This study explores how Open Science practices shaped the early international response to the COVID-19 pandemic, particularly the rapid development of vaccines in 2020. Using a mixed-methods approach, it integrates bibliometric analysis from Web of Science, PubMed, and Scopus with a qualitative timeline of World Health Organization (WHO) communications to contextualise key scientific milestones. Findings indicate a strong correlation between open-access publishing – over 80% of pandemic-related publications were freely accessible – and accelerated scientific progress. The study highlights the critical role of digital infrastructures in enabling rapid knowledge dissemination and identifies the reemergence of economic barriers during vaccine commercialisation. From a Library and Information Science (LIS) perspective, this article underscores the importance of information professionals and equitable access policies in crisis response. It concludes by advocating for resilient and sustainable Open Science frameworks that extend beyond emergency contexts to support global scientific equity and innovation.

Immigrant entrepreneurs are increasingly reliant on digital information sources for crucial business choices. This study investigates how digital information sources shape the information-seeking behaviour of Finnish and Vietnamese immigrants situated in Finland, and examines the influence of digital information sources on business operations and expansion. Our results indicate that digital information sources give entrepreneurs timely access to market tendencies and regulatory updates, bolstering business progress. This research presents practical approaches to surmount obstacles such as information overload and source reliability, suggesting continued investigation to explore digital information source effects across diverse cultural and industrial settings, refining digital instruments for varied entrepreneurial environments. We extend theoretical knowledge on information seeking to the domain of entrepreneurship by demonstrating how entrepreneurs choose and employ digital information sources based on informational requirements, alternating between static and interactive resources.

In the scientific community and among various evaluators, Web of Science is regarded as a leading and reliable data source. Given the distinctive features of legal publishing, the metadata quality of law journals in Web of Science may be seriously affected which hinders the reliability of literature retrieval, research evaluation and ranking. Focusing on this under-explored topic, this study identifies a significant proportion of metadata issues in the two ‘Trump Cards’ meet scenario. Specifically, the established Web of Science meets the leading journal Harvard Law Review, which contrasts with the low rates of metadata defects reported in previous studies. Four representative scenarios are identified: mis-indexing of non-Harvard Law Review records, omission of a large proportion of published records, inconsistent assignment of document types for similar content and inaccurate identification of authors’ names. This study not only highlights the serious metadata issues of a leading law review within the Web of Science for the scientific community, evaluators and database providers, but also probes the possible causes and potential impacts. Several recommendations are proposed to enhance the metadata quality of legal journals in Web of Science.

The role of geographical proximity in facilitating inter-regional or inter-organisational collaborations has been studied thoroughly in recent years. However, the effect of geographical proximity on forming scientific collaborations at the individual level still needs to be addressed. Using publication data in the field of artificial intelligence from 2001 to 2019, in this work, the effect of geographical proximity on the likelihood of forming future scientific collaborations among researchers is studied. In addition, the interaction between geographical and network proximities is examined to see whether network proximity can substitute for geographical proximity in encouraging long-distance scientific collaborations. Employing conventional and machine learning techniques, our results suggest that geographical distance impedes scientific collaboration at the individual level despite the tremendous improvements in transportation and communication technologies during recent decades. Moreover, our findings show that the effect of network proximity on the likelihood of scientific collaboration increases with geographical distance, implying that network proximity can act as a substitute for geographical proximity.

This study aims to map the evolving relationship between Internet technology and innovation performance by conducting a comprehensive bibliometric analysis. The study collects and merges data from the Web of Science and Scopus databases, comprising 906 research articles published between 1994 and 2023. Internet technology, defined as digital communication tools and infrastructures (e.g. the Internet, mobile technologies and ICT), has profoundly transformed how organisations innovate. We analyse co-occurrence and co-citation networks to identify key trends, thematic clusters and influential research contributions via the Biblioshiny tool in RStudio. The findings reveal emerging themes such as artificial intelligence, digital transformation and sustainable development, which are reshaping innovation strategies across industries. This analysis displays enduring themes like ICT, innovation performance and China, signalling their sustained relevance. The study also highlights research gaps, particularly in the areas of digital integration in small-scale industries and the impact of Internet technologies on firm performance. Our findings provide a roadmap for future research, guiding scholars and practitioners towards the most pressing areas for further exploration in this rapidly evolving field.

Wikipedia is one of the most widely accessed sources of information worldwide, playing a crucial role in global knowledge dissemination across languages and cultures. Understanding how information complexity varies among different language editions is essential to