Computer Science

The particular day of the week when an event occurs seems to have unexpected consequences. For example, the day of the week when a paper is submitted to a peer reviewed journal correlates with whether that paper is accepted. Using an econometric analysis (a mix of log-log and semi-log based on undated and panel structured data) we find that more papers are submitted to certain peer review journals on particular weekdays than others, with fewer papers being submitted on weekends. Seasonal effects, geographical information as well as potential changes over time are examined. This finding rests on a large (178 000) and reliable sample; the journals polled are broadly recognized (Nature, Cell, PLOS ONE and Physica A). Day of the week effect in the submission of accepted papers should be of interest to many researchers, editors and publishers, and perhaps also to managers and psychologists.

Deep learning belongs to the field of artificial intelligence, where machines perform tasks that typically require some kind of human intelligence. Deep learning tries to achieve this by mimicking the learning of a human brain. Similar to the basic structure of a brain, which consists of (billions of) neurons and connections between them, a deep learning algorithm consists of an artificial neural network, which resembles the biological brain structure. Mimicking the learning process of humans with their senses, deep learning networks are fed with (sensory) data, like texts, images, videos or sounds. These networks outperform the state-of-the-art methods in different tasks and, because of this, the whole field saw an exponential growth during the last years. This growth resulted in way over 10 000 publications per year in the last years. For example, the search engine PubMed alone, which covers only a sub-set of all publications in the medical field, provides over 11 000 results for the search term ′ deep learning ′ in Q3 2020, and ~90% of these results are from the last three years. Consequently, a complete overview over the field of deep learning is already impossible to obtain and, in the near future, it will potentially become difficult to obtain an overview over a subfield. However, there are several review articles about deep learning, which are focused on specific scientific fields or applications, for example deep learning advances in computer vision or in specific tasks like object detection. With these surveys as a foundation, the aim of this contribution is to provide a first high-level, categorized meta-analysis of selected reviews on deep learning across different scientific disciplines and outline the research impact that they already have during a short period of time.

A Sleeping Beauty is a publication that is apparently unrecognized for some period of time before experiencing sudden recognition by citation. Various reasons, including resistance to new ideas, have been attributed to such delayed recognition. We examine this phenomenon in the special case of co-citations, which represent new ideas generated through the combination of existing ones. Using relatively stringent selection criteria derived from the work of others, we analyze a very large dataset of over 940 million unique co-cited article pairs, and identified 1,196 cases of delayed co-citations. We further classify these 1,196 cases with respect to amplitude, rate of citation, and disciplinary origin and discuss alternative approaches towards identifying such instances.

Figures are an important channel for scientific communication, used to express complex ideas, models and data in ways that words cannot. However, this visual information is mostly ignored in analyses of the scientific literature. In this paper, we demonstrate the utility of using scientific figures as markers of knowledge domains in science, which can be used for classification, recommender systems, and studies of scientific information exchange. We encode sets of images into a visual signature, then use distances between these signatures to understand how patterns of visual communication compare with patterns of jargon and citation structures. We find that figures can be as effective for differentiating communities of practice as text or citation patterns. We then consider where these metrics disagree to understand how different disciplines use visualization to express ideas. Finally, we further consider how specific figure types propagate through the literature, suggesting a new mechanism for understanding the flow of ideas apart from conventional channels of text and citations. Our ultimate aim is to better leverage these information-dense objects to improve scientific communication across disciplinary boundaries.

Scholars have long hypothesized that democratic forms of government are more compatible with scientific advancement. However, empirical analysis testing the democracy-science compatibility hypothesis remains underdeveloped. This article explores the effect of democratic governance on scientific performance using panel data on 124 countries between 2007 and 2018. We find evidence supporting the democracy-science hypothesis, with strongest effects of egalitarian democracy on national scientific performance. Further, using both internal and external measures of complexity, we estimate the effects of complexity as a moderating factor between the democracy-science connection. The results show differential main effects of economic complexity, globalization, and international collaboration on scientific performance, as well as significant interaction effects that moderate the effect of democracy on scientific performance. The findings show the significance of democratic governance and complex systems in national scientific performance.

