–49 years group was significantly higher than that of male patients (<i>P</i> &lt; .05). Age, number of hospital visits and severity of DED were showed a significant influence on the direct economic burden both in univariate linear regression analysis and multiple linear regression analysis. Subtype of DED was showed a significant influence on the direct economic burden in multiple linear regression analysis after eliminating confounding factors. This study preliminarily analyzed the direct economic burden of Chinese DED patients. Age, number of hospital visits, severity of DED, mixed and evaporative dry eye (EDE) subtypes are shown to be the significant influencing factors of the direct economic burden and sex is a potential influencing factor.
DOI: 10.6084/m9.figshare.15090717
title: Annual Direct Economic Burden and Influencing Factors of Dry Eye Diseases in Central China

description: To study the direct economic burden of dry eye diseases (DED) on Chinese residents and analyze the influencing factors of the direct economic burden of patients with DED. Two hundred and twenty-one Chinese adults with DED who underwent treatment in Wuhan Aier Hankou Eye Hospital were enrolled in this health economics research from January 2018 to August 2018 and followed for at least 1 year. Examination, pharmacological therapy, and nonpharmacological therapy costs were collected to calculate the annual direct economic burden of DED on patients through the outpatient medical record system. Annual direct economic burden caused by DED on each patient was $465.54 ± 303.08. The direct economic burden of female patients in the 40–49 years group was significantly higher than that of male patients (<i>P</i> &lt; .05). Age, number of hospital visits and severity of DED were showed a significant influence on the direct economic burden both in univariate linear regression analysis and multiple linear regression analysis. Subtype of DED was showed a significant influence on the direct economic burden in multiple linear regression analysis after eliminating confounding factors. This study preliminarily analyzed the direct economic burden of Chinese DED patients. Age, number of hospital visits, severity of DED, mixed and evaporative dry eye (EDE) subtypes are shown to be the significant influencing factors of the direct economic burden and sex is a potential influencing factor.
DOI: 10.6084/m9.figshare.15090717.v1
title: Annual direct economic burden and influencing factors of dry eye disease in Central China

description: To study the direct economic burden of dry eye diseases (DED) on Chinese residents and analyze the influencing factors of the direct economic burden of patients with DED. Two hundred and twenty-one Chinese adults with DED who underwent treatment in Wuhan Aier Hankou Eye Hospital were enrolled in this health economics research from January 2018 to August 2018 and followed for at least 1 year. Examination, pharmacological therapy, and nonpharmacological therapy costs were collected to calculate the annual direct economic burden of DED on patients through the outpatient medical record system. Annual direct economic burden caused by DED on each patient was $465.54 ± 303.08. The direct economic burden of female patients in the 40–49 years group was significantly higher than that of male patients (<i>P</i> &lt; .05). Age, number of hospital visits and severity of DED were showed a significant influence on the direct economic burden both in univariate linear regression analysis and multiple linear regression analysis. Subtype of DED was showed a significant influence on the direct economic burden in multiple linear regression analysis after eliminating confounding factors. This study preliminarily analyzed the direct economic burden of Chinese DED patients. Age, number of hospital visits, severity of DED, mixed and evaporative dry eye (EDE) subtypes are shown to be the significant influencing factors of the direct economic burden and sex is a potential influencing factor.