cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/cc138wr0
title: CCDC 1021442: Experimental Crystal Structure Determination

description: Related Article: Timo Gneuß, Markus J. Leitl, Lars H. Finger, Nicholas Rau, Hartmut Yersin, Jörg Sundermeyer|2015|Dalton Trans.|44|8506|doi:10.1039/C4DT02631D
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/cc138ws1
title: CCDC 1021443: Experimental Crystal Structure Determination

description: Related Article: Timo Gneuß, Markus J. Leitl, Lars H. Finger, Nicholas Rau, Hartmut Yersin, Jörg Sundermeyer|2015|Dalton Trans.|44|8506|doi:10.1039/C4DT02631D
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/cc138wt2
title: CCDC 1021444: Experimental Crystal Structure Determination

description: Related Article: Timo Gneuß, Markus J. Leitl, Lars H. Finger, Nicholas Rau, Hartmut Yersin, Jörg Sundermeyer|2015|Dalton Trans.|44|8506|doi:10.1039/C4DT02631D
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/cc138wv3
title: CCDC 1021445: Experimental Crystal Structure Determination

description: Related Article: Timo Gneuß, Markus J. Leitl, Lars H. Finger, Nicholas Rau, Hartmut Yersin, Jörg Sundermeyer|2015|Dalton Trans.|44|8506|doi:10.1039/C4DT02631D
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/cc138ww4
title: CCDC 1021446: Experimental Crystal Structure Determination

description: Related Article: Timo Gneuß, Markus J. Leitl, Lars H. Finger, Nicholas Rau, Hartmut Yersin, Jörg Sundermeyer|2015|Dalton Trans.|44|8506|doi:10.1039/C4DT02631D
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/cc138wx5
title: CCDC 1021447: Experimental Crystal Structure Determination

description: Related Article: Carsten Jenne, Mathias Keßler, Jonas Warneke|2015|Chem.-Eur.J.|21|5887|doi:10.1002/chem.201500034
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/cc138x4f
title: CCDC 1021454: Experimental Crystal Structure Determination

description: Related Article: Joshua H. Palmer, Gerard Parkin|2015|J.Mol.Struct.|1081|530|doi:10.1016/j.molstruc.2014.10.023
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/cc138x5g
title: CCDC 1021455: Experimental Crystal Structure Determination

description: Related Article: Ze-Shui Liu, Wen-Ke Li, Tai-Ran Kang, Long He, Quan-Zhong Liu|2015|Org.Lett.|17|150|doi:10.1021/ol503383x
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/cc138x6h
title: CCDC 1021456: Experimental Crystal Structure Determination

description: Related Article: Ze-Shui Liu, Wen-Ke Li, Tai-Ran Kang, Long He, Quan-Zhong Liu|2015|Org.Lett.|17|150|doi:10.1021/ol503383x
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/cc138wz7
title: CCDC 1021449: Experimental Crystal Structure Determination

description: Related Article: N.R. Penthala, S. Bommagani, V. Janganati, S. Parkin, P.A. Crooks|2014|Acta Crystallogr.,Sect.E:Struct.Rep.Online|70|o1092|doi:10.1107/S1600536814019333
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.cc138x09
title: CCDC 1021450: Experimental Crystal Structure Determination

description: Related Article: Stephan Enthaler, Andreas Brück, Anja Kammer, Henrik Junge, Elisabeth Irran, Samet Gülak|2015|ChemCatChem|7|65|doi:10.1002/cctc.201402716
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: 10.15468/dl.fwbb6w
title: Occurrence Download

