cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc299zk5
title: CCDC 2124757: Experimental Crystal Structure Determination

description: Related Article: Sunil K. Rai, Anilkumar Gunnam, Gregory J. O. Beran, James A. Kaduk, Ashwini K. Nangia|2023|Cryst.Growth Des.|23|1179|doi:10.1021/acs.cgd.2c01289
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.cc299zl6
title: CCDC 2124758: Experimental Crystal Structure Determination

description: Related Article: Sunil K. Rai, Anilkumar Gunnam, Gregory J. O. Beran, James A. Kaduk, Ashwini K. Nangia|2023|Cryst.Growth Des.|23|1179|doi:10.1021/acs.cgd.2c01289
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.cc299zm7
title: CCDC 2124759: Experimental Crystal Structure Determination

description: Related Article: Sunil K. Rai, Anilkumar Gunnam, Gregory J. O. Beran, James A. Kaduk, Ashwini K. Nangia|2023|Cryst.Growth Des.|23|1179|doi:10.1021/acs.cgd.2c01289
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.cc29bfqt
title: CCDC 2125196: Experimental Crystal Structure Determination

description: Related Article: Jinling Wang, Na Xu, Zhihan Chang, Xiu-Li Wang|2022|Cryst.Growth Des.|23|694|doi:10.1021/acs.cgd.2c00596
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: 10.3886/e112907v1-132283
title: Replication data for: Punishment and Deterrence: Evidence from Drunk Driving

description: I test the effect of harsher punishments and sanctions on driving under the influence (DUI). In this setting, punishments are determined by strict rules on blood alcohol content (BAC) and previous offenses. Regression discontinuity derived estimates suggest that having a BAC above the DUI threshold reduces recidivism by up to 2 percentage points (17 percent). Likewise having a BAC over the aggravated DUI threshold reduces recidivism by an additional percentage point (9 percent). The results suggest that the additional sanctions experienced by drunk drivers at BAC thresholds are effective in reducing repeat drunk driving. (JEL I12, K42, R41)
DOI: 10.5281/zenodo.7576909
title: Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for EnviroAtlas/6-class segmentation of RGB 512x512 high-res. images

description: <em><strong>Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for EnviroAtlas/6-class segmentation of RGB 512x512 high-res. images</strong></em> These Residual-UNet model data are based on [EnviroAtlas](https://www.mdpi.com/2072-4292/12/12/1909) images and associated labels. Models have been created using Segmentation Gym* using the following dataset**: https://zenodo.org/record/6268150#.Y91Q-BzMLRa Image size used by model: 512 x 512 x 3 pixels classes:<br> nodata<br> water<br> impervious<br> barren<br> trees<br> herbaceous<br> shrubland <br> File descriptions For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU References<br> *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym **Pilant, A., Endres, K., Rosenbaum, D. and Gundersen, G., 2020. US EPA EnviroAtlas meter-scale urban land cover (MULC): 1-m pixel land cover class definitions and guidance. Remote sensing, 12(12), p.1909.
DOI: 10.5281/zenodo.7576908
title: Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for EnviroAtlas/6-class segmentation of RGB 512x512 high-res. images

description: <em><strong>Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for EnviroAtlas/6-class segmentation of RGB 512x512 high-res. images</strong></em> These Residual-UNet model data are based on [EnviroAtlas](https://www.mdpi.com/2072-4292/12/12/1909) images and associated labels. Models have been created using Segmentation Gym* using the following dataset**: https://zenodo.org/record/6268150#.Y91Q-BzMLRa Image size used by model: 512 x 512 x 3 pixels classes:<br> nodata<br> water<br> impervious<br> barren<br> trees<br> herbaceous<br> shrubland <br> File descriptions For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU References<br> *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym **Pilant, A., Endres, K., Rosenbaum, D. and Gundersen, G., 2020. US EPA EnviroAtlas meter-scale urban land cover (MULC): 1-m pixel land cover class definitions and guidance. Remote sensing, 12(12), p.1909.
DOI: 10.5281/zenodo.7605712
title: Repeat photography of a joint military operation involving the Ethiopian and Eritrean armies in Inderta, Tigray, Ethiopia (November 2020)

description: The Eritrean PFDJ government has actively participated in the conflict against Ethiopia's Tigray region since it began in November 2020, supporting the Ethiopian government. Regularly, Eritrea dispatched Eritrean Defence Force (EDF) troops to aid the Ethiopian National Defence Forces (ENDF) in their conflict with the Tigray region. Following the Eritrean admission of its involvement, the Ethiopian Prime Minister Abiy Ahmed, however, minimized the scope of the Eritrean participation throughout the conflict and tended to confine it to the Ethio-Eritrean border regions. Here we present a dataset of repeated video images, allowing the examination of the coordination between the Ethiopian and Eritrean armies in the war in late 2020, deep within Tigray. The warfare imagery was accurately placed near Miheni in Inderta district, and shows unequivocally that Eritrean forces participated in fighting in Tigray in close coordination with the Ethiopian army. It is 180 kilometers, or 102 kilometers as the crow flies, to the Eritrean border from Miheni.
DOI: 10.5281/zenodo.7605711
title: Repeat photography of a joint military operation involving the Ethiopian and Eritrean armies in Inderta, Tigray, Ethiopia (November 2020)

