Volumen 6 (2021): Edición 4 (November 2021)

Methods to tackle Covid-19 have been developed by a wave of biomedical research but the pandemic has also influenced many aspects of society, generating a need for research into its consequences, and potentially changing the way existing topics are investigated. This article investigates the nature of this influence on the wider academic research mission.

This article reports an inductive content analysis of 500 randomly selected journal articles mentioning Covid-19, as recorded by the Dimensions scholarly database on 19 March 2021. Covid-19 mentions were coded for the influence of the disease on the research.

Whilst two thirds of these articles were about biomedicine (e.g. treatments, vaccines, virology), or health services in response to Covid-19, others covered the pandemic economy, society, safety, or education. In addition, some articles were not about the pandemic but stated that Covid-19 had increased or decreased the value of the reported research or changed the context in which it was conducted.

The findings relate only to Covid-19 influences declared in published journal articles.

Research managers and funders should consider whether their current procedures are effective in supporting researchers to address the evolving demands of pandemic societies, particularly in terms of timeliness.

The results show that although health research dominates the academic response to Covid-19, it is more widely disrupting academic research with new demands and challenges.

Interdisciplinarity is a hot topic in science and technology policy. However, the concept of interdisciplinarity is both abstract and complex, and therefore difficult to measure using a single indicator. A variety of metrics for measuring the diversity and interdisciplinarity of articles, journals, and fields have been proposed in the literature. In this article, we ask whether institutions can be ranked in terms of their (inter-)disciplinary diversity.

We developed a software application (interd_vb.exe) that outputs the values of relevant diversity indicators for any document set or network structure. The software is made available, free to the public, online. The indicators it considers include the advanced diversity indicators Rao-Stirling (RS) diversity and DIV*, as well as standard measures of diversity, such as the Gini coefficient, Shannon entropy, and the Simpson Index. As an empirical demonstration of how the application works, we compared the research portfolios of 42 “Double First-Class” Chinese universities across Web of Science Subject Categories (WCs).

The empirical results suggest that DIV* provides results that are more in line with one's intuitive impressions than RS, particularly when the results are based on sample-dependent disparity measures. Furthermore, the scores for diversity are more consistent when based on a global disparity matrix than on a local map.

“Interdisciplinarity” can be operationalized as bibliographic coupling among (sets of) documents with references to disciplines. At the institutional level, however, diversity may also indicate comprehensiveness. Unlike impact (e.g. citation), diversity and interdisciplinarity are context-specific and therefore provide a second dimension to the evaluation.

Operationalization and quantification make it necessary for analysts to make their choices and options clear. Although the equations used to calculate diversity are often mathematically transparent, the specification in terms of computer code helps the analyst to further precision in decisions. Although diversity is not necessarily a goal of universities, a high diversity score may inform potential policies concerning interdisciplinarity at the university level.

This article introduces a non-commercial online application to the public domain that allows researchers and policy analysts to measure “diversity” and “interdisciplinarity” using the various indicators as encompassing as possible for any document set or network structure (e.g. a network of co-authors). Insofar as we know, such a professional computing tool for evaluating data sets using diversity indicators has not yet been made available online.

Building upon pioneering work by Francis Narin and others, a new methodological approach to assessing the technological impact of scientific research is presented.

It is based on the analysis of citations made in patent families included in the PATSTAT database that is to scientific papers indexed in Scopus.

An advanced citation matching procedure is applied to the data in order to construct two indicators of technological impact: on the citing (patent) side, the country/region in which protection is sought and a patent family's propensity to cite scientific papers are taken into account, and on the cited (paper) side, a relative citation rate is defined for patent citations to papers that is similar to the scientific paper-to-paper citation rate in classical bibliometrics.

The results are limited by the available data, in our case Scopus and PATSTAT, and especially by the lack of standardization of references in patents. This required a matching procedure that is neither trivial nor exact.

