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

doi:10.20309/jdis.201612

Journal of Data and Information Science >

2016 , Vol. 1 >Issue 2: 32 - 44

DOI: https://doi.org/10.20309/jdis.201612

Research Paper

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

Expand

Institute of Medical Information and Library, Chinese Academy of Medical Sciences, Beijing 100020, China

Received date: 2016-01-20

Revised date: 2016-04-28

Online published: 2016-06-15

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.

Fold

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

Cite this article

Jiao Li , Si Zheng , Hongyu Kang , Zhen Hou & Qing Qian . Identifying Scientific Project-generated Data Citation from Full-text Articles: An Investigation of TCGA Data Citation[J]. Journal of Data and Information Science, 2016 , 1(2) : 32 -44 . DOI: 10.20309/jdis.201612

References

Bourne, P.E., Lorsch, J.R., & Green, E.D. (2015). Perspective: Sustaining the big-data ecosystem. Nature, 527(7576), S16-17.
Brennan, C.W., Verhaak, R.G., McKenna, A., Campos, B., Noushmehr, H., Salama, S.R. (2013). The somatic genomic landscape of glioblastoma. Cell, 155(2), 462-477.
Cancer Genome Atlas Research Network (2008). Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature, 455(7216), 1061-1068.
Chin, L., Hahn, W.C., Getz, G., & Meyerson, M. (2011). Making sense of cancer genomic data. Genes & Development, 25(6), 534-555.
Green, E.D., Watson, J.D., & Collins, F.S. (2015). Human Genome Project: Twenty-five years of big biology. Nature, 526(7571), 29-31.
Kafkas, S., Kim, J.H., & McEntyre, J.R. (2013). Database citation in full text biomedical articles. PLoS One, 8(5), e63184.
Kafkas, S., Kim, J.H., Pi, X., & McEntyre, J.R. (2015). Database citation in supplementary data linked to Europe PubMed Central full text biomedical articles. Journal of Biomedical Semantics, 6, 1.
Kibbe, W.A., Arze, C., Felix, V., Mitraka, E., Bolton, E., Fu, G., Mungall, C.J., Binder, J.X., Malone, J., Vasant, D., Parkinson, H., & Schriml, L.M. (2015). Disease Ontology 2015 Update: an expanded and updated database of human diseases for linking biomedical knowledge through disease data. Nucleic Acids Research, 43(Database issue), D1071-1078.
Leaman, R., Islamaj, D.R., & Lu, Z. (2013). DNorm: Disease name normalization with pairwise learning to rank. Bioinformatics, 29(22), 2909-2917.
National Science Board (2005). Long-lived digital data collections: Enabling research and education in the 21st century. Retrieved on Oct 20, 2015, from http://www.nsf.gov/pubs/2005/nsb0540/
Neveol, A., Wilbur, W.J., & Lu, Z. (2011) Extraction of data deposition statements from the literature: A method for automatically tracking research results. Bioinformatics, 27, 3306-3312.
Neveol, A., Wilbur, W.J., & Lu, Z. (2012). Improving links between literature and biological data with text mining: A case study with GEO, PDB and MEDLINE. Database (Oxford), 2012, bas026.
Peng, L., Bian, X.W., Li, D.K., Xu, C., Wang, G.M., Xia, Q.Y., & Xiong, Q. (2015). Large-scale RNA-Seq transcriptome analysis of 4043 cancers and 548 normal tissue controls across 12 TCGA cancer types. Scientific Report, 5, 13413.
Piwowar, H., & Chapman, W. (2010). Recall and bias of retrieving gene expression microarray datasets through PubMed identifiers. Journal of Biomedical Discovery and Collaboration, 5, 7-20.
Piwowar, H., & Vision, T.J. (2013). Data reuse and the open data citation advantage. Peer J, 1, e175.
TCGA Data Matrix (2015). Retrieved on Oct. 20, 2015, from https://tcga-data.nci.nih.gov/tcga/dataAccessMatrix.htm
TCGA Publications (2016). Retrieved on Jan. 28, 2016, from http://cancergenome.nih.gov/publications.
Tomczak, K., Czerwinska, P., & Wiznerowicz, M. (2015). The Cancer Genome Atlas (TCGA): An immeasurable source of knowledge. Contemporary Oncology, 19(1A), A68-77.
Wang, Z., Gerstein, M., & Snyder, M. (2009). RNA-Seq: A revolutionary tool for transcriptomics. Nature Reviews Genetics, 10(1), 57-63.
Yu, Q., Ding, Y., Song, M., Song, S., Liu, J., & Zhang, B. (2015). Tracing database usage: Dectecting main paths in database link network, Journal of Informetrics, 9(1), 1-15.

Options

Outlines

模态框（Modal）标题

Abstract

Cite this article

References