cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.21376693
title: Data for 'Statistics of the number of defects after quantum annealing in a thermal environment'

description: We study the statistics of the kink number generated by quantum annealing in one dimensional transverse Ising model coupled to a bosonic thermal bath. Using the freezing ansatz for quantum annealing in the thermal environment, we show the relation between the ratio of the second to the first cumulant of the kink number distribution and the average kink density. The theoretical result is confirmed thoroughly by numerical simulation using the non-Markovian infinite time-evolving block decimation which we proposed recently. The simulation using D-Wave’s quantum annealer is also discussed.This article is part of the theme issue ‘Quantum annealing and computation: challenges and perspectives’.
DOI: 10.6084/m9.figshare.21376693.v1
title: Data for 'Statistics of the number of defects after quantum annealing in a thermal environment'

description: We study the statistics of the kink number generated by quantum annealing in one dimensional transverse Ising model coupled to a bosonic thermal bath. Using the freezing ansatz for quantum annealing in the thermal environment, we show the relation between the ratio of the second to the first cumulant of the kink number distribution and the average kink density. The theoretical result is confirmed thoroughly by numerical simulation using the non-Markovian infinite time-evolving block decimation which we proposed recently. The simulation using D-Wave’s quantum annealer is also discussed.This article is part of the theme issue ‘Quantum annealing and computation: challenges and perspectives’.
DOI: 10.6084/m9.figshare.12237314.v3
title: FGF9 variant in 46,XY DSD patient suggests a role for dimerization in sex determination

description: Supplemetary Material
DOI: 10.5281/zenodo.6954869
title: Mutation Testing of Deep Reinforcement Learning Based on Real Faults

description: Trained agents to be used in the replication package of the paper "<em>Mutation Testing of Deep Reinforcement Learning Based on Real Faults</em>" submitted to the International Conference on Software Engineering (ICST) 2023. Authors are set to anonymous for the peer-review process.
DOI: 10.5281/zenodo.6954868
title: Mutation Testing of Deep Reinforcement Learning Based on Real Faults

description: Trained agents to be used in the replication package of the paper "<em>Mutation Testing of Deep Reinforcement Learning Based on Real Faults</em>" submitted to the International Conference on Software Engineering (ICST) 2023. Authors are set to anonymous for the peer-review process.
DOI: 10.6084/m9.figshare.21376905
title: Table S2: LGTV determinants

description: Comparison of predicted determinants and aa residues in a LGTV polyprotein
DOI: 10.6084/m9.figshare.21376905.v1
title: Table S2: LGTV determinants

description: Comparison of predicted determinants and aa residues in a LGTV polyprotein
DOI: 10.6084/m9.figshare.13185410
title: CNRDv1.0: the China natural runoff dataset version 1.0

description: The quality-controlled gridded naturalized runoff dataset, the China natural runoff dataset version 1.0 (CNRD v1.0), is reconstructed by using a comprehensive model parameter uncertainty analysis framework within a land-surface model. CNRD v1.0 provides daily, monthly and annual 0.25° runoff estimates for the period 1961–2018 over China. CNRD v1.0 was generated using the Variable Infiltration Capacity model with a comprehensive parameter uncertainty analysis framework incorporating parameter sensitivity analysis, optimization and regionalization based on 200 natural or near-natural gauge catchments. <br> Global attributes: Variables = Total runoff (qtot) Data Types = Histrorical reconstruction Date format = NetCDF4 Temporal Range = 1961-01-01 to 2018-12-31 Spatial extent = 18.6250° N ~53.6250° N, 73.375° E ~135.1250° E Units = mm Missing value = -9999 _FillValue = -9999 <br> Publications: 1. Gou, J. J., Miao, C. Y., Duan, Q. Y., Tang, Q. H., Di, Z. H., Liao, W. H., Wu, J. W., & Zhou, R. (2020). Sensitivity analysis-based automatic parameter calibration of the VIC model for streamflow simulations over China. Water Resources Research, 56: 1-19. https://doi.org/10.1029/2019wr025968;<br> 2. Gou, J. J., Miao, C. Y., Samaniego, L., Xiao, M., Wu, J. W. & Guo, X. Y. (2021). CNRD v1.0: a high-quality natural runoff dataset for hydrological and climate studies in China. Bulletin of the American Meteorological Society, 102(5), E929-E947. https://doi.org/10.1175/BAMS-D-20-0094.1; 3. Miao, C.Y., Gou, J.J., Fu, B.J., Tang, Q.H., Duan, Q.Y., Chen, Z.S., Lei, H.M., Chen, J., Guo, J.L., Borthwick, A.G.L., Ding, W.F., Duan, X.W., Li, Y.G., Kong, D.X., Guo, X.Y., Wu, J.W. High-quality reconstruction of China’s natural streamflow. Science Bulletin, 2022, 67(5): 547-556. https://doi.org/10.1016/j.scib.2021.09.022. <br>
DOI: 10.6084/m9.figshare.19419228
title: Speech-language pathologists’ perspectives of interprofessional collaboration