Results at the country/region, document type, and publication age levels are presented. The country/region-level results in particular reveal features that have remained hidden in analyses of straight counts. Especially notable is that the rankings of some Asian countries/regions move upwards when the proposed normalized indicator of technological impact is applied as against the case with straight counts of patent citations to those countries/regions’ published papers.

In our opinion, the level of sophistication of the indicators proposed in the current paper is unparalleled in the scientific literature, and provides a solid basis for the assessment of the technological impact of scientific research in countries/regions and institutions.

We attempt to find out whether OA or TA really affects the dissemination of scientific discoveries.

We design the indicators, hot-degree, and R-index to indicate a topic OA or TA advantages. First, according to the OA classification of the Web of Science (WoS), we collect data from the WoS by downloading OA and TA articles, letters, and reviews published in Nature and Science during 2010–2019. These papers are divided into three broad disciplines, namely biomedicine, physics, and others. Then, taking a discipline in a journal and using the classical Latent Dirichlet Allocation (LDA) to cluster 100 topics of OA and TA papers respectively, we apply the Pearson correlation coefficient to match the topics of OA and TA, and calculate the hot-degree and R-index of every OA-TA topic pair. Finally, characteristics of the discipline can be presented. In qualitative comparison, we choose some high-quality papers which belong to Nature remarkable papers or Science breakthroughs, and analyze the relations between OA/TA and citation numbers.

The result shows that OA hot-degree in biomedicine is significantly greater than that of TA, but significantly less than that of TA in physics. Based on the R-index, it is found that OA advantages exist in biomedicine and TA advantages do in physics. Therefore, the dissemination of average scientific discoveries in all fields is not necessarily affected by OA or TA. However, OA promotes the spread of important scientific discoveries in high-quality papers.

We lost some citations by ignoring other open sources such as arXiv and bioArxiv. Another limitation came from that Nature employs some strong measures for access-promoting subscription-based articles, on which the boundary between OA and TA became fuzzy.

It is useful to select hot topics in a set of publications by the hot-degree index. The finding comprehensively reflects the differences of OA and TA in different disciplines, which is a useful reference when researchers choose the publishing way as OA or TA.

We propose a new method, including two indicators, to explore and measure OA or TA advantages.

The adequacy of research performance of universities or research institutes have often been evaluated and understood in two axes: “quantity” (i.e. size or volume) and “quality” (i.e. what we define here as a measure of excellence that is considered theoretically independent of size or volume, such as clarity in diamond grading). The purpose of this article is, however, to introduce a third construct named “substantiality” (“ATSUMI” in Japanese) of research performance and to demonstrate its importance in evaluating/understanding research universities.

We take a two-step approach to demonstrate the effectiveness of the proposed construct by showing that (1) some characteristics of research universities are not well captured by the conventional constructs (“quantity” and “quality”)-based indicators, and (2) the “substantiality” indicators can capture them. Furthermore, by suggesting that “substantiality” indicators appear linked to the reputation that appeared in university reputation rankings by simple statistical analysis, we reveal additional benefits of the construct.

We propose a new construct named “substantiality” for measuring research performance. We show that indicators based on “substantiality” can capture important characteristics of research institutes. “Substantiality” indicators demonstrate their “predictive powers” on research reputation.

The concept of “substantiality” originated from IGO game; therefore the ease/difficulty of accepting the concept is culturally dependent. In other words, while it is easily accepted by people from Japan and other East Asian countries and regions, it might be difficult for researchers from other cultural regions to accept it.

There is no simple solution to the challenge of evaluating research universities’ research performance. It is vital to combine different types of indicators to understand the excellence of research institutes. Substantiality indicators could be part of such a combination of indicators.

The authors propose a new construct named substantiality for measuring research performance. They show that indicators based on this construct can capture the important characteristics of research institutes.

This paper studies the relationship between the impact factor (IF) and the number of journal papers in Chinese publishing system.

The method proposed by Huang (2016) is used whereas to analysis the data of Chinese journals in this study.

