cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2c0pvy
title: CCDC 2175420: Experimental Crystal Structure Determination

description: Related Article: Yun Wei, Jianjian Gao, Ling Jiang, Zeming Huang, Qin Bao, Qingbing Yuan, Lijun Zhang, Shuangliu Zhou, Shaowu Wang|2022|Organometallics|41|2985|doi:10.1021/acs.organomet.2c00412
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.cc2c0pwz
title: CCDC 2175421: Experimental Crystal Structure Determination

description: Related Article: Yun Wei, Jianjian Gao, Ling Jiang, Zeming Huang, Qin Bao, Qingbing Yuan, Lijun Zhang, Shuangliu Zhou, Shaowu Wang|2022|Organometallics|41|2985|doi:10.1021/acs.organomet.2c00412
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.cc2c0px0
title: CCDC 2175422: Experimental Crystal Structure Determination

description: Related Article: Yun Wei, Jianjian Gao, Ling Jiang, Zeming Huang, Qin Bao, Qingbing Yuan, Lijun Zhang, Shuangliu Zhou, Shaowu Wang|2022|Organometallics|41|2985|doi:10.1021/acs.organomet.2c00412
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.cc2c0py1
title: CCDC 2175423: Experimental Crystal Structure Determination

description: Related Article: Yun Wei, Jianjian Gao, Ling Jiang, Zeming Huang, Qin Bao, Qingbing Yuan, Lijun Zhang, Shuangliu Zhou, Shaowu Wang|2022|Organometallics|41|2985|doi:10.1021/acs.organomet.2c00412
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.cc2c0pz2
title: CCDC 2175424: Experimental Crystal Structure Determination

description: Related Article: Yun Wei, Jianjian Gao, Ling Jiang, Zeming Huang, Qin Bao, Qingbing Yuan, Lijun Zhang, Shuangliu Zhou, Shaowu Wang|2022|Organometallics|41|2985|doi:10.1021/acs.organomet.2c00412
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.cc2c0q04
title: CCDC 2175425: Experimental Crystal Structure Determination

description: Related Article: Yun Wei, Jianjian Gao, Ling Jiang, Zeming Huang, Qin Bao, Qingbing Yuan, Lijun Zhang, Shuangliu Zhou, Shaowu Wang|2022|Organometallics|41|2985|doi:10.1021/acs.organomet.2c00412
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.cc2c0q15
title: CCDC 2175426: Experimental Crystal Structure Determination

description: Related Article: Yun Wei, Jianjian Gao, Ling Jiang, Zeming Huang, Qin Bao, Qingbing Yuan, Lijun Zhang, Shuangliu Zhou, Shaowu Wang|2022|Organometallics|41|2985|doi:10.1021/acs.organomet.2c00412
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.cc2c0q26
title: CCDC 2175427: Experimental Crystal Structure Determination

description: Related Article: Yun Wei, Jianjian Gao, Ling Jiang, Zeming Huang, Qin Bao, Qingbing Yuan, Lijun Zhang, Shuangliu Zhou, Shaowu Wang|2022|Organometallics|41|2985|doi:10.1021/acs.organomet.2c00412
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.7334228
title: Dataset Literature Review digital forensic AND discriminant analysis

description: data ini digunakan untuk membuat penelitian berdasarkan tinjauan literatur dengan kata kunci "digital forensic" dan discriminant analysis"
DOI: 10.5281/zenodo.7334227
title: Dataset Literature Review digital forensic AND discriminant analysis

description: data ini digunakan untuk membuat penelitian berdasarkan tinjauan literatur dengan kata kunci "digital forensic" dan discriminant analysis"
DOI: 10.5281/zenodo.7334238
title: Data of the Paper: Stateful Depletion and Scheduling of Containers on Cloud Nodes for Efficient Resource Usage