description: The Eritrean PFDJ government has actively participated in the conflict against Ethiopia's Tigray region since it began in November 2020, supporting the Ethiopian government. Regularly, Eritrea dispatched Eritrean Defence Force (EDF) troops to aid the Ethiopian National Defence Forces (ENDF) in their conflict with the Tigray region. Following the Eritrean admission of its involvement, the Ethiopian Prime Minister Abiy Ahmed, however, minimized the scope of the Eritrean participation throughout the conflict and tended to confine it to the Ethio-Eritrean border regions. Here we present a dataset of repeated video images, allowing the examination of the coordination between the Ethiopian and Eritrean armies in the war in late 2020, deep within Tigray. The warfare imagery was accurately placed near Miheni in Inderta district, and shows unequivocally that Eritrean forces participated in fighting in Tigray in close coordination with the Ethiopian army. It is 180 kilometers, or 102 kilometers as the crow flies, to the Eritrean border from Miheni.
DOI: 10.6084/m9.figshare.13653955
title: Geometric control using the state-dependent Riccati equation: application to aerial-acrobatic maneuvers

description: Acrobatic flip is one of the most challenging representatives of aggressive maneuvers to test the performance of an aerial system’s capability or a controller. A variable-pitch rotor quadcopter generates thrust in both vertical directions for the special design of the rotor’s actuation mechanism. This research proposes two possible solutions for the flip: a regulation solution based on the geometric control approach; and tracking a predefined optimal smooth trajectory covering a turnover. The first solution uses a geometric control approach that is immune to singular points since the rotation matrix is integrated on the manifold on SO(3). The second solution proposes an optimal trajectory generation for flip maneuver using open-loop optimal control, two-point boundary value problem (TPBVP) approach. Since generated open-loop state information is not applicable without a controller, the state-dependent differential Riccati equation (SDDRE) is chosen for trajectory tracking.
DOI: 10.6084/m9.figshare.13653955.v2
title: Geometric control using the state-dependent Riccati equation: application to aerial-acrobatic maneuvers

description: Acrobatic flip is one of the most challenging representatives of aggressive maneuvers to test the performance of an aerial system’s capability or a controller. A variable-pitch rotor quadcopter generates thrust in both vertical directions for the special design of the rotor’s actuation mechanism. This research proposes two possible solutions for the flip: a regulation solution based on the geometric control approach; and tracking a predefined optimal smooth trajectory covering a turnover. The first solution uses a geometric control approach that is immune to singular points since the rotation matrix is integrated on the manifold on SO(3). The second solution proposes an optimal trajectory generation for flip maneuver using open-loop optimal control, two-point boundary value problem (TPBVP) approach. Since generated open-loop state information is not applicable without a controller, the state-dependent differential Riccati equation (SDDRE) is chosen for trajectory tracking.
DOI: 10.6084/m9.figshare.22010552
title: Ações de vigilância sanitária em serviços de saúde: revisão Integrativa

description: Vigilância sanitária lida com a qualidade e segurança de serviços e produtos disponíveis aos cidadãos. Seus objetos de atuação amplia-se a cada dia, com incorporação de novas tecnologias pelos serviços de saúde (COSTA, 2017). A análise das evidências disponíveis na literatura sobre as ações de vigilância sanitária nos serviços de saúde justifica-se, dada a ausência de revisões da literatura semelhantes que possam contribuir para as práticas de vigilância sanitária que garantam serviços de saúde seguros e de qualidade.
DOI: 10.6084/m9.figshare.22010552.v2
title: Ações de vigilância sanitária em serviços de saúde: revisão Integrativa

description: Vigilância sanitária lida com a qualidade e segurança de serviços e produtos disponíveis aos cidadãos. Seus objetos de atuação amplia-se a cada dia, com incorporação de novas tecnologias pelos serviços de saúde (COSTA, 2017). A análise das evidências disponíveis na literatura sobre as ações de vigilância sanitária nos serviços de saúde justifica-se, dada a ausência de revisões da literatura semelhantes que possam contribuir para as práticas de vigilância sanitária que garantam serviços de saúde seguros e de qualidade.
DOI: 10.6084/m9.figshare.22010552.v1
title: Protocolo Revisão Integrativa (31-01-2023).docx

