cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.14559533.v1
title: Correlation between collum angle and lower lip position in different Class II malocclusions - A retrospective cephalometric study

description: <b>Purpose</b>: Significance of analysing collum angle in class II malocclusions with respect to lip position may lend greater credibility to variable torque brackets (mainly in the anteriors) that are fast gaining traction. Determine the magnitude of collum angle of maxillary central incisors in Class II division 1, division 2 malocclusions and changes in measurement brought about by variations in lower lip position and incisal contact. <b>Materials and methods</b>: 154 lateral cephalograms were divided into three groups- 55(Angle’s class I), 52 (Angle’s class II division 1) and 47 (Angle’s class II division 2). The collum angle was measured along with lower lip line (in Class II division 2) and lower lip contact (in Class II division 1) malocclusions. <b>Results</b>: The mean collum angles were 2.12 ± 1.43°, −0.84 ± 0.87° and 4.67 ± 2.23° in Groups 1, 2, and 3, respectively. Mean collum angle is significantly increased when the lower lip was found to contact the middle-third of the central incisor(in Group 3)and significantly decreased when the anterior one-third of the lower lip was in contact with the incisal edge of the central incisor (in Group 2). ANOVA followed by Tukey HSD test was carried out to determine statistically significant differences (p <b>Conclusion</b>: Collum angle is significantly decreased in Class II division 1 malocclusions necessitating variable torque prescriptions and segmental mechanics of maxillary anteriors. Appropriate alveolar bone support must be assessed prior to en-masse retraction and intrusion respecting the cortical boundaries. <b>KEY MESSAGES</b>: Variable torque brackets are rapidly gaining credence for their ability to provide sufficient alveolar bone support in different clinical scenarios. In this study, we have emphasized upon utilizing similar mechanics to treat class II malocclusions by utilizing the lower lip position as a reference guide.
DOI: 10.26036/cnp0000676
title: stLFR Library prep kit development

description: Development of a long DNA fragment sequencing method by using short reading platform.information kit
DOI: 10.26036/cnp0001190
title: 多能性干细胞-新增数据 (iPS cells)

description: 马诱导多能性干细胞的建立及表征分析
DOI: 10.26036/cnp0001310
title: Callithrix jacchus (common marmoset) Genome , mCalJac1, paternal haplotype

description: The common marmoset (Callithrix jacchus) is a small, social monkey native to Brazil. With numerous attributes making it advantageous to study in the laboratory, the marmoset has become a prominent model in biomedical research. Sponsored by Guojie Zhang of the University of Copenhagen, a trio of samples were collected from a male juvenile and its parents by Stephanie Marcus of Erich Jarvis’ Lab in the colony of the Winrich Freiwald Lab at The Rockefeller University, and were used to generate a high-quality reference genome as part of the G10K-VGP.
DOI: 10.6084/m9.figshare.14452662
title: Transcriptome-based phylogenomic studies of liverworts

description: liverwort transcriptome data, phylogenomics analyses, and trees
DOI: 10.6084/m9.figshare.14559663.v1
title: Optimization of Running-in surface morphology parameters based on Auto-ML model

description: Running-in is an important and relatively complicated process. Surface morphology prior to run-ning-in affects the surface morphology following the running-in process, which in turn influences friction and wear characteristics of the workpiece. Therefore, it is important to establish a running-in model based on surface morphology to investigate the process and optimize the surface design. According to the existing research, the application of machine learning in running-in wear modeling is not sufficiently investigated. The black-box model has a robust complex object simulation per-formance. In this paper, five machine learning methods are applied for running-in modeling per-formance based on surface morphology parameters. It is observed that the support vector machine method has the best performance. Then, the support vector machine model is carried out and tested. Finally, the change law of surface morphology parameters is obtained and optimization direction of surface morphology design is proposed. When the surface is required to have good oil storage per-formance, the root mean square deviation (Sq), the root mean square slope (Sdq), and the height of surface core roughness (Sk) should be increased, and the height difference of surface morphology (Sdc) and the surface area ratio (Sdr) should be moderately high. When surface improved surface supporting performance is desired, larger values of parameters the height difference of surface morphology (Sdc), the root mean square slope (Sdq), and the surface area ratio (Sdr) should be used.
DOI: 10.5281/zenodo.4743415
title: Vapor pressure deficits and sunlight explain seasonality of leaf phenology and photosynthesis across Amazonian evergreen broadleaved forest