description: This study aims to provide insight into speech-language pathologists’ experiences of and preparation for interprofessional collaborative practice across various settings and geographical locations in the United States. We disseminated an online survey via Qualtrics© to reach a representative sample of speech-language pathologists. We questioned respondents about the extend to which they engage in interprofessional collaborative practice, professionals with whom they engage in interprofessional collaborative practice, preparation for interprofessional collaborative practice, and barriers to engaging in interprofessional collaborative practice. Responses from 296 participants were analyzed to describe details regarding speech-language pathologists’ experiences in interprofessional collaboration. Quantitative data included means, ranges, standard deviations, and frequency counts. Open-ended responses underwent analysis through a consensual qualitative approach. Most speech-language pathologists in this study (59%) reported feeling prepared for interprofessional collaboration. Participants reported that they engage in interprofessional collaborative practice with other professionals from disciplines such as nursing, occupational therapy, teaching, physical therapy, and school psychology. To best prepare students for future speech-language pathology practice, participants recommended that students engage in interprofessional education to learn about collaborating with these disciplines. These results could have implications for future design and implementation of interprofessional education activities for students and practicing clinicians.
DOI: 10.6084/m9.figshare.19419228.v1
title: Speech-language pathologists’ perspectives of interprofessional collaboration

description: This study aims to provide insight into speech-language pathologists’ experiences of and preparation for interprofessional collaborative practice across various settings and geographical locations in the United States. We disseminated an online survey via Qualtrics© to reach a representative sample of speech-language pathologists. We questioned respondents about the extend to which they engage in interprofessional collaborative practice, professionals with whom they engage in interprofessional collaborative practice, preparation for interprofessional collaborative practice, and barriers to engaging in interprofessional collaborative practice. Responses from 296 participants were analyzed to describe details regarding speech-language pathologists’ experiences in interprofessional collaboration. Quantitative data included means, ranges, standard deviations, and frequency counts. Open-ended responses underwent analysis through a consensual qualitative approach. Most speech-language pathologists in this study (59%) reported feeling prepared for interprofessional collaboration. Participants reported that they engage in interprofessional collaborative practice with other professionals from disciplines such as nursing, occupational therapy, teaching, physical therapy, and school psychology. To best prepare students for future speech-language pathology practice, participants recommended that students engage in interprofessional education to learn about collaborating with these disciplines. These results could have implications for future design and implementation of interprofessional education activities for students and practicing clinicians.
DOI: 10.6084/m9.figshare.21377296
title: supplemental materials.zip from Kirigami tiled surfaces with multiple configurations

description: This paper presents new kirigami patterns consisting of <i>tiles</i> connected by <i>sub-folds</i> that can approximate multiple specified target surfaces. The curvature of the surfaces approximated by the tiles varies as the patterns are folded, allowing access to a wide range of curvatures. A numerical framework is developed for the synthesis of the fold patterns that approximate a given set of target surfaces. The pattern synthesis process is framed as a tile placement problem, where compatible tile arrangements associated with each target surface are computed by solving a constrained optimization problem. After computing a set of tile arrangements, sub-folds are added to connect adjacent tiles. The resulting patterns are rigid foldable with many kinematic degrees of freedom, allowing them to achieve configurations that approximate the specified target surfaces. Kinematic simulations verify the existence of continuous paths between the target surfaces. A prototype pattern with six target surfaces is fabricated using three-dimensional printed components.
DOI: 10.6084/m9.figshare.21377296.v1
title: supplemental materials.zip from Kirigami tiled surfaces with multiple configurations

