cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccrwx3m
title: CCDC 712011: Experimental Crystal Structure Determination

description: Related Article: Ying-Feng Han, Yue Fei, Guo-Xin Jin|2010|Dalton Trans.|39|3976|doi:10.1039/b925098k
description: Related Article: Ying-Feng Han, Wei-Guo Jia, Yue-Jian Lin, Guo-Xin Jin|2009|Angew.Chem.,Int.Ed.|48|6234|doi:10.1002/anie.200805949
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/ccrwx4n
title: CCDC 712012: Experimental Crystal Structure Determination

description: Related Article: Ying-Feng Han, Yue Fei, Guo-Xin Jin|2010|Dalton Trans.|39|3976|doi:10.1039/b925098k
description: Related Article: Ying-Feng Han, Wei-Guo Jia, Yue-Jian Lin, Guo-Xin Jin|2009|Angew.Chem.,Int.Ed.|48|6234|doi:10.1002/anie.200805949
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: 10.13140/rg.2.2.18053.27361
title: Towards marker assisted selection
DOI: 10.5517/ccrwx5p
title: CCDC 712013: Experimental Crystal Structure Determination

description: Related Article: Ying-Feng Han, Yue Fei, Guo-Xin Jin|2010|Dalton Trans.|39|3976|doi:10.1039/b925098k
description: Related Article: Ying-Feng Han, Wei-Guo Jia, Yue-Jian Lin, Guo-Xin Jin|2009|Angew.Chem.,Int.Ed.|48|6234|doi:10.1002/anie.200805949
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/ccrwx6q
title: CCDC 712014: Experimental Crystal Structure Determination

description: Related Article: Ying-Feng Han, Wei-Guo Jia, Yue-Jian Lin, Guo-Xin Jin|2009|Angew.Chem.,Int.Ed.|48|6234|doi:10.1002/anie.200805949
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/ccrwx7r
title: CCDC 712015: Experimental Crystal Structure Determination

description: Related Article: Ying-Feng Han, Wei-Guo Jia, Yue-Jian Lin, Guo-Xin Jin|2009|Angew.Chem.,Int.Ed.|48|6234|doi:10.1002/anie.200805949
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/ccrwx8s
title: CCDC 712016: Experimental Crystal Structure Determination

description: Related Article: Ying-Feng Han, Wei-Guo Jia, Yue-Jian Lin, Guo-Xin Jin|2009|Angew.Chem.,Int.Ed.|48|6234|doi:10.1002/anie.200805949
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/ccrwxfy
title: CCDC 712021: Experimental Crystal Structure Determination

description: Related Article: Ying-Feng Han, Wei-Guo Jia, Yue-Jian Lin, Guo-Xin Jin|2009|Angew.Chem.,Int.Ed.|48|6234|doi:10.1002/anie.200805949
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/ccrwxgz
title: CCDC 712022: Experimental Crystal Structure Determination

description: Related Article: Ying-Feng Han, Wei-Guo Jia, Yue-Jian Lin, Guo-Xin Jin|2009|Angew.Chem.,Int.Ed.|48|6234|doi:10.1002/anie.200805949
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/ccrwxh0
title: CCDC 712023: Experimental Crystal Structure Determination

description: Related Article: Ying-Feng Han, Wei-Guo Jia, Yue-Jian Lin, Guo-Xin Jin|2009|Angew.Chem.,Int.Ed.|48|6234|doi:10.1002/anie.200805949
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/ccrwxj1
title: CCDC 712024: Experimental Crystal Structure Determination

description: Related Article: Ying-Feng Han, Yue Fei, Guo-Xin Jin|2010|Dalton Trans.|39|3976|doi:10.1039/b925098k
description: Related Article: Ying-Feng Han, Wei-Guo Jia, Yue-Jian Lin, Guo-Xin Jin|2009|Angew.Chem.,Int.Ed.|48|6234|doi:10.1002/anie.200805949
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/ccrwxk2
title: CCDC 712025: Experimental Crystal Structure Determination

description: Related Article: Ying-Feng Han, Yue Fei, Guo-Xin Jin|2010|Dalton Trans.|39|3976|doi:10.1039/b925098k
description: Related Article: Ying-Feng Han, Wei-Guo Jia, Yue-Jian Lin, Guo-Xin Jin|2009|Angew.Chem.,Int.Ed.|48|6234|doi:10.1002/anie.200805949
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/ccsglqj
title: CCDC 729018: Experimental Crystal Structure Determination

description: Related Article: Y.Matsuo, K.Tahara, T.Fujita, E.Nakamura|2009|Angew.Chem.,Int.Ed.|48|6239|doi:10.1002/anie.200902185
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: 10.17632/nrfmk2wn9z.1
title: Corporate Personal Data Protection and Goodwill Impairment

description: This study employs a sample of Chinese A-share listed firms spanning the period 2016 to 2023, with financial and governance data sourced from the CSMAR database. Subsequent regulatory milestones—including the 2019 Data Security Management Measures (Draft for Comment), the 2021 Data Security Law, and the 2021 Personal Information Protection Law—further solidified the legal infrastructure governing corporate data practices. This temporal scope, therefore, captures the critical period of institutional development and corporate adaptation, enabling a comprehensive analysis of PDPS evolution and its economic consequences.
The sample construction adheres to the following criteria: (1) Firms designated as Special Treatment are excluded to mitigate potential distortions arising from financial distress; (2) Financial institutions are omitted due to fundamental differences in business models, regulatory environments, and goodwill accounting practices that could introduce confounding heterogeneity; (3) Observations with missing values for core model variables are removed. To address the influence of extreme values, all continuous variables are winsorized at the 1st and 99th percentiles. The final dataset comprises an unbalanced panel of 11,965 firm-year observations, representing 2,044 unique listed entities.
DOI: 10.17632/m888gxyk5r
title: ImmunoMTL: A Multi-Task Learning Framework for Immunogenicity Prediction

description: ImmunoMTL is a machine learning framework designed to improve immunogenicity prediction by incorporating MHC-specific preferences. It supports downstream applications such as neoantigen prioritization and immune response modeling.

