cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5109415
title: UniToBrain Dataset

description: The University of Turin (UniTO) released the open-access dataset UniTOBrain collected for the homonymous Use Case 3 in the DeepHealth project (https://deephealth-project.eu/). UniToBrain is a dataset of Computed Tomography (CT) perfusion images (CTP). The dataset includes 100 training subjects and 15 testing subjects used in a submitted publication for the training and the testing of a Convolutional Neural Network (CNN, see for details: https://arxiv.org/abs/2101.05992, https://paperswithcode.com/paper/neural-network-derived-perfusion-maps-a-model, https://www.medrxiv.org/content/10.1101/2021.01.13.21249757v1). The UniTO team released this dataset publicly. This is a subsample of a greater dataset of 258 subjects that will be soon available for download at https://ieee-dataport.org/.<br> CTP data from 258 consecutive patients were retrospectively obtained from the hospital PACS of Città della Salute e della Scienza di Torino (Molinette). CTP acquisition parameters were as follows: Scanner GE, 64 slices, 80 kV, 150 mAs, 44.5 sec duration, 89 volumes (40 mm axial coverage), injection of 40 ml of Iodine contrast agent (300 mg/ml) at 4 ml/s speed. Along with the dataset, we provide some utility files. dicomtonpy.py: It converts the dicom files in the dataset to numpy arrays. These are 3D arrays, where CT slices at the same height are piled-up over the temporal acquisition. dataloader_pytorch.py: Dataloader for the pytorch deep learning framework. It converts the numpy arrays in normalized tensors, which can be provided as input to standard deep learning models. dataloader_pyeddl.py: Dataloader for the pyeddl deep learning framework. It converts the numpy arrays in normalized tensors, which can be provided as input to standard deep learning models using the european library EDDL. Visit https://github.com/EIDOSlab/UC3-UNITOBrain to have a full companion code where a U-Net model is trained over the dataset. As for UniToBrain Data and Metadata in machine-readable format see https://openview.metadatacenter.org/templates/https:%2F%2Frepo.metadatacenter.org%2Ftemplates%2Fe30d8369-6c31-45fa-a10a-2122283a28f2.
description: {"references": ["Bennink E, Oosterbroek J, Kudo K, Viergever MA, Velthuis BK, de Jong HWAM. Fast nonlinear regression method for CT brain perfusion analysis. Journal of Medical Imaging 2016. https://doi.org/10.1117/1.jmi.3.2.026003.", "Klein S, Staring M, Murphy K, Viergever MA, Pluim JPW. Elastix: A toolbox for intensity-based medical image registration. IEEE Transactions on Medical Imaging 2010. https://doi.org/10.1109/TMI.2009.2035616.", "Tomasi C, Manduchi R. Bilateral filtering for gray and color images. Proceedings of the IEEE International Conference on Computer Vision, 1998. https://doi.org/10.1109/iccv.1998.710815."]}
DOI: 10.5281/zenodo.4817604
title: UniToBrain Dataset

description: The University of Turin (UniTO) released the open-access dataset UniTOBrain collected for the homonymous Use Case 3 in the DeepHealth project (https://deephealth-project.eu/). UniToBrain is a dataset of Computed Tomography (CT) perfusion images (CTP). The dataset includes 100 training subjects and 15 testing subjects used in a submitted publication for the training and the testing of a Convolutional Neural Network (CNN, see for details: https://arxiv.org/abs/2101.05992, https://paperswithcode.com/paper/neural-network-derived-perfusion-maps-a-model, https://www.medrxiv.org/content/10.1101/2021.01.13.21249757v1). The UniTO team released this dataset publicly. This is a subsample of a greater dataset of 258 subjects that will be soon available for download at https://ieee-dataport.org/.<br> CTP data from 258 consecutive patients were retrospectively obtained from the hospital PACS of Città della Salute e della Scienza di Torino (Molinette). CTP acquisition parameters were as follows: Scanner GE, 64 slices, 80 kV, 150 mAs, 44.5 sec duration, 89 volumes (40 mm axial coverage), injection of 40 ml of Iodine contrast agent (300 mg/ml) at 4 ml/s speed. Along with the dataset, we provide some utility files. dicomtonpy.py: It converts the dicom files in the dataset to numpy arrays. These are 3D arrays, where CT slices at the same height are piled-up over the temporal acquisition. dataloader_pytorch.py: Dataloader for the pytorch deep learning framework. It converts the numpy arrays in normalized tensors, which can be provided as input to standard deep learning models. dataloader_pyeddl.py: Dataloader for the pyeddl deep learning framework. It converts the numpy arrays in normalized tensors, which can be provided as input to standard deep learning models using the european library EDDL. Visit https://github.com/EIDOSlab/UC3-UNITOBrain to have a full companion code where a U-Net model is trained over the dataset. As for UniToBrain Data and Metadata in machine-readable format see https://openview.metadatacenter.org/templates/https:%2F%2Frepo.metadatacenter.org%2Ftemplates%2Fe30d8369-6c31-45fa-a10a-2122283a28f2.
description: {"references": ["Bennink E, Oosterbroek J, Kudo K, Viergever MA, Velthuis BK, de Jong HWAM. Fast nonlinear regression method for CT brain perfusion analysis. Journal of Medical Imaging 2016. https://doi.org/10.1117/1.jmi.3.2.026003.", "Klein S, Staring M, Murphy K, Viergever MA, Pluim JPW. Elastix: A toolbox for intensity-based medical image registration. IEEE Transactions on Medical Imaging 2010. https://doi.org/10.1109/TMI.2009.2035616.", "Tomasi C, Manduchi R. Bilateral filtering for gray and color images. Proceedings of the IEEE International Conference on Computer Vision, 1998. https://doi.org/10.1109/iccv.1998.710815."]}
DOI: 10.15468/yru6id
title: Varietes de plantain echantillonnees et les zones de prelevements