description: This dataset contains all of the figures and corresponding data of paper 'Vapor pressure deficits and sunlight explain seasonality of leaf phenology and photosynthesis across Amazonian evergreen broadleaved forest'. Data is organized with geotiff, matlab and tables (.xlsx) etc. <br> If you want to know more detail please contact: chenxzh73@mail.sysu.edu.cn
DOI: 10.5281/zenodo.4718510
title: Vapor pressure deficits and sunlight explain seasonality of leaf phenology and photosynthesis across Amazonian evergreen broadleaved forest

description: This dataset contains all of the figures and corresponding data of paper 'Vapor pressure deficits and sunlight explain seasonality of leaf phenology and photosynthesis across Amazonian evergreen broadleaved forest'. Data is organized with geotiff, matlab and tables (.xlsx) etc. <br> If you want to know more detail please contact: chenxzh73@mail.sysu.edu.cn
DOI: 10.6084/m9.figshare.14559663.v2
title: Optimization of Running-in surface morphology parameters based on Auto-ML model

description: Running-in is an important and relatively complicated process. Surface morphology prior to run-ning-in affects the surface morphology following the running-in process, which in turn influences friction and wear characteristics of the workpiece. Therefore, it is important to establish a running-in model based on surface morphology to investigate the process and optimize the surface design. According to the existing research, the application of machine learning in running-in wear modeling is not sufficiently investigated. The black-box model has a robust complex object simulation per-formance. In this paper, five machine learning methods are applied for running-in modeling per-formance based on surface morphology parameters. It is observed that the support vector machine method has the best performance. Then, the support vector machine model is carried out and tested. Finally, the change law of surface morphology parameters is obtained and optimization direction of surface morphology design is proposed. When the surface is required to have good oil storage per-formance, the root mean square deviation (Sq), the root mean square slope (Sdq), and the height of surface core roughness (Sk) should be increased, and the height difference of surface morphology (Sdc) and the surface area ratio (Sdr) should be moderately high. When surface improved surface supporting performance is desired, larger values of parameters the height difference of surface morphology (Sdc), the root mean square slope (Sdq), and the surface area ratio (Sdr) should be used.
DOI: 10.6084/m9.figshare.14559666
title: 2021 JGR: Solid Earth-Lyu Chao-A novel hybrid wave numerical simulation

description: The seismic structure of Earth is multi-scale, yet capturing such a broad range of complex heterogeneities by the available global tomography is still computationally prohibitive nowadays. It is of significance to lighten this computational burden by only inverting a small region, namely, the so-called box tomography. As the forward part of box tomography, the elegant implementation of the hybrid numerical simulation plays a crucial role. After analyzing the two main existing hybrid simulations including five different subcategories, we propose a new more flexible, and efficient hybrid method, which is promising to be used to efficiently probe the key 3-D structures anywhere in the earth.
DOI: 10.6084/m9.figshare.14559663
title: Optimization of Running-in surface morphology parameters based on Auto-ML model

description: Running-in is an important and relatively complicated process. Surface morphology prior to run-ning-in affects the surface morphology following the running-in process, which in turn influences friction and wear characteristics of the workpiece. Therefore, it is important to establish a running-in model based on surface morphology to investigate the process and optimize the surface design. According to the existing research, the application of machine learning in running-in wear modeling is not sufficiently investigated. The black-box model has a robust complex object simulation per-formance. In this paper, five machine learning methods are applied for running-in modeling per-formance based on surface morphology parameters. It is observed that the support vector machine method has the best performance. Then, the support vector machine model is carried out and tested. Finally, the change law of surface morphology parameters is obtained and optimization direction of surface morphology design is proposed. When the surface is required to have good oil storage per-formance, the root mean square deviation (Sq), the root mean square slope (Sdq), and the height of surface core roughness (Sk) should be increased, and the height difference of surface morphology (Sdc) and the surface area ratio (Sdr) should be moderately high. When surface improved surface supporting performance is desired, larger values of parameters the height difference of surface morphology (Sdc), the root mean square slope (Sdq), and the surface area ratio (Sdr) should be used.
DOI: 10.6084/m9.figshare.14559663.v4
title: Optimization of Running-in surface morphology parameters based on Auto-ML model

