Global visibility of publications through Digital Object Identifiers.

This brief research report analyzes the availability of Digital Object Identifiers (DOIs) worldwide, highlighting the dominance of large publishing houses and the need for unique persistent identifiers to increase the visibility of publications from developing countries. The study reveals that a considerable amount of publications from developing countries are excluded from the global flow of scientific information due to the absence of DOIs, emphasizing the need for alternative publishing models. The authors suggest that the availability of DOIs should receive more attention in scholarly communication and scientometrics, contributing to a necessary debate on DOIs relevant for librarians, publishers, and scientometricians.

Letter to the Editor: FHIR RDF - Why the world needs structured electronic health records.

This comment discusses the benefits of representing and reusing the information in Electronic Health Record databases as knowledge graphs in the RDF format based on the FHIR RDF specification. As a structured representation of clinical data, FHIR RDF-based electronic health records allow a simpler and more effective integration of biomedical information using semantic alignment, queries, interoperability, and federation to provide better support for health practice and research.

Awakening sleeping beauties during the COVID-19 pandemic influences the citation impact of their references.

In this research letter, we build upon recent studies about the sleeping beauties awakened by the COVID-19 pandemic. We prove that a peak of citations for sleeping beauties is associated with a sharp increase in the number of citations received by their references. This demonstrates the existence of a cascading activation of citation-based sleeping beauties.

Multilingual topic modeling for tracking COVID-19 trends based on Facebook data analysis.

Social data has shown important role in tracking, monitoring and risk management of disasters. Indeed, several works focused on the benefits of social data analysis for the healthcare practices and curing domain. Similarly, these data are exploited now for tracking the COVID-19 pandemic but the majority of works exploited Twitter as source. In this paper, we choose to exploit Facebook, rarely used, for tracking the evolution of COVID-19 related trends. In fact, a multilingual dataset covering 7 languages (English (EN), Arabic (AR), Spanish (ES), Italian (IT), German (DE), French (FR) and Japanese (JP)) is extracted from Facebook public posts. The proposal is an analytics process including a data gathering step, pre-processing, LDA-based topic modeling and presentation module using graph structure. Data analysing covers the duration spanned from January 1st, 2020 to May 15, 2020 divided on three periods in cumulative way: first period January-February, second period March-April and the last one to 15 May. The results showed that the extracted topics correspond to the chronological development of what has been circulated around the pandemic and the measures that have been taken according to the various languages under discussion representing several countries.

Enhancing filter-based parenthetic abbreviation extraction methods.

This letter discusses the limitations of the use of filters to enhance the accuracy of the extraction of parenthetic abbreviations from scholarly publications and proposes the usage of the parentheses level count algorithm to efficiently extract entities between parentheses from raw texts as well as of machine learning-based supervised classification techniques for the identification of biomedical abbreviations to significantly reduce the removal of acronyms including disallowed punctuations.

Reproducibility dataset for a large experimental survey on word embeddings and ontology-based methods for word similarity.

This data article introduces a reproducibility dataset with the aim of allowing the exact replication of all experiments, results and data tables introduced in our companion paper (Lastra-Díaz et al., 2019), which introduces the largest experimental survey on ontology-based semantic similarity methods and Word Embeddings (WE) for word similarity reported in the literature. The implementation of all our experiments, as well as the gathering of all raw data derived from them, was based on the software implementation and evaluation of all methods in HESML library (Lastra-Díaz et al., 2017), and their subsequent recording with Reprozip (Chirigati et al., 2016). Raw data is made up by a collection of data files gathering the raw word-similarity values returned by each method for each word pair evaluated in any benchmark. Raw data files were processed by running a R-language script with the aim of computing all evaluation metrics reported in (Lastra-Díaz et al., 2019), such as Pearson and Spearman correlation, harmonic score and statistical significance p-values, as well as to generate automatically all data tables shown in our companion paper. Our dataset provides all input data files, resources and complementary software tools to reproduce from scratch all our experimental data, statistical analysis and reported data. Finally, our reproducibility dataset provides a self-contained experimentation platform which allows to run new word similarity benchmarks by setting up new experiments including other unconsidered methods or word similarity benchmarks.

Wikidata: A large-scale collaborative ontological medical database.

Created in October 2012, Wikidata is a large-scale, human-readable, machine-readable, multilingual, multidisciplinary, centralized, editable, structured, and linked knowledge-base with an increasing diversity of use cases. Here, we raise awareness of the potential use of Wikidata as a useful resource for biomedical data integration and semantic interoperability between biomedical computer systems. We show the data model and characteristics of Wikidata and explain how this database can be automatically processed by users as well as by computer methods and programs. Then, we give an overview of the medical entities and relations provided by the database and how they can be useful for various medical purposes such as clinical decision support.

Computing semantic similarity between biomedical concepts using new information content approach.

The exploitation of heterogeneous clinical sources and healthcare records is fundamental in clinical and translational research. The determination of semantic similarity between word pairs is an important component of text understanding that enables the processing and structuring of textual resources. Some of these measures have been adapted to the biomedical field by incorporating domain information extracted from clinical data or from medical ontologies such as MeSH. This study focuses on Information Content (IC) based measures that exploit the topological parameters of the taxonomy to express the semantics of a concept. A new intrinsic IC computing method based on the taxonomical parameters of the ancestors' subgraph is then assigned to a biomedical concept into the "is a" hierarchy. Moreover, we present a study of the topological parameters through the MeSH taxonomy. This study treats the semantic interpretation and the different ways of expressing the parameters of depth and the descendants' subgraph. Using MeSH as an input ontology, the accuracy of our proposal is evaluated and compared against other IC-based measures according to several widely-used benchmarks of biomedical terms. The correlation between the results obtained for the evaluated measure using the proposed approach and those from the ratings of human' experts shows that our proposal outperforms the previous measures.