description: This is the online artifact containing the code, data and evaluation log of the experiment performed for the research paper accepted at IEEE QRS 2022 with the title: <strong>Stateful Depletion and Scheduling of Containers on Cloud Nodes for Efficient Resource Usage</strong> <strong>Abstract:</strong> Container scheduling is a fundamental part of today’s service and cloud-based applications. Schedulers operate at different levels depending on how much control the system developers have. On the one hand, container orchestration managers such as Google Kubernetes manage the scheduling of containers to different nodes. On the other hand, serverless managers, such as Google Autopilot, take care of the underlying infrastructure automatically, and developers do not need to manage the nodes. However, when it comes to container depletion, i.e., removing the assigned cloud resources to an idle container, current scheduling technologies have limitations. In this paper, we propose our approach to managing cloud resource usage when containers are idle efficiently. For this purpose, we deplete idle containers statefully, i.e., propose a novel manager that monitors idle containers, saves their state, and efficiently depletes them. This manager reconstructs a depleted container using the saved state when reconstruction is needed. In our approach, we suggest an Infrastructure as Code component to automate the creation of new nodes if a depleted container cannot be scheduled on the same node, e.g., because of being overloaded. We provide an analytical model for the stateful depletion of containers and their rescheduling and empirically evaluate the accuracy of our model. For this purpose, we ran an experiment on a private cloud infrastructure and Google Cloud Platform. Our model has a low error rate of 4.28% averaged over public and private clouds.
DOI: 10.5281/zenodo.7067104
title: Data of the Paper: Stateful Depletion and Scheduling of Containers on Cloud Nodes for Efficient Resource Usage

description: This is the online artifact containing the code, data and evaluation log of the experiment performed for the research paper accepted at IEEE QRS 2022 with the title: <strong>Stateful Depletion and Scheduling of Containers on Cloud Nodes for Efficient Resource Usage</strong> <strong>Abstract:</strong> Container scheduling is a fundamental part of today’s service and cloud-based applications. Schedulers operate at different levels depending on how much control the system developers have. On the one hand, container orchestration managers such as Google Kubernetes manage the scheduling of containers to different nodes. On the other hand, serverless managers, such as Google Autopilot, take care of the underlying infrastructure automatically, and developers do not need to manage the nodes. However, when it comes to container depletion, i.e., removing the assigned cloud resources to an idle container, current scheduling technologies have limitations. In this paper, we propose our approach to managing cloud resource usage when containers are idle efficiently. For this purpose, we deplete idle containers statefully, i.e., propose a novel manager that monitors idle containers, saves their state, and efficiently depletes them. This manager reconstructs a depleted container using the saved state when reconstruction is needed. In our approach, we suggest an Infrastructure as Code component to automate the creation of new nodes if a depleted container cannot be scheduled on the same node, e.g., because of being overloaded. We provide an analytical model for the stateful depletion of containers and their rescheduling and empirically evaluate the accuracy of our model. For this purpose, we ran an experiment on a private cloud infrastructure and Google Cloud Platform. Our model has a low error rate of 4.28% averaged over public and private clouds.
DOI: 10.34740/kaggle/dsv/4536147
title: Elon Musk Tweets
DOI: 10.5281/zenodo.7067105
title: Data of the Paper: Stateful Depletion and Scheduling of Containers on Cloud Nodes for Efficient Resource Usage