This repository includes:
• Training and validation data for MMS clustering
• Training and validation data for ImmunoMTL 

All datasets were obtained from public resources.
DOI: 10.17632/m888gxyk5r.1
title: ImmunoMTL: A Multi-Task Learning Framework for Immunogenicity Prediction

description: ImmunoMTL is a machine learning framework designed to improve immunogenicity prediction by incorporating MHC-specific preferences. It supports downstream applications such as neoantigen prioritization and immune response modeling.

This repository includes:
• Training and validation data for MMS clustering
• Training and validation data for ImmunoMTL 

All datasets were obtained from public resources.
DOI: 10.5281/zenodo.15743860
title: Dataset LC MS Manool disarms Streptococcus mutans by targeting ABC trasporters protein

description: The emergency of antibiotic resistance is serious. We must identify molecules that escape the self-defense mechanisms of microorganisms. In this study, we optimized a protocol for identifying Streptococcus mutans proteins impaired by manool, a metabolite from Salvia officinalis with antimicrobial activity. The research showed that S. mutans can evade antibiotics and grow. This study shows the potential of manool as a new antimicrobial agent and the importance of understanding the molecular mechanisms of plant-derived compounds in the fight against AMR. Samples were obtained using the Drug Affinity Responsive Target Stability (DART-S) assay. A new protocol was performed for bacterial cells that allows the DARTS assay to be applied to a prokaryotic model using a minimal medium. The DARTS assay was initially performed by incubating manool with a protein extract (pe-DARTS) from S. mutans, followed by limited proteolysis, SDS-PAGE analysis of digestion products, and proteomic identification of the separated proteins. The bc-DARTS assay involved the incubation of manool with Streptococcus mutans bacterial cells, followed by proteolysis, SDS-PAGE analysis, and LC-MS. The S. mutans overnight inoculum was centrifuged at 3000 rpm for 15 minutes, and the cells were resuspended in BHI medium to a density of 0.05 OD/ml. The cells were incubated at 37°C and cultured to the established optical densities (0.5 OD/ml and 1 OD/ml). Then cells were centrifuged at 3000 rpm for 15 minutes. After discarding the supernatant, the pellet was resuspended in M9 minimal medium 1X supplemented with 0.4% glucose for both control and treated samples, at a density of 0,5 OD/ml and 1 OD/mL. The treated samples were incubated with manool at 5 µM and 10 µM for 90 minutes.
DOI: 10.5281/zenodo.15743861
title: Dataset LC MS Manool disarms Streptococcus mutans by targeting ABC trasporters protein

description: The emergency of antibiotic resistance is serious. We must identify molecules that escape the self-defense mechanisms of microorganisms. In this study, we optimized a protocol for identifying Streptococcus mutans proteins impaired by manool, a metabolite from Salvia officinalis with antimicrobial activity. The research showed that S. mutans can evade antibiotics and grow. This study shows the potential of manool as a new antimicrobial agent and the importance of understanding the molecular mechanisms of plant-derived compounds in the fight against AMR. Samples were obtained using the Drug Affinity Responsive Target Stability (DART-S) assay. A new protocol was performed for bacterial cells that allows the DARTS assay to be applied to a prokaryotic model using a minimal medium. The DARTS assay was initially performed by incubating manool with a protein extract (pe-DARTS) from S. mutans, followed by limited proteolysis, SDS-PAGE analysis of digestion products, and proteomic identification of the separated proteins. The bc-DARTS assay involved the incubation of manool with Streptococcus mutans bacterial cells, followed by proteolysis, SDS-PAGE analysis, and LC-MS. The S. mutans overnight inoculum was centrifuged at 3000 rpm for 15 minutes, and the cells were resuspended in BHI medium to a density of 0.05 OD/ml. The cells were incubated at 37°C and cultured to the established optical densities (0.5 OD/ml and 1 OD/ml). Then cells were centrifuged at 3000 rpm for 15 minutes. After discarding the supernatant, the pellet was resuspended in M9 minimal medium 1X supplemented with 0.4% glucose for both control and treated samples, at a density of 0,5 OD/ml and 1 OD/mL. The treated samples were incubated with manool at 5 µM and 10 µM for 90 minutes.
DOI: 10.15156/bio/sh0198710.10fu
title: SH0198710.10FU

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/sh0198711.10fu
title: SH0198711.10FU

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: 10.15468/dl.qt4m9z
title: Occurrence Download

description: A dataset containing 11363 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is Amsinckia menziesii",
 "Continent is North America",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 11363 records from 146 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002124-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh0198712.10fu
title: SH0198712.10FU

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/sh0198713.10fu
title: SH0198713.10FU

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/sh0198714.10fu
title: SH0198714.10FU

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/sh0198715.10fu
title: SH0198715.10FU

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/sh0198716.10fu
title: SH0198716.10FU

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/sh0198717.10fu
title: SH0198717.10FU

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.