cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6940097
title: One or Many? Assyrian Perceptions of Arabian Populations. Data.

description: Excel spreadsheet that includes all the attestations of Arabian populations from the Neo-Assyrian period that are referenced in the publication "One or Many? Assyrian Perceptions of Arabian Populations".
DOI: 10.14284/573
title: Plankton-specific protein sequence database

description: Plankton-specific protein sequence database suitable for profiling environmental metagenomic/metatranscriptomic samples
description: Each entry in this fasta file corresponds to a predicted protein sequence for prokaryotic/eukaryotic species. Sequences (2867766) have been taxonomically affiliated (NCBI taxID) and annotated against the UniRef90 database of protein families (clusters).
DOI: 10.6084/m9.figshare.20383014.v12
title: Complete assembly, annotation of virulence genes and CRISPR editing of the genome of Leishmania amazonensis PH8 strain

description: <em>Leishmania amazonensis</em> is one of the etiological agents of tegumentary and visceral leishmaniasis. Genome studies focusing on genes encoding virulence factors that play crucial roles in the establishment of the infection constitute an essential step towards the molecular characterization of a protozoan parasite that is being increasingly recognized as a significant human pathogen. Here, we report the sequencing and assembly of the <em>L</em><em>. amazonensis </em>PH8 strain combining data from long PacBio reads, short Illumina reads and synteny with the <em>Leishmania mexicana</em> genome. The final assembly, composed of 34 chromosomes, represents a genome of ~32 Mb with 8317 annotated genes. Several multigene families encoding virulence factors, such as A2, amastins, metalloproteins GP63 and cysteine proteases, were identified and compared to their annotation in the genomes of other <em>Leishmania</em> species. The <em>L. amazonensis </em>PH8 strain genome has 27 genes encoding all four sub-classes of amastins (α, β, γ and δ), 5 genes encoding A2 antigens, 9 genes encoding the metalloprotease GP63 and 76 genes encoding cysteine proteases. For all gene families analysed, we identified a larger number of copies and presented a more complete gene repertoire associated with <em>L. amazonensis </em>virulence factors, compared to previously published genomes. As they have been recently recognized as virulence factors essential for disease establishment and progression of the infection, we have also identified 14 genes encoding proteins involved in the parasite iron and heme metabolism and compared them to this gene repertoire in other Trypanosomatids. To follow these studies with a genetic approach that would allow us to directly address the role of these virulence factors, we tested two CRISPR-Cas9 protocols to generate <em>L. amazonensis</em> knockout cell lines. Using the Miltefosine Transporter (MT) gene as a proof of concept, we transfected promastigotes expressing the <em>Streptococcus pyogenes</em> Cas9 with <em>in vitro</em> transcribed sgRNA targeting this gene. As an alternative protocol, promastigotes were transfected with recombinant <em>Staphylococcus aureus </em>Cas9 complexed with sgRNAs. With both strategies, we were able to disrupt the MT gene with high efficiency, as shown by PCR and restriction digestion analysis as well as the development of miltefosine-resistant parasites.
DOI: 10.5281/zenodo.6958046
title: Passive gas plume database for metrics comparison

description: Plume database used for the evaluation of different metrics that are presented in the submitted paper "New plume comparison metrics for the inversion of passive gases emissions". The synthetical CO2 plumes presented in the NetCDF file entitled "Synthetical_CO2_plume_database.nc" are the results of chemical transport model simulations described in the preprint available here https://amt.copernicus.org/preprints/amt-2022-48/.
DOI: 10.5517/ccdc.csd.cc24f26w
title: CCDC 1978767: Experimental Crystal Structure Determination

description: Related Article: Yang Zhao, Xinyue Niu, Hong Yang, Jing Yang, Zhizeng Wang, Qilin Wang|2022|Chem.Commun.|58|8576|doi:10.1039/D2CC02686D
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.cc26ppxj
title: CCDC 2046648: Experimental Crystal Structure Determination

description: Related Article: Amit Vijay Sasane, Tung-Chun Kuo, Mu-Jeng Cheng, Rai-Shung Liu|2022|Org.Lett.|24|5220|doi:10.1021/acs.orglett.2c02117
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.cc27wbhz
title: CCDC 2081851: Experimental Crystal Structure Determination

