cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6481534
title: Data for "How flexible electrification can integrate fluctuating renewables"

description: This upload provides the files used to compile and compute the input data used in the aforementioned working paper.
DOI: 10.5281/zenodo.6481533
title: Data for "How flexible electrification can integrate fluctuating renewables"

description: This upload provides the files used to compile and compute the input data used in the aforementioned working paper.
DOI: 10.6084/m9.figshare.19643082
title: SRR5836473_1_sub_val_1_bismark_bt2_pe.coverage.bigWig

description: gfvffvfv
DOI: 10.6084/m9.figshare.19643082.v1
title: SRR5836473_1_sub_val_1_bismark_bt2_pe.coverage.bigWig

description: gfvffvfv
DOI: 10.5281/zenodo.6477084
title: SuperDARN data in netCDF format (2014-Feb)

description: 2014-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/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.6478186
title: SuperDARN data in netCDF format (2014-Jan)

description: 2014-Jan 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.6476585
title: SuperDARN data in netCDF format (2014-Jul)

description: 2014-Jul 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.6476433
title: SuperDARN data in netCDF format (2014-Sep)

description: 2014-Sep 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.6476683
title: SuperDARN data in netCDF format (2014-May)

description: 2014-May 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.6476826
title: SuperDARN data in netCDF format (2014-Apr)

description: 2014-Apr 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.6476241
title: SuperDARN data in netCDF format (2014-Oct)

description: 2014-Oct 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.6476643
title: SuperDARN data in netCDF format (2014-Jun)

description: 2014-Jun 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.6476520
title: SuperDARN data in netCDF format (2014-Aug)

description: 2014-Aug 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.6476932
title: SuperDARN data in netCDF format (2014-Mar)

description: 2014-Mar 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.6481549
title: Data 280609

description: Data 280609
DOI: 10.5281/zenodo.5761191
title: Data 280609

description: Data 280609
DOI: 10.6084/m9.figshare.19643070
title: Appendix - logistic regression analysis basic and full model for FRAIL and FI BP groups.docx

description: This is the extended data containing the appendix for the paper ''Older adults identified as frail by Frailty Index and FRAIL scale who were intensively treated for hypertension were at increased risk of 2-year adverse health outcomes in The Irish Longitudinal Study on Ageing (TILDA)'' that is in the process of being submitted to HRB Open journal. <br>
DOI: 10.5281/zenodo.6481513
title: CYP NoCryo dataset

description: CYP NoCryo dataset (PDB ID: 7ZB9) for a review
DOI: 10.5281/zenodo.6480295
title: CYP NoCryo dataset

description: CYP NoCryo dataset (PDB ID: 7ZB9) for a review
DOI: 10.5281/zenodo.6476481
title: Original Video Dataset

description: Since news speakers and journalists tend to be targets of video manipulation, this dataset consists of 300 short videos of news speakers and journalists of various backgrounds. All of the videos are completely pristine (unaltered).
DOI: 10.5281/zenodo.6477772
title: data set to bioRxiv preprint 'Persistent cross-species SARS-CoV-2 variant infectivity predicted via comparative molecular dynamics simulation

description: This is supporting data and software code for the following preprint in bioRxiv <strong>Persistent cross-species SARS-CoV-2 variant infectivity predicted via comparative molecular dynamics simulation</strong> https://www.biorxiv.org/content/10.1101/2022.04.18.488629v1
DOI: 10.5281/zenodo.6477771
title: data set to bioRxiv preprint 'Persistent cross-species SARS-CoV-2 variant infectivity predicted via comparative molecular dynamics simulation

description: This is supporting data and software code for the following preprint in bioRxiv <strong>Persistent cross-species SARS-CoV-2 variant infectivity predicted via comparative molecular dynamics simulation</strong> https://www.biorxiv.org/content/10.1101/2022.04.18.488629v1
DOI: 10.6084/m9.figshare.19643088
title: SRR5836473_1_sub_val_1_bismark_bt2_pe.methylation.bigWig

description: vvfv
DOI: 10.6084/m9.figshare.19643088.v1
title: SRR5836473_1_sub_val_1_bismark_bt2_pe.methylation.bigWig

description: vvfv
DOI: 10.5281/zenodo.6480767
title: FIVE FIGURES OF KIFILIDEEN (POWER OF BASE 11) AND ANTIKIFILIDEEN (ANTIPOWER OF BASE 11) TABLES

description: The four figures of power of base numbers tables is need to be created for easy synchronization with existing Logarithm table of power of base 10 which is working on four figures. Although the five figures of power of base numbers tables is more accurate because it is having less approximation in its establishment. The four figure tables is easy to compute doing utilization because it is having less digit to work with. This paper presents the four and five figures of Kifilideen (Power of base 11) and AntiKifilideen (Antipower of base 11) tables for the computation of mathematical problem. The four and five figures are both reliable to work with. However there is tradeoff between easy computation (as related to four figure table) and more accuracy (as related to five figure table) in their utilization.
DOI: 10.5281/zenodo.6480766
title: FIVE FIGURES OF KIFILIDEEN (POWER OF BASE 11) AND ANTIKIFILIDEEN (ANTIPOWER OF BASE 11) TABLES

