cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2f1vy9
title: CCDC 2236121: Experimental Crystal Structure Determination

description: Related Article: Constantin G. Daniliuc, Jörg Wonnemann, 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.cc2f1wq3
title: CCDC 2236145: Experimental Crystal Structure Determination

description: Related Article: Constantin G. Daniliuc, Klaus Kunz, 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.cc2f203n
title: CCDC 2236250: 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: 10.5517/ccdc.csd.cc2f204p
title: CCDC 2236251: 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: 10.5517/ccdc.csd.cc2f205q
title: CCDC 2236252: 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: 10.5517/ccdc.csd.cc2f236v
title: CCDC 2236346: 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: 10.5517/ccdc.csd.cc2f2bwr
title: CCDC 2236584: 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: 10.5517/ccdc.csd.cc2f2bxs
title: CCDC 2236585: 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: 10.5517/ccdc.csd.cc2f2byt
title: CCDC 2236586: 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: 10.5517/ccdc.csd.cc2f2gff
title: CCDC 2236694: Experimental Crystal Structure Determination

description: Related Article: D. Schollmeyer, H. Detert|2023|IUCrData|8|x230042|doi:10.1107/S2414314623000421
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.cc2f2kwz
title: CCDC 2236801: Experimental Crystal Structure Determination

description: Related Article: Arnold L. Rheingold, Stephen Cope and Joseph O'Connor|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.cc2f2lch
title: CCDC 2236816: Experimental Crystal Structure Determination

description: Related Article: Arnold L. Rheingold, Phil Baran|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.cc2f2ldj
title: CCDC 2236817: 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: 10.5517/ccdc.csd.cc2f2t6k
title: CCDC 2237028: Experimental Crystal Structure Determination

description: Related Article: Martin Nieger, Jahir U. Ahmad, Markku Leskelä, Timo Repo|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.cc123l6z
title: CCDC 986550: Experimental Crystal Structure Determination

description: Related Article: W. Clegg, R.W. Harrington|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.cc2f35lb
title: CCDC 2237381: Experimental Crystal Structure Determination

description: Related Article: W. Clegg, R.W. Harrington|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.cc2f3h35
title: CCDC 2237676: Experimental Crystal Structure Determination

description: Related Article: Arnold L. Rheingold and Phil Baran|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.cc2f3h46
title: CCDC 2237677: 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: 10.5517/ccdc.csd.cc2f3h68
title: CCDC 2237679: 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: 10.5517/ccdc.csd.cc2f3h79
title: CCDC 2237680: Experimental Crystal Structure Determination

description: Related Article: Arnold L. Rheingold and David R. Weinberg|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: 10.5281/zenodo.7623641
title: Computer Vision Datasets for Visual Blockage Assessment at Culverts

