cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2fryms
title: CCDC 2257346: Experimental Crystal Structure Determination

description: Related Article: Wenhao Xiang, Ziqi Hu, Jinpeng Xin, Huaimin Jin, Zhanxin Jiang, Xinyi Han, Muqing Chen, Yang-Rong Yao, Shangfeng Yang|2023|J.Am.Chem.Soc.|145|22599|doi:10.1021/jacs.3c07686
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.cc2gmgdz
title: CCDC 2282821: Experimental Crystal Structure Determination

description: Related Article: Miao-Miao Zhang, Kai-Kai Gao, Xi-Yan Dong, Yubing Si, Teng Jia, Zhen Han, Shuang-Quan Zang, Thomas C. W. Mak|2023|J.Am.Chem.Soc.|145|22310|doi:10.1021/jacs.3c08241
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.cc2gmgg1
title: CCDC 2282823: Experimental Crystal Structure Determination

description: Related Article: Miao-Miao Zhang, Kai-Kai Gao, Xi-Yan Dong, Yubing Si, Teng Jia, Zhen Han, Shuang-Quan Zang, Thomas C. W. Mak|2023|J.Am.Chem.Soc.|145|22310|doi:10.1021/jacs.3c08241
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.cc2gnx9c
title: CCDC 2284213: Experimental Crystal Structure Determination

description: Related Article: Jeffrey S. Price, Ignacio Vargas-Baca, David J. H. Emslie, James F. Britten|2023|Dalton Trans.|52|14880|doi:10.1039/D3DT02437G
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.cc2gnxbd
title: CCDC 2284214: Experimental Crystal Structure Determination

description: Related Article: Jeffrey S. Price, Ignacio Vargas-Baca, David J. H. Emslie, James F. Britten|2023|Dalton Trans.|52|14880|doi:10.1039/D3DT02437G
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.cc2gnxcf
title: CCDC 2284215: Experimental Crystal Structure Determination

description: Related Article: Jeffrey S. Price, Ignacio Vargas-Baca, David J. H. Emslie, James F. Britten|2023|Dalton Trans.|52|14880|doi:10.1039/D3DT02437G
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.cc2gnxdg
title: CCDC 2284216: Experimental Crystal Structure Determination

description: Related Article: Jeffrey S. Price, Ignacio Vargas-Baca, David J. H. Emslie, James F. Britten|2023|Dalton Trans.|52|14880|doi:10.1039/D3DT02437G
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.cc2cz7m7
title: CCDC 2203809: Experimental Crystal Structure Determination

description: Related Article: Peng Tao, Xiao-Kang Zheng, He Jiang, Xinghao Sheng, Yongjing Deng, Yuk Yin Ian Chan, Qiang Zhao, Wai-Yeung Wong|2023|Dalton Trans.|52|15496|doi:10.1039/D3DT02061D
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.cc2g1hjk
title: CCDC 2265558: Experimental Crystal Structure Determination

description: Related Article: Jin-Hua Wang, Mohammad Khurram Javed, Jia-Xin Li, Yi-Quan Zhang, Zhao-Yang Li, Masahiro Yamashita|2023|Dalton Trans.|52|15510|doi:10.1039/D3DT01043K
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.cc2grpkh
title: CCDC 2286887: Experimental Crystal Structure Determination

description: Related Article: Tobias Morack, Tyler E. Myers, Lucas J. Karas, Melissa A. Hardy, Brandon Q. Mercado, Matthew S. Sigman, Scott J. Miller|2023|J.Am.Chem.Soc.|145|22322|doi:10.1021/jacs.3c08727
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.cc2grxlr
title: CCDC 2287105: Experimental Crystal Structure Determination

description: Related Article: Anqi Zhu, Ying Li, Yi Man, Yudong Pang, Chunbo Duan, Chunmiao Han, Jing Zhang, Chenhui Cao, Ying Wei, Xinfeng Shui, Hui Xu|2023|J.Mater.Chem.C|11|14119|doi:10.1039/D3TC02802J
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: 10.6084/m9.figshare.24471277
title: Zircon dataset in core s3 of the southwestern Okinawa Trough

description: Zircon dataset in core s3 of the southwestern Okinawa Trough
DOI: 10.6084/m9.figshare.24471277.v1
title: Zircon dataset in core s3 of the southwestern Okinawa Trough

description: Zircon dataset in core s3 of the southwestern Okinawa Trough
DOI: 10.5281/zenodo.7997397
title: MaDroid: A Maliciousness-aware Multifeatured Dataset for Detecting Android Malware

description: MaDroid is a maliciousness-aware multifeatured dataset of system calls focused on APK anomaly detection. The dataset includes 50,429 well-marked normal and abnormal system call sequences, with 24,789 and 25,640 sets of normal and abnormal sequences, respectively, for a total of 1.1 billion system call feature information. Each APK is labeled with the latest VT checksum information, and the sequence data includes 81 groups of system calls, system call parameters, and return values. The size of the whole dataset is 457 GB (19 GB after compression), including 236 GB of malicious system call sequence data. The APKs from which the system call feature sequences are derived cover mobile apps of different types released at different times in the past 14 years (2010-2023), covering 10 mainstream app markets, including Google Play, PlayDrone, Anzhi, etc. The APKs are also used as the source of the system call feature sequences, and the system call sequence data is used as the source of the APKs. anzhi, etc. We store the source code and dataset in two open platforms, GitHub and Zenodo, respectively.
DataSet
Release address: http://doi.org/10.5281/zenodo.7997398

