cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc1qbgxf
title: CCDC 1588995: Experimental Crystal Structure Determination

description: Related Article: Riccardo Montis, Massimiliano Arca, M. Carla Aragoni, Alexander J. Blake, Carlo Castellano, Francesco Demartin, Francesco Isaia, Vito Lippolis, Anna Pintus, Eder J. Lenardão, Gelson Perin, Alice E. O'Connor, Samuel Thurow|2018|New J.Chem.|42|10592|doi:10.1039/C8NJ00495A
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.cc1qbcdt
title: CCDC 1588886: Experimental Crystal Structure Determination

description: Related Article: Wuji Sun, Shubiao Fang, Hong Yan|2018|MedChemComm|9|1054|doi:10.1039/C8MD00057C
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.cc1qbcfv
title: CCDC 1588887: Experimental Crystal Structure Determination

description: Related Article: Yuanting Su,  Yongxin Li,  Rakesh Ganguly,  Rei Kinjo|2018|Eur.J.Inorg.Chem.|2018|2228|doi:10.1002/ejic.201701483
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.cc1qbcgw
title: CCDC 1588888: Experimental Crystal Structure Determination

description: Related Article: Yuanting Su,  Yongxin Li,  Rakesh Ganguly,  Rei Kinjo|2018|Eur.J.Inorg.Chem.|2018|2228|doi:10.1002/ejic.201701483
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.cc1qbchx
title: CCDC 1588889: Experimental Crystal Structure Determination

description: Related Article: Wuji Sun, Shubiao Fang, Hong Yan|2018|MedChemComm|9|1054|doi:10.1039/C8MD00057C
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.cc1qbcq4
title: CCDC 1588896: Experimental Crystal Structure Determination

description: Related Article: Zi-Qiang Rong,  Li-Cheng Yang,  Song Liu,  Zhaoyuan Yu,  Ya-Nong Wang,  Zher Yin Tan,  Rui-Zhi Huang,  Yu Lan,  Yu Zhao|2017|J.Am.Chem.Soc.|139|15304|doi:10.1021/jacs.7b09161
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.cc1qbcr5
title: CCDC 1588897: Experimental Crystal Structure Determination

description: Related Article: Zi-Qiang Rong,  Li-Cheng Yang,  Song Liu,  Zhaoyuan Yu,  Ya-Nong Wang,  Zher Yin Tan,  Rui-Zhi Huang,  Yu Lan,  Yu Zhao|2017|J.Am.Chem.Soc.|139|15304|doi:10.1021/jacs.7b09161
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.cc1qbcs6
title: CCDC 1588898: Experimental Crystal Structure Determination

description: Related Article: Zi-Qiang Rong,  Li-Cheng Yang,  Song Liu,  Zhaoyuan Yu,  Ya-Nong Wang,  Zher Yin Tan,  Rui-Zhi Huang,  Yu Lan,  Yu Zhao|2017|J.Am.Chem.Soc.|139|15304|doi:10.1021/jacs.7b09161
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.cc1qbct7
title: CCDC 1588899: Experimental Crystal Structure Determination

description: Related Article: Zi-Qiang Rong,  Li-Cheng Yang,  Song Liu,  Zhaoyuan Yu,  Ya-Nong Wang,  Zher Yin Tan,  Rui-Zhi Huang,  Yu Lan,  Yu Zhao|2017|J.Am.Chem.Soc.|139|15304|doi:10.1021/jacs.7b09161
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.cc1qbcv8
title: CCDC 1588900: Experimental Crystal Structure Determination

description: Related Article: Zi-Qiang Rong,  Li-Cheng Yang,  Song Liu,  Zhaoyuan Yu,  Ya-Nong Wang,  Zher Yin Tan,  Rui-Zhi Huang,  Yu Lan,  Yu Zhao|2017|J.Am.Chem.Soc.|139|15304|doi:10.1021/jacs.7b09161
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.cc1qbgfy
title: CCDC 1588980: Experimental Crystal Structure Determination

description: Related Article: Anup Mandal,  Jayaraman Selvakumar,  Suman Dana,  Upasana Mukherjee,  Mahiuddin Baidya|2018|Chem.-Eur.J.|24|3448|doi:10.1002/chem.201800337
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.cc1qbggz
title: CCDC 1588981: Experimental Crystal Structure Determination

description: Related Article: Anup Mandal,  Jayaraman Selvakumar,  Suman Dana,  Upasana Mukherjee,  Mahiuddin Baidya|2018|Chem.-Eur.J.|24|3448|doi:10.1002/chem.201800337
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.cc1qbgh0
title: CCDC 1588982: Experimental Crystal Structure Determination

description: Related Article: Anup Mandal,  Jayaraman Selvakumar,  Suman Dana,  Upasana Mukherjee,  Mahiuddin Baidya|2018|Chem.-Eur.J.|24|3448|doi:10.1002/chem.201800337
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.cc1qbgk2
title: CCDC 1588984: Experimental Crystal Structure Determination

