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

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.

Google Scholar University Ranking Algorithm to Evaluate the Quality of Institutional Research

Education quality has undoubtedly become an important local and international benchmark for education,and an institute’s ranking is assessed based on the quality of education,research projects,theses,and dissertations,which has always been controversial.Hence,this research paper is influenced by the institutes ranking all over the world.The data of institutes are obtained through Google Scholar(GS),as input to investigate the United Kingdom’s Research Excellence Framework(UK-REF)process.For this purpose,the current research used a Bespoke Program to evaluate the institutes’ranking based on their source.The bespoke program requires changes to improve the results by addressing these methodological issues:Firstly,Redundant profiles,which increased their citation and rank to produce false results.Secondly,the exclusion of theses and dissertation documents to retrieve the actual publications to count for citations.Thirdly,the elimination of falsely owned articles from scholars’profiles.To accomplish this task,the experimental design referred to collecting data from 120 UK-REF institutes and GS for the present year to enhance its correlation analysis in this new evaluation.The data extracted from GS is processed into structured data,and afterward,it is utilized to generate statistical computations of citations’analysis that contribute to the ranking based on their citations.The research promoted the predictive approach of correlational research.Furthermore,experimental evaluation reported encouraging results in comparison to the previous modi-fication made by the proposed taxonomy.This paper discussed the limitations of the current evaluation and suggested the potential paths to improve the research impact algorithm.

FAIR Data Reuse-the Path through Data Citation

One of the key goals of the FAIR guiding principles is defined by its final principle-to optimize data sets for reuse by both humans and machines.To do so,data providers need to implement and support consistent machine readable metadata to describe their data sets.This can seem like a daunting task for data providers,whether it is determining what level of detail should be provided in the provenance metadata or figuring out what common shared vocabularies should be used.Additionally,for existing data sets it is often unclear what steps should be taken to enable maximal,appropriate reuse.Data citation already plays an important role in making data findable and accessible,providing persistent and unique identifiers plus metadata on over 16 million data sets.In this paper,we discuss how data citation and its underlying infrastructures,in particular associated metadata,provide an important pathway for enabling FAIR data reuse.

Studies on the characteristics of scientific data citation in Chinese researchers:Case studies of twelve academic journals

Scientific data citation is a common behavior in the process of scientific research and writing academic papers under the context of data-intensive scientific research paradigm. Standardized citation of scientific data has received continuous attention from academia and policy management departments in recent years. In order to explore the characteristics and the correlation of scientific data citations of Chinese researchers, based on the results of scientific data citations presented in academic papers, this study use CNKI as the data source to extract771 papers in 12 academic journals during 2017 to 2019. Combining with the Chinese national standard Information Technology-Scientific Data Citation(GB/T 35294-2017), a set of variables were given to reflect the reference characteristics. First, 4992 citation records of scientific data were manually identified and coded one by one, and the citation characteristics were presented with the statistical distribution of data frequency. Then, the chi-square test, log-linear model analysis, and correspondence analysis methods were used to analyze and explore the significant correlation among the characteristic variables. The study found that in general, the phenomenon of scientific data citations in Chinese researchers is widespread, and the number of citations has increased year by year, but there are also a large number of irregular citations. At present, there are roughly two modes of citation labeling behavior, and the traditional document citation mode is still the mainstream citation method for data citation. Furthermore, distributor type of scientific data may affect the reference in marked way. In addition, the completeness of the labeling elements differed in different bibliographic elements of scientific data. Irregular references to Unique Identifiers and parsing addresses are particularly prominent, which may be related to the type of distributor.

Paving the Way to Open Data

It is easy to argue that open data are critical to enabling faster and more effective research discovery.In this article,we describe the approach we have taken at Wiley to support open data and to start enabling more data to be FAIR data(Findable,Accessible,Interoperable and Reusable)with the implementation of four data policies:“Encourages”,“Expects”,“Mandates”and“Mandates and Peer Reviews Data”.We describe the rationale for these policies and levels of adoption so far.In the coming months we plan to measure and monitor the implementation of these policies via the publication of data availability statements and data citations.With this information,we’ll be able to celebrate adoption of data-sharing practices by the research communities we work with and serve,and we hope to showcase researchers from those communities leading in open research.

Playing Well on the Data FAIRground:Initiatives and Infrastructure in Research Data Management

Over the past five years,Elsevier has focused on implementing FAIR and best practices in data management,from data preservation through reuse.In this paper we describe a series of efforts undertaken in this time to support proper data management practices.In particular,we discuss our journal data policies and their implementation,the current status and future goals for the research data management platform Mendeley Data,and clear and persistent linkages to individual data sets stored on external data repositories from corresponding published papers through partnership with Scholix.Early analysis of our data policies implementation confirms significant disparities at the subject level regarding data sharing practices,with most uptake within disciplines of Physical Sciences.Future directions at Elsevier include implementing better discoverability of linked data within an article and incorporating research data usage metrics.