cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh097953.07fu
title: SH097953.07FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh097954.07fu
title: SH097954.07FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh097955.07fu
title: SH097955.07FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh097956.07fu
title: SH097956.07FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh097957.07fu
title: SH097957.07FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh097958.07fu
title: SH097958.07FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh097959.07fu
title: SH097959.07FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.6084/m9.figshare.24079611
title: Data of MTP for the Ti-Al system

description: Data related to our work of "Machine Learning Moment Tensor Potential for Modelling Dislocation and Fracture in L10-TiAl and D019-Ti3Al Alloys" published at https://arxiv.org/abs/2305.11825. The shared data includes:1. Two MTPs trained in this work. The "fitted.mtp" is trained with all training data including surface structures. The "fitted.bulk.mtp" is trained without surface structures.2. Two sets for training data with and without surface structures. The json files can be loaded with monty python package. For example, "<i>from</i><i> monty.serialization </i><i>import</i><i> loadfn; </i><i>energies = loadfn(</i><i>'train_energies.json'</i><i>)</i>" in a python script.3. An example python jupyter notebook for the generation of the five groups of training structures. This notebook can be viewed online as well.
DOI: 10.6084/m9.figshare.24079611.v1
title: Data of MTP for the Ti-Al system

description: Data related to our work of "Machine Learning Moment Tensor Potential for Modelling Dislocation and Fracture in L10-TiAl and D019-Ti3Al Alloys" published at https://arxiv.org/abs/2305.11825. The shared data includes:1. Two MTPs trained in this work. The "fitted.mtp" is trained with all training data including surface structures. The "fitted.bulk.mtp" is trained without surface structures.2. Two sets for training data with and without surface structures.3. An example python jupyter notebook for the generation of the five groups of training structures. This notebook can be viewed online as well.
DOI: 10.6084/m9.figshare.24079611.v2
title: Data of MTP for the Ti-Al system

description: Data related to our work of "Machine Learning Moment Tensor Potential for Modelling Dislocation and Fracture in L10-TiAl and D019-Ti3Al Alloys" published at https://arxiv.org/abs/2305.11825. The shared data includes:1. Two MTPs trained in this work. The "fitted.mtp" is trained with all training data including surface structures. The "fitted.bulk.mtp" is trained without surface structures.2. Two sets for training data with and without surface structures. The json files can be loaded with monty python package. For example, "<i>from</i><i> monty.serialization </i><i>import</i><i> loadfn; </i><i>energies = loadfn(</i><i>'train_energies.json'</i><i>)</i>" in a python script.3. An example python jupyter notebook for the generation of the five groups of training structures. This notebook can be viewed online as well.
DOI: 10.5281/zenodo.7256611
title: Finding Aid for Harold Garfinkel Papers on "Occasion Maps"

description: Finding Aid for Harold Garfinkel Papers on "Occasion Maps"
DOI: 10.5281/zenodo.7256610
title: Finding Aid for Harold Garfinkel Papers on "Occasion Maps"

description: Finding Aid for Harold Garfinkel Papers on "Occasion Maps"
DOI: 10.5281/zenodo.8315978
title: Finding Aid for Harold Garfinkel Papers on "Occasion Maps"

description: Finding Aid for Harold Garfinkel Papers on "Occasion Maps"
DOI: 10.17903/fk2/lvgrhp
title: Female Immigration in Greece: Femint_GR

description: The subject of the study is the identification of the various parameters related to the process of arrival, installation, living and integration of immigrant women in Greece. The interest focuses on the differentiated conditions of immigrant women's integration into Greek society. The field research aims at investigating the degree and quality of social integration of migrant women and the definition of relevant indicators identified in the following parameters: 1) Labor market integration, 2) Access to housing, 3) Access to health services and welfare, 4) Access to education, 5) Political and social participation, 6) Family life.
DOI: 10.5517/ccdc.csd.cc2d4cqn
title: CCDC 2208741: Experimental Crystal Structure Determination

description: Related Article: Liaw Chia-Ching|2023|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.cc2g284z
title: CCDC 2266290: Experimental Crystal Structure Determination

description: Related Article: Ju Ho Yang, Sung Kuk Kim|2023|Chem.Commun.|59|9988|doi:10.1039/D3CC02611F
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.cc2g2q5g
title: CCDC 2266725: Experimental Crystal Structure Determination