description: Related Article: Anup Mandal,  Jayaraman Selvakumar,  Suman Dana,  Upasana Mukherjee,  Mahiuddin Baidya|2018|Chem.-Eur.J.|24|3448|doi:10.1002/chem.201800337
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.cc1qbczd
title: CCDC 1588904: Experimental Crystal Structure Determination

description: Related Article: Niklas B. Thompson,  Michael T. Green,  Jonas C. Peters|2017|J.Am.Chem.Soc.|139|15312|doi:10.1021/jacs.7b09364
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.cc1qbgj1
title: CCDC 1588983: Experimental Crystal Structure Determination

description: Related Article: Anup Mandal,  Jayaraman Selvakumar,  Suman Dana,  Upasana Mukherjee,  Mahiuddin Baidya|2018|Chem.-Eur.J.|24|3448|doi:10.1002/chem.201800337
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.cc1qbd0g
title: CCDC 1588905: Experimental Crystal Structure Determination

description: Related Article: Niklas B. Thompson,  Michael T. Green,  Jonas C. Peters|2017|J.Am.Chem.Soc.|139|15312|doi:10.1021/jacs.7b09364
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.t6cyuu
title: Occurrence Download

description: A dataset containing 3589 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is IOW Macrozoobenthos monitoring Baltic Sea (1980-2005)"
 ]
}

 The dataset includes 3589 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055486-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13012/b2idb-2680180_v1
title: Data for MALDI-MS Screening of Microbial Colonies With Isomer Resolution to Select Fatty Acid Desaturase Variants

description: Creating controlled lipid unsaturation locations in oleochemicals can be a key to many bioengineered products. However, evaluating the effects of modifications to the acyl-ACP desaturase on lipid unsaturation is not currently amenable to high-throughput assays, limiting the scale of redesign efforts to &lt;200 variants. Here, we report a rapid mass spectrometry (MS) assay for profiling the positions of double bonds on membrane lipids produced by Escherichia coli colonies after treatment with ozone gas. By MS measurement of the ozonolysis products of Δ6 and Δ8 isomers of membrane lipids from colonies expressing recombinant Thunbergia alata desaturase, we screened a randomly mutagenized library of the desaturase gene at 5 s per sample. Two variants with altered regiospecificity were isolated, indicated by an increase in 16:1 Δ8 proportion. We also demonstrated the ability of these desaturase variants to influence the membrane composition and fatty acid distribution of E. coli strains deficient in the native acyl-ACP desaturase gene, fabA. Finally, we used the fabA deficient chassis to concomitantly express a non-native acyl-ACP desaturase and a medium-chain thioesterase from Umbellularia californica, demonstrating production of only saturated free fatty acids.
DOI: 10.5255/ukda-sn-858124
title: UK Farms Survey: Pollinator-dependent Crops, 2014-2015

description: This dataset contains information about UK Farms sociodemographic, knowledge about pollinators, conservation actions and determinants of pro-pollinator behaviour based on the Theory of Planned Behaviour from 89 farmers of pollinator-dependent fruit crops across the UK. 

The main crops are apple, cider apple, strawberry, raspberry and blackcurrant. Data was obtained through a paper-printed questionnaire, with the most interviews carried out in-person, during the years of 2014-2015. 

The data was obtained to understand the sociopsychological drivers of farmers in adopting pollinator-friendly management practices in agriculture. One questionnaire (AP08) is missing data for PART 4. The work was supported by Natural Environment Research Council (grant codes: NE/K015419/1, NE/N014472/1&2).
DOI: 10.5281/zenodo.13180906
title: Alternative splicing in response to spaceflight environment in Arabidopsis thaliana for OSD-37 NASA-GeneLab study
title: Alternative splicing and variation (SNPs) analysis in OSD-38

description: The data source for bam files and differential gene expression is NASA-GeneLab (https://osdr.nasa.gov/bio/repo/data/studies/OSD-37). We surveyed the data for genome-wide identification of alternative splicing genes and events.
DOI: 10.5281/zenodo.13180907
title: Alternative splicing in response to spaceflight environment in Arabidopsis thaliana for OSD-37 NASA-GeneLab study
title: Alternative splicing and variation (SNPs) analysis in OSD-38

description: The data source for bam files and differential gene expression is NASA-GeneLab (https://osdr.nasa.gov/bio/repo/data/studies/OSD-37). We surveyed the data for genome-wide identification of alternative splicing genes and events.
DOI: 10.15468/dl.ryhnan
title: Occurrence Download

description: A dataset containing 1055 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Jacaratia mexicana"
 ]
}

 The dataset includes 1055 records from 72 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055487-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.30442820.v1
title: Database of original and synthetic images of regions of interest in microstructures of aluminum alloys.

