cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.20472684
title: Influence of niche dimensionality on functional diversity, beta diversity and community assembly in plant communities

description: To study on niche dimensionality of species coexistence in natural ecosystem also should broadened its view beyond a focus on local alpha diversity, simultaneously considering the influence of niche dimensionality on functional diversity, the temporal dynamics of species composition, as well the transition of niche and neutral processes
DOI: 10.34740/kaggle/dsv/4056988
title: Supplementary data for GRN-classifier
DOI: 10.6084/m9.figshare.20199986
title: Feature sets in just-in-time defect prediction: An empirical evaluation

description: This archive was created for evaluation purposes of the work described in detail in "Feature sets in just-in-time defect prediction: An empirical evaluation" submitted to PROMISE 2022. It contains all data sets and evaluation results created and used in the study.
DOI: 10.25919/ceep-zv67
title: Parkes observations for project P456 semester 2022APRS_09

description: The Parkes Pulsar Timing Array (PPTA) project has three primary goals: (a) detection of gravitational waves from astronomical sources, (b) establishment of a pulsar timescale, and (c) improvement of our understanding of Solar-system dynamics. We are now in an exciting regime where a signal detected in our (and other data sets) has the form expected for a gravitational wave background. We are therefore now attempting to confirm, or deny, this exciting result. With this proposal we aim to maintain our pre-eminent position in the field. Unlike most observing proposals, this is a continuing proposal for which the observations will continue to improve bounds on ultra-low-frequency gravitational waves until they are finally detected. Continued Parkes observations will remain valuable at least until the first stage of the SKA is able to improve on our sensitivity and observing cadence. Even after the gravitational wave background is detected we will want to continue observations in support of the nascent field of gravitational wave astronomy!
DOI: 10.25919/vnzc-gk56
title: Parkes observations for project P1145 semester 2022APRS_07

description: Pulsar surveys find pulsars, but follow-up observations are crucial to establish the nature of the pulsars, including their spin-down rate, their magnetic field strength, their polarization properties and whether they are part of a binary system and therefore potentially interesting for further study. We aim to determine timing solutions for 26 pulsars discovered at Parkes but which have not been observed since their discovery.
DOI: 10.25402/bmm.20472729
title: Supplementary Table 3: Investigating and modeling the differential DNA methylation for early lung adenocarcinoma diagnosis

description: Background: Aberrant DNA methylations serve as rich sources of diagnostic biomarkers, but a further improvement in their accuracy and clinical utility is warranted. Methods: Large panel bisulfite sequencing were performed on paired normal and stage I/IV tumors from 226 lung adenocarcinoma cancer patients to characterize the differentially methylated regions (DMRs). Results: Random forest model achieved high prediction accuracy (sensitivity 96% and specificity 97.56%) to separate normal controls from both early and advanced cancer samples, which is superior to most previous prediction models tested in lung adenocarcinoma. Conclusion: Our results suggest that combining the random forest model with targeted bisulfite sequencing have great clinical potentials to accurately predict and early diagnose lung adenocarcinoma during cancer screening.
DOI: 10.25402/bmm.20472729.v1
title: Supplementary Table 3: Investigating and modeling the differential DNA methylation for early lung adenocarcinoma diagnosis

description: Background: Aberrant DNA methylations serve as rich sources of diagnostic biomarkers, but a further improvement in their accuracy and clinical utility is warranted. Methods: Large panel bisulfite sequencing were performed on paired normal and stage I/IV tumors from 226 lung adenocarcinoma cancer patients to characterize the differentially methylated regions (DMRs). Results: Random forest model achieved high prediction accuracy (sensitivity 96% and specificity 97.56%) to separate normal controls from both early and advanced cancer samples, which is superior to most previous prediction models tested in lung adenocarcinoma. Conclusion: Our results suggest that combining the random forest model with targeted bisulfite sequencing have great clinical potentials to accurately predict and early diagnose lung adenocarcinoma during cancer screening.
DOI: 10.25402/bmm.20472732
title: Supplementary Figure 2: Investigating and modeling the differential DNA methylation for early lung adenocarcinoma diagnosis

description: Background: Aberrant DNA methylations serve as rich sources of diagnostic biomarkers, but a further improvement in their accuracy and clinical utility is warranted. Methods: Large panel bisulfite sequencing were performed on paired normal and stage I/IV tumors from 226 lung adenocarcinoma cancer patients to characterize the differentially methylated regions (DMRs). Results: Random forest model achieved high prediction accuracy (sensitivity 96% and specificity 97.56%) to separate normal controls from both early and advanced cancer samples, which is superior to most previous prediction models tested in lung adenocarcinoma. Conclusion: Our results suggest that combining the random forest model with targeted bisulfite sequencing have great clinical potentials to accurately predict and early diagnose lung adenocarcinoma during cancer screening.
DOI: 10.25402/bmm.20472732.v1
title: Supplementary Figure 2: Investigating and modeling the differential DNA methylation for early lung adenocarcinoma diagnosis

