cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7309427
title: SuperDARN data in netCDF format (2020-May)

description: 2020-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.7309042
title: SuperDARN data in netCDF format (2020-Sep)

description: 2020-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.7309614
title: SuperDARN data in netCDF format (2020-Mar)

description: 2020-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.7308951
title: SuperDARN data in netCDF format (2020-Nov)

description: 2020-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.7309074
title: SuperDARN data in netCDF format (2020-Aug)

description: 2020-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.7309187
title: SuperDARN data in netCDF format (2020-Jul)

description: 2020-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.7308997
title: SuperDARN data in netCDF format (2020-Oct)

description: 2020-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.7309505
title: SuperDARN data in netCDF format (2020-Apr)

description: 2020-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.7309960
title: SuperDARN data in netCDF format (2020-Jan)

description: 2020-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.7150797
title: Thermosphere Hough Mode Extensions (HMEs) for Solar Tides in Earth's Atmosphere

description: The scientific basis for the atmospheric solar tide HME files contained here is described in Forbes et al. (2022). The directory "HME2022-Hough-UV functions-Tides" contains solutions to Laplace's Tidal Equation for the eastward(westward) propagating diurnal tides with zonal wavenumbers s = -1, -2, -3(+1, +2); the eastward(westward) propagating semidiurnal tides with zonal wavenumbers s = -1, -2, -3(+1, +2, +3, +4, +6); the zonally-symmetric (s = 0) diurnal and semidiurnal tides; and the westward-propagating terdiurnal tide with s = +3. In a widely-used abbreviated notation, these are, respectively: DE1, DE2, DE3, DW1, DW2; SE1, SE2, SE3, SW1, SW2, SW3, SW4, SW6; D0, S0; and TW3. These tides correspond to those measured in Earth's mesosphere and thermosphere. The directory "HME2022-output files-Tides" provides the amplitudes and phases (UT hour of maximum at 0 longitude) of eastward, southward, and vertical winds (m/s), temperatures (K), density perturbations relative to mean, and geopotential height (m) as a function of height (z, every ~4 km) and latitude (deg, every 3 deg), corresponding to several (2 to 7) HMEs for each of the aforementioned tides. Generally the number of HMEs corresponding to each tide is determined by the vertical wavelength Lz of each HME; HMEs with Lz &lt; 30 km are excluded due to their inability to effectively penetrate into the thermosphere above 100 km altitude. The directory "HME_Legacy output files-Tides" includes a prior version of these HMEs that were used in several papers in the literature, but not publicly distributed. The data for each HME extend from pole to pole and from 0 to 400 km altitude. Each directory contains a README file containing further information on the data files.
description: JMF acknowledges support for this work under the ICON mission, which is supported by NASA's Explorers Program through contracts NNG12FA45C and NNG12FA42I.
description: {"references": ["Forbes, J. M., & Zhang, X. (2022). Hough Mode Extensions (HMEs) and solar tide behavior in the dissipative thermosphere. Journal of Geophysical Research: Space Physics, 127, e2022JA030962. https://doi.org/10.1029/2022JA030962"]}
DOI: 10.6084/m9.figshare.21574536
title: Cao et al., 2022-Melting-associated thermal overpressure of pseudotachylyte and host rock in an earthquakes fault

description: Supplementary-Electron microprobe analysis Data
DOI: 10.6084/m9.figshare.21574536.v1
title: Cao et al., 2022-Melting-associated thermal overpressure of pseudotachylyte and host rock in an earthquakes fault

description: Supplementary-Electron microprobe analysis Data
DOI: 10.25612/837.nzgj784p20jd
title: American Community Survey (ACS) - Five-Year Estimates - Frequently Used Variables

description: American Community Survey (ACS) 5-year estimates datasets that Urban Data Platform acquired and processed over the years.
DOI: 10.12751/g-node.rovhrd
title: Data set - Cortical neuron number in schizophrenia with focus on area 24: a post-mortem case-control study

description: Data set accompanying Gaus, R., Popal, M., Heinsen, H. et al. Reduced cortical neuron number and neuron density in schizophrenia with focus on area 24: a post-mortem case–control study. Eur Arch Psychiatry Clin Neurosci (2022). https://doi.org/10.1007/s00406-022-01513-6
description: IsSupplementTo: Gaus, R., Popal, M., Heinsen, H. et al. Reduced cortical neuron number and neuron density in schizophrenia with focus on area 24: a post-mortem case–control study. Eur Arch Psychiatry Clin Neurosci (2022). https://doi.org/10.1007/s00406-022-01513-6
DOI: 10.3886/e120570v1-130662
title: Data and Code for: The Legacy of Colonial Medicine in Central Africa