The dataset consists of two classifications, Normal and Malware, with a total of 21 zip files. The installation files of each sequence come from 10 application markets such as Google Play, PlayDrone, Anzhi, etc. The Malware classification contains information about the running system call sequences of some APKs in the Drebin dataset.
RF, MLP and GBDT models were used to establish benchmarks for the dataset, the use of the models can be found in the source code.
The file `merge_all_csv_count_online_check_replenish.csv` is the dataset APK information. We provide APK name (SHA256 name for APK only), classification, APK capacity, number of sequences, log capacity, CVT, OVT value, check time, etc.
Source Code
Release address: https://github.com/HNUSystemsLab/MaDroid

The released source code contains two folders, `Source_Code` and `ml_metadata`. Where `Source_Code` is the automated framework for data collection, the tool chain and some notes on the structure of the source files. `ml_metadata` contains the metadata used for machine learning, the partitioned data on which the article builds its benchmark.
The automation framework is described in detail in the `Readme.md` document in the `Source_Code` directory. It consists of four main parts: environment requirements, program structure, quick start (working steps), model training and evaluation (including training and evaluation). It describes in detail the preparation of the environment, the data import method, the functional description of each file in the source code directory, the working principle of model training and evaluation, and other related contents.
Tips: 

MAS is another name of MaDroid, the content shown here is the final version of "Readme.md".
A Large-scale Multi-feature Dataset for Anomaly Detection of Mobile Applications, which is the name of the document during our experiment.
DOI: 10.5281/zenodo.7997398
title: MaDroid: A Maliciousness-aware Multifeatured Dataset for Detecting Android Malware

description: MaDroid is a maliciousness-aware multifeatured dataset of system calls focused on APK anomaly detection. The dataset includes 50,429 well-marked normal and abnormal system call sequences, with 24,789 and 25,640 sets of normal and abnormal sequences, respectively, for a total of 1.1 billion system call feature information. Each APK is labeled with the latest VT checksum information, and the sequence data includes 81 groups of system calls, system call parameters, and return values. The size of the whole dataset is 457 GB (19 GB after compression), including 236 GB of malicious system call sequence data. The APKs from which the system call feature sequences are derived cover mobile apps of different types released at different times in the past 14 years (2010-2023), covering 10 mainstream app markets, including Google Play, PlayDrone, Anzhi, etc. The APKs are also used as the source of the system call feature sequences, and the system call sequence data is used as the source of the APKs. anzhi, etc. We store the source code and dataset in two open platforms, GitHub and Zenodo, respectively.
DataSet
Release address: http://doi.org/10.5281/zenodo.7997398

The dataset consists of two classifications, Normal and Malware, with a total of 21 zip files. The installation files of each sequence come from 10 application markets such as Google Play, PlayDrone, Anzhi, etc. The Malware classification contains information about the running system call sequences of some APKs in the Drebin dataset.
RF, MLP and GBDT models were used to establish benchmarks for the dataset, the use of the models can be found in the source code.
The file `merge_all_csv_count_online_check_replenish.csv` is the dataset APK information. We provide APK name (SHA256 name for APK only), classification, APK capacity, number of sequences, log capacity, CVT, OVT value, check time, etc.
Source Code
Release address: https://github.com/HNUSystemsLab/MaDroid

The released source code contains two folders, `Source_Code` and `ml_metadata`. Where `Source_Code` is the automated framework for data collection, the tool chain and some notes on the structure of the source files. `ml_metadata` contains the metadata used for machine learning, the partitioned data on which the article builds its benchmark.
The automation framework is described in detail in the `Readme.md` document in the `Source_Code` directory. It consists of four main parts: environment requirements, program structure, quick start (working steps), model training and evaluation (including training and evaluation). It describes in detail the preparation of the environment, the data import method, the functional description of each file in the source code directory, the working principle of model training and evaluation, and other related contents.
Tips: 