description: La banane contribue de manière significative, à la sécurité alimentaire, à la création d’emplois, à la diversification des revenus dans les zones rurales et urbaines et même sur le plan mondial. La banane constitue la quatrième récolte importante de fruit dans le monde (Lassoudière, 2007). La production mondiale de banane est estimée à 102,687 millions de tonnes avec 40 millions pour la banane plantain en 2003 (Anonyme 1, 2003). Cette production demeure cependant, insuffisante à cause de l’évolution démographique. Les exploitations actuelles de banane sont insuffisantes pour répondre aux besoins de la population à cause de l’indisponibilité du matériel végétal de bonne qualité, des techniques agricoles mal adaptées. Au Togo, les recherches pour l’amélioration de la production de banane sont peu avancées. Rares sont les documents qui abordent des thématiques relatives à la production, l’amélioration, la phytopathologie, la transformation et surtout les ressources phytogénétiques de cette culture. Faisant le constat d’une insuffisance d’informations scientifiques sur le genre Musa au Togo, une prospection et une collecte des variétés locales de bananiers ont été menées en 2012 par une équipe de l’ITRA dans la Région Maritime, au sud du Togo. Cette région est celle où on enregistre une plus grande production de banane par rapport aux autres régions du Togo. Ainsi, il a été identifié dans cette région trois préfectures comme zones de production les plus importantes de bananes. A l’issue de ce travail, il a été identifié d’une manière générale, 19 accessions de banane plantain et 15 accessions de banane douce.
DOI: 10.6084/m9.figshare.14554252
title: Changes in pesticide residues in field-treated fresh grapes during raisin production by different methods of drying

description: Drying is one of the best food preservation methods, especially for grapes. This study aimed to determine the fate of pesticides after grapes are being subjected to different drying methods, namely, sun-, oven-, shade-, and alkaline-drying, which are employed in raisin production. Pesticide residues were determined by gas chromatography–tandem mass spectrometry (GC–MS/MS). Washing before drying decreased the diazinon, ethion, phosalone, penconazole, and hexaconazole levels in the grape samples by 52.04%, 6.75%, 9.99%, 56.54%, and 20.97%, respectively. In the raisins, the concentration of all the pesticide residues increased based on wet weight, and this result was due to dehydration; by contrast, the concentrations of the pesticides decreased based on dry weight. The sun-drying method demonstrated the greatest efficiency in residue removal wherein it decreased the diazinon, ethion, phosalone, penconazole, and hexaconazole levels by 76.65%, 68.40%, 69.55%, 72.74%, and 82.19% (dry weight), respectively. On the contrary, the shade-drying method demonstrated the least efficiency in reducing the residue levels (19.57%–42.55%). The evaluation of the processing results showed pesticide residue level in the raisins prepared using the investigated drying methods was higher than the maximum residual limits (MRL). This finding indicates that it is necessary to consider the pre-harvest interval in pesticide application in grape.
DOI: 10.6084/m9.figshare.14554252.v1
title: Changes in pesticide residues in field-treated fresh grapes during raisin production by different methods of drying