description: Background: Aberrant DNA methylations serve as rich sources of diagnostic biomarkers, but a further improvement in their accuracy and clinical utility is warranted. Methods: Large panel bisulfite sequencing were performed on paired normal and stage I/IV tumors from 226 lung adenocarcinoma cancer patients to characterize the differentially methylated regions (DMRs). Results: Random forest model achieved high prediction accuracy (sensitivity 96% and specificity 97.56%) to separate normal controls from both early and advanced cancer samples, which is superior to most previous prediction models tested in lung adenocarcinoma. Conclusion: Our results suggest that combining the random forest model with targeted bisulfite sequencing have great clinical potentials to accurately predict and early diagnose lung adenocarcinoma during cancer screening.
DOI: 10.25402/bmm.20472738
title: Supplementary Figure 3: Investigating and modeling the differential DNA methylation for early lung adenocarcinoma diagnosis

description: Background: Aberrant DNA methylations serve as rich sources of diagnostic biomarkers, but a further improvement in their accuracy and clinical utility is warranted. Methods: Large panel bisulfite sequencing were performed on paired normal and stage I/IV tumors from 226 lung adenocarcinoma cancer patients to characterize the differentially methylated regions (DMRs). Results: Random forest model achieved high prediction accuracy (sensitivity 96% and specificity 97.56%) to separate normal controls from both early and advanced cancer samples, which is superior to most previous prediction models tested in lung adenocarcinoma. Conclusion: Our results suggest that combining the random forest model with targeted bisulfite sequencing have great clinical potentials to accurately predict and early diagnose lung adenocarcinoma during cancer screening.
DOI: 10.25402/bmm.20472738.v1
title: Supplementary Figure 3: Investigating and modeling the differential DNA methylation for early lung adenocarcinoma diagnosis

description: Background: Aberrant DNA methylations serve as rich sources of diagnostic biomarkers, but a further improvement in their accuracy and clinical utility is warranted. Methods: Large panel bisulfite sequencing were performed on paired normal and stage I/IV tumors from 226 lung adenocarcinoma cancer patients to characterize the differentially methylated regions (DMRs). Results: Random forest model achieved high prediction accuracy (sensitivity 96% and specificity 97.56%) to separate normal controls from both early and advanced cancer samples, which is superior to most previous prediction models tested in lung adenocarcinoma. Conclusion: Our results suggest that combining the random forest model with targeted bisulfite sequencing have great clinical potentials to accurately predict and early diagnose lung adenocarcinoma during cancer screening.
DOI: 10.25402/bmm.20472735
title: Supplementary Figure 1: Investigating and modeling the differential DNA methylation for early lung adenocarcinoma diagnosis

description: Background: Aberrant DNA methylations serve as rich sources of diagnostic biomarkers, but a further improvement in their accuracy and clinical utility is warranted. Methods: Large panel bisulfite sequencing were performed on paired normal and stage I/IV tumors from 226 lung adenocarcinoma cancer patients to characterize the differentially methylated regions (DMRs). Results: Random forest model achieved high prediction accuracy (sensitivity 96% and specificity 97.56%) to separate normal controls from both early and advanced cancer samples, which is superior to most previous prediction models tested in lung adenocarcinoma. Conclusion: Our results suggest that combining the random forest model with targeted bisulfite sequencing have great clinical potentials to accurately predict and early diagnose lung adenocarcinoma during cancer screening.
DOI: 10.25402/bmm.20472723
title: Supplementary Table 4: Investigating and modeling the differential DNA methylation for early lung adenocarcinoma diagnosis

description: Background: Aberrant DNA methylations serve as rich sources of diagnostic biomarkers, but a further improvement in their accuracy and clinical utility is warranted. Methods: Large panel bisulfite sequencing were performed on paired normal and stage I/IV tumors from 226 lung adenocarcinoma cancer patients to characterize the differentially methylated regions (DMRs). Results: Random forest model achieved high prediction accuracy (sensitivity 96% and specificity 97.56%) to separate normal controls from both early and advanced cancer samples, which is superior to most previous prediction models tested in lung adenocarcinoma. Conclusion: Our results suggest that combining the random forest model with targeted bisulfite sequencing have great clinical potentials to accurately predict and early diagnose lung adenocarcinoma during cancer screening.
DOI: 10.25402/bmm.20472723.v1
title: Supplementary Table 4: Investigating and modeling the differential DNA methylation for early lung adenocarcinoma diagnosis

