Journal of Data and Information Science ›› 2016, Vol. 1 ›› Issue (2): 32-44.doi: 10.20309/jdis.201612

• Research Paper • Previous Articles     Next Articles

Identifying Scientific Project-generated Data Citation from Full-text Articles: An Investigation of TCGA Data Citation

Jiao Li, Si Zheng, Hongyu Kang, Zhen Hou & Qing Qian   

  1. Institute of Medical Information and Library, Chinese Academy of Medical Sciences, Beijing 100020, China
  • Received:2016-01-20 Revised:2016-04-28 Online:2016-06-15 Published:2016-06-15
  • Contact: Qing Qian E-mail:qian.qing@imicams.ac.cn
  • Supported by:
    This work was supported by the National Population and Health Scientific Data Sharing Program of China, the Knowledge Centre for Engineering Sciences and Technology (Medical Centre), and the Fundamental Research Funds for the Central Universities (Grant No.: 13R0101). The authors thank Yang Pan for the data processing during his summer internship.

Abstract: 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 PubMed 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.


http://ir.las.ac.cn/handle/12502/8596

Key words: Scientific data, Full-text literature, Open access, PubMed Central, Data citation