基于阻抗张肌数据， tipper 数据，和conjugate 坡度算法的分析，我们发展一三维( 3D )为转换结合坡度算法完整的信息数据决定了从的 magnetotelluric 五电并且磁场部件并且讨论方法为 3D 倒置结果的量的解释使用完整的信息数据。从合...基于阻抗张肌数据， tipper 数据，和conjugate 坡度算法的分析，我们发展一三维( 3D )为转换结合坡度算法完整的信息数据决定了从的 magnetotelluric 五电并且磁场部件并且讨论方法为 3D 倒置结果的量的解释使用完整的信息数据。从合成数据的 3D 倒置的结果显示从转换的结果更好联合张肌和 tipper 数据是的阻抗比的完整的信息数据源于转换仅仅阻抗张肌数据(或 tipper 数据) 在改进分辨率和可靠性。合成例子也表明这个 3D 倒置算法的有效性和稳定性。展开更多
Purpose： In the open science era, it is typical to share project-generated scientific data by depositing it in an open and accessible database. Moreover, scientific publications are preserved in a digital library arc...Purpose： In the open science era, it is typical to share project-generated scientific data by depositing it in an open and accessible database. Moreover, scientific publications are preserved in a digital library archive. It is challenging to identify the data usage that is mentioned in literature and associate it with its source. Here, we investigated the data usage of a government-funded cancer genomics project, The Cancer Genome Atlas（TCGA）, via a full-text literature analysis.Design/methodology/approach： We focused on identifying articles using the TCGA dataset and constructing linkages between the articles and the specific TCGA dataset. First, we collected 5,372 TCGA-related articles from Pub Med Central（PMC）. Second, we constructed a benchmark set with 25 full-text articles that truly used the TCGA data in their studies, and we summarized the key features of the benchmark set. Third, the key features were applied to the remaining PMC full-text articles that were collected from PMC.Findings： The amount of publications that use TCGA data has increased significantly since 2011, although the TCGA project was launched in 2005. Additionally, we found that the critical areas of focus in the studies that use the TCGA data were glioblastoma multiforme, lung cancer, and breast cancer; meanwhile, data from the RNA-sequencing（RNA-seq） platform is the most preferable for use.Research limitations： The current workflow to identify articles that truly used TCGA data is labor-intensive. An automatic method is expected to improve the performance.Practical implications： This study will help cancer genomics researchers determine the latest advancements in cancer molecular therapy, and it will promote data sharing and data-intensive scientific discovery.Originality/value： Few studies have been conducted to investigate data usage by governmentfunded projects/programs since their launch. In this preliminary study, we extracted articles that use TCGA data from PMC, and we created a link between the full-text articles and the source data.展开更多
In allusion to the difficulty of integrating data with different models in integrating spatial information, the characteristics of raster structure, vector structure and mixed model were analyzed, and a hierarchical v...In allusion to the difficulty of integrating data with different models in integrating spatial information, the characteristics of raster structure, vector structure and mixed model were analyzed, and a hierarchical vector-raster integrative full feature model was put forward by integrating the advantage of vector and raster model and using the object-oriented method. The data structures of the four basic features, i.e. point, line, surface and solid, were described. An application was analyzed and described, and the characteristics of this model were described. In this model, all objects in the real world are divided into and described as features with hierarchy, and all the data are organized in vector. This model can describe data based on feature, field, network and other models, and avoid the disadvantage of inability to integrate data based on different models and perform spatial analysis on them in spatial information integration.展开更多
基金supported by the National Hi-tech Research and Development Program of China（863Program）（No.2007AA09Z310） National Natural Science Foundation of China（Grant No.40774029 40374024）+1 种基金 the Fundamental Research Funds for the Central Universities（Grant No.2010ZY53） the Program for New Century Excellent Talents in University（NCET）