description: Related Article: Amit Vijay Sasane, Tung-Chun Kuo, Mu-Jeng Cheng, Rai-Shung Liu|2022|Org.Lett.|24|5220|doi:10.1021/acs.orglett.2c02117
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.cc28j6qr
title: CCDC 2100954: Experimental Crystal Structure Determination

description: Related Article: Franz A. Mautner, Florian Bierbaumer, Roland C. Fischer, Ànnia Tubau, Saskia Speed, Eliseo Ruiz, Salah S. Massoud, Ramon Vicente, Silvia Gómez-Coca|2022|Inorg.Chem.|61|11124|doi:10.1021/acs.inorgchem.2c00958
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.cc28j6rs
title: CCDC 2100955: Experimental Crystal Structure Determination

description: Related Article: Franz A. Mautner, Florian Bierbaumer, Roland C. Fischer, Ànnia Tubau, Saskia Speed, Eliseo Ruiz, Salah S. Massoud, Ramon Vicente, Silvia Gómez-Coca|2022|Inorg.Chem.|61|11124|doi:10.1021/acs.inorgchem.2c00958
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.cc28j6st
title: CCDC 2100956: Experimental Crystal Structure Determination

description: Related Article: Franz A. Mautner, Florian Bierbaumer, Roland C. Fischer, Ànnia Tubau, Saskia Speed, Eliseo Ruiz, Salah S. Massoud, Ramon Vicente, Silvia Gómez-Coca|2022|Inorg.Chem.|61|11124|doi:10.1021/acs.inorgchem.2c00958
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.cc28j6tv
title: CCDC 2100957: Experimental Crystal Structure Determination

description: Related Article: Franz A. Mautner, Florian Bierbaumer, Roland C. Fischer, Ànnia Tubau, Saskia Speed, Eliseo Ruiz, Salah S. Massoud, Ramon Vicente, Silvia Gómez-Coca|2022|Inorg.Chem.|61|11124|doi:10.1021/acs.inorgchem.2c00958
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.cc28tn3w
title: CCDC 2110018: Experimental Crystal Structure Determination

description: Related Article: Yingge Gu, Yajie Yang, Ye Wang, Zongkang Wang, Yilin Zhu, Yanzhong Li|2022|New J.Chem.|46|14309|doi:10.1039/D2NJ01197B
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.cc294h21
title: CCDC 2119472: Experimental Crystal Structure Determination

description: Related Article: Krishna Ravi Singh, Chikkappaiahnayaka Santhosh, Toreshettahally R. Swaroop, Maralinganadoddi P. Sadashiva|2022|Org.Biomol.Chem.|20|5771|doi:10.1039/D2OB00837H
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.cc294h32
title: CCDC 2119473: Experimental Crystal Structure Determination

description: Related Article: Krishna Ravi Singh, Chikkappaiahnayaka Santhosh, Toreshettahally R. Swaroop, Maralinganadoddi P. Sadashiva|2022|Org.Biomol.Chem.|20|5771|doi:10.1039/D2OB00837H
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: 10.5281/zenodo.6833046
title: SuperDARN data in netCDF format (1994-Sep)

description: 1994-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.6833004
title: SuperDARN data in netCDF format (1994-Oct)

description: 1994-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.6833090
title: SuperDARN data in netCDF format (1994-Aug)

description: 1994-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.6833116
title: SuperDARN data in netCDF format (1994-Jul)

description: 1994-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.6833143
title: SuperDARN data in netCDF format (1994-Jun)

description: 1994-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.6833176
title: SuperDARN data in netCDF format (1994-May)

description: 1994-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.6833198
title: SuperDARN data in netCDF format (1994-Apr)

description: 1994-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.6833220
title: SuperDARN data in netCDF format (1994-Mar)

description: 1994-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.6833276
title: SuperDARN data in netCDF format (1994-Jan)

description: 1994-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.6833246
title: SuperDARN data in netCDF format (1994-Feb)

description: 1994-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.'