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Abstract

Purpose

The number of citations has been widely used to measure the significance of a paper. However, there is a need in introducing another index to determine superiority or inferiority of papers with the same number of citations. We determine superiority or inferiority of papers by using the ranking based on the number of citations and PageRank.

Design/methodology/approach

We show the positive linear correlation between Citation Rank (the ranking of the number of citation) and PageRank. On this basis, we identify high-quality, prestige, emerging, and popular papers.

Findings

We found that the high-quality papers belong to the subjects of biochemistry and molecular biology, chemistry, and multidisciplinary sciences. The prestige papers correspond to the subjects of computer science, engineering, and information science. The emerging papers are related to biochemistry and molecular biology, as well as those published in the journal “Cell.” The popular papers belong to the subject of multidisciplinary sciences.

Research limitations

We analyze the Science Citation Index Expanded (SCIE) from 1981 to 2015 to calculate Citation Rank and PageRank within a citation network consisting of 34,666,719 papers and 591,321,826 citations.

Practical implications

Our method is applicable to forecast emerging fields of research subjects in science and helps policymakers to consider science policy.

Originality/value

We calculated PageRank for a giant citation network which is extremely larger than the citation networks investigated by previous researchers.

Abstract

Purpose

Research dynamics have long been a research interest. It is a macro perspective tool for discovering temporal research trends of a certain discipline or subject. A micro perspective of research dynamics, however, concerning a single researcher or a highly cited paper in terms of their citations and “citations of citations” (forward chaining) remains unexplored.

Design/methodology/approach

In this paper, we use a cross-collection topic model to reveal the research dynamics of topic disappearance topic inheritance, and topic innovation in each generation of forward chaining.

Findings

For highly cited work, scientific influence exists in indirect citations. Topic modeling can reveal how long this influence exists in forward chaining, as well as its influence.

Research limitations

This paper measures scientific influence and indirect scientific influence only if the relevant words or phrases are borrowed or used in direct or indirect citations. Paraphrasing or semantically similar concept may be neglected in this research.

Practical implications

This paper demonstrates that a scientific influence exists in indirect citations through its analysis of forward chaining. This can serve as an inspiration on how to adequately evaluate research influence.

Originality

The main contributions of this paper are the following three aspects. First, besides research dynamics of topic inheritance and topic innovation, we model topic disappearance by using a cross-collection topic model. Second, we explore the length and character of the research impact through “citations of citations” content analysis. Finally, we analyze the research dynamics of artificial intelligence researcher Geoffrey Hinton's publications and the topic dynamics of forward chaining.

Abstract

Purpose

This paper proposes a discrimination index method based on the Jain's fairness index to distinguish researchers with the same H-index.

Design/methodology/approach

A validity test is used to measure the correlation of D-offset with the parameters, i.e. H-index, the number of cited papers, the total number of citations, the number of indexed papers, and the number of uncited papers. The correlation test is based on the Saphiro-Wilk method and Pearson's product-moment correlation.

Findings

The result from the discrimination index calculation is a two-digit decimal value called the discrimination-offset (D-offset), with a range of D-offset from 0.00 to 0.99. The result of the correlation value between the D-offset and the number of uncited papers is 0.35, D-offset with the number of indexed papers is 0.24, and the number of cited papers is 0.27. The test provides the result that it is very unlikely that there exists no relationship between the parameters.

Practical implications

For this reason, D-offset is proposed as an additional parameter for H-index to differentiate researchers with the same H-index. The H-index for researchers can be written with the format of “H-index: D-offset”.

Originality/value

D-offset is worthy to be considered as a complement value to add the H-index value. If the D-offset is added in the H-index value, the H-index will have more discrimination power to differentiate the rank of the researchers who have the same H-index.

Abstract

Purpose

Digital literacy and related fields have received interests from scholars and practitioners for more than 20 years; nonetheless, academic communities need to systematically review how the fields have developed. This study aims to investigate the research trends of digital literacy and related concepts since the year of 2000, especially in education.

