Comparing different search methods for the open access journal recommendation tool B!SON

Finding a suitable open access journal to publish academic work is a complex task: Researchers have to navigate a constantly growing number of journals, institutional agreements with publishers, funders’ conditions and the risk of predatory publishers. To help with these challenges, we introduce a web-based journal recommendation system called B!SON. A systematic requirements analysis was conducted in the form of a survey. The developed tool suggests open access journals based on title, abstract and references provided by the user. The recommendations are built on open data, publisher-independent and work across domains and languages. Transparency is provided by its open source nature, an open application programming interface (API) and by specifying which matches the shown recommendations are based on. The recommendation quality has been evaluated using two different evaluation techniques, including several new recommendation methods. We were able to improve the results from our previous paper with a pre-trained transformer model. The beta version of the tool received positive feedback from the community and in several test sessions. We developed a recommendation system for open access journals to help researchers find a suitable journal. The open tool has been extensively tested, and we found possible improvements for our current recommendation technique. Development by two German academic libraries ensures the longevity and sustainability of the system.German Federal Ministry of Education and Research (BMBF)/Projekt DEAL/16TOA034A/E

Identifying collaborations among researchers: a pattern-based approach

In recent years a huge amount of publications and scientific reports has become available through digital libraries and online databases. Digital libraries commonly provide advanced search interfaces, through which researchers can find and explore the most related scientific studies. Even though the publications of a single author can be easily retrieved and explored, understanding how authors have collaborated with each other on specific research topics and to what extent their collaboration have been fruitful is, in general, a challenging task. This paper proposes a new pattern-based approach to analyzing the correlations among the authors of most influential research studies. To this purpose, it analyzes publication data retrieved from digital libraries and online databases by means of an itemset-based data mining algorithm. It automatically extracts patterns representing the most relevant collaborations among authors on specific research topics. Patterns are evaluated and ranked according to the number of citations received by the corresponding publications. The proposed approach was validated in a real case study, i.e., the analysis of scientific literature on genomics. Specifically, we first analyzed scientific studies on genomics acquired from the OMIM database to discover correlations between authors and genes or genetic disorders. Then, the reliability of the discovered patterns was assessed using the PubMed search engine. The results show that, for the majority of the mined patterns, the most influential (top ranked) studies retrieved by performing author-driven PubMed queries range over the same gene/genetic disorder indicated by the top ranked pattern

unarXive: a large scholarly data set with publications’ full-text, annotated in-text citations, and links to metadata

In recent years, scholarly data sets have been used for various purposes, such as paper recommendation, citation recommendation, citation context analysis, and citation context-based document summarization. The evaluation of approaches to such tasks and their applicability in real-world scenarios heavily depend on the used data set. However, existing scholarly data sets are limited in several regards. Here, we propose a new data set based on all publications from all scientific disciplines available on arXiv.org. Apart from providing the papers' plain text, in-text citations were annotated via global identifiers. Furthermore, citing and cited publications were linked to the Microsoft Academic Graph, providing access to rich metadata. Our data set consists of over one million documents and 29.2 million citation contexts. The data set, which is made freely available for research purposes, not only can enhance the future evaluation of research paper-based and citation context-based approaches but also serve as a basis for new ways to analyze in-text citations. See https://github.com/IllDepence/unarXive for the source code which has been used for creating the data set. For citing our data set and for further information we can refer to our journal article Tarek Saier, Michael Färber: "unarXive: A Large Scholarly Data Set with Publications’ Full-Text, Annotated In-Text Citations, and Links to Metadata", Scientometrics, 2020, http://dx.doi.org/10.1007/s11192-020-03382-z