description: Vigilância sanitária lida com a qualidade e segurança de serviços e produtos disponíveis aos cidadãos. Seus objetos de atuação amplia-se a cada dia, com incorporação de novas tecnologias pelos serviços de saúde (COSTA, 2017). A análise das evidências disponíveis na literatura sobre as ações de vigilância sanitária nos serviços de saúde justifica-se, dada a ausência de revisões da literatura semelhantes que possam contribuir para as práticas de vigilância sanitária que garantam serviços de saúde seguros e de qualidade.
DOI: 10.6084/m9.figshare.21799793.v5
title: Precision Oncology-Based Targeting of Signaling Pathways Toward a Holistic Approach to Cancer Therapeutics

description: The mutational changes occurring in the regulatory genes responsible for the maintenance of optimal cell growth and proliferation gradually lead to cancer progression and metastasis. Furthermore, patients with the same cancer types often respond differently to different cancer therapies, indicating the need for a patient-specific treatment regimen for the disease. Precision oncology is the new field of cancer research that relies on genetic profiling of individual tumors to identify targetable alterations for custom-tailored personalized treatment of the disease. It is to rely upon the genomic study of cancer cells to get a clear picture of the prognosis and pathways involved in disease progression and to look for the means to selectively target them for a cure. This article intends to briefly explain the foundations and frontiers of precision oncology in the context of technological advances being made in this direction to assess its scope and importance in the realization of a proper cure for cancer.
DOI: 10.5517/ccdc.csd.cc2d72d4
title: CCDC 2211335: Experimental Crystal Structure Determination

description: Related Article: Bo-Sheng Zhang, Sheng-Yan Zhao, Shun-Xi Li, Wan-Yuan Jia, Yu-Xi Yang, Yi-Ming Wang, Xue-Ya Gou, Yong-Min Liang, Xi-Cun Wang, Zheng-Jun Quan|2023|J.Org.Chem.|88|1786|doi:10.1021/acs.joc.2c02988
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.cc2d9wqb
title: CCDC 2214042: Experimental Crystal Structure Determination

description: Related Article: Alejandro Portugués, Miguel Ángel Martínez-Nortes, Delia Bautista, Pablo González-Herrero, Juan Gil-Rubio|2023|Inorg.Chem.|62|1708|doi:10.1021/acs.inorgchem.2c04166
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.cc2d9wrc
title: CCDC 2214043: Experimental Crystal Structure Determination

description: Related Article: Alejandro Portugués, Miguel Ángel Martínez-Nortes, Delia Bautista, Pablo González-Herrero, Juan Gil-Rubio|2023|Inorg.Chem.|62|1708|doi:10.1021/acs.inorgchem.2c04166
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.cc2cx3gw
title: CCDC 2201758: Experimental Crystal Structure Determination

description: Related Article: T. Steinke, E. Engelage, S.M. Huber|2023|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|79|26|doi:10.1107/S2053229622011536
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.cc2cx3hx
title: CCDC 2201759: Experimental Crystal Structure Determination

description: Related Article: T. Steinke, E. Engelage, S.M. Huber|2023|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|79|26|doi:10.1107/S2053229622011536
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.cc2cx3jy
title: CCDC 2201760: Experimental Crystal Structure Determination

description: Related Article: T. Steinke, E. Engelage, S.M. Huber|2023|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|79|26|doi:10.1107/S2053229622011536
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.cc2cx3kz
title: CCDC 2201761: Experimental Crystal Structure Determination

description: Related Article: T. Steinke, E. Engelage, S.M. Huber|2023|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|79|26|doi:10.1107/S2053229622011536
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.cc2dn0bf
title: CCDC 2223764: Experimental Crystal Structure Determination

description: Related Article: Agnieszka A. Kaczor, Ewa Kędzierska,Tomasz M. Wróbel, Angelika Grudzińska, Angelika Pawlak, Tuomo Laitinen, Agata Bartyzel |2023|Molecules|28|383|doi:10.3390/molecules28010383
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.cc2dngh1
title: CCDC 2224203: Experimental Crystal Structure Determination

description: Related Article: Xia-Bin Xiang, Sheng Wang, Teng Xu, Shanshan Chen|2023|Org.Lett.|25|587|doi:10.1021/acs.orglett.2c04103
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.cc2dp4y6
title: CCDC 2224868: Experimental Crystal Structure Determination

description: Related Article: Li-Ping Zhong, Rui Feng, Jing-Jing Wang, Chuang-Chuang Li|2023|J.Am.Chem.Soc.|145|2098|doi:10.1021/jacs.2c13494
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.cc2dp4z7
title: CCDC 2224869: Experimental Crystal Structure Determination

description: Related Article: Li-Ping Zhong, Rui Feng, Jing-Jing Wang, Chuang-Chuang Li|2023|J.Am.Chem.Soc.|145|2098|doi:10.1021/jacs.2c13494
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.