Computer Science

This is the first ever attempt of application in a country other than Italy of a research efficiency indicator (FSS), to assess and compare the performance of professors and universities, within and between countries. A special attention has been devoted to the presentation of the methodology developed to set up a common field classification scheme of professors, and to overcome the limited availability of comparable input data. Results of the comparison between countries, carried out in the 2011-2015 period, show similar average performances of professors, but noticeable differences in the distributions, whereby Norwegian professors are more concentrated in the tails. Norway shows notable higher performance in Mathematics and Earth and Space Sciences, while Italy in Biomedical Research and Engineering.

This study focuses on a recently introduced type of indicator measuring disruptiveness in science. Disruptive research diverges from current lines of research by opening up new lines. In the current study, we included the initially proposed indicator of this new type (Wu, Wang, & Evans, 2019) and several variants with DI1: DI5, DI1n, DI5n, and DEP. Since indicators should measure what they propose to measure, we investigated the convergent validity of the indicators. We used a list of milestone papers, selected and published by editors of Physical Review Letters, and investigated whether this human (experts - based list is related to values of the several disruption indicators variants and - if so - which variants show the highest correlation with expert judgements. We used bivariate statistics, multiple regression models, and (coarsened) exact matching (CEM) to investigate the convergent validity of the indicators. The results show that the indicators correlate differently with the milestone paper assignments by the editors. It is not the initially proposed disruption index that performed best (DI1), but the variant DI5 which has been introduced by Bornmann, Devarakonda, Tekles, and Chacko (2019). In the CEM analysis of this study, the DEP variant - introduced by Bu, Waltman, and Huang (2019) - also showed favorable results.

Proper field delineation plays an important role in scientometric studies, although it is a tough task. Based on an emerging and interdisciplinary field, nanoscience and nanotechnology, this paper highlights the problem of field delineation. First, we review the related literature. Then, three different approaches to delineate a field of knowledge were applied at three different levels of aggregation: subject category, publication level, and journal level. Expert opinion interviews served to assess the data, and precision and recall of each approach were calculated for comparison. Our findings confirm that field delineation is a complicated issue at both the quantitative and the qualitative level, even when experts validate results.

The speed with which biomedical researchers were able to identify and characterise COVID-19 was clearly due to prior research with other coronaviruses. Early epidemiological comparisons with two previous coronaviruses, Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS), also made it easier to predict COVID-19's likely spread and lethality. This article assesses whether academic interest in prior coronavirus research has translated into interest in the primary source material, using Mendeley reader counts for early academic impact evidence. The results confirm that SARS and MERS research 2008-2017 experienced anomalously high increases in Mendeley readers in April-May 2020. Nevertheless, studies learning COVID-19 lessons from SARS and MERS or using them as a benchmark for COVID-19 have generated much more academic interest than primary studies of SARS or MERS. Thus, research that interprets prior relevant research for new diseases when they are discovered seems to be particularly important to help researchers to understand its implications in the new context.

The purpose of this study is to analyse the correlation between content and traffic of 21,485 academic websites (universities and research institutes). The achieved result is used as an indicator which shows the performance of the websites for attracting more visitors. This inspires a best practice for developing new websites or promoting the traffic of the existing websites. At the first step, content of the site is divided into three major items which are: Size, Papers and Rich Files. Then, the Spearman correlation between traffic of the websites and these items are calculated for each country and for the world, respectively. At the next step, countries are ranked based on their correlations, also a new indicator is proposed from combining these three correlations of the countries. Results show that in most countries, correlation between traffic of the websites and Papers is less than correlations between traffic of the websites and Rich Files and Size.

The present review of bibliometric counting methods investigates 1) the number of unique counting methods in the bibliometric research literature, 2) to what extent the counting methods can be categorized according to selected characteristics of the counting methods, 3) methods and elements to assess the internal validity of the counting methods, and 4) to what extent and with which characteristics the counting methods are used in research evaluations. The review identifies 32 counting methods introduced during the period 1981 - 2018. Two frameworks categorize these counting methods. Framework 1 describes selected mathematical properties of counting methods, and Framework 2 describes arguments for choosing a counting method. Twenty of the 32 counting methods are rank-dependent, fractionalized, and introduced to measure contribution, participation, etc. of an object of study. Next, three criteria for internal validity are used to identify five methods that test the adequacy of counting methods, two elements that test sensitivity, and three elements that test homogeneity of the counting methods. These methods and elements may be used to assess the internal validity of counting methods. Finally, a literature search finds research evaluations that use the counting methods. Only three of the 32 counting methods are used by four research evaluations or more. Of these three counting methods, two are used with the same characteristics as defined in the studies that introduced the counting methods. The review provides practitioners in research evaluation and researchers in bibliometrics with a detailed foundation for working with counting methods. At the same time, many of the findings in the review provide bases for future investigations of counting methods.