description: Running-in is an important and relatively complicated process. Surface morphology prior to run-ning-in affects the surface morphology following the running-in process, which in turn influences friction and wear characteristics of the workpiece. Therefore, it is important to establish a running-in model based on surface morphology to investigate the process and optimize the surface design. According to the existing research, the application of machine learning in running-in wear modeling is not sufficiently investigated. The black-box model has a robust complex object simulation per-formance. In this paper, five machine learning methods are applied for running-in modeling per-formance based on surface morphology parameters. It is observed that the support vector machine method has the best performance. Then, the support vector machine model is carried out and tested. Finally, the change law of surface morphology parameters is obtained and optimization direction of surface morphology design is proposed. When the surface is required to have good oil storage per-formance, the root mean square deviation (Sq), the root mean square slope (Sdq), and the height of surface core roughness (Sk) should be increased, and the height difference of surface morphology (Sdc) and the surface area ratio (Sdr) should be moderately high. When surface improved surface supporting performance is desired, larger values of parameters the height difference of surface morphology (Sdc), the root mean square slope (Sdq), and the surface area ratio (Sdr) should be used.
DOI: 10.6084/m9.figshare.14559663.v3
title: Optimization of Running-in surface morphology parameters based on Auto-ML model

description: Running-in is an important and relatively complicated process. Surface morphology prior to run-ning-in affects the surface morphology following the running-in process, which in turn influences friction and wear characteristics of the workpiece. Therefore, it is important to establish a running-in model based on surface morphology to investigate the process and optimize the surface design. According to the existing research, the application of machine learning in running-in wear modeling is not sufficiently investigated. The black-box model has a robust complex object simulation per-formance. In this paper, five machine learning methods are applied for running-in modeling per-formance based on surface morphology parameters. It is observed that the support vector machine method has the best performance. Then, the support vector machine model is carried out and tested. Finally, the change law of surface morphology parameters is obtained and optimization direction of surface morphology design is proposed. When the surface is required to have good oil storage per-formance, the root mean square deviation (Sq), the root mean square slope (Sdq), and the height of surface core roughness (Sk) should be increased, and the height difference of surface morphology (Sdc) and the surface area ratio (Sdr) should be moderately high. When surface improved surface supporting performance is desired, larger values of parameters the height difference of surface morphology (Sdc), the root mean square slope (Sdq), and the surface area ratio (Sdr) should be used.
DOI: 10.5517/ccdc.csd.cc26wpsl
title: CCDC 2052410: Experimental Crystal Structure Determination

description: Related Article: Li Tan, Zhishan Luo, Xiaoyong Chang, Yi Wei, Min Tang, Wen Chen, Qian Li, Pengfei Shen, Zewei Quan|2021|Inorg.Chem.|60|6600|doi:10.1021/acs.inorgchem.1c00393
DOI: 10.5517/ccdc.csd.cc27g4pk
title: CCDC 2069178: Experimental Crystal Structure Determination

description: Related Article: Liselle Atkin, Angus Robertson, Jonathan M. White, Mark A. Rizzacasa|2021|Org.Lett.|23|3557|doi:10.1021/acs.orglett.1c00971
DOI: 10.5517/ccdc.csd.cc27hhdn
title: CCDC 2070471: Experimental Crystal Structure Determination

description: Related Article: Li Tan, Zhishan Luo, Xiaoyong Chang, Yi Wei, Min Tang, Wen Chen, Qian Li, Pengfei Shen, Zewei Quan|2021|Inorg.Chem.|60|6600|doi:10.1021/acs.inorgchem.1c00393
DOI: 10.5517/ccdc.csd.cc27j6zz
title: CCDC 2071171: Experimental Crystal Structure Determination

description: Related Article: Yousra Hamdane, Pradeep S. Chauhan, Suresh Vutla, Mukandila Mulumba, Huy Ong, William D. Lubell|2021|Org.Lett.|23|3491|doi:10.1021/acs.orglett.1c00936
DOI: 10.5517/ccdc.csd.cc25w9s5
title: CCDC 2022247: Experimental Crystal Structure Determination

description: Related Article: Jennifer N. Wacker, Aaron D. Nicholas, Monica Vasiliu, Alexander C. Marwitz, Jeffery A. Bertke, David A. Dixon, Karah E. Knope|2021|Inorg.Chem.|60|6375|doi:10.1021/acs.inorgchem.1c00099
DOI: 10.5517/ccdc.csd.cc25w9t6
title: CCDC 2022248: Experimental Crystal Structure Determination

description: Related Article: Jennifer N. Wacker, Aaron D. Nicholas, Monica Vasiliu, Alexander C. Marwitz, Jeffery A. Bertke, David A. Dixon, Karah E. Knope|2021|Inorg.Chem.|60|6375|doi:10.1021/acs.inorgchem.1c00099
DOI: 10.5517/ccdc.csd.cc25w9v7
title: CCDC 2022249: Experimental Crystal Structure Determination