Design/methodology/approach

The current study analyzes keywords, co-authorship, and cited publications in digital literacy through the scientometric method. The journal articles have been retrieved from the WoS (Web of Science) using four keywords: “Digital literacy,” “ICT literacy,” “information literacy,” and “media literacy.” Further, keywords, publications, and co-authorship are examined and further classified into clusters for more in-depth investigation.

Findings

Digital literacy is a multidisciplinary field that widely embraces literacy, ICT, the Internet, computer skill proficiency, science, nursing, health, and language education. The participants, or study subjects, in digital literacy research range from primary students to professionals, and the co-authorship clusters are distinctive by countries in America and Europe.

Research limitations

This paper analyzes one fixed chunk of a dataset obtained by searching for all four keywords at once. Further studies will retrieve the data from diverse disciplines and will trace the change of the leading research themes by time spans.

Practical implications

To shed light on the findings, using customized digital literacy curriculums and technology is critical for learners at different ages to nurture digital literacy according to their learning aims. They need to cultivate their understanding of the social impact of exploiting technology and computational thinking. To increase the originality of digital literacy-related studies, researchers from different countries and cultures may collaborate to investigate a broader range of digital literacy environments.

Originality/value

The present study reviews research trends in digital literacy and related areas by performing a scientometric study to analyze multidimensional aspects in the fields, including keywords, journal titles, co-authorship, and cited publications.

Abstract

Purpose

Our work seeks to overcome data quality issues related to incomplete author affiliation data in bibliographic records in order to support accurate and reliable measurement of international research collaboration (IRC).

Design/methodology/approch

We propose, implement, and evaluate a method that leverages the Web-based knowledge graph Wikidata to resolve publication affiliation data to particular countries. The method is tested with general and domain-specific data sets.

Findings

Our evaluation covers the magnitude of improvement, accuracy, and consistency. Results suggest the method is beneficial, reliable, and consistent, and thus a viable and improved approach to measuring IRC.

Research limitations

Though our evaluation suggests the method works with both general and domain-specific bibliographic data sets, it may perform differently with data sets not tested here. Further limitations stem from the use of the R programming language and R libraries for country identification as well as imbalanced data coverage and quality in Wikidata that may also change over time.

Practical implications

The new method helps to increase the accuracy in IRC studies and provides a basis for further development into a general tool that enriches bibliographic data using the Wikidata knowledge graph.

Originality

This is the first attempt to enrich bibliographic data using a peer-produced, Web-based knowledge graph like Wikidata.

Abstract

Purpose

This paper relates the definition of data quality procedures for knowledge organizations such as Higher Education Institutions. The main purpose is to present the flexible approach developed for monitoring the data quality of the European Tertiary Education Register (ETER) database, illustrating its functioning and highlighting the main challenges that still have to be faced in this domain.

Design/methodology/approach

The proposed data quality methodology is based on two kinds of checks, one to assess the consistency of cross-sectional data and the other to evaluate the stability of multiannual data. This methodology has an operational and empirical orientation. This means that the proposed checks do not assume any theoretical distribution for the determination of the threshold parameters that identify potential outliers, inconsistencies, and errors in the data.

Findings

We show that the proposed cross-sectional checks and multiannual checks are helpful to identify outliers, extreme observations and to detect ontological inconsistencies not described in the available meta-data. For this reason, they may be a useful complement to integrate the processing of the available information.

Research limitations

The coverage of the study is limited to European Higher Education Institutions. The cross-sectional and multiannual checks are not yet completely integrated.

Practical implications

The consideration of the quality of the available data and information is important to enhance data quality-aware empirical investigations, highlighting problems, and areas where to invest for improving the coverage and interoperability of data in future data collection initiatives.

Originality/value

The data-driven quality checks proposed in this paper may be useful as a reference for building and monitoring the data quality of new databases or of existing databases available for other countries or systems characterized by high heterogeneity and complexity of the units of analysis without relying on pre-specified theoretical distributions.

Abstract

Purpose

This study examines acknowledgments to libraries in the journal literature, as well as the efficacy of using Web of Science (WoS) to locate general acknowledgment text.