This study describes a method to detect hijacked journals based on the analysis of the archives of clone journals. This approach is most effective in discovering a network of hijacked journals that have the same organizer(s). Analysis of the archives of clone journals allowed to detect 62 URLs of hijacked journals. It also provided the possibility to predict two clone websites before they became operational. This study shows that most detected hijacked journals represent a network of clone journals organized by one or several fraudulent individuals. The information and content of nine legitimate journals were compromised in international and national scientometric databases.

Many libraries offer free access to digitised historical newspapers via user interfaces. After an initial period of search and filter options as the only features, the availability of more advanced tools and the desire for more options among users has ushered in a period of interface development. However, this raises a number of open questions and challenges. For example, how can we provide interfaces for different user groups? What tools should be available on interfaces and how can we avoid too much complexity? What tools are helpful and how can we improve usability? This paper will not provide definite answers to these questions, but it gives an insight into the difficulties, challenges and risks of using interfaces to investigate historical newspapers. More importantly, it provides ideas and recommendations for the improvement of user interfaces and digital tools.

Objective: This paper gives context on recent literature regarding the development of digital personal health libraries (PHL) and provides insights into the potential application of consumer health informatics in diverse clinical specialties. Materials and Methods: A systematic literature review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Here, 2,850 records were retrieved from PubMed and EMBASE in March 2020 using search terms: personal, health, and library. Information related to the health topic, target population, study purpose, library function, data source, data science method, evaluation measure, and status were extracted from each eligible study. In addition, knowledge discovery methods, including co-occurrence analysis and multiple correspondence analysis, were used to explore research trends of PHL. Results: After screening, this systematic review focused on a dozen articles related to PHL. These encompassed health topics such as infectious diseases, congestive heart failure, electronic prescribing. Data science methods included relational database, information retrieval technology, ontology construction technology. Evaluation measures were heterogeneous regarding PHL functions and settings. At the time of writing, only one of the PHLs described in these articles is available for the public while the others are either prototypes or in the pilot stage. Discussion: Although PHL researches have used different methods to address problems in diverse health domains, there is a lack of an effective PHL to meet the needs of older adults. Conclusion: The development of PHLs may create an unprecedented opportunity for promoting the health of older consumers by providing diverse health information.

Mathematical notation, i.e., the writing system used to communicate concepts in mathematics, encodes valuable information for a variety of information search and retrieval systems. Yet, mathematical notations remain mostly unutilized by today's systems. In this paper, we present the first in-depth study on the distributions of mathematical notation in two large scientific corpora: the open access arXiv (2.5B mathematical objects) and the mathematical reviewing service for pure and applied mathematics zbMATH (61M mathematical objects). Our study lays a foundation for future research projects on mathematical information retrieval for large scientific corpora. Further, we demonstrate the relevance of our results to a variety of use-cases. For example, to assist semantic extraction systems, to improve scientific search engines, and to facilitate specialized math recommendation systems. The contributions of our presented research are as follows: (1) we present the first distributional analysis of mathematical formulae on arXiv and zbMATH; (2) we retrieve relevant mathematical objects for given textual search queries (e.g., linking P (α,β) n (x) with `Jacobi polynomial'); (3) we extend zbMATH's search engine by providing relevant mathematical formulae; and (4) we exemplify the applicability of the results by presenting auto-completion for math inputs as the first contribution to math recommendation systems. To expedite future research projects, we have made available our source code and data.

Bibliometric information retrieval in databases can employ different strategies. Com-monly, queries are performed by searching in title, abstract and/or author keywords (author vocabulary). More advanced queries employ database keywords to search in a controlled vo-cabulary. Queries based on search terms can be augmented with their citing papers if a re-search field cannot be curtailed by the search query alone. Here, we present another strategy to discover the most important papers of a research field. A marker paper is used to reveal the most important works for the relevant community. All papers co-cited with the marker paper are analyzed using reference publication year spectroscopy (RPYS). For demonstration of the marker paper approach, density functional theory (DFT) is used as a research field. Compari-sons between a prior RPYS on a publication set compiled using a keyword-based search in a controlled vocabulary and three different co-citation RPYS (RPYS-CO) analyses show very similar results. Similarities and differences are discussed.