description: This paper presents new kirigami patterns consisting of <i>tiles</i> connected by <i>sub-folds</i> that can approximate multiple specified target surfaces. The curvature of the surfaces approximated by the tiles varies as the patterns are folded, allowing access to a wide range of curvatures. A numerical framework is developed for the synthesis of the fold patterns that approximate a given set of target surfaces. The pattern synthesis process is framed as a tile placement problem, where compatible tile arrangements associated with each target surface are computed by solving a constrained optimization problem. After computing a set of tile arrangements, sub-folds are added to connect adjacent tiles. The resulting patterns are rigid foldable with many kinematic degrees of freedom, allowing them to achieve configurations that approximate the specified target surfaces. Kinematic simulations verify the existence of continuous paths between the target surfaces. A prototype pattern with six target surfaces is fabricated using three-dimensional printed components.
DOI: 10.5281/zenodo.7233150
title: Text Mining as a Support Tool for Research on Climate Change: Theoretical and Technical Considerations

description: 839 companies listed on the Australian Stock Exchange were surveyed for climate risk disclosures, with 201 such disclosures identified.
DOI: 10.5281/zenodo.7233149
title: Text Mining as a Support Tool for Research on Climate Change: Theoretical and Technical Considerations

description: 839 companies listed on the Australian Stock Exchange were surveyed for climate risk disclosures, with 201 such disclosures identified.
DOI: 10.5517/ccdc.csd.cc2c8tp5
title: CCDC 2183227: Experimental Crystal Structure Determination

description: Related Article: Neil A. Dodd, Yu Cao, John Bacsa, Eric C. Towles, Thomas G. Gray, Joseph P. Sadighi|2022|Inorg.Chem.|61|16317|doi:10.1021/acs.inorgchem.2c02291
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.cc2c8tq6
title: CCDC 2183228: Experimental Crystal Structure Determination

description: Related Article: Neil A. Dodd, Yu Cao, John Bacsa, Eric C. Towles, Thomas G. Gray, Joseph P. Sadighi|2022|Inorg.Chem.|61|16317|doi:10.1021/acs.inorgchem.2c02291
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.cc2byskn
title: CCDC 2173582: Experimental Crystal Structure Determination

description: Related Article: Kangting Zheng, Yajing Ye, Yanan Shi, Yaxuan Xu, Ziyu Yang, Pengtao Ma, Jingping Wang, Jingyang Niu|2022|Inorg.Chem.|61|15871|doi:10.1021/acs.inorgchem.2c01806
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.cc2byslp
title: CCDC 2173583: Experimental Crystal Structure Determination

description: Related Article: Kangting Zheng, Yajing Ye, Yanan Shi, Yaxuan Xu, Ziyu Yang, Pengtao Ma, Jingping Wang, Jingyang Niu|2022|Inorg.Chem.|61|15871|doi:10.1021/acs.inorgchem.2c01806
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.cc2bysmq
title: CCDC 2173584: Experimental Crystal Structure Determination

description: Related Article: Kangting Zheng, Yajing Ye, Yanan Shi, Yaxuan Xu, Ziyu Yang, Pengtao Ma, Jingping Wang, Jingyang Niu|2022|Inorg.Chem.|61|15871|doi:10.1021/acs.inorgchem.2c01806
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.cc2c9l6h
title: CCDC 2183956: Experimental Crystal Structure Determination

description: Related Article: Denis A. Gazizov, Evgeny B. Gorbunov, Ekaterina F. Zhilina, Pavel A. Slepukhin, Gennady L. Rusinov|2022|J.Org.Chem.|87|13011|doi:10.1021/acs.joc.2c01558
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.cc2c9l7j
title: CCDC 2183957: Experimental Crystal Structure Determination

description: Related Article: Denis A. Gazizov, Evgeny B. Gorbunov, Ekaterina F. Zhilina, Pavel A. Slepukhin, Gennady L. Rusinov|2022|J.Org.Chem.|87|13011|doi:10.1021/acs.joc.2c01558
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.cc2c9l8k
title: CCDC 2183958: Experimental Crystal Structure Determination