description: The four figures of power of base numbers tables is need to be created for easy synchronization with existing Logarithm table of power of base 10 which is working on four figures. Although the five figures of power of base numbers tables is more accurate because it is having less approximation in its establishment. The four figure tables is easy to compute doing utilization because it is having less digit to work with. This paper presents the four and five figures of Kifilideen (Power of base 11) and AntiKifilideen (Antipower of base 11) tables for the computation of mathematical problem. The four and five figures are both reliable to work with. However there is tradeoff between easy computation (as related to four figure table) and more accuracy (as related to five figure table) in their utilization.
DOI: 10.6084/m9.figshare.3761562.v2198
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.6084/m9.figshare.19137512
title: Data of "Phenological sensitivity and seasonal variability explain climate-driven trends in Mediterranean butterflies"

description: Data of "Phenological sensitivity and seasonal variability explain climate-driven trends in Mediterranean butterflies".
DOI: 10.5517/ccdc.csd.cc26tprh
title: CCDC 2050487: Experimental Crystal Structure Determination

description: Related Article: Bin Hou, Jing Li, Xiaodi Yang, Jianwei Zhang, Hanshen Xin, Cong-Wu Ge, Xike Gao|2022|Chin.Chem.Lett.|33|2147|doi:10.1016/j.cclet.2021.08.079
DOI: 10.6084/m9.figshare.19643217
title: Data set for Social Scienfe and Medicine

description: Data on psychological, social, and health variables (N = 130 countries) relevant to study submitted at SSM. <br>
DOI: 10.6084/m9.figshare.19643217.v1
title: Data set for Social Scienfe and Medicine

description: Data on psychological, social, and health variables (N = 130 countries) relevant to study submitted at SSM. <br>
DOI: 10.3886/e113563v1-116100
title: Replication data for: Early Childhood Intervention and Life-Cycle Skill Development: Evidence from Head Start

description: This paper provides new evidence on the long-term benefits of Head Start using the National Longitudinal Survey of Youth. I compare siblings who differ in their participation in the program, controlling for a variety of pre-treatment covariates. I estimate that Head Start participants gain 0.23 standard deviations on a summary index of young adult outcomes. This closes one-third of the gap between children with median and bottom quartile family income, and is about 80 percent as large as model programs such as Perry Preschool. The long-term impact for disadvantaged children is large despite "fadeout" of test score gains. (JEL H52, J13, I28, I38)
DOI: 10.6084/m9.figshare.19643238
title: ADC data anonyme.xlsx

description: Data on the article : <strong>Adenocarcinoma arising from a chronic perianal fistula in a Crohn’s disease patient: case report and review of the literature</strong>
DOI: 10.6084/m9.figshare.19643238.v1
title: ADC data anonyme.xlsx

description: Data on the article : <strong>Adenocarcinoma arising from a chronic perianal fistula in a Crohn’s disease patient: case report and review of the literature</strong>
DOI: 10.5281/zenodo.6477941
title: Fire exclusion changes belowground bud bank and bud-bearing organ composition jeopardizing open savanna resilience

description: Dataset from the article: Bombo, A.B.; Appezzato-da-Glória, B.; Fidelis, A. (2022) Fire exclusion changes belowground bud bank and bud-bearing organ composition jeopardizing open savanna resilience. <em>Oecologia</em> DOI 10.1007/s00442-022-05172-1, Article type: Community ecology – original research.
DOI: 10.5281/zenodo.6477940
title: Fire exclusion changes belowground bud bank and bud-bearing organ composition jeopardizing open savanna resilience

description: Dataset from the article: Bombo, A.B.; Appezzato-da-Glória, B.; Fidelis, A. (2022) Fire exclusion changes belowground bud bank and bud-bearing organ composition jeopardizing open savanna resilience. <em>Oecologia</em> DOI 10.1007/s00442-022-05172-1, Article type: Community ecology – original research.
DOI: 10.13020/9b7h-0973
title: Appendix 1: Ring-billed and Franklin's Gull AIV rT-PCR and ELISA Results
DOI: 10.34740/kaggle/dsv/3522044
title: US counties COVID 19 dataset
DOI: 10.25919/zm4k-re51
title: Parkes observations for project PX500 semester 2022APRS_05

description: This proposal relates to time purchased by the Chinese Academy of Sciences for Parkes follow-up confirmation and timing of FAST discoveries. As the observing time has been purchased this proposal (and the related PX501) does not require TAC grading. The primary reason for submitting a proposal is to ensure an outreach statement and that the proposal is entered into OPAL for the data archive to access. The Chinese team have two time requests. The time scheduled for the first, relating to this proposal, will be allocated in blocks of 24 hours and the data will become available in the data archive with an 18 month embargo period. The second type of time will be scheduled in shorter blocks and the data archived with a 10 year embargo.
DOI: 10.6084/m9.figshare.19643277.v1
title: Data Columns Explained

description: An overview of what is contained within each of the columns of the main database.
DOI: 10.6084/m9.figshare.19643277
title: Data Columns Explained

description: An overview of what is contained within each of the columns of the main database.