description: A dataset containing 1084576 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 one of (Geranium bicknellii, Geranium bohemicum, Betonica scardica, Teucrium bergadense, Teucrium bullatum, Teucrium intricatum, Teucrium lini-vaccarii, Alchemilla bandericensis, Alchemilla vetteri, Angianthus newbeyi, Amorpha apiculata, Ceratogyne obionoides, Geranium latum, Beta trigyna, Nivenia binata, Festuca karsiana, Lachenalia martleyi, Leptomeria aphylla, Ranunculus chaerophyllos, Ranunculus acris, Paronychia bornmuelleri, Alchemilla acutiformis, Alchemilla catachnoa, Alchemilla catalaunica, Argyrolobium stipulaceum, Atriplex dioica, Atriplex hymenotheca, Atriplex hystrix, Festuca dimorpha, Alchemilla quinqueloba, Ranunculus millefolius, Stemodia kingii, Geranium canescens, Heliosperma retzdorffianum, Baccharis caramavidensis, Lachenalia splendida, Lachenalia rosea, Lachenalia perryae, Festuca pseudosupina, Ranunculus crateris, Ranunculus angustanus, Ranunculus productus, Ranunculus undosus, Ranunculus polymorphus, Halothamnus iraqensis, Lachenalia komsbergensis, Teucrium estevei, Teucrium grosii, Teucrium lanigerum, Teucrium radicans, Teucrium rivas-martinezii, Teucrium zaianum, Knautia byzantina, Knautia involucrata, Ranunculus cochlearifer, Atriplex semibaccata, Barbarea lutea, Knautia subcanescens, Ranunculus bilobus, Tragia novae-hollandiae, Festuca californica, Festuca cratericola, Festuca eskia, Festuca imperatrix, Festuca lazistanica, Festuca litardiereana, Festuca macedonica, Loeselia mexicana, Ranunculus damascenus, Ranunculus orbiculatus, Ranunculus peyronii, Alchemilla bolusii, Alchemilla delphinensis, Alchemilla glomerulans, Alchemilla heteropoda, Alchemilla holocycla, Alchemilla ilerdensis, Alchemilla speciosa, Alchemilla neglecta, Alchemilla obtusa, Atriplex halimus, Alchemilla procumbens, Alchemilla pseudocartalinica, Alchemilla undulata, Heliosperma macranthum, Heliosperma veselskyi, Heracleum pastinacifolium, Alchemilla martinii, Stemodia palmeri, Disparago anomala, Geranium baurianum, Geranium caeruleatum, Geranium robustum, Geranium seemannii, Lachenalia valeriae, Lachenalia algoensis, Lachenalia multifolia, Lachenalia thomasiae, Eriochloa aristata, Eriochloa lemmonii)",
 "Year is greater than or equal to 1950"
 ]
}

 The dataset includes 1084576 records from 3232 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0051086-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh1048955.10fu
title: SH1048955.10FU

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/sh1048958.10fu
title: SH1048958.10FU

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/sh1048960.10fu
title: SH1048960.10FU

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/sh1048961.10fu
title: SH1048961.10FU

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/sh1048964.10fu
title: SH1048964.10FU

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/sh1048966.10fu
title: SH1048966.10FU

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/sh1048967.10fu
title: SH1048967.10FU

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/sh1048969.10fu
title: SH1048969.10FU

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/sh1048971.10fu
title: SH1048971.10FU

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/sh1048973.10fu
title: SH1048973.10FU

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: 10.3886/icpsr39161
title: Assessment of Sexual Assault Kit (SAK) Evidence Selection Leading to Development of SAK Evidence Machine-Learning Model (SAK-ML Model), California, Idaho, Utah, 2010-2022
title: Archival Version

description: Few studies have explored aggregated DNA analysis findings from sexual assault kits (SAKs) and predictive features of developing useful DNA information related to the foreign contributor(s). Information gleaned from evaluating DNA analysis findings have significant practice and policy implications for both forensic medical examiners/sexual assault nurse examiners and forensic scientists. Results from this innovative study were obtained by tracking SAKs from evidence collection, data from sexual assault medical forensic examinations, through DNA analysis results, and data from publicly funded laboratories.


This study does not include data files. It includes 13 Python files used for statistical analysis.
description: The study had two purposes:


To evaluate decision-making protocols on DNA evidence contained in sexual assault kits (SAKs) to develop research-based guidelines regarding which swabs and how many swabs should be tested by crime lab.


To develop, implement and evaluate a machine learning statistical model, SAK Evidence Machine-Learning Model (SAK-ML Model) to guide forensic scientists within publicly funded forensic laboratories on the selection of the most probative SAK swabs to analyze.


The overarching goal of the study was to extract and analyze information related to SAK evidence collection and analysis to inform practice and policy.
description: Three publicly funded crime laboratories were collaborative research partners: Utah Bureau of Forensic Services (UBFS), state crime laboratory in Utah; Orange County Crime Lab (OCCL), county crime laboratory in Orange County, California; and Idaho State Police Forensic Services (ISPFS), state crime laboratory in Idaho. As the DNA analysis interpretation methods utilized by crime labs impacts findings, it is important to note that binary interpretation approach was employed during the study period at the sites.
description: Datasets: 
DS1: Assessment of Sexual Assault Kit (SAK) Evidence Selection Leading to Development of SAK Evidence Machine-Learning Model (SAK-ML Model), California, Idaho, Utah, 2010-2022
description: Victims age 14 years and older who received a sexual assault medical forensic examinations (SAMFE) from one of the participating forensic nursing teams and had an unrestricted sexual assault kit (SAK) collected. Years of inclusion are 2010-2022 in Utah, 2015-2020 in Orange County, and 2013-2020 in Idaho.
description: record abstractsThe statistical models are available publicly through Zenodo: https://zenodo.org/records/11061747