description: Drying is one of the best food preservation methods, especially for grapes. This study aimed to determine the fate of pesticides after grapes are being subjected to different drying methods, namely, sun-, oven-, shade-, and alkaline-drying, which are employed in raisin production. Pesticide residues were determined by gas chromatography–tandem mass spectrometry (GC–MS/MS). Washing before drying decreased the diazinon, ethion, phosalone, penconazole, and hexaconazole levels in the grape samples by 52.04%, 6.75%, 9.99%, 56.54%, and 20.97%, respectively. In the raisins, the concentration of all the pesticide residues increased based on wet weight, and this result was due to dehydration; by contrast, the concentrations of the pesticides decreased based on dry weight. The sun-drying method demonstrated the greatest efficiency in residue removal wherein it decreased the diazinon, ethion, phosalone, penconazole, and hexaconazole levels by 76.65%, 68.40%, 69.55%, 72.74%, and 82.19% (dry weight), respectively. On the contrary, the shade-drying method demonstrated the least efficiency in reducing the residue levels (19.57%–42.55%). The evaluation of the processing results showed pesticide residue level in the raisins prepared using the investigated drying methods was higher than the maximum residual limits (MRL). This finding indicates that it is necessary to consider the pre-harvest interval in pesticide application in grape.
DOI: 10.34740/kaggle/dsv/3922118
title: 2021 World Population (updated daily)
DOI: 10.25505/fiz.icsd.cc25gr1g
title: CSD 2010165: Experimental Crystal Structure Determination

description: Related Article: Lu Qi, Zhaohui Chen, Lu Li, Qun Jing, Na Li, Zhongqi Jiang, Xiaoyu Dong, Ming‐Hsien Lee|2020|Eur.J.Inorg.Chem.|2020|4007|doi:10.1002/ejic.202000716
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc24plt8
title: CSD 1986970: Experimental Crystal Structure Determination

description: Related Article: Yingjie Zhang, Tao Wei, Toan Trong Tran, Kimbal T. Lu, Zhaoming Zhang, Jason R. Price∥, Igor Aharonovich, Rongkun Zheng|2020|Inorg.Chem.|59|12166|doi:10.1021/acs.inorgchem.0c01099
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc25h6zw
title: CSD 2010628: Experimental Crystal Structure Determination

description: Related Article: Callista M. Skaggs, Chang-Jong Kang$, Christopher J. Perez, Joke Hadermann, Thomas J. Emge, Corey E. Frank, Chongin Pak, Saul H. Lapidus, David Walker, Gabriel Kotliar, Susan M. Kauzlarich, Xiaoyan Tan, Martha Greenblatt|2020|Inorg.Chem.|59|14058|doi:10.1021/acs.inorgchem.0c01848
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc25hr8q
title: CCDC 2011133: Experimental Crystal Structure Determination

description: Related Article: Congjun Zhu, Jie Dong, Xueting Liu, Liuzhou Gao, Yue Zhao, Jin Xie, Shuhua Li, Chengjian Zhu|2020|Angew.Chem.,Int.Ed.|59|12817|doi:10.1002/anie.202005749
description: Related Article: Dewmi A. Ekanayake, Arundhoti Chakraborty, Jeanette A. Krause, Hairong Guan|2020|Inorg.Chem.|59|12817|doi:10.1021/acs.inorgchem.0c01865
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.cc25hr9r
title: CCDC 2011134: Experimental Crystal Structure Determination

description: Related Article: Congjun Zhu, Jie Dong, Xueting Liu, Liuzhou Gao, Yue Zhao, Jin Xie, Shuhua Li, Chengjian Zhu|2020|Angew.Chem.,Int.Ed.|59|12817|doi:10.1002/anie.202005749
description: Related Article: Dewmi A. Ekanayake, Arundhoti Chakraborty, Jeanette A. Krause, Hairong Guan|2020|Inorg.Chem.|59|12817|doi:10.1021/acs.inorgchem.0c01865
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.cc25hrbs
title: CCDC 2011135: Experimental Crystal Structure Determination

description: Related Article: Congjun Zhu, Jie Dong, Xueting Liu, Liuzhou Gao, Yue Zhao, Jin Xie, Shuhua Li, Chengjian Zhu|2020|Angew.Chem.,Int.Ed.|59|12817|doi:10.1002/anie.202005749
description: Related Article: Dewmi A. Ekanayake, Arundhoti Chakraborty, Jeanette A. Krause, Hairong Guan|2020|Inorg.Chem.|59|12817|doi:10.1021/acs.inorgchem.0c01865
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.cc25hrct
title: CCDC 2011136: Experimental Crystal Structure Determination