description: --- PLEASE SEE THE NEWER VERSION: https://zenodo.org/record/7334238 --- This is the online artifact containing the code, data and evaluation log of the experiment performed for the research paper submitted to QRS22 with the title: <strong>Stateful Depletion and Scheduling of Containers on Cloud Nodes for Efficient Resource Usage</strong> <strong>Abstract:</strong> Container scheduling is a fundamental part of today’s service and cloud-based applications. Schedulers operate at different levels depending on how much control the system developers have. On the one hand, container orchestration managers such as Google Kubernetes manage the scheduling of containers to different nodes. On the other hand, serverless managers, such as Google Autopilot, take care of the underlying infrastructure automatically and developers do not need to manage the nodes. However, when it comes to container depletion, i.e., removing the assigned cloud resources to a container when it is idle, current scheduling technologies have limitations. In this paper, we propose our approach to efficiently manage cloud resource usage when containers are idle. For this purpose, we deplete idle containers in a stateful manner, i.e., we propose a novel manager that monitors idle containers, saves their state and efficiently depletes them. When a reconstruction is needed, this manager reconstructs a depleted container using the saved state. In our approach, we suggest an Infrastructure as Code component to automate the creation of new nodes if a depleted container cannot be scheduled on the same node, e.g., because of being overloaded. We provide an analytical model for the stateful depletion of containers and their rescheduling, and empirically evaluate the accuracy of our model. For this purpose, we ran an experiment on a private cloud infrastructure and Google Cloud Platform. Our model has a low error rate of 7.71% averaged over public and private clouds.
DOI: 10.6084/m9.figshare.17134706
title: Neogene restoration of geometry of the Neotethyan suture zone in Central Anatolia (Turkey)

description: We have restored the geometries of suture zones that involved various continental blocks in central Anatolia during the Neogene, using palaeomagnetic data. Previously, the Kırşehir Block was proposed to be an NNE-SSW striking tectonic block broken into three fragments that they underwent clockwise rotations in the north and counter-clockwise rotations in the south during the Palaeogene, due to collision and N-S shortening of the Kırşehir Block between Tauride-Menderes Block in the south and the Pontides in the north. Our new results point out five distinct Neogene tectonic domains with distinct rotation patterns that indicate the rotational deformation of Central Anatolia is far more complex than generally presumed. Among these, 1) Kırıkkale-Bala Domain is rotated ~18° clockwise, 2) the Tuz Gölü Domain underwent ~14° counter-clockwise rotation, 3) the Alcı-Orhaniye Domain rotated ~35° counter-clockwise sense, 4) the Northern Haymana Domain underwent ~12° counter-clockwise rotation while 5) the Southern Haymana Domain underwent very small (~5° clockwise) net rotation since the early Miocene. The results also indicated that pre-Neogene configuration of the Izmir-Ankara Suture Zone was striking almost E-W while Inner-Tauride and Ankara-Erzincan suture zones were almost N-S in the study area. In addition, the fault zones that define the these domains extend from the Kırşehir Block into the over-riding blocks (Pontides) continuously without any deflection indicating that they were active during the Neogene and fragmented and dislocated the suture zones in the region.
DOI: 10.6084/m9.figshare.17134706.v1
title: Neogene restoration of geometry of the Neotethyan suture zone in Central Anatolia (Turkey)

description: We have restored the geometries of suture zones that involved various continental blocks in central Anatolia during the Neogene, using palaeomagnetic data. Previously, the Kırşehir Block was proposed to be an NNE-SSW striking tectonic block broken into three fragments that they underwent clockwise rotations in the north and counter-clockwise rotations in the south during the Palaeogene, due to collision and N-S shortening of the Kırşehir Block between Tauride-Menderes Block in the south and the Pontides in the north. Our new results point out five distinct Neogene tectonic domains with distinct rotation patterns that indicate the rotational deformation of Central Anatolia is far more complex than generally presumed. Among these, 1) Kırıkkale-Bala Domain is rotated ~18° clockwise, 2) the Tuz Gölü Domain underwent ~14° counter-clockwise rotation, 3) the Alcı-Orhaniye Domain rotated ~35° counter-clockwise sense, 4) the Northern Haymana Domain underwent ~12° counter-clockwise rotation while 5) the Southern Haymana Domain underwent very small (~5° clockwise) net rotation since the early Miocene. The results also indicated that pre-Neogene configuration of the Izmir-Ankara Suture Zone was striking almost E-W while Inner-Tauride and Ankara-Erzincan suture zones were almost N-S in the study area. In addition, the fault zones that define the these domains extend from the Kırşehir Block into the over-riding blocks (Pontides) continuously without any deflection indicating that they were active during the Neogene and fragmented and dislocated the suture zones in the region.
DOI: 10.5281/zenodo.4627197
title: TeCla: Text Classification Catalan dataset