description: Related Article: Denis A. Gazizov, Evgeny B. Gorbunov, Ekaterina F. Zhilina, Pavel A. Slepukhin, Gennady L. Rusinov|2022|J.Org.Chem.|87|13011|doi:10.1021/acs.joc.2c01558
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.cc2c9l9l
title: CCDC 2183959: Experimental Crystal Structure Determination

description: Related Article: Denis A. Gazizov, Evgeny B. Gorbunov, Ekaterina F. Zhilina, Pavel A. Slepukhin, Gennady L. Rusinov|2022|J.Org.Chem.|87|13011|doi:10.1021/acs.joc.2c01558
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.cc2c9llw
title: CCDC 2183968: Experimental Crystal Structure Determination

description: Related Article: Ya-Li Wu, Min Jiang, Li Rao, Ying Cheng, Wen-Jing Xiao, Jia-Rong Chen|2022|Org.Lett.|24|7470|doi:10.1021/acs.orglett.2c03124
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.cc2c9zf3
title: CCDC 2184335: Experimental Crystal Structure Determination

description: Related Article: Honami Iguchi, Nobuki Katayama, Takeyuki Suzuki, Tetsuaki Fujihara, Yuan Jing, Takashi Toyao, Zen Maeno, Ken-ichi Shimizu, Yasushi Obora|2022|Chem.Commun.|58|11851|doi:10.1039/D2CC04133B
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.cc2cgqxh
title: CCDC 2188907: Experimental Crystal Structure Determination

description: Related Article: Nicolai A. Aksenov, Nikolai A. Arutiunov, Alexander V. Aksenov, Inna V. Aksenova, Elena V. Aleksandrova, Dmitrii A. Aksenov, Michael Rubin|2022|Org.Lett.|24|7062|doi:10.1021/acs.orglett.2c02483
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.cc2cgxtl
title: CCDC 2189090: Experimental Crystal Structure Determination

description: Related Article: Xinyi Wan, Haiwen Li, Shan Wang, Cunde Wang|2022|J.Org.Chem.|87|13375|doi:10.1021/acs.joc.2c01610
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.cc2cgxvm
title: CCDC 2189091: Experimental Crystal Structure Determination

description: Related Article: Xinyi Wan, Haiwen Li, Shan Wang, Cunde Wang|2022|J.Org.Chem.|87|13375|doi:10.1021/acs.joc.2c01610
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.cc28pvb5
title: CCDC 2106367: Experimental Crystal Structure Determination

description: Related Article: Yongzhen Chen, Zhihan Chang, Yuchen Zhang, Keke Chen, Xiuli Wang|2022|Inorg.Chem.|61|16020|doi:10.1021/acs.inorgchem.2c02424
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.cc2chm0j
title: CCDC 2189747: Experimental Crystal Structure Determination

description: Related Article: Rohit Kumar, Sanjukta Pahar, Joy Chatterjee, Soumya Ranjan Dash, Rajesh G. Gonnade, Kumar Vanka, Sakya S. Sen|2022|Chem.Commun.|58|11843|doi:10.1039/D2CC04142A
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.cc2chm1k
title: CCDC 2189748: Experimental Crystal Structure Determination

description: Related Article: Rohit Kumar, Sanjukta Pahar, Joy Chatterjee, Soumya Ranjan Dash, Rajesh G. Gonnade, Kumar Vanka, Sakya S. Sen|2022|Chem.Commun.|58|11843|doi:10.1039/D2CC04142A
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.21377607
title: Untitled Item

description: test
DOI: 10.6084/m9.figshare.21377607.v1
title: Untitled Item

description: test
DOI: 10.21227/ahtr-rh57
title: COVID-19-CHU-Sfax

description: The dataset  includes annotated Computed Tomography (CT)  scanned images. The labels consist of three types:COVID-19 labels indicate whether an expert detects the presence of a COVID-19 lesion. NON-COVID-Normal labels indicate the normal clinical outcome diagnosed by the medical expert.Pathological-NON-COVID labels indicate the presence of disease other than COVID-19 detected by the expert.All the CT examinations were performed in the imaging department of Hedi Chaker Hospital (Sfax-Tunisia) between January and May 2022, using a 64-slice multi-detector CT scanner. A lung parenchymal window reconstruction with a hard filter (B50- B70) is essential for the interpretation of CT images. 1017 CT images of COVID-19 pneumonia as well as 723 CT images of normal lung aspects are collected.