description: Database of original and synthetic images of regions of interest in microstructures of aluminum alloys.
DOI: 10.15468/dl.ezrdny
title: Occurrence Download

description: A dataset containing 983 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Colombia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Panthera onca"
 ]
}

 The dataset includes 983 records from 87 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055490-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hjj9ck
title: Occurrence Download

description: A dataset containing 51 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-75.66102 1.49468,-75.65716 1.49468,-75.65716 1.50016,-75.66102 1.50016,-75.66102 1.49468))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 51 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055491-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dyc78j
title: Occurrence Download

description: A dataset containing 19334 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-72.9683 6.81555,-72.81614 6.9151,-72.81614 7.21455,-72.9394 7.21455,-72.9683 6.81555))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 19334 records from 73 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055492-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.30058190
title: A Bayesian Nonparametric Stochastic Block Model for Directed Acyclic Graphs

description: Random graphs have been widely used in statistics, for example in network analysis and graphical models. In some applications, the data may contain an inherent hierarchical ordering among its vertices, which prevents directed edges between pairs of vertices that do not respect this order. For example, in bibliometrics, older papers cannot cite newer ones. In such situations, the resulting graph forms a Directed Acyclic Graph. In this article, we extend the Stochastic Block Model (SBM) to account for the presence of such ordering in the data, ignoring which can lead to biased estimates of the number of blocks. The proposed approach includes in the model likelihood a topological ordering, which is treated as an unknown parameter and endowed with a prior distribution. We describe how to formalize the model and perform posterior inference for a Bayesian nonparametric version of the SBM in which both the hierarchical ordering and the number of latent blocks are learnt from the data. Finally, an illustration with real-world datasets from bibliometrics is presented. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.30142471
title: Influential Observations Detection by Random Projection in High-Dimensional Multivariate Response Linear Model

description: In this article, we consider the challenging problem of influential point detection in high-dimensional linear regressions with multivariate responses. A Multivariate Response Influential Point (MRIP) detection algorithm is proposed based on a novel random projection method, which takes into account the dependence among the responses. When the number of projected directions tends to infinity, the limit statistic is derived, which simplifies the computations greatly. The proposed MRIP algorithm can mitigate the adverse effects of masking and swamping effectively. The experimental results on both simulated and real datasets demonstrate that the proposed method outperforms existing state-of-the-art methods. The proposed method is computationally efficient and scalable to large datasets, making it practical for real-world applications. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.30058190.v1
title: A Bayesian Nonparametric Stochastic Block Model for Directed Acyclic Graphs

description: Random graphs have been widely used in statistics, for example in network analysis and graphical models. In some applications, the data may contain an inherent hierarchical ordering among its vertices, which prevents directed edges between pairs of vertices that do not respect this order. For example, in bibliometrics, older papers cannot cite newer ones. In such situations, the resulting graph forms a Directed Acyclic Graph. In this article, we extend the Stochastic Block Model (SBM) to account for the presence of such ordering in the data, ignoring which can lead to biased estimates of the number of blocks. The proposed approach includes in the model likelihood a topological ordering, which is treated as an unknown parameter and endowed with a prior distribution. We describe how to formalize the model and perform posterior inference for a Bayesian nonparametric version of the SBM in which both the hierarchical ordering and the number of latent blocks are learnt from the data. Finally, an illustration with real-world datasets from bibliometrics is presented. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.30142471.v1
title: Influential observations detection by random projection in high-dimensional multivariate response linear model

description: In this paper, we consider the challenging problem of influential point detection in high-dimensional linear regressions with multivariate responses. A Multivariate Response Influential Point (MRIP) detection algorithm is proposed based on a novel random projection method, which takes into account the dependence among the responses. When the number of projected directions tends to infinity, the limit statistic is derived, which simplifies the computations greatly. The proposed MRIP algorithm can mitigate the adverse effects of masking and swamping effectively. The experimental results on both simulated and real datasets demonstrate that the proposed method outperforms existing state-of-the-art methods. The proposed method is computationally efficient and scalable to large datasets, making it practical for real-world applications.
DOI: 10.6084/m9.figshare.30142471.v2
title: Influential Observations Detection by Random Projection in High-Dimensional Multivariate Response Linear Model

description: In this article, we consider the challenging problem of influential point detection in high-dimensional linear regressions with multivariate responses. A Multivariate Response Influential Point (MRIP) detection algorithm is proposed based on a novel random projection method, which takes into account the dependence among the responses. When the number of projected directions tends to infinity, the limit statistic is derived, which simplifies the computations greatly. The proposed MRIP algorithm can mitigate the adverse effects of masking and swamping effectively. The experimental results on both simulated and real datasets demonstrate that the proposed method outperforms existing state-of-the-art methods. The proposed method is computationally efficient and scalable to large datasets, making it practical for real-world applications. Supplementary materials for this article are available online.