At the start of 2020, COVID-19 became the most urgent threat to global public health. Uniquely in recent times, governments have imposed partly voluntary, partly compulsory restrictions on the population to slow the spread of the virus. In this context, public attitudes and behaviors are vitally important for reducing the death rate. Analyzing tweets about the disease may therefore give insights into public reactions that may help guide public information campaigns. This article analyses 3,038,026 English tweets about COVID-19 from March 10 to 23, 2020. It focuses on one relevant aspect of public reaction: gender differences. The results show that females are more likely to tweet about the virus in the context of family, social distancing and healthcare whereas males are more likely to tweet about sports cancellations, the global spread of the virus and political reactions. Thus, women seem to be taking a disproportionate share of the responsibility for directly keeping the population safe. The detailed results may be useful to inform public information announcements and to help understand the spread of the virus. For example, failure to impose a sporting bans whilst encouraging social distancing may send mixed messages to males.

During the current century, each major coronavirus outbreak has triggered a quick surge of academic publications on this topic. The spike in research publications following the 2019 Novel Coronavirus (Covid-19), however, has been like no other. The global crisis caused by the Covid-19 pandemic has mobilised scientific efforts in an unprecedented way. In less than five months, more than 12,000 research items have been indexed while the number increasing every day. With the crisis affecting all aspects of life, research on Covid-19 seems to have become a focal point of interest across many academic disciplines. Here, scientometric aspects of the Covid-19 literature are analysed and contrasted with those of the two previous major Coronavirus diseases, i.e. SARS and MERS. The focus is on the co-occurrence of key-terms, bibliographic coupling and citation relations of journals and collaborations between countries. Certain recurring patterns across all three literatures were discovered. All three outbreaks have commonly generated three distinct and major cohort of studies: (i) studies linked to the public health response and epidemic control, (ii) studies associated with the chemical constitution of the virus and (iii) studies related to treatment, vaccine and clinical care. While studies affiliated with the category (i) seem to have been the first to emerge, they overall received least numbers of citations compared to those of the two other categories. Covid-19 studies seem to have been distributed across a broader variety of journals and subject areas. Clear links are observed between the geographical origins of each outbreak or the local geographical severity of each outbreak and the magnitude of research originated from regions. Covid-19 studies also display the involvement of authors from a broader variety of countries compared to SARS and MRS.

Due to the lack of structure, scholarly knowledge remains hardly accessible for machines. Scholarly knowledge graphs have been proposed as a solution. Creating such a knowledge graph requires manual effort and domain experts, and is therefore time-consuming and cumbersome. In this work, we present a human-in-the-loop methodology used to build a scholarly knowledge graph leveraging literature survey articles. Survey articles often contain manually curated and high-quality tabular information that summarizes findings published in the scientific literature. Consequently, survey articles are an excellent resource for generating a scholarly knowledge graph. The presented methodology consists of five steps, in which tables and references are extracted from PDF articles, tables are formatted and finally ingested into the knowledge graph. To evaluate the methodology, 92 survey articles, containing 160 survey tables, have been imported in the graph. In total, 2,626 papers have been added to the knowledge graph using the presented methodology. The results demonstrate the feasibility of our approach, but also indicate that manual effort is required and thus underscore the important role of human experts.

A fair assignment of credit for multi-authored publications is a long-standing issue in scientometrics. In the calculation of the h -index, for instance, all co-authors receive equal credit for a given publication, independent of a given author's contribution to the work or of the total number of co-authors. Several attempts have been made to distribute the credit in a more appropriate manner. In a recent paper, Hirsch has suggested a new way of credit assignment that is fundamentally different from the previous ones: All credit for a multi-author paper goes to a single author, the called `` α -author'', defined as the person with the highest current h -index not the highest h -index at the time of the paper's publication) (J. E. Hirsch, Scientometrics 118, 673 (2019)). The collection of papers this author has received credit for as α -author is then used to calculate a new index, h α , following the same recipe as for the usual h index. The objective of this new assignment is not a fairer distribution of credit, but rather the determination of an altogether different property, the degree of a person's scientific leadership. We show that given the complex time dependence of h for individual scientists, the approach of using the current h value instead of the historic one is problematic, and we argue that it would be feasible to determine the α -author at the time of the paper's publication instead. On the other hand, there are other practical considerations that make the calculation of the proposed h α very difficult. As an alternative, we explore other ways of crediting papers to a single author in order to test early career achievement or scientific leadership.