description: Related Article: Sotaro Kusumoto, Youssef Atoini, Yoshihiro Koide, Kittipong Chainok, Shinya Hayami, Yang Kim, Jack Harrowfield, Pierre Thuéry|2023|Chem.Commun.|59|10004|doi:10.1039/D3CC02636A
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.cc2gr41f
title: CCDC 2286344: Experimental Crystal Structure Determination

description: Related Article: W. Clegg, J.D. Kennedy, C. O'Dowd|2023|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.cc2gr44j
title: CCDC 2286347: Experimental Crystal Structure Determination

description: Related Article: W. Clegg, J.D. Kennedy, C. O'Dowd, M. Thornton-Pett|2023|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.cc2gr45k
title: CCDC 2286348: Experimental Crystal Structure Determination

description: Related Article: W. Clegg, J.D. Kennedy, C. O'Dowd, M.G.S. Londesborough|2023|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.cc2grn52
title: CCDC 2286844: Experimental Crystal Structure Determination

description: Related Article: Constantin G. Daniliuc, Vasily Kotov, Roland Fröhlich, Gerhard Erker|2023|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.cc2gs5yd
title: CCDC 2287364: Experimental Crystal Structure Determination

description: Related Article: Frank R.  Fronczek|2023|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.cc2gsnc9
title: CCDC 2287811: Experimental Crystal Structure Determination

description: Related Article: Frank R.  Fronczek|2023|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.cc2gsnvs
title: CCDC 2287826: Experimental Crystal Structure Determination

description: Related Article: Arnold L. Rheingold|2023|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: 10.25377/sussex.23703159
title: Data for paper: Targeted Synthesis of End-on Dinitrogen-bridged Lanthanide Metallocenes and their Reactivity as Divalent Synthons

description: Dataset for <em>Targeted Synthesis of End-on Dinitrogen-bridged Lanthanide Metallocenes and their Reactivity as Divalent Synthons </em>published in Journal of the American Chemical Society September 2023 Data files CIF and checkCIF files for all crystal structures. IR spectroscopy data for all compounds. Raman spectroscopy data for all compounds. UV/visible spectroscopy data for all compounds. Magnetic measurement data for all compounds. <br> Article abstract High-yield syntheses of the lanthanide dinitrogen complexes [(Cp2tttM)2(μ-1,2-N2)] (<strong>1M</strong>, M = Gd, Tb, Dy; Cpttt = 1,2,4-C5<em>t</em>Bu3H2), in which the [N2]2– ligands solely adopt the rare end-on or 1,2-bridging mode, are reported. The bulk of the <em>tert</em>-butyl substituents and the smaller radii of gadolinium, terbium, and dysprosium preclude formation of the side-on dinitrogen bonding mode on steric grounds. Elongation of the nitrogen-nitrogen bond relative to N2 is observed in <strong>1M</strong>, and their Raman spectra show a major absorption consistent with N═N double bonds. Computational analysis of <strong>1Gd</strong> identifies that the local symmetry of the metallocene units lifts the degeneracy of two 5dπ orbitals, leading to differing overlap with the π* orbitals of [N2]2–, a consequence of which is that the dinitrogen ligand occupies a singlet ground state. Magnetic measurements reveal antiferromagnetic exchange in <strong>1M</strong> and single-molecule magnet (SMM) behavior in <strong>1Dy</strong>. Ab initio calculations show that the magnetic easy axis in the ground doublets of <strong>1Tb</strong> and <strong>1Dy</strong> align with the {M–N═N–M} connectivity, in contrast to the usual scenario in dysprosium metallocene SMMs, where the axis passes through the cyclopentadienyl ligands. The [N2]2– ligands in <strong>1M</strong> allow these compounds to be regarded as two-electron reducing agents, serving as synthons for divalent gadolinium, terbium, and dysprosium. Proof of principle for this concept is obtained in the reactions of <strong>1M</strong> with 2,2′-bipyridyl (bipy) to give [Cp2tttM(κ2-bipy)] (<strong>2M</strong>, M = Gd, Tb, Dy), in which the lanthanide is ligated by a bipy radical anion, with strong metal–ligand direct exchange coupling.
DOI: 10.3886/e119690v1-141247
title: Data and Code for: Arrival of Young Talent: The Send-down Movement and Rural Education in China