description: Related Article: Jennifer N. Wacker, Aaron D. Nicholas, Monica Vasiliu, Alexander C. Marwitz, Jeffery A. Bertke, David A. Dixon, Karah E. Knope|2021|Inorg.Chem.|60|6375|doi:10.1021/acs.inorgchem.1c00099
DOI: 10.5517/ccdc.csd.cc25w9w8
title: CCDC 2022250: Experimental Crystal Structure Determination

description: Related Article: Jennifer N. Wacker, Aaron D. Nicholas, Monica Vasiliu, Alexander C. Marwitz, Jeffery A. Bertke, David A. Dixon, Karah E. Knope|2021|Inorg.Chem.|60|6375|doi:10.1021/acs.inorgchem.1c00099
DOI: 10.5517/ccdc.csd.cc25w9x9
title: CCDC 2022251: Experimental Crystal Structure Determination

description: Related Article: Jennifer N. Wacker, Aaron D. Nicholas, Monica Vasiliu, Alexander C. Marwitz, Jeffery A. Bertke, David A. Dixon, Karah E. Knope|2021|Inorg.Chem.|60|6375|doi:10.1021/acs.inorgchem.1c00099
DOI: 10.6084/m9.figshare.14559672
title: for niche dynamics

description: linear mixed models for examining factors responsible for niche dynamics<br>
DOI: 10.6084/m9.figshare.14559672.v1
title: for niche dynamics

description: linear mixed models for examining factors responsible for niche dynamics<br>
DOI: 10.5517/ccdc.csd.cc27w1kr
title: CCDC 2081574: Experimental Crystal Structure Determination

description: Related Article: W. Clegg, K. Guille|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc27w1t0
title: CCDC 2081582: Experimental Crystal Structure Determination

description: Related Article: Frank R. Fronczek|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc27w9ct
title: CCDC 2081816: Experimental Crystal Structure Determination

description: Related Article: Richard R. Thompson, Frank R. Fronczek, Semin Lee|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc27wc9t
title: CCDC 2081876: Experimental Crystal Structure Determination

description: Related Article: Frank R. Fronczek|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc27wk6x
title: CCDC 2082059: Experimental Crystal Structure Determination

description: Related Article: Michael Bolte|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc27wk7y
title: CCDC 2082060: Experimental Crystal Structure Determination

description: Related Article: Michael Bolte|2021|CSD Communication|||
DOI: 10.6084/m9.figshare.14559702
title: sources data

description: occurrence records of all species and datasets of all predictors
DOI: 10.6084/m9.figshare.14559702.v1
title: sources data

description: occurrence records of all species and datasets of all predictors
DOI: 10.5281/zenodo.4743423
title: Supplementary and Video files

description: The practice exercise of hazard perception test in this study
DOI: 10.5281/zenodo.4632415
title: Supplementary and Video files

description: The practice exercise of hazard perception test in this study
DOI: 10.6084/m9.figshare.13066629
title: Genetic structure and demographic history of <i>Indirana semipalmata</i>, an endemic frog species of the Western Ghats, India

description: The evolutionary potential of a species mainly depends on the level of genetic variation in their populations. Maintenance of gene variation enables populations to adapt more quickly to environmental changes. The geographical gaps also influence the distribution and evolutionary history of many mountain frogs in the world. Hence, a sound knowledge in population genetic structure of a species will help understand its population dynamics and develop conservation strategies. In the context of facing threats to the amphibian fauna of Western Ghats due to habitat loss, we used both mitochondrial and nuclear DNA markers to investigate the genetic structure of an endemic frog species of the Western Ghats (<i>Indirana semipalmata</i>) with restricted distribution. The present study showed the importance of mountain gaps in shaping the species’ structuring in the Western Ghats. Though a high genetic diversity was observed for the species when considering a single unit in the southern Western Ghats, the restricted gene flow on/between either side of the Shencottah gap with genetic clustering of the sampled populations may warrant a unique management plan for the species. The habitat fragmentation of the Western Ghats through anthropogenic activities may result in severe setbacks to the survival of the species in the future.
DOI: 10.6084/m9.figshare.13066629.v1
title: Genetic structure and demographic history of <i>Indirana semipalmata</i>, an endemic frog species of the Western Ghats, India