MAS is another name of MaDroid, the content shown here is the final version of "Readme.md".
A Large-scale Multi-feature Dataset for Anomaly Detection of Mobile Applications, which is the name of the document during our experiment.
DOI: 10.34740/kaggle/dsv/6849703
title: WallStreetBets 2022
DOI: 10.5439/2007149
title: Planetary Boundary Layer Height (PBLH) over SGP from 1998 to 2023

description: PBLH is a critical parameter influencing weather phenomena, air quality, and various meteorological processes. However, accurately retrieving PBLH has been a challenging task due to limitations such as coarse temporal resolution and measurement drift in traditional radiosonde observations. To address these limitations, we have devised a lidar-based methodology that capitalizes on a newly developed algorithm for PBLH retrieval. This algorithm demonstrates enhanced capabilities in capturing diurnal fluctuations in PBLH compared to existing lidar-based methods (Su et al. 2020). In addition, we have refined this algorithm specifically for PBLH retrieval under cloudy conditions through a novel scheme (Su et al. 2022). To ensure data reliability, a quality-control process has been implemented to filter out questionable data points. Accompanying the data set is a quality-control flag for ease of reference. It should be noted that we have assimilated all available radiosonde observations to provide a more robust estimate of PBLH, making the data set valuable for a variety of related studies.
DOI: 10.25582/data.2020-12.2099180/2160250
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2106507/2160251
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2021-01.2118468/2160252
title: Data for EMSL Project 51287 from January 2021
DOI: 10.25582/data.2021-01.2124472/2160253
title: Data for EMSL Project 51287 from January 2021
DOI: 10.25582/data.2020-12.2105564/2160254
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2100048/2160255
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2107602/2160256
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2104330/2160257
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2106561/2160258
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2021-01.2124786/2160259
title: Data for EMSL Project 51287 from January 2021
DOI: 10.25582/data.2020-12.2101139/2160260
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2105711/2160261
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2101142/2160262
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2099138/2160263
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2099797/2160264
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2100167/2160265
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2115853/2160266
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2021-01.2119019/2160267
title: Data for EMSL Project 51287 from January 2021
DOI: 10.25582/data.2020-12.2104162/2160268
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2115680/2160269
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2021-01.2125043/2160270
title: Data for EMSL Project 51287 from January 2021
DOI: 10.25582/data.2020-12.2115216/2160271
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2021-01.2118614/2160272
title: Data for EMSL Project 51287 from January 2021
DOI: 10.25582/data.2020-12.2115634/2160273
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2021-01.2124894/2160274
title: Data for EMSL Project 51287 from January 2021
DOI: 10.25582/data.2020-12.2115166/2160275
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2116280/2160276
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2021-01.2119055/2160277
title: Data for EMSL Project 51287 from January 2021
DOI: 10.25582/data.2020-12.2103121/2160278
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2115596/2160279
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2099128/2160280
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2099135/2160281
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2021-01.2118860/2160282
title: Data for EMSL Project 51287 from January 2021
DOI: 10.25582/data.2020-12.2099174/2160283
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2099193/2160284
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2099214/2160285
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2099991/2160286
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2099875/2160287
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2107697/2160288
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2099839/2160289
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2103113/2160290
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2099496/2160291
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2021-01.2118981/2160292
title: Data for EMSL Project 51287 from January 2021
DOI: 10.25582/data.2020-12.2105789/2160293
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2104189/2160294
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2099229/2160295
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2105059/2160296
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2104653/2160297
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2107708/2160298
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2113287/2160299
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2115947/2160300
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2021-01.2122931/2160301
title: Data for EMSL Project 51287 from January 2021
DOI: 10.25582/data.2020-12.2099874/2160302
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2101089/2160303
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2107547/2160304
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2099493/2160305
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2021-01.2124933/2160306
title: Data for EMSL Project 51287 from January 2021
DOI: 10.25582/data.2020-12.2106917/2160307
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2115611/2160308
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2099834/2160309
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2099837/2160310
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2115626/2160311
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2105640/2160312
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2021-01.2124932/2160313
title: Data for EMSL Project 51287 from January 2021
DOI: 10.25582/data.2020-12.2099549/2160314
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2021-01.2124833/2160315
title: Data for EMSL Project 51287 from January 2021
DOI: 10.25582/data.2020-12.2099840/2160316
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2104756/2160317
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2099124/2160318
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2115416/2160319
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2107752/2160320
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2107060/2160321
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2114794/2160322
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2115052/2160323
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2021-01.2118206/2160324
title: Data for EMSL Project 51287 from January 2021
DOI: 10.25582/data.2020-12.2099835/2160325
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2100069/2160326
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2106832/2160327
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2099206/2160328
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2021-01.2124945/2160329
title: Data for EMSL Project 51287 from January 2021
DOI: 10.25582/data.2020-12.2114853/2160330
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2021-01.2124672/2160331
title: Data for EMSL Project 51287 from January 2021
DOI: 10.25582/data.2020-12.2105814/2160332
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2100801/2160333
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2021-01.2123087/2160334
title: Data for EMSL Project 51287 from January 2021
DOI: 10.25582/data.2021-01.2124907/2160335
title: Data for EMSL Project 51287 from January 2021
DOI: 10.25582/data.2021-01.2118999/2160336
title: Data for EMSL Project 51287 from January 2021
DOI: 10.25582/data.2020-12.2104702/2160337
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2115690/2160338
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2021-01.2117971/2160339
title: Data for EMSL Project 51287 from January 2021
DOI: 10.25582/data.2021-01.2118596/2160340
title: Data for EMSL Project 51287 from January 2021
DOI: 10.25582/data.2020-12.2105610/2160341
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2105704/2160342
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2115682/2160343
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2104577/2160344
title: Data for EMSL Project 51287 from December 2020