<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,...

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

Article Category: Research Paper

Online veröffentlicht: 01. Sept. 2017

Seitenbereich: 32 - 44

Eingereicht: 20. Jan. 2016

Akzeptiert: 15. Mai 2016

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

Schlüsselwörter
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.

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

Article Category: Research Paper

Online veröffentlicht: 01. Sept. 2017

Seitenbereich: 32 - 44

Eingereicht: 20. Jan. 2016

Akzeptiert: 15. Mai 2016

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

SchlüsselwörterScientific 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.

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