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
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.
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。