description: Blockage of culverts caused by transported debris is a major factor in causing flash floods in urban areas. Traditional hydraulic models have been unsuccessful in solving this problem due to a lack of data on peak flood hydraulics and the complex behavior of debris at culverts. To address this problem, a new approach of developing intelligent video analytics (IVA) algorithms is being proposed, which uses computer vision algorithms to extract information about visual blockage. This approach is expected to help in timely and safe maintenance operations and reduce the risk of culverts being blocked. To support the development of computer vision solutions, two datasets have been created: the Synthetic Images of Culvert (SIC) and the Visual Hydraulics Lab Dataset (VHD). The Synthetic Images of Culvert (SIC) dataset consists of synthetic images of culverts that were generated using a 3D computer application built on the Unity3D gaming engine. The application was designed to simulate various blockage scenarios by allowing users to place different types of debris materials in the scene in various orientations and locations. These blockage scenarios were captured as images using batch capture functionality. The dataset offers diversity in terms of the type of debris (urban, vegetative, mixed), culvert types (pipe, single circular, double circular, single box, double box, triple box), camera viewpoints, time of day, and water levels. However, it has some limitations, such as a single natural background and unrealistic effects and animations. The Visual Hydraulics-Lab Dataset (VHD) is a dataset of simulated images of culverts that were captured during controlled hydrology lab experiments. The experiments involved a series of tests using scaled physical models of culverts under different flooding conditions. The experiments were recorded using two high definition (HD) cameras and images of culverts in both blocked and unblocked conditions were extracted. The VHD dataset includes a variety of images with different culvert configurations (single circular, double circular, single box, double box), blockage types (urban, vegetative, mixed), simulated lighting conditions, camera viewpoints, and flood levels controlled by inlet water discharge. The limitations of the dataset include reflections from the water surface and flume walls, an identical background and scaling, and clear water.
description: {"references": ["Iqbal, Umair, Application of Artificial Intelligence Approaches in the Flood Management Process for Assessing Blockage at Cross-Drainage Hydraulic Structures, Doctor of Philosophy thesis, SMART Infrastructure Facility, University of Wollongong, 2022. https://ro.uow.edu.au/theses1/1340", "Iqbal, U.; Barthelemy, J.; Li, W.; Perez, P. Automating Visual Blockage Classification of Culverts with Deep Learning. Appl. Sci. 2021, 11, 7561. https://doi.org/10.3390/app11167561"]}
DOI: 10.5281/zenodo.7623640
title: Computer Vision Datasets for Visual Blockage Assessment at Culverts

description: Blockage of culverts caused by transported debris is a major factor in causing flash floods in urban areas. Traditional hydraulic models have been unsuccessful in solving this problem due to a lack of data on peak flood hydraulics and the complex behavior of debris at culverts. To address this problem, a new approach of developing intelligent video analytics (IVA) algorithms is being proposed, which uses computer vision algorithms to extract information about visual blockage. This approach is expected to help in timely and safe maintenance operations and reduce the risk of culverts being blocked. To support the development of computer vision solutions, two datasets have been created: the Synthetic Images of Culvert (SIC) and the Visual Hydraulics Lab Dataset (VHD). The Synthetic Images of Culvert (SIC) dataset consists of synthetic images of culverts that were generated using a 3D computer application built on the Unity3D gaming engine. The application was designed to simulate various blockage scenarios by allowing users to place different types of debris materials in the scene in various orientations and locations. These blockage scenarios were captured as images using batch capture functionality. The dataset offers diversity in terms of the type of debris (urban, vegetative, mixed), culvert types (pipe, single circular, double circular, single box, double box, triple box), camera viewpoints, time of day, and water levels. However, it has some limitations, such as a single natural background and unrealistic effects and animations. The Visual Hydraulics-Lab Dataset (VHD) is a dataset of simulated images of culverts that were captured during controlled hydrology lab experiments. The experiments involved a series of tests using scaled physical models of culverts under different flooding conditions. The experiments were recorded using two high definition (HD) cameras and images of culverts in both blocked and unblocked conditions were extracted. The VHD dataset includes a variety of images with different culvert configurations (single circular, double circular, single box, double box), blockage types (urban, vegetative, mixed), simulated lighting conditions, camera viewpoints, and flood levels controlled by inlet water discharge. The limitations of the dataset include reflections from the water surface and flume walls, an identical background and scaling, and clear water.
description: {"references": ["Iqbal, Umair, Application of Artificial Intelligence Approaches in the Flood Management Process for Assessing Blockage at Cross-Drainage Hydraulic Structures, Doctor of Philosophy thesis, SMART Infrastructure Facility, University of Wollongong, 2022. https://ro.uow.edu.au/theses1/1340", "Iqbal, U.; Barthelemy, J.; Li, W.; Perez, P. Automating Visual Blockage Classification of Culverts with Deep Learning. Appl. Sci. 2021, 11, 7561. https://doi.org/10.3390/app11167561"]}
DOI: 10.6084/m9.figshare.19508192
title: How shall we measure programmed cell death in eukaryotic microalgae?