description: <em>Corpus de notícies en català per a classificació textual, extret del web de l'Agència Catalana de Notícies sota llicència CC-BY-NC-ND</em> TeCla (Text Classification) is a Catalan News corpus for thematic multi-class Text Classification tasks. The present version (2.0) contains 113.376 articles classified under a hierarchical class structure consisting of a coarse-grained and a fine-grained class. Each of the 4 coarse-grained classes accept a subset of fine-grained ones, 53 in total. The source data is crawled from the ACN (Catalan News Agency) site: http://www.acn.cat, and used under CC-BY-NC-ND 4.0 licence. The dataset is released under the same licence, and is intended exclusively for training Machine Learning models. This dataset was developed by BSC TeMU as part of the AINA project, and intended as part of CLUB (Catalan Language Understanding Benchmark).
DOI: 10.5281/zenodo.4627198
title: TeCla: Text Classification Catalan dataset

description: Catalan News corpus for Text classification, crawled from ACN (Catalan News Agency) site: www.acn.cat Corpus de notícies en català per a classificació textual, extret del web de l'Agència Catalana de Notícies - www.acn.cat
DOI: 10.5281/zenodo.4761505
title: TeCla: Text Classification Catalan dataset

description: If you use this resource in your work, please cite our latest paper: @inproceedings{armengol-estape-etal-2021-multilingual,<br> title = "Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? {A} Comprehensive Assessment for {C}atalan",<br> author = "Armengol-Estap{\'e}, Jordi and<br> Carrino, Casimiro Pio and<br> Rodriguez-Penagos, Carlos and<br> de Gibert Bonet, Ona and<br> Armentano-Oller, Carme and<br> Gonzalez-Agirre, Aitor and<br> Melero, Maite and<br> Villegas, Marta",<br> booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",<br> month = aug,<br> year = "2021",<br> address = "Online",<br> publisher = "Association for Computational Linguistics",<br> url = "https://aclanthology.org/2021.findings-acl.437",<br> doi = "10.18653/v1/2021.findings-acl.437",<br> pages = "4933--4946",<br> } <em>Corpus de notícies en català per a classificació textual, extret del web de l'Agència Catalana de Notícies sota llicència CC-BY-NC-ND</em> TeCla is a Catalan News corpus for thematic Text Classification tasks. It contains 153.265 articles classified under 30 different categories. The source data is crawled from the ACN (Catalan News Agency) site: http://www.acn.cat, and used under CC-BY-NC-ND 4.0 licence. The dataset is released under the same licence, and is intended exclusively for training Machine Learning models. This dataset was developed by BSC TeMU as part of the AINA project, and intended as part of CLUB (Catalan Language Understanding Benchmark).
DOI: 10.5281/zenodo.7334110
title: TeCla: Text Classification Catalan dataset

description: <em>Corpus de notícies en català per a classificació textual, extret del web de l'Agència Catalana de Notícies sota llicència CC-BY-NC-ND</em> TeCla (Text Classification) is a Catalan News corpus for thematic multi-class Text Classification tasks. The present version (2.0) contains 113.376 articles classified under a hierarchical class structure consisting of a coarse-grained and a fine-grained class. Each of the 4 coarse-grained classes accept a subset of fine-grained ones, 53 in total. The source data is crawled from the ACN (Catalan News Agency) site: http://www.acn.cat, and used under CC-BY-NC-ND 4.0 licence. The dataset is released under the same licence, and is intended exclusively for training Machine Learning models. This dataset was developed by BSC TeMU as part of the AINA project, and intended as part of CLUB (Catalan Language Understanding Benchmark).
DOI: 10.5281/zenodo.6619130
title: Analyzing Satellite-Derived 3D Building Inventories and Quantifying Urban Growth towards Active Faults: A Case Study of Bishkek, Kyrgyzstan