description: Background: Aberrant DNA methylations serve as rich sources of diagnostic biomarkers, but a further improvement in their accuracy and clinical utility is warranted. Methods: Large panel bisulfite sequencing were performed on paired normal and stage I/IV tumors from 226 lung adenocarcinoma cancer patients to characterize the differentially methylated regions (DMRs). Results: Random forest model achieved high prediction accuracy (sensitivity 96% and specificity 97.56%) to separate normal controls from both early and advanced cancer samples, which is superior to most previous prediction models tested in lung adenocarcinoma. Conclusion: Our results suggest that combining the random forest model with targeted bisulfite sequencing have great clinical potentials to accurately predict and early diagnose lung adenocarcinoma during cancer screening.
DOI: 10.25402/bmm.20472726
title: Supplementary Table 5: Investigating and modeling the differential DNA methylation for early lung adenocarcinoma diagnosis

description: Background: Aberrant DNA methylations serve as rich sources of diagnostic biomarkers, but a further improvement in their accuracy and clinical utility is warranted. Methods: Large panel bisulfite sequencing were performed on paired normal and stage I/IV tumors from 226 lung adenocarcinoma cancer patients to characterize the differentially methylated regions (DMRs). Results: Random forest model achieved high prediction accuracy (sensitivity 96% and specificity 97.56%) to separate normal controls from both early and advanced cancer samples, which is superior to most previous prediction models tested in lung adenocarcinoma. Conclusion: Our results suggest that combining the random forest model with targeted bisulfite sequencing have great clinical potentials to accurately predict and early diagnose lung adenocarcinoma during cancer screening.
DOI: 10.25402/bmm.20472726.v1
title: Supplementary Table 5: Investigating and modeling the differential DNA methylation for early lung adenocarcinoma diagnosis

description: Background: Aberrant DNA methylations serve as rich sources of diagnostic biomarkers, but a further improvement in their accuracy and clinical utility is warranted. Methods: Large panel bisulfite sequencing were performed on paired normal and stage I/IV tumors from 226 lung adenocarcinoma cancer patients to characterize the differentially methylated regions (DMRs). Results: Random forest model achieved high prediction accuracy (sensitivity 96% and specificity 97.56%) to separate normal controls from both early and advanced cancer samples, which is superior to most previous prediction models tested in lung adenocarcinoma. Conclusion: Our results suggest that combining the random forest model with targeted bisulfite sequencing have great clinical potentials to accurately predict and early diagnose lung adenocarcinoma during cancer screening.
DOI: 10.25402/bmm.20472735.v1
title: Supplementary Figure 1: Investigating and modeling the differential DNA methylation for early lung adenocarcinoma diagnosis

description: Background: Aberrant DNA methylations serve as rich sources of diagnostic biomarkers, but a further improvement in their accuracy and clinical utility is warranted. Methods: Large panel bisulfite sequencing were performed on paired normal and stage I/IV tumors from 226 lung adenocarcinoma cancer patients to characterize the differentially methylated regions (DMRs). Results: Random forest model achieved high prediction accuracy (sensitivity 96% and specificity 97.56%) to separate normal controls from both early and advanced cancer samples, which is superior to most previous prediction models tested in lung adenocarcinoma. Conclusion: Our results suggest that combining the random forest model with targeted bisulfite sequencing have great clinical potentials to accurately predict and early diagnose lung adenocarcinoma during cancer screening.
DOI: 10.25402/bmm.20472720
title: Supplementary Table 2: Investigating and modeling the differential DNA methylation for early lung adenocarcinoma diagnosis

description: Background: Aberrant DNA methylations serve as rich sources of diagnostic biomarkers, but a further improvement in their accuracy and clinical utility is warranted. Methods: Large panel bisulfite sequencing were performed on paired normal and stage I/IV tumors from 226 lung adenocarcinoma cancer patients to characterize the differentially methylated regions (DMRs). Results: Random forest model achieved high prediction accuracy (sensitivity 96% and specificity 97.56%) to separate normal controls from both early and advanced cancer samples, which is superior to most previous prediction models tested in lung adenocarcinoma. Conclusion: Our results suggest that combining the random forest model with targeted bisulfite sequencing have great clinical potentials to accurately predict and early diagnose lung adenocarcinoma during cancer screening.
DOI: 10.25402/bmm.20472720.v1
title: Supplementary Table 2: Investigating and modeling the differential DNA methylation for early lung adenocarcinoma diagnosis

description: Background: Aberrant DNA methylations serve as rich sources of diagnostic biomarkers, but a further improvement in their accuracy and clinical utility is warranted. Methods: Large panel bisulfite sequencing were performed on paired normal and stage I/IV tumors from 226 lung adenocarcinoma cancer patients to characterize the differentially methylated regions (DMRs). Results: Random forest model achieved high prediction accuracy (sensitivity 96% and specificity 97.56%) to separate normal controls from both early and advanced cancer samples, which is superior to most previous prediction models tested in lung adenocarcinoma. Conclusion: Our results suggest that combining the random forest model with targeted bisulfite sequencing have great clinical potentials to accurately predict and early diagnose lung adenocarcinoma during cancer screening.
DOI: 10.34740/kaggle/dsv/4057186
title: AutoXGB
DOI: 10.5517/ccdc.csd.cc1nwrs2
title: CCDC 1546025: Experimental Crystal Structure Determination