Design/methodology/approach

This mixed-methods approach quantifies and characterizes acknowledgments to libraries in the journal literature. Using WoS's Funding Text field, the acknowledgments for six peer universities were identified and then characterized. The efficacy of using WoS to locate library acknowledgments was assessed by comparing the WoS Funding Text search results to the actual acknowledgment text found in the articles.

Findings

Acknowledgments to libraries were found in articles at all six peer universities, though the absolute and relative numbers were quite low (< 0.5%). Most of the library acknowledgments were for resources (collections, funding, etc.), and many were concentrated in natural history (e.g. zoology). Examination of Texas A&M University zoology articles found that 91.7% of the funding information came from “acknowledgments” and not specifically a funding acknowledgment section. The WoS Funding Text search found 56% of the library acknowledgments compared to a search of the actual acknowledgment text in the articles.

Research limitations

Limiting publications to journals, using a single truncated search term, and including only six research universities in the United States.

Practical implications

This study examined library acknowledgments, but the same approach could be applied to searches of other keywords, institutions/organizations, individuals, etc. While not specifically designed to search general acknowledgments, WoS's Funding Text field can be used as an exploratory tool to search acknowledgments beyond funding.

Originality/value

There are a few studies that have examined library acknowledgments in the scholarly literature, though to date none of those studies have examined the efficacy of using the WoS Funding Text field to locate those library acknowledgments within the journal literature.

Abstract

Purpose

To give a theoretical framework to measure the relative impact of bibliometric methodology on the subfields of a scientific discipline, and how that impact depends on the method of evaluation used to credit individual scientists with citations and publications. The authors include a study of the discipline of physics to illustrate the method. Indicators are introduced to measure the proportion of a credit space awarded to a subfield or a set of authors.

Design/methodology/approach

The theoretical methodology introduces the notion of credit spaces for a discipline. These quantify the total citation or publication credit accumulated by the scientists in the discipline. One can then examine how the credit is divided among the subfields. The design of the physics study uses the American Physical Society print journals to assign subdiscipline classifications to articles and gather citation, publication, and author information. Credit spaces for the collection of Physical Review Journal articles are computed as a proxy for physics.

Findings

There is a substantial difference in the value or impact of a specific subfield depending on the credit system employed to credit individual authors.

Research limitations

Subfield classification information is difficult to obtain. In the illustrative physics study, subfields are treated in groups designated by the Physical Review journals. While this collection of articles represents a broad part of the physics literature, it is not all the literature nor a random sample.

Practical implications

The method of crediting individual scientists has consequences beyond the individual and affects the perceived impact of whole subfields and institutions.

Originality/value

The article reveals the consequences of bibliometric methodology on subfields of a disciple by introducing a systematic theoretical framework for measuring the consequences.

Abstract

Purpose

The use of in vitro cell culture and experimentation is a cornerstone of biomedical research, however, more attention has recently been given to the potential consequences of using such artificial basal medias and undefined supplements. As a first step towards better understanding and measuring the impact these systems have on experimental results, we use text mining to capture typical research practices and trends around cell culture.

Design/methodology/approach

To measure the scale of in vitro cell culture use, we have analyzed a corpus of 94,695 research articles that appear in biomedical research journals published in ScienceDirect from 2000–2018. Central to our investigation is the observation that studies using cell culture describe conditions using the typical sentence structure of cell line, basal media, and supplemented compounds. Here we tag our corpus with a curated list of basal medias and the Cellosaurus ontology using the Aho-Corasick algorithm. We also processed the corpus with Stanford CoreNLP to find nouns that follow the basal media, in an attempt to identify supplements used.

Findings

Interestingly, we find that researchers frequently use DMEM even if a cell line's vendor recommends less concentrated media. We see long-tailed distributions for the usage of media and cell lines, with DMEM and RPMI dominating the media, and HEK293, HEK293T, and HeLa dominating cell lines used.

Research limitations

Our analysis was restricted to documents in ScienceDirect, and our text mining method achieved high recall but low precision and mandated manual inspection of many tokens.

Practical implications

Our findings document current cell culture practices in the biomedical research community, which can be used as a resource for future experimental design.

Originality/value

No other work has taken a text mining approach to surveying cell culture practices in biomedical research.