description: #############################################################################################################<br> Datasets supporting the publication:<br> Analyzing satellite-derived 3D building inventories and quantifying urban growth towards active faults:<br> a case study of Bishkek, Kyrgyzstan.<br> https://doi.org/10.3390/rs14225790 -Please refer to the publication for details on the production of each dataset.<br> -Datasets are ordered following the publication figures.<br> -Please cite the publication and this dataset repository when using the data.<br> ############################################################################################################# ------------------------<br> Structure:<br> File ID<br> -[fields:] description<br> ------------------------ KH9_1979_builtup.shp<br> -KH9 1979 built-up area classification S2_2021_builtup.tif<br> -Sentinel-2 2021 built-up area classification. S2_2021_corine_land_cover_class.tif<br> -Sentinel-2 2021 land cover classification in Corine 2018 land-cover classes. S2_KH9_DN_change_aggregated.shp<br> -Proportional DN change aggregated to a 1 km^2 grid for areas ≥50% built-up. building_characteristics.shp<br> -build_count: building count in 500 m square grid cell.<br> -mean_area: mean building size (m^2) in 500 m square grid cell.<br> -median_area: median building size(m^2) in 500 m square grid cell.<br> -cell_coverage: %building coverage of 500 m square grid cell. pleiades_buildings_all.shp<br> -All building detections from Pleiades data. Confidence values are output from the deep learning model. pleiades_buildings_heights.shp<br> -Building detections from the Pleiades data that were allocated heights (m).<br> -Zmean, Zmedian,... refer to heights (m) wv2_buildings_all.shp<br> -All building detections from WorldView-2 data. Confidence values are output from the deep learning model. wv2_buildings_heights.shp<br> -Building detections from the WorldView-2 data that were allocated heights (m).<br> -Zmean, Zmedian,... refer to heights (m) trained_rcnn.zip<br> -ArcGIS Pro deep learning model (DLPK) used to extract building footprints.
DOI: 10.5281/zenodo.6619129
title: Analyzing Satellite-Derived 3D Building Inventories and Quantifying Urban Growth towards Active Faults: A Case Study of Bishkek, Kyrgyzstan

description: #############################################################################################################<br> Datasets supporting the publication:<br> Analyzing satellite-derived 3D building inventories and quantifying urban growth towards active faults:<br> a case study of Bishkek, Kyrgyzstan.<br> https://doi.org/10.3390/rs14225790 -Please refer to the publication for details on the production of each dataset.<br> -Datasets are ordered following the publication figures.<br> -Please cite the publication and this dataset repository when using the data.<br> ############################################################################################################# ------------------------<br> Structure:<br> File ID<br> -[fields:] description<br> ------------------------ KH9_1979_builtup.shp<br> -KH9 1979 built-up area classification S2_2021_builtup.tif<br> -Sentinel-2 2021 built-up area classification. S2_2021_corine_land_cover_class.tif<br> -Sentinel-2 2021 land cover classification in Corine 2018 land-cover classes. S2_KH9_DN_change_aggregated.shp<br> -Proportional DN change aggregated to a 1 km^2 grid for areas ≥50% built-up. building_characteristics.shp<br> -build_count: building count in 500 m square grid cell.<br> -mean_area: mean building size (m^2) in 500 m square grid cell.<br> -median_area: median building size(m^2) in 500 m square grid cell.<br> -cell_coverage: %building coverage of 500 m square grid cell. pleiades_buildings_all.shp<br> -All building detections from Pleiades data. Confidence values are output from the deep learning model. pleiades_buildings_heights.shp<br> -Building detections from the Pleiades data that were allocated heights (m).<br> -Zmean, Zmedian,... refer to heights (m) wv2_buildings_all.shp<br> -All building detections from WorldView-2 data. Confidence values are output from the deep learning model. wv2_buildings_heights.shp<br> -Building detections from the WorldView-2 data that were allocated heights (m).<br> -Zmean, Zmedian,... refer to heights (m) trained_rcnn.zip<br> -ArcGIS Pro deep learning model (DLPK) used to extract building footprints.