description: Related Article: Claudio Pettinari, Jean Ngoune, Eleuterio Álvarez|2022|Inorg.Chim.Acta|542|121103|doi:10.1016/j.ica.2022.121103
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.cc1nwrt3
title: CCDC 1546026: Experimental Crystal Structure Determination

description: Related Article: Claudio Pettinari, Jean Ngoune, Eleuterio Álvarez|2022|Inorg.Chim.Acta|542|121103|doi:10.1016/j.ica.2022.121103
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.cc293kdd
title: CCDC 2118583: Experimental Crystal Structure Determination

description: Related Article: Juan Shi, Fengmin Song, Hongguang Ge, Yanhong Gao, Shaobo Guo|2022|J.Inorg.Biochem.|236|111939|doi:10.1016/j.jinorgbio.2022.111939
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.cc29jjft
title: CCDC 2131046: Experimental Crystal Structure Determination

description: Related Article: Guanyu Ding, Jialin Tong, Yingchen Duan, Shuang Wang, Zhongmin Su, Kuizhan Shao, Lingyu Zhang, Daoming Zhu, Li-Li Wen, Yuanyuan Li, Guo-Gang Shan|2022|J.Mater.Chem.B|10|5818|doi:10.1039/D2TB01152B
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.cc29jjgv
title: CCDC 2131047: Experimental Crystal Structure Determination

description: Related Article: Guanyu Ding, Jialin Tong, Yingchen Duan, Shuang Wang, Zhongmin Su, Kuizhan Shao, Lingyu Zhang, Daoming Zhu, Li-Li Wen, Yuanyuan Li, Guo-Gang Shan|2022|J.Mater.Chem.B|10|5818|doi:10.1039/D2TB01152B
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.cc2bq92g
title: CCDC 2166375: Experimental Crystal Structure Determination

description: Related Article: Rubén Turo-Cortés, Manuel Meneses-Sánchez, Teresa Delgado, Carlos Bartual-Murgui, M. Carmen Muñoz, José Antonio Real|2022|J.Mater.Chem.C|10|10686|doi:10.1039/D2TC02039D
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.cc2bq93h
title: CCDC 2166376: Experimental Crystal Structure Determination

description: Related Article: Rubén Turo-Cortés, Manuel Meneses-Sánchez, Teresa Delgado, Carlos Bartual-Murgui, M. Carmen Muñoz, José Antonio Real|2022|J.Mater.Chem.C|10|10686|doi:10.1039/D2TC02039D
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.cc2c2vkw
title: CCDC 2177488: Experimental Crystal Structure Determination

description: Related Article: Yang Chen, Geng-Xie Li, Ai-Qing Peng, Yong Tang, Lijia Wang|2022|Org.Lett.|24|5525|doi:10.1021/acs.orglett.2c01933
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: 10.34740/kaggle/dsv/4057237
title: Herbarium 2022 Train TFREC 480
DOI: 10.20375/0000-000f-5eb6-b
title: DIF_Crimea_2016-05.sav
DOI: 10.20375/0000-000f-5eb7-a
title: DIF_Crimea_2016-05.xlsx
DOI: 10.20375/0000-000f-5eb8-9
title: DIF_Crimea_2018-08.sav
DOI: 10.20375/0000-000f-5eb9-8
title: DIF_Crimea_2018-08.xlsx
DOI: 10.20375/0000-000f-5eba-7
title: DIF_Crimea_2021-07.sav
DOI: 10.20375/0000-000f-5ebb-6
title: DIF_Crimea_2021-07.xlsx
DOI: 10.20375/0000-000f-5ebc-5
title: Documentation-Data-Collection.pdf
DOI: 10.48320/bbcd9ce6-ec82-40ed-b9f2-5c0a9b2a060d
title: Ukrainian public opinion about the future of Crimea (Ilko Kucheriv Democratic Initiatives Foundation)
DOI: 10.5517/cc7nlm2
title: CCDC 228334: Experimental Crystal Structure Determination

description: Related Article: G.V.Romanenko, D.V.Ovcharenko, S.F.Vasilevskii|2003|Zh.Strukt.Khim.|44|351|
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/cc6hsct
title: CCDC 193916: Experimental Crystal Structure Determination

description: Related Article: Chen-Xi Zhang, Zhan-Quan Liu, Dai-Zheng Liao, Zong-Hui Jiang, Shi-Ping Yan|2004|Jiegou Huaxue|23|247|
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.