description: The evolutionary potential of a species mainly depends on the level of genetic variation in their populations. Maintenance of gene variation enables populations to adapt more quickly to environmental changes. The geographical gaps also influence the distribution and evolutionary history of many mountain frogs in the world. Hence, a sound knowledge in population genetic structure of a species will help understand its population dynamics and develop conservation strategies. In the context of facing threats to the amphibian fauna of Western Ghats due to habitat loss, we used both mitochondrial and nuclear DNA markers to investigate the genetic structure of an endemic frog species of the Western Ghats (<i>Indirana semipalmata</i>) with restricted distribution. The present study showed the importance of mountain gaps in shaping the species’ structuring in the Western Ghats. Though a high genetic diversity was observed for the species when considering a single unit in the southern Western Ghats, the restricted gene flow on/between either side of the Shencottah gap with genetic clustering of the sampled populations may warrant a unique management plan for the species. The habitat fragmentation of the Western Ghats through anthropogenic activities may result in severe setbacks to the survival of the species in the future.
DOI: 10.17876/cosmosim/bolshoi/003
title: BDMW

description: <b>BDM catalogue</b> for all available snapshots, calculated using <b>200*ρ<sub>crit</sub></b> (critical density) for defining the halo boundary.
DOI: 10.17876/cosmosim/bolshoi/012
title: Dens256

description: <b>Density fields</b> for a number of snapshot numbers (see Redshifts-table or check with <code>SELECT DISTINCT snapnum FROM bolshoi.dens256</code>), for 256<sup>3</sup> grid cells; less strongly smoothed than for dens256_z0.
DOI: 10.17876/cosmosim/bolshoi/013
title: Dens256_z0

description: <b>Density field</b> for redshift 0, for 256<sup>3</sup> grid cells; strongly smoothed
DOI: 10.17876/cosmosim/bolshoi/014
title: Dens512

description: <b>Density field</b> for redshift 0, for 512<sup>3</sup> grid cells
DOI: 10.17876/cosmosim/bolshoi/015
title: Dens512_z0
DOI: 10.17876/cosmosim/bolshoi/006
title: FOF1

description: <b>Friends-of-Friends catalogue</b> for all available snapshots, same as FOF-table, but for <b>smaller (relative) linking lengths</b>, levels 1 (linking length 0.085) to 5 (linking length 0.005313). Thus these tables contain <b>substructures</b> of the FOF groups.
DOI: 10.17876/cosmosim/bolshoi/007
title: FOF2

description: <b>Friends-of-Friends catalogue</b> for all available snapshots, same as FOF-table, but for <b>smaller (relative) linking lengths</b>, levels 1 (linking length 0.085) to 5 (linking length 0.005313). Thus these tables contain <b>substructures</b> of the FOF groups.
DOI: 10.17876/cosmosim/bolshoi/008
title: FOF3

description: <b>Friends-of-Friends catalogue</b> for all available snapshots, same as FOF-table, but for <b>smaller (relative) linking lengths</b>, levels 1 (linking length 0.085) to 5 (linking length 0.005313). Thus these tables contain <b>substructures</b> of the FOF groups.
DOI: 10.17876/cosmosim/bolshoi/009
title: FOF4

description: <b>Friends-of-Friends catalogue</b> for all available snapshots, same as FOF-table, but for <b>smaller (relative) linking lengths</b>, levels 1 (linking length 0.085) to 5 (linking length 0.005313). Thus these tables contain <b>substructures</b> of the FOF groups.
DOI: 10.17876/cosmosim/bolshoi/005
title: FOF

description: <b>Main Friends-of-Friends catalogue</b> for all available snapshots, <b>level 0 (relative linking length 0.17)</b>;<br> <code>fofid = (snapnum\*10 + level)\*10<sup>8</sup> +(rank in FOF-file)</code>
DOI: 10.17876/cosmosim/bolshoi/010
title: FOF5

description: <b>Friends-of-Friends catalogue</b> for all available snapshots, same as FOF-table, but for <b>smaller (relative) linking lengths</b>, levels 1 (linking length 0.085) to 5 (linking length 0.005313). Thus these tables contain <b>substructures</b> of the FOF groups.
DOI: 10.17876/cosmosim/bolshoip/001
title: BDMV

description: BDM (Bound Density Maximum) catalogue for all available snapshots, calculated using the standard overdensity criterion with <b>360\*ρ<sub>back</sub></b> (background density).<br> <code>bdmid = ( snapnum\*10<sup>8</sup> +(number in BDM catalogue) )</code>