文摘基于阻抗张肌数据， tipper 数据，和conjugate 坡度算法的分析，我们发展一三维( 3D )为转换结合坡度算法完整的信息数据决定了从的 magnetotelluric 五电并且磁场部件并且讨论方法为 3D 倒置结果的量的解释使用完整的信息数据。从合成数据的 3D 倒置的结果显示从转换的结果更好联合张肌和 tipper 数据是的阻抗比的完整的信息数据源于转换仅仅阻抗张肌数据(或 tipper 数据) 在改进分辨率和可靠性。合成例子也表明这个 3D 倒置算法的有效性和稳定性。
基金supported by the National Population and Health Scientific Data Sharing Program of Chinathe Knowledge Centre for Engineering Sciences and Technology (Medical Centre)the Fundamental Research Funds for the Central Universities (Grant No.: 13R0101)
文摘Purpose： In the open science era, it is typical to share project-generated scientific data by depositing it in an open and accessible database. Moreover, scientific publications are preserved in a digital library archive. It is challenging to identify the data usage that is mentioned in literature and associate it with its source. Here, we investigated the data usage of a government-funded cancer genomics project, The Cancer Genome Atlas（TCGA）, via a full-text literature analysis.Design/methodology/approach： We focused on identifying articles using the TCGA dataset and constructing linkages between the articles and the specific TCGA dataset. First, we collected 5,372 TCGA-related articles from Pub Med Central（PMC）. Second, we constructed a benchmark set with 25 full-text articles that truly used the TCGA data in their studies, and we summarized the key features of the benchmark set. Third, the key features were applied to the remaining PMC full-text articles that were collected from PMC.Findings： The amount of publications that use TCGA data has increased significantly since 2011, although the TCGA project was launched in 2005. Additionally, we found that the critical areas of focus in the studies that use the TCGA data were glioblastoma multiforme, lung cancer, and breast cancer; meanwhile, data from the RNA-sequencing（RNA-seq） platform is the most preferable for use.Research limitations： The current workflow to identify articles that truly used TCGA data is labor-intensive. An automatic method is expected to improve the performance.Practical implications： This study will help cancer genomics researchers determine the latest advancements in cancer molecular therapy, and it will promote data sharing and data-intensive scientific discovery.Originality/value： Few studies have been conducted to investigate data usage by governmentfunded projects/programs since their launch. In this preliminary study, we extracted articles that use TCGA data from PMC, and we created a link between the full-text articles and the source data.
基金Project (40473029) supported bythe National Natural Science Foundation of China project (04JJ3046) supported bytheNatural Science Foundation of Hunan Province , China
文摘In allusion to the difficulty of integrating data with different models in integrating spatial information, the characteristics of raster structure, vector structure and mixed model were analyzed, and a hierarchical vector-raster integrative full feature model was put forward by integrating the advantage of vector and raster model and using the object-oriented method. The data structures of the four basic features, i.e. point, line, surface and solid, were described. An application was analyzed and described, and the characteristics of this model were described. In this model, all objects in the real world are divided into and described as features with hierarchy, and all the data are organized in vector. This model can describe data based on feature, field, network and other models, and avoid the disadvantage of inability to integrate data based on different models and perform spatial analysis on them in spatial information integration.
文摘探究教师注意力对于评估课堂教师行为具有极其重要的研究价值。然而,现有的教师注意力识别算法存在无法应对极端头部姿态角度等问题。为此,提出一种基于6DRep Net360模型的教师注意力状态识别算法,提升极端角度中头部姿态估计算法的准确性。相较于传统的依赖条件判断来分类教师注意力状态的方法,设计一种基于支持向量机(SVM)的教师注意力分类模型,对复杂头部姿态角度进行注意力状态的精准识别。为进一步解决算法稳定性和准确性带来的误差数据,提出基于滑动窗口的数据清洗算法,有效提高整体识别结果的真实性和可靠性。通过在构建的CCNUTeacherS tat e数据集上进行一系列的算法评估,实验结果表明,所提出的教师注意力识别算法在CCNUTeacherS tate数据集上达到了90.67%的准确率。