description: This paper estimates the effects of the send-down movement during the Cultural Revolution---when about 16 million urban youth were mandated to resettle in the countryside---on rural education. Using a county-level dataset compiled from local gazetteers and population censuses, we show that greater exposure to the sent-down youths significantly increased rural children's educational achievement. This positive effect diminished after the urban youth left the countryside in the late 1970s but never disappeared. Rural children who interacted with the sent-down youths were also more likely to pursue more-skilled occupations, marry later, and have smaller families than those who did not.
description: rural population in China.
DOI: 10.6084/m9.figshare.24081438.v3
title: lunar_surface_chemistry_maps

description: The data files depict the Mg# values and the abundances of SiO2, TiO2, Al2O3, FeO, MgO and CaO observed by the KAGUYA multiband imager. The data files are in the format of .tif and can be opend by the software ArcGIS.
DOI: 10.5281/zenodo.6020481
title: Drug repurposing using mechanistic models of signal transduction machine learning

description: (DREM³L) Drug REpurposing using Mechanistic Models of signal transduction and Machine Learning
description: The authors acknowledge Junta de Andalucía for the postdoctoral contract of Carlos Loucera (PAIDI2020- DOC_00350) co-funded by the European Social Fund (FSE) 2014-2020.
DOI: 10.5281/zenodo.6037213
title: (DRExM³L) Drug REpurposing using eXplainable Machine Learning and Mechanistic Models of signal transduction

description: Training data for the "Drug REpurposing using eXplainable Machine Learning and Mechanistic Models of signal transduction" (DRExM³L) package.
description: The authors acknowledge Junta de Andalucía for the postdoctoral contract of Carlos Loucera (PAIDI2020- DOC_00350) co-funded by the European Social Fund (FSE) 2014-2020.
DOI: 10.5281/zenodo.7736710
title: Drug REpurposing using Mechanistic Models of signal transduction and Machine Learning

description: (DREM³L) Drug REpurposing using Mechanistic Models of signal transduction and Machine Learning
description: The authors acknowledge Junta de Andalucía for the postdoctoral contract of Carlos Loucera (PAIDI2020- DOC_00350) co-funded by the European Social Fund (FSE) 2014-2020.
DOI: 10.5281/zenodo.7737166
title: Data for DRExM³L: Drug REpurposing using eXplainable Machine Learning and Mechanistic Models of signal transduction

description: (DREM³L) Drug REpurposing using Mechanistic Models of signal transduction and Machine Learning
description: The authors acknowledge Junta de Andalucía for the postdoctoral contract of Carlos Loucera (PAIDI2020- DOC_00350) co-funded by the European Social Fund (FSE) 2014-2020.
DOI: 10.5281/zenodo.8028472
title: Data for DRExM³L: Drug REpurposing using eXplainable Machine Learning and Mechanistic Models of signal transduction

description: (DREM³L) Drug REpurposing using Mechanistic Models of signal transduction and Machine Learning
description: The authors acknowledge Junta de Andalucía for the postdoctoral contract of Carlos Loucera (PAIDI2020- DOC_00350) co-funded by the European Social Fund (FSE) 2014-2020.
DOI: 10.5281/zenodo.8043253
title: Data for DRExM³L: Drug REpurposing using eXplainable Machine Learning and Mechanistic Models of signal transduction

description: (DREM³L) Drug REpurposing using Mechanistic Models of signal transduction and Machine Learning
description: The authors acknowledge Junta de Andalucía for the postdoctoral contract of Carlos Loucera (PAIDI2020- DOC_00350) co-funded by the European Social Fund (FSE) 2014-2020.
DOI: 10.5281/zenodo.8043743
title: Data for DRExM³L: Drug REpurposing using eXplainable Machine Learning and Mechanistic Models of signal transduction

description: (DREM³L) Drug REpurposing using Mechanistic Models of signal transduction and Machine Learning
description: The authors acknowledge Junta de Andalucía for the postdoctoral contract of Carlos Loucera (PAIDI2020- DOC_00350) co-funded by the European Social Fund (FSE) 2014-2020.