Computer Science

The main objective of this work is to analyse the contributions of Judit Bar-Ilan to the search engines studies. To do this, two complementary approaches have been carried out. First, a systematic literature review of 47 publications authored and co-authored by Judit and devoted to this topic. Second, an interdisciplinarity analysis based on the cited references (publications cited by Judit) and citing documents (publications that cite Judit's work) through Scopus. The systematic literature review unravels an immense amount of search engines studied (43) and indicators measured (especially technical precision, overlap and fluctuation over time). In addition to this, an evolution over the years is detected from descriptive statistical studies towards empirical user studies, with a mixture of quantitative and qualitative methods. Otherwise, the interdisciplinary analysis evidences that a significant portion of Judit's oeuvre was intellectually founded on the computer sciences, achieving a significant, but not exclusively, impact on library and information sciences.

In this paper, we analyse the current availability of open citations data in one particular dataset, namely COCI (the OpenCitations Index of Crossref open DOI-to-DOI citations; this http URL) provided by OpenCitations. The results of these analyses show a persistent gap in the coverage of the currently available open citation data. In order to address this specific issue, we propose a strategy whereby the community (e.g. scholars and publishers) can directly involve themselves in crowdsourcing open citations, by uploading their citation data via the OpenCitations infrastructure into our new index, CROCI, the Crowdsourced Open Citations Index.

Scientific collaboration is often not perfectly reciprocal. Scientifically strong countries/institutions/laboratories may help their less prominent partners with leading scholars, or finance, or other resources. What is interesting in such type of collaboration is that (1) it may be measured by bibliometrics and (2) it may shed more light on the scholarly level of both collaborating organizations themselves. In this sense measuring institutions in collaboration sometimes may tell more than attempts to assess them as stand-alone organizations. Evaluation of collaborative patterns was explained in detail, for example, by Glanzel (2001; 2003). Here we combine these methods with a new one, made available by separating 'the best' journals from 'others' on the same platform of Russian Index of Science Citation (RISC). Such sub-universes of journals from 'different leagues' provide additional methods to study how collaboration influences the quality of papers published by organizations.

A new clinical literature search engine, called CupQ, is presented. It aims to help clinicians stay updated with medical knowledge. Although PubMed is currently one of the most widely used digital libraries for biomedical information, it frequently does not return clinically relevant results. CupQ utilizes a ranking algorithm that filters non-medical journals, compares semantic similarity between queries, and incorporates journal impact factor and publication date. It organizes search results into useful categories for medical practitioners: reviews, guidelines, and studies. Qualitative comparisons suggest that CupQ may return more clinically relevant information than PubMed. CupQ is available at this https URL.

The increase in the number of publications has made more difficult for authors to situate their work within previous literature, especially on subjects studied from different disciplinary viewpoints. Besides, new data analysis techniques and new bibliometrics data sources provide an opportunity to map and navigate scientific landscapes. We introduce here an open-source and open-access web application designed for the multi-dimensional exploration of a journal content, including the mapping of geographical, semantic and citations networks. The application is profiled and implemented for the geography journal Cybergeo, a generalist geography journal which receives contributions from multiple sub-disciplines. We suggest that such initiatives are crucial to promote open science and reflexivity.

Many research questions can be answered quickly and efficiently using data already collected for previous research. This practice is called secondary data analysis (SDA), and has gained popularity due to lower costs and improved research efficiency. In this paper we propose DFS, a file system to standardize the metadata representation of datasets, and DDU, a scalable architecture based on DFS for semi-automated metadata generation and data recommendation on the cloud. We discuss how DFS and DDU lays groundwork for automatic dataset aggregation, how it integrates with existing data wrangling and machine learning tools, and explores their implications on datasets stored in digital libraries.

This paper presents DIALOG (Digital Investigation Ontology); a framework for the management, reuse, and analysis of Digital Investigation knowledge. DIALOG provides a general, application independent vocabulary that can be used to describe an investigation at different levels of detail. DIALOG is defined to encapsulate all concepts of the digital forensics field and the relationships between them. In particular, we concentrate on the Windows Registry, where registry keys are modeled in terms of both their structure and function. Registry analysis software tools are modeled in a similar manner and we illustrate how the interpretation of their results can be done using the reasoning capabilities of ontology

We present DINGO (Data INtegration for Grants Ontology), an ontology that provides a machine readable extensible framework to model data for semantically-enabled applications relative to projects, funding, actors, and, notably, funding policies in the research landscape. DINGO is designed to yield high modeling power and elasticity to cope with the huge variety in funding, research and policy practices, which makes it applicable also to other areas besides research where funding is an important aspect. We discuss its main features, the principles followed for its development, its community uptake, its maintenance and evolution.

The scientific community is facing one of its greatest challenges in solving a global health problem: COVID-19 pandemic. This situation has generated an unprecedented volume of publications. What is the volume, in terms of publications, of research on COVID-19? The general objective of this research work is to obtain a global vision of the daily growth of scientific production on COVID-19 in different databases (Dimensions, Web of Science Core Collection, Scopus-Elsevier, Pubmed and eight repositories). In relation to the results obtained, Dimensions indexes a total of 9435 publications (69% with peer review and 2677 preprints) well above Scopus (1568) and WoS (718). This is a classic biliometric phenomenon of exponential growth (R2 = 0.92). The global growth rate is 500 publications and the production doubles every 15 days. In the case of Pubmed the weekly growth is around 1000 publications. Of the eight repositories analysed, Pubmed Central, Medrxiv and SSRN are the leaders. Despite their enormous contribution, the journals continue to be the core of scientific communication. Finally, it has been established that three out of every four publications on the COVID-19 are available in open access. The information explosion demands a serious and coordinated response from information professionals, which places us at the centre of the information pandemic.

The data paper, an emerging scholarly genre, describes research datasets and is intended to bridge the gap between the publication of research data and scientific articles. Research examining how data papers report data events, such as data transactions and manipulations, is limited. The research reported on in this paper addresses this limitation and investigated how data events are inscribed in data papers. A content analysis was conducted examining the full texts of 82 data papers, drawn from the curated list of data papers connected to the Global Biodiversity Information Facility (GBIF). Data events recorded for each paper were organized into a set of 17 categories. Many of these categories are described together in the same sentence, which indicates the messiness of data events in the laboratory space. The findings challenge the degrees to which data papers are a distinct genre compared to research papers and they describe data-centric research processes in a through way. This paper also discusses how our results could inform a better data publication ecosystem in the future.