Computer Science

Scientific publishing is the means by which we communicate and share scientific knowledge, but this process currently often lacks transparency and machine-interpretable representations. Scientific articles are published in long coarse-grained text with complicated structures, and they are optimized for human readers and not for automated means of organization and access. Peer reviewing is the main method of quality assessment, but these peer reviews are nowadays rarely published and their own complicated structure and linking to the respective articles is not accessible. In order to address these problems and to better align scientific publishing with the principles of the Web and Linked Data, we propose here an approach to use nanopublications as a unifying model to represent in a semantic way the elements of publications, their assessments, as well as the involved processes, actors, and provenance in general. To evaluate our approach, we present a dataset of 627 nanopublications representing an interlinked network of the elements of articles (such as individual paragraphs) and their reviews (such as individual review comments). Focusing on the specific scenario of editors performing a meta-review, we introduce seven competency questions and show how they can be executed as SPARQL queries. We then present a prototype of a user interface for that scenario that shows different views on the set of review comments provided for a given manuscript, and we show in a user study that editors find the interface useful to answer their competency questions. In summary, we demonstrate that a unified and semantic publication model based on nanopublications can make scientific communication more effective and user-friendly.

Blockchain technology, and more specifically Bitcoin (one of its foremost applications), have been receiving increasing attention in the scientific community. The first publications with Bitcoin as a topic, can be traced back to 2012. In spite of this short time span, the production magnitude (1162 papers) makes it necessary to make a bibliometric study in order to observe research clusters, emerging topics, and leading scholars. Our paper is aimed at studying the scientific production only around bitcoin, excluding other blockchain applications. Thus, we restricted our search to papers indexed in the Web of Science Core Collection, whose topic is "bitcoin". This database is suitable for such diverse disciplines such as economics, engineering, mathematics, and computer science. This bibliometric study draws the landscape of the current state and trends of Bitcoin-related research in different scientific disciplines.

In this paper we develop a methodology to assess the scientific wealth of territories at field level. Our methodology uses a bibliometric approach based on the observation of academic research performance and overall scientific production in each territory. We apply it to assess disparities in the Italian territories in the medical specialties at the front line of the COVID-19 emergency. Italy has been the first among western countries to be severely affected by the onset of the COVID-19 pandemic. The study reveals remarkable inequities across territories, with scientific weaknesses concentrated in the south. Policies for rebalancing the north-south divide should also consider, in addition to tangible assets, the gap in production and availability of quality medical knowledge.

The EXFOR library is a useful resource for many people in the field of nuclear physics. In particular, the experimental data in the EXFOR library serves as a starting point for nuclear data evaluations. There is an ongoing discussion about how to make evaluations more transparent and reproducible. One important ingredient may be convenient programmatic access to the data in the EXFOR library from high-level languages. To this end, the complete EXFOR library can be converted to a MongoDB database. This database can be conveniently searched and accessed from a wide variety of programming languages, such as C++, Python, Java, Matlab, and R. This contribution provides some details about the successful conversion of the EXFOR library to a MongoDB database and shows simple usage examples to underline its merits. All codes required for the conversion have been made available online and are open-source. In addition, a Dockerfile has been created to facilitate the installation process.

The ATLAS collaboration defines methods, establishes procedures, and organises advisory groups to manage the publication processes of scientific papers, conference papers, and public notes. All stages are managed through web systems, computing programs, and tools that are designed and developed by the collaboration. A framework called FENCE is integrated into the CERN GitLab software repository, to automatically configure workspaces where each analysis can be documented by the analysis team and managed by the relevant coordinators. Continuous integration is used to guide the writers in applying consistent and correct formatting when preparing papers to be submitted to scientific journals. Additional software assures the correctness of other aspects of each paper, such as the lists of collaboration authors, funding agencies, and foundations. The framework and the workflow therein provide automatic and easy support to the researchers and facilitates each phase of the publication process, allowing authors to focus on the article contents. The framework and its integration with the most up to date and efficient tools has consequently provided a more professional and efficient automatized work environment to the whole collaboration.

