<abstract xmlns="http://www.w3.org/1999/xhtml"><sec id="j_jdis.201612_s_007_w2aab2b8c69b1b7b1aab1c15b1Aa"><h3>Purpose</h3><p>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.</p></sec><sec id="j_jdis.201612_s_008_w2aab2b8c69b1b7b1aab1c15b2Aa"><h3>Design/methodology/approach</h3><p>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.</p></sec><sec id="j_jdis.201612_s_009_w2aab2b8c69b1b7b1aab1c15b3Aa"><h3>Findings</h3><p>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.</p></sec><sec id="j_jdis.201612_s_010_w2aab2b8c69b1b7b1aab1c15b4Aa"><h3>Research limitations</h3><p>The current workflow to identify articles that truly used TCGA data is labor-intensive. An automatic method is expected to improve the performance.</p></sec><sec id="j_jdis.201612_s_011_w2aab2b8c69b1b7b1aab1c15b5Aa"><h3>Practical implications</h3><p>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.</p></sec><sec id="j_jdis.201612_s_012_w2aab2b8c69b1b7b1aab1c15b6Aa"><h3>Originality/value</h3><p>Few studies have been conducted to investigate data usage by government-funded 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.</p></sec></abstract>

PurposeIn 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/approachWe 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.FindingsThe 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 limitationsThe current workflow to identify articles that truly used TCGA data is labor-intensive. An automatic method is expected to improve the performance.Practical implicationsThis 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/valueFew studies have been conducted to investigate data usage by government-funded 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.

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

Institute of Medical Information and Library

Journal of Data and Information Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

PurposeIn the open science era, it is typical to share project-generated scientific data by depositing it in an open and accessible database. Moreover,...

Concept ID	Name	TCGA defined terms [abbr] – [full name]	Synonyms	DO mapping
D0001	Glioblastoma	GBM – Glioblastoma Multiforme	Glioblastoma, GBM, adult glioblastoma multiforme, primary glioblastoma multiforme, spongioblastoma multiforme	DOID: 3068
D0002	Breast cancer	BRCA – Breast Invasive Carcinoma	Breast cancer, breast tumor, breast neoplasm, mammary cancer, mammary tumor, mammary neoplasm, malignant tumor of breast,	DOID: 1612
D0003	Ovarian cancer	OV – Ovarian Serous Cystadenocarcinoma	Ovarian cancer, ovarian tumor, ovarian neoplasm, ovary cancer, ovary tumor, ovary neoplasm, malignant tumor of ovary	DOID: 2394
D0004	Acute myeloid leukemia	LAML – Acute Myeloid Leukemia	Acute myeloid leukemia, AML, acute myeloblastic leukemia, acute myelogenous leukemia	DOID: 9119

Feature		Retrieved PMC article set (%)	Benchmark dataset (%)
TCGA term positon	Title	1	4
	Abstract	11	28
	Introduction/Background	12	20
	Method/Material	31	68
	Result	74	96
	Discussion/Conclusion	20	36
TCGA related concept	Cancer type mention	73	100
mention	Platform mention	66	96

Concept ID	Name	TCGA-defined terms	Generated data
P0001	RNASeq	IlluminaGA_RNASeq,	Nucleotide sequence, gene expression
		IlluminaHiSeq_RNASeq
P0002	miRNASeq	IlluminaGA_miRNASeq	miRNAs, microRNA, microRNA sequence
P0003	SNP	Genome_Wide_SNP	SNPs, single nucleotide polymorphisms, CNV, copy number variation
P0004	Methylation	Human methylation	DNA methylation

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

Article Category: Research Paper

Published Online: Sep 01, 2017

Page range: 32 - 44

Received: Jan 20, 2016

Accepted: May 15, 2016

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

Keywords
Scientific data, Full-text literature, Open access, PubMed Central, Data citation

© 2016 Jiao Li, Si Zheng, Hongyu Kang, Zhen Hou, Qing Qian

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Examples of TCGA cancer-type concepts.

Distribution of TCGA key terms in full-text articles.

Examples of TCGA high-throughput platform concepts.

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

Article Category: Research Paper

Published Online: Sep 01, 2017

Page range: 32 - 44

Received: Jan 20, 2016

Accepted: May 15, 2016

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

KeywordsScientific data, Full-text literature, Open access, PubMed Central, Data citation

© 2016 Jiao Li, Si Zheng, Hongyu Kang, Zhen Hou, Qing Qian

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Examples of TCGA cancer-type concepts.

Distribution of TCGA key terms in full-text articles.

Examples of TCGA high-throughput platform concepts.

Keywords
Scientific data, Full-text literature, Open access, PubMed Central, Data citation