cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6653721
title: SuperDARN data in netCDF format (2019-Sep)

description: 2019-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.6633809
title: SuperDARN data in netCDF format (2005-May)

description: 2005-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.6633682
title: SuperDARN data in netCDF format (2005-Sep)

description: 2005-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.6633845
title: SuperDARN data in netCDF format (2005-Mar)

description: 2005-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.6633870
title: SuperDARN data in netCDF format (2005-Jan)

description: 2005-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.6633542
title: SuperDARN data in netCDF format (2005-Dec)

description: 2005-Dec 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.6633768
title: SuperDARN data in netCDF format (2005-Jun)

description: 2005-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.6633862
title: SuperDARN data in netCDF format (2005-Feb)

description: 2005-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.6633733
title: SuperDARN data in netCDF format (2005-Jul)

description: 2005-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.6633617
title: SuperDARN data in netCDF format (2005-Nov)

description: 2005-Nov 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.6633712
title: SuperDARN data in netCDF format (2005-Aug)

description: 2005-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.6633824
title: SuperDARN data in netCDF format (2005-Apr)

description: 2005-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: 10.13026/083h-vh54
title: BRAX, a Brazilian labeled chest X-ray dataset

description: The Brazilian labeled chest x-ray dataset (BRAX) is an automatically labeled
dataset designed to assist researchers in the validation of machine learning
models. The dataset contains 24,959 chest radiography studies from patients
presenting to a large general Brazilian hospital. A total of 40,967 images are
available in the BRAX dataset. All images have been verified by trained
radiologists and de-identified to protect patient privacy. Fourteen labels
were derived from free-text radiology reports written in Brazilian Portuguese
using Natural Language Processing.
DOI: 10.34681/xa3v-br87
title: Maltreatment and the Academic and Social Adjustment of School Children, 1987-1988

description: This study examined the effects of child abuse and neglect on academic achievement and social adjustment in the school setting. The data were derived from a population of 8600 children attending public schools (grades K to 12) in a small city in New York State between 1987 and 1988. From this population of children, 1239 were found to have been substantiated victims of abuse or neglect on at least one occasion. A stratified sampling procedure was employed to select a sample of maltreated children. The final sample selected for the study consists of 420 maltreated children and 420 matched controls from regular classrooms (i.e., not from special education classrooms). Children selected for the control group were matched by school, gender, grade in school, residential location, and when possible by classroom. The machine-readable data file contains 840 observations and 430 variables that describe maltreatment history, school behavioral problems, educational services received, and academic achievement. These variables were derived from social service and school records.
DOI: 10.6084/m9.figshare.20085293.v1
title: GL reference annotations

description: Temporary holding location for GeneLab reference databases
DOI: 10.6084/m9.figshare.20090297
title: Factors determining success of the chronically instrumented unanesthetized fetal sheep model of human development: the accompanying dataset

description: The fetal sheep model closely mirrors the physiology of human fetal development. In addition to its high translational potential, this model is known to have some generally not reported rate of experimental failure. Here we present a dataset of outcomes in 82 experiments and the SAS code to analyze it. We show that factors such as animal characteristics & surgical complexity do not influence the experimental outcomes. Instead, the key factor in model experimental success is the level of the research team’s experience. The key factors to improve the animal model outcomes are an intra- and inter-team communication.
DOI: 10.6084/m9.figshare.20085293.v2
title: GL reference annotations

description: Temporary holding location for GeneLab reference databases
DOI: 10.6084/m9.figshare.20092544
title: SPP_new_220108.sav

description: Plasma thymidine kinase activity for improved prognostic precision in pancreatic ductal adenocarcinoma
DOI: 10.6084/m9.figshare.20092544.v1
title: SPP_new_220108.sav

description: Plasma thymidine kinase activity for improved prognostic precision in pancreatic ductal adenocarcinoma
DOI: 10.6084/m9.figshare.20092547.v1
title: Academic Medical Center Degree Distribution

description: Degree distribution plots for 40 academic medical center websites.
DOI: 10.5066/p9eacmix
title: Gridded earthquake ground motions for the 2023 AASHTO Design Specifications

description: For designing bridges to safely resist earthquakes, the 2023 editions of the AASHTO (1) Guide Specifications for LRFD Seismic Bridge Design, (2) LRFD Bridge Design Specifications, and (3) Guide Specifications for Seismic Isolation Design provide risk-targeted design ground motions via this data release and the corresponding USGS Seismic Design Web Services (listed below). The web services spatially interpolate the gridded ground motions of this data release for user-specified latitudes and longitudes. All of the data files within the regional "Child Items" below were derived from respective USGS National Seismic Hazard Models (cited below). Details of the derivation will be added to this data release upon publication of the 2023 AASHTO Design Specifications.
DOI: 10.34681/47m4-hn91
title: Youth Information Form: Data from Runaway and Homeless Youth Programs, 1987-1990

description: These data were gathered from federally funded runaway and homeless youth shelters during 1988-1989. Data were collected on approximately 87,000 youth who received services from runaway and homeless youth programs. The following types of information are included in the data set: demographic characteristics, youth and family presenting problems at intake (including history of abuse and neglect), and services provided by the shelter.
DOI: 10.34681/884e-z988
title: National Family Violence Survey, 1985

description: A representative national sample of 6,002 families were studied with an emphasis on psychological and medical consequences, costs, and help-seeking patterns of intra-family violence. The study is a follow-up to the 1975 National Family Violence Survey.
DOI: 10.34681/dm0h-bc44
title: National Study of the Incidence of Child Abuse and Neglect 1980 (NIS-1)

description: Conducted by NCCAN and mandated by Congress, this study was the first large scale effort to assemble information, based on standardized definitions, about the scope and character of recognized child abuse and neglect in the United States. From findings obtained in a probability sample of 26 counties in 10 states, national estimates were derived from the numbers and characteristics of abused and neglected children who were: a) referred to CPS agencies and treatment, b) not referred to CPS but were known to other investigatory agencies which may handle abuse/neglect situations (e.g., police, court, public health departments, etc.), or c) were not known to any of the investigatory agencies but were identifiable from other community institutions, particularly schools and hospitals.
DOI: 10.5517/ccdc.csd.cc29x90k
title: CCDC 2142348: Experimental Crystal Structure Determination

description: Related Article: Eugene A. Katlenok, Anton V. Rozhkov, Ruslan R. Ramazanov, Rashid R. Valiev, Oleg V. Levin, Dmitrii O. Goryachiy, Ilya V. Taydakov, Maxim L. Kuznetsov, Vadim Yu Kukushkin|2022|Inorg.Chem.|61|8670|doi:10.1021/acs.inorgchem.2c00321
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc29x91l
title: CCDC 2142349: Experimental Crystal Structure Determination

description: Related Article: Eugene A. Katlenok, Anton V. Rozhkov, Ruslan R. Ramazanov, Rashid R. Valiev, Oleg V. Levin, Dmitrii O. Goryachiy, Ilya V. Taydakov, Maxim L. Kuznetsov, Vadim Yu Kukushkin|2022|Inorg.Chem.|61|8670|doi:10.1021/acs.inorgchem.2c00321
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.