Computer Science

New researchers are usually very curious about the recipe that could accelerate the chances of their paper getting accepted in a reputed forum (journal/conference). In search of such a recipe, we investigate the profile and peer review text of authors whose papers almost always get accepted at a venue (Journal of High Energy Physics in our current work). We find authors with high acceptance rate are likely to have a high number of citations, high h -index, higher number of collaborators etc. We notice that they receive relatively lengthy and positive reviews for their papers. In addition, we also construct three networks -- co-reviewer, co-citation and collaboration network and study the network-centric features and intra- and inter-category edge interactions. We find that the authors with high acceptance rate are more `central' in these networks; the volume of intra- and inter-category interactions are also drastically different for the authors with high acceptance rate compared to the other authors. Finally, using the above set of features, we train standard machine learning models (random forest, XGBoost) and obtain very high class wise precision and recall. In a followup discussion we also narrate how apart from the author characteristics, the peer-review system might itself have a role in propelling the distinction among the different categories which could lead to potential discrimination and unfairness and calls for further investigation by the system admins.

Secondary analysis or the reuse of existing survey data is a common practice among social scientists. Searching for relevant datasets in Digital Libraries is a somehow unfamiliar behaviour for this community. Dataset retrieval, especially in the social sciences, incorporates additional material such as codebooks, questionnaires, raw data files and more. Our assumption is that due to the diverse nature of datasets, document retrieval models often do not work as efficiently for retrieving datasets. One way of enhancing these types of searches is to incorporate the users' interaction context in order to personalise dataset retrieval sessions. As a first step towards this long term goal, we study characteristics of dataset retrieval sessions from a real-life Digital Library for the social sciences that incorporates both: research data and publications. Previous studies reported a way of discerning queries between document search and dataset search by query length. In this paper, we argue the claim and report our findings of an indistinguishability of queries, whether aiming for a dataset or a document. Amongst others, we report our findings of dataset retrieval sessions with respect to query characteristics, interaction sequences and topical drift within 65,000 unique sessions.

Multidisciplinary cooperation is now common in research since social issues inevitably involve multiple disciplines. In research articles, reference information, especially citation content, is an important representation of communication among different disciplines. Analyzing the distribution characteristics of references from different disciplines in research articles is basic to detecting the sources of referred information and identifying contributions of different disciplines. This work takes articles in PLoS as the data and characterizes the references from different disciplines based on Citation Content Analysis (CCA). First, we download 210,334 full-text articles from PLoS and collect the information of the in-text citations. Then, we identify the discipline of each reference in these academic articles. To characterize the distribution of these references, we analyze three characteristics, namely, the number of citations, the average cited intensity and the average citation length. Finally, we conclude that the distributions of references from different disciplines are significantly different. Although most references come from Natural Science, Humanities and Social Sciences play important roles in the Introduction and Background sections of the articles. Basic disciplines, such as Mathematics, mainly provide research methods in the articles in PLoS. Citations mentioned in the Results and Discussion sections of articles are mainly in-discipline citations, such as citations from Nursing and Medicine in PLoS.

Research collaborations, especially long-distance and cross-border collaborations, have become increasingly prevalent worldwide. Recent studies highlighted the significant role of research leadership in collaborations. However, existing measures of the research leadership do not take into account the intensity of leadership in the co-authorship network. More importantly, the spatial features, which influence the collaboration patterns and research outcomes, have not been incorporated in measuring the research leadership. To fill the gap, we construct an institution-level weighted co-authorship network that has two types of weight on the edges: the intensity of collaborations and the spatial score (the geographic distance adjusted by the cross-border nature). Based on this network, we propose a novel metric, namely the spatial research leadership rank (SpatialLeaderRank), to identify the leading institutions while considering both the collaboration intensity and the spatial features. Harnessing a dataset of 323,146 journal publications in pharmaceutical sciences during 2010-2018, we perform a comprehensive analysis of the geographical distribution and dynamic patterns of research leadership flows at the institution level. The results demonstrate that the SpatialLeaderRank outperforms baseline metrics in predicting the scholarly impact of institutions. And the result remains robust in the field of Information Science & Library Science.

Papers from India are cited 14.68 times on average compared to cites per paper of 45.34 for Singapore, 30.47 for USA, 23.12 for China, 26.51 for the UK, 21.77 for South Korea and 24.77 for Germany. Less than 39% of papers from India are found in quartile 1 (high impact factor) journals, compared to 53.6% for China and 53.8% for South Korea. Percent share of papers in quartile 1 journals from India is lower than that for the world for all of chemistry and for each one of the eight categories, viz. analytical, applied, inorganic & nuclear, medicinal, multidisciplinary, organic, physical and electrochemistry whether one considers data for the entire five-year period or for 2015 alone. About 20% of Indian chemistry papers are in collaboration with international coauthors. Researchers from only 160 Indian institutions have published at least 100 papers (compared to 362 in USA and 399 in China) and these include 67 state, 14 central and 11 private universities, 27 institutions under the Ministry of Human Resource Development, 20 CSIR laboratories, seven Department of Atomic Energy institutions, and seven Department of Science & Technology institutions. About 40% of all Indian chemistry papers have come from public universities. Only three Indian institutions, viz Bhabha Atomic Research Centre, Indian Institute of Science and Indian Institute of Chemical Technology, have published more than 2,000 papers. None of the Indian universities has performed as well as leading Asian universities. Amrita Vishwa Vidyapeetham, a small institution with less than 200 papers, has performed reasonably well.