Author name disambiguation results are often evaluated by measures such as Cluster-F, K-metric, Pairwise-F, Splitting & Lumping Error, and B-cubed. Although these measures have distinctive evaluation schemes, this paper shows that they can be calculated in a single framework by a set of common steps that compare truth and predicted clusters through two hash tables recording information about name instances with their predicted cluster indices and frequencies of those indices per truth cluster. This integrative calculation reduces greatly calculation runtime, which is scalable to a clustering task involving millions of name instances within a few seconds. During the integration process, B-cubed and K-metric are shown to produce the same precision and recall scores. In this framework, especially, name instance pairs for Pairwise-F are counted using a heuristic, surpassing a state-of-the-art algorithm in speedy calculation. Details of the integrative calculation are described with examples and pseudo-code to assist scholars to implement each measure easily and validate the correctness of implementation. The integrative calculation will help scholars compare similarities and differences of multiple measures before they select ones that characterize best the clustering performances of their disambiguation methods.

This work analyzes the differences in collaboration behavior between males and females among a particular type of scholars: top scientists, and as compared to non top scientists. The field of observation consists of the Italian academic system and the co-authorships of scientific publications by 11,145 professors. The results obtained from a cross-sectional analysis covering the five-year period 2006-2010 show that there are no significant differences in the overall propensity to collaborate in the top scientists of the two genders. At the level of single disciplines there are no differences in collaboration behavior, except in the case of: i) international collaborations, for Mathematics and Chemistry - where the propensity for collaboration is greater for males; and ii) extramural domestic collaborations in Physics, in which it is the females that show greater propensity for collaboration. Because international collaboration is positively correlated to research performance, findings can inform science policy aimed at increasing the representation of female top performers.

Current attempts to address the shortfall of female researchers in Science, Technology, Engineering and Mathematics (STEM) have not yet succeeded despite other academic subjects having female majorities. This article investigates the extent to which gender disparities are subject-wide or nation-specific by a first author gender comparison of 30 million articles from all 27 Scopus broad fields within the 31 countries with the most Scopus-indexed articles 2014-18. The results show overall and geocultural patterns as well as individual national differences. Almost half of the subjects were always more male (7; e.g., Mathematics) or always more female (6; e.g., Immunology & Microbiology) than the national average. A strong overall trend (Spearman correlation 0.546) is for countries with a higher proportion of female first-authored research to also have larger differences in gender disparities between fields (correlation 0.314 for gender ratios). This confirms the international gender equality paradox previously found for degree subject choices: increased gender equality overall associates with moderately greater gender differentiation between subjects. This is consistent with previous USA-based claims that gender differences in academic careers are partly due to (socially constrained) gender differences in personal preferences. Radical solutions may therefore be needed for some STEM subjects to overcome gender disparities.

The increased social media attention to scholarly articles has resulted in efforts to create platforms & services to track and measure the social media transactions around scholarly articles in different social platforms (such as Twitter, Blog, Facebook) and academic social networks (such as Mendeley, Academia and ResearchGate). this http URL and PlumX are two popular aggregators that track social media activity around scholarly articles from a variety of social platforms and provide the coverage and transaction data to researchers for various purposes. However, some previous studies have shown that the social media data captured by the two aggregators have differences in terms of coverage and magnitude of mentions. This paper aims to revisit the question by doing a large-scale analysis of social media mentions of a data sample of 1,785,149 publication records (drawn from multiple disciplines, demographies, publishers). Results obtained show that PlumX tracks more wide sources and more articles as compared to this http URL. However, the coverage and average mentions of the two aggregators vary across different social media platforms, with this http URL recording higher mentions in Twitter and Blog, and PlumX recording higher mentions in Facebook and Mendeley, for the same set of articles. The coverage and average mentions captured by the two aggregators across different document types, disciplines and publishers is also analyzed.

Web of Science Core Collection, one of the most authoritative bibliographic databases, is widely used in academia to track high-quality research. This database has begun to index online-first articles since December 2017. This new practice has introduced two different publication dates (online and final publication dates) into the database for more and more early access publications. It may confuse many users who want to search or analyze literature by using the publication-year related tools provided by Web of Science Core Collection. By developing custom retrieval strategies and checking manually, this study finds that the "year published" field in search page searches in both online and final publication date fields of indexed records. Each indexed record is allocated to only one "publication year" on the left of the search results page which will inherit first from online publication date field even when the online publication date is later than the final publication date. The "publication year" field in the results analysis page and the timespan "custom year range" field in the search page have the same function as that of the filter "publication year" in search results page. The potential impact of the availability of two different publication dates in calculating bibliometric indicators is also discussed at the end of the article.