description: Related Article: Congjun Zhu, Jie Dong, Xueting Liu, Liuzhou Gao, Yue Zhao, Jin Xie, Shuhua Li, Chengjian Zhu|2020|Angew.Chem.,Int.Ed.|59|12817|doi:10.1002/anie.202005749
description: Related Article: Dewmi A. Ekanayake, Arundhoti Chakraborty, Jeanette A. Krause, Hairong Guan|2020|Inorg.Chem.|59|12817|doi:10.1021/acs.inorgchem.0c01865
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: 10.25505/fiz.icsd.cc25hyd1
title: CSD 2011323: Experimental Crystal Structure Determination

description: Related Article: M.Debost, P.B.Klar, N.Barrier, E.B.Clatworthy, J.Grand, F.Laine, P.Prázda, L.Palatinus, N.Nesterenko, P.Boullay, S.Mintova|2020|ICSD Communication|||
description: Related Article: Maxime Debost, Paul B. Klar, Nicolas Barrier, Edwin B. Clatworthy, Julien Grand, Fabien Laine, Petr Brázda, Lukas Palatinus, Nikolai Nesterenko, Philippe Boullay, Svetlana Mintova|2020|Angew.Chem.,Int.Ed.|59|23491|doi:10.1002/anie.202009397
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc25jnx9
title: CSD 2012021: Experimental Crystal Structure Determination

description: Related Article: Maxime Dufaye, Sylvain Duval, Gregory Stoclet, Dimitrije Mara, Rik Van Deun, Thierry Loiseau|2020|Eur.J.Inorg.Chem.|2020|3837|doi:10.1002/ejic.202000613
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc25jnyb
title: CSD 2012022: Experimental Crystal Structure Determination

description: Related Article: Maxime Dufaye, Sylvain Duval, Gregory Stoclet, Dimitrije Mara, Rik Van Deun, Thierry Loiseau|2020|Eur.J.Inorg.Chem.|2020|3837|doi:10.1002/ejic.202000613
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc25jnzc
title: CSD 2012023: Experimental Crystal Structure Determination

description: Related Article: Maxime Dufaye, Sylvain Duval, Gregory Stoclet, Dimitrije Mara, Rik Van Deun, Thierry Loiseau|2020|Eur.J.Inorg.Chem.|2020|3837|doi:10.1002/ejic.202000613
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc25jp0f
title: CSD 2012024: Experimental Crystal Structure Determination

description: Related Article: Maxime Dufaye, Sylvain Duval, Gregory Stoclet, Dimitrije Mara, Rik Van Deun, Thierry Loiseau|2020|Eur.J.Inorg.Chem.|2020|3837|doi:10.1002/ejic.202000613
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc25jp2h
title: CSD 2012026: Experimental Crystal Structure Determination

description: Related Article: Maxime Dufaye, Sylvain Duval, Gregory Stoclet, Dimitrije Mara, Rik Van Deun, Thierry Loiseau|2020|Eur.J.Inorg.Chem.|2020|3837|doi:10.1002/ejic.202000613
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc25jp3j
title: CSD 2012027: Experimental Crystal Structure Determination

description: Related Article: Maxime Dufaye, Sylvain Duval, Gregory Stoclet, Dimitrije Mara, Rik Van Deun, Thierry Loiseau|2020|Eur.J.Inorg.Chem.|2020|3837|doi:10.1002/ejic.202000613
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc25jp4k
title: CSD 2012028: Experimental Crystal Structure Determination

description: Related Article: Maxime Dufaye, Sylvain Duval, Gregory Stoclet, Dimitrije Mara, Rik Van Deun, Thierry Loiseau|2020|Eur.J.Inorg.Chem.|2020|3837|doi:10.1002/ejic.202000613
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc25jv5r
title: CSD 2012184: Experimental Crystal Structure Determination

description: Related Article: Maria A. Kirsanova, Alexey S. Akmaev, Dmitry A. Aksyonov, Sergey V. Ryazantsev, Victoria A. Nikitina, Dmitry S. Filimonov, Maxim Avdeev, Artem M. Abakumov|2020|Inorg.Chem.|59|16225|doi:10.1021/acs.inorgchem.0c01961
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc25jv6s
title: CSD 2012185: Experimental Crystal Structure Determination

description: Related Article: Maria A. Kirsanova, Alexey S. Akmaev, Dmitry A. Aksyonov, Sergey V. Ryazantsev, Victoria A. Nikitina, Dmitry S. Filimonov, Maxim Avdeev, Artem M. Abakumov|2020|Inorg.Chem.|59|16225|doi:10.1021/acs.inorgchem.0c01961
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.