description: Programmed cell death (PCD) plays a key role in unicellular microalgal ecology. However, the methodologies for detecting PCD are problematic. Clearly, to interpret the empirical data, clarity on how to measure microalgal PCD is essential. Here, we critically review the current measurements of PCD and provide suggestions for future methodological developments and interpretations. We review the traditional measures of PCD and associated cellular responses in microalgae and provide assessments of their frequencies of use and true positive rates. Traditional physiological measurements of photosynthetic activity, change in gene regulation, measurements of reactive oxygen species and terminal deoxynucleotidyl transferase dUTP nick end labelling are highly sensitive assays. They provide important measures of cellular physiological responses but are not unique to PCD. Both caspase-like and metacaspase activity reveal useful information about stress responses and demonstrate high (94% and 100%, respectively) positivity rates, however, they can play a role in cell activities other than death. Furthermore, the controversy surrounding positive caspase assays, even though microalgae encode metacaspases rather than orthologous caspases, is highlighted. DNA laddering had the lowest true positive rate (64%) and was not reported in diatoms while phosphatidylserine externalization was consistently positive in all taxa except dinoflagellates. These data illustrate the limitations of some PCD markers across different taxa. Ultrastructural alterations (transmission electron microscopy) were highly correlated with PCD across all microalgal taxa (true positive rate of 94%) and seem essential for the initial assessments of whether a cell is dying in an organized, ‘programmed’ way. However, in the face of the complexity of PCD phenotypes and the non-specific nature of the methodologies, no single indicator can be used to diagnose PCD. Here, we highlight the importance of employing a time-sensitive multi-assay approach to detect PCD in the eukaryotic microalgae before any ecological or evolutionary interpretations can be made. Measurements of PCD have different specificities and sensitivities.TEM appears essential as part of an initial investigation.Complementary markers provide information about cell stress and death responses. Measurements of PCD have different specificities and sensitivities. TEM appears essential as part of an initial investigation. Complementary markers provide information about cell stress and death responses.
DOI: 10.6084/m9.figshare.19508192.v1
title: How shall we measure programmed cell death in eukaryotic microalgae?

description: Programmed cell death (PCD) plays a key role in unicellular microalgal ecology. However, the methodologies for detecting PCD are problematic. Clearly, to interpret the empirical data, clarity on how to measure microalgal PCD is essential. Here, we critically review the current measurements of PCD and provide suggestions for future methodological developments and interpretations. We review the traditional measures of PCD and associated cellular responses in microalgae and provide assessments of their frequencies of use and true positive rates. Traditional physiological measurements of photosynthetic activity, change in gene regulation, measurements of reactive oxygen species and terminal deoxynucleotidyl transferase dUTP nick end labelling are highly sensitive assays. They provide important measures of cellular physiological responses but are not unique to PCD. Both caspase-like and metacaspase activity reveal useful information about stress responses and demonstrate high (94% and 100%, respectively) positivity rates, however, they can play a role in cell activities other than death. Furthermore, the controversy surrounding positive caspase assays, even though microalgae encode metacaspases rather than orthologous caspases, is highlighted. DNA laddering had the lowest true positive rate (64%) and was not reported in diatoms while phosphatidylserine externalization was consistently positive in all taxa except dinoflagellates. These data illustrate the limitations of some PCD markers across different taxa. Ultrastructural alterations (transmission electron microscopy) were highly correlated with PCD across all microalgal taxa (true positive rate of 94%) and seem essential for the initial assessments of whether a cell is dying in an organized, ‘programmed’ way. However, in the face of the complexity of PCD phenotypes and the non-specific nature of the methodologies, no single indicator can be used to diagnose PCD. Here, we highlight the importance of employing a time-sensitive multi-assay approach to detect PCD in the eukaryotic microalgae before any ecological or evolutionary interpretations can be made. Measurements of PCD have different specificities and sensitivities.TEM appears essential as part of an initial investigation.Complementary markers provide information about cell stress and death responses. Measurements of PCD have different specificities and sensitivities. TEM appears essential as part of an initial investigation. Complementary markers provide information about cell stress and death responses.