Based on the analysis, we find the following. (1) The average impact factor (AIF) of journals in all disciplines maintained a growth trend from 2007 to 2017. Whether before or after removing outlier journals that may garner publication fees, the IF and its growth rate for most social sciences disciplines are larger than those of most natural sciences disciplines, and the number of journal papers on social sciences disciplines decreased while that of natural sciences disciplines increased from 2007 to 2017. (2) The removal of outlier journals has a greater impact on the relationship between the IF and the number of journal papers in some disciplines such as Geosciences because there may be journals that publish many papers to garner publication fees. (3) The success-breeds-success (SBS) principle is applicable in Chinese journals on natural sciences disciplines but not in Chinese journals on social sciences disciplines, and the relationship is the reverse of the SBS principle in Economics and Education & Educational Research. (4) Based on interviews and surveys, the difference in the relationship between the IF and the number of journal papers for Chinese natural sciences disciplines and Chinese social sciences disciplines may be due to the influence of the international publishing system. Chinese natural sciences journals are losing their academic power while Chinese social sciences journals that are less influenced by the international publishing system are in fierce competition.

More implications could be found if long-term tracking and comparing the international publishing system with Chinese publishing system are taken.

It is suggested that researchers from different countries study natural science and social sciences journals in their languages and observe the influence of the international publishing system.

This paper presents an overview of the relationship between IF and the number of journal papers in Chinese publishing system from 2007 to 2017, provides insights into the relationship in different disciplines in Chinese publishing system, and points out the similarities and differences between Chinese publishing system and international publishing system.

This study aims to provide a new framework for analyzing the path of technology diffusion in the innovation network at the regional level and industrial level respectively, which is conducive to the integration of innovation resources, the coordinated development of innovative subjects, and the improvement of innovation abilities.

Based on the Z-Park patent cooperation data, we establish Inter-Enterprise Technology Transfer Network model and apply the concept of Pivotability to describe the key links of technology diffusion and quantify the importance of innovative partnerships. By measuring the topologically structural characteristics in the levels of branch park and the technosphere, this paper demonstrates how technology spreads and promotes overall innovation activities within the innovation network.

The results indicate that: (1) Patent cooperation network of the Z-Park displays heterogeneity and the connections between the innovative subjects distribute extremely uneven. (2) Haidian park owns the highest pivotability in the IETTN model, yet the related inter-enterprise patent cooperation is mainly concentrated in its internal, failing to facilitate the technology diffusion across multiple branch parks. (3) Such fields as “electronics and information” and “advanced manufacturing” are prominent in the cross-technosphere cooperation, while fields such as “new energy” and “environmental protection technology” can better promote industrial integration.

Only the part of the joint patent application is taken into account while establishing the patent cooperation network. The other factors that influence the mechanism of technology diffusion in the innovation network need to be further studied, such as financial capital, market competition, and personnel mobility, etc.

The findings of this paper will provide useful information and suggestions for the administration and policy-making of high-tech parks.

The value of this paper is to build a bridge between the massive amount of patent data and the nature of technology diffusion, and to develop a set of tools to analyze the nonlinear relations between innovative subjects.

We proposed a method to represent scientific papers by a complex network, which combines the approaches of neural and complex networks.

Its novelty is representing a paper by a word branch, which carries the sequential structure of words in sentences. The branches are generated by the attention mechanism in deep learning models. We connected those branches at the positions of their common words to generate networks, called word-attention networks, and then detect their communities, defined as topics.

Those detected topics can carry the sequential structure of words in sentences, represent the intra- and inter-sentential dependencies among words, and reveal the roles of words playing in them by network indexes.

The parameter setting of our method may depend on practical data. Thus it needs human experience to find proper settings.

Our method is applied to the papers of the PNAS, where the discipline designations provided by authors are used as the golden labels of papers’ topics.

This empirical study shows that the proposed method outperforms the Latent Dirichlet Allocation and is more stable.