Purpose:In relation to the boom in China's SCI-indexed publications, this opinion piece examines this phenomenon and looks at future possible directions for the reform of China's research evaluation processes. Design/Approach/Methods:This opinion piece uses bibliographic data for the past decade (2010-2019) from the Science Citation Index Expanded in the Web of Science Core Collection to examine the rise in China's SCI-indexed publications. Findings: China has surpassed the United States and been the largest contributor of SCI publications since 2018. However, while the impact of China's SCI publications is rising, the scale of this impact still lags behind that of other major contributing countries. China's SCI publications are also overrepresented in some journals. Originality/Value: Reporting the latest facts about China's SCI-indexed publications, this article will benefit the reform of China's research evaluation system.

In recent years, the full text of papers are increasingly available electronically which opens up the possibility of quantitatively investigating citation contexts in more detail. In this study, we introduce a new form of citation analysis, which we call citation concept analysis (CCA). CCA is intended to reveal the cognitive impact certain concepts -- published in a document -- have on the citing authors. It counts the number of times the concepts are mentioned (cited) in the citation context of citing publications. We demonstrate the method using three classical examples: (1) The structure of scientific revolutions by Thomas S. Kuhn, (2) The logic of scientific discovery - Logik der Forschung: Zur Erkenntnistheorie der modernen Naturwissenschaft in German -, and (3) Conjectures and refutations: the growth of scientific knowledge by Karl R. Popper. It is not surprising -- as our results show -- that Kuhn's "paradigm" concept has had a significant impact. What is surprising is that it has had such a disproportionately larger impact than Kuhn's other concepts, e.g., "scientific revolution". The paradigm concept accounts for over 80% of the concept-related citations to Kuhn's work, and its impact is resilient across all disciplines and over time. With respect to Popper, "falsification" is the most used concept derived from his books. Falsification, after all, is the cornerstone of Popper's critical rationalism.

It is several years since national research evaluation systems around the globe started making use of quantitative indicators to measure the performance of researchers. Nevertheless, the effects on these systems on the behavior of the evaluated researchers are still largely unknown. We attempt to shed light on this topic by investigating how Italian researchers reacted to the introduction in 2011 of national regulations in which key passages of professional careers are governed by bibliometric indicators. A new inwardness measure, able to gauge the degree of scientific self-referentiality of a country, is defined as the proportion of citations coming from the country itself compared to the total number of citations gathered by the country. Compared to the trends of the other G10 countries in the period 2000-2016, Italy's inwardness shows a net increase after the introduction of the new evaluation rules. Indeed, globally and also for a large majority of the research fields, Italy became the European country with the highest inwardness. Possible explanations are proposed and discussed, concluding that the observed trends are strongly suggestive of a generalized strategic use of citations, both in the form of author self-citations and of citation clubs. We argue that the Italian case offers crucial insights on the constitutive effects of evaluation systems. As such, it could become a paradigmatic case in the debate about the use of indicators in science-policy contexts.

Many factors could affect the number of citations to a paper. Citations have an important role in research policy and in measuring the excellence of research and researchers. This work is the first study in software engineering (SE) to assess multiple factors affecting the number of citations to SE papers. We use (a) negative binomial regression and (b) quantile regression to study arithmetic mean and median expected citations of a paper. Our dataset includes all the 25,113 papers which have been published in a set of 16 main SE journals, between 1970 and 2018. Our results indicate that publication venue, author team's past citations, paper length, the number of references, and the recency of references are the most influential factors on the number of citations to SE papers. From our empirical findings, we present several implications and advice to researchers for getting higher citations on their papers, which are in addition to the obvious case of conducting high-quality technical research, e.g. (1) Aim for high-profile venues, (2) Build a high-quality author team with highly cited past papers, and (3) Aim for high-quality work that has comprehensive content (thus longer paper length and reference list).

Peer review and citation metrics are two means of gauging the value of scientific research, but the lack of publicly available peer review data makes the comparison of these methods difficult. Mathematics can serve as a useful laboratory for considering these questions because as an exact science, there is a narrow range of reasons for citations. In mathematics, virtually all published articles are post-publication reviewed by mathematicians in Mathematical Reviews (MathSciNet) and so the data set was essentially the Web of Science mathematics publications from 1993 to 2004. For a decade, especially important articles were singled out in Mathematical Reviews for featured reviews. In this study, we analyze the bibliometrics of elite articles selected by peer review and by citation count. We conclude that the two notions of significance described by being a featured review article and being highly cited are distinct. This indicates that peer review and citation counts give largely independent determinations of highly distinguished articles. We also consider whether hiring patterns of subfields and mathematicians' interest in subfields reflect subfields of featured review or highly cited articles. We reexamine data from two earlier studies in light of our methods for implications on the peer review/citation count relationship to a diversity of disciplines.