description: Between 1921 and 1956, French colonial governments organized medical campaigns to treat and prevent sleeping sickness. Villagers were forcibly examined and injected with medications with severe, sometimes fatal, side effects. We digitized thirty years of archival records to document the locations of campaign visits at a granular geographic level for five central African countries. We find that greater campaign exposure reduces vaccination rates and trust in medicine -- as measured by willingness to consent to a blood test. We examine relevance for present day health initiatives; World Bank projects in the health sector are less successful in areas with greater exposure.
DOI: 10.5517/ccdc.csd.cc26nyb5
title: CCDC 2045917: Experimental Crystal Structure Determination

description: Related Article: Jun Ying, Chenxi Sun, Liang Jin, Aixiang Tian, Xiuli Wang|2021|CSD Communication|||
description: Related Article: Jun Ying, Chenxi Sun, Liang Jin, Aixiang Tian, Xiuli Wang|2021|CrystEngComm|23|5385|doi:10.1039/D1CE00775K
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.cc26qsmc
title: CCDC 2047693: Experimental Crystal Structure Determination

description: Related Article: Jun Ying, Chenxi Sun, Liang Jin, Aixiang Tian, Xiuli Wang|2021|CSD Communication|||
description: Related Article: Jun Ying, Chenxi Sun, Liang Jin, Aixiang Tian, Xiuli Wang|2021|CrystEngComm|23|5385|doi:10.1039/D1CE00775K
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.cc26w4bl
title: CCDC 2051869: Experimental Crystal Structure Determination

description: Related Article: Jun Ying, Chenxi Sun, Liang Jin, Aixiang Tian, Xiuli Wang|2021|CSD Communication|||
description: Related Article: Jun Ying, Chenxi Sun, Liang Jin, Aixiang Tian, Xiuli Wang|2021|CrystEngComm|23|5385|doi:10.1039/D1CE00775K
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.cc2750bs
title: CCDC 2060394: Experimental Crystal Structure Determination

description: Related Article: Jie Zheng, Shuyu Meng, Quanrui Wang|2021|CSD Communication|||
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.cc27h5lj
title: CCDC 2070167: Experimental Crystal Structure Determination

description: Related Article: Michael Ryan Reynolds, Fraser S Pick, John James Hayward, John Frederick Trant|2021|CSD Communication|||
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.cc27nqzm
title: CCDC 2075511: Experimental Crystal Structure Determination

description: Related Article: Jun Ying, Chenxi Sun, Liang Jin, Aixiang Tian, Xiuli Wang|2021|CSD Communication|||
description: Related Article: Jun Ying, Chenxi Sun, Liang Jin, Aixiang Tian, Xiuli Wang|2021|CrystEngComm|23|5385|doi:10.1039/D1CE00775K
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.cc292r5b
title: CCDC 2117801: Experimental Crystal Structure Determination

description: Related Article: Angélica Navarrete-Gutiérrez, Gerardo Aguirre, Jonathan Cisterna|2022|J.Mol.Struct.|1274|134436|doi:10.1016/j.molstruc.2022.134436
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.cc292rkq
title: CCDC 2117813: Experimental Crystal Structure Determination

description: Related Article: Angélica Navarrete-Gutiérrez, Gerardo Aguirre, Jonathan Cisterna|2022|J.Mol.Struct.|1274|134436|doi:10.1016/j.molstruc.2022.134436
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.cc292rx2
title: CCDC 2117824: Experimental Crystal Structure Determination

description: Related Article: Angélica Navarrete-Gutiérrez, Gerardo Aguirre, Jonathan Cisterna|2022|J.Mol.Struct.|1274|134436|doi:10.1016/j.molstruc.2022.134436
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: 10.25612/837.wzpelappjgkd
title: 2020 Census State Redistricting Data (Public Law 94-171) for Texas

description: Population and housing data in Texas from the 2020 Census State Redistricting Data (PL 94-171) Summary File. Data is available at 12 different geographic levels.
DOI: 10.25612/837.40mkjm6y1w78
title: Community Tabulation Areas 2010

description: Boundary files for Community Tabulation Areas. The Community Tabulation Areas subdivide the metropolitan area into geographic areas that are comparable in size to city of Houston Super Neighborhoods but extend past the city limits and build upon census geographies.
DOI: 10.7945/yevs-9z66
title: Coherence_Evaluations - nws_pollution.csv
DOI: 10.5281/zenodo.7307589
title: Surfalex HF formability study - Workflow 5 - Simulate Marciniak-Kuczynski

description: This group of MatFlow workflows is the fifth in a set of eight workflow groups developed during our formability study of the Surfalex HF (AA6016A) material. In these workflows, we used Abaqus to perform deformation of a grooved sheet using the finite element method, for different displacement boundary conditions and different groove angles (relative to the major loading direction). Thus, we performed a numerical Marciniak-Kuczynski analysis on the Surfalex material, which allowed us to predict the forming limit curve. We also include various sensitivity studies. This workflow can be downloaded and explored in a Jupyter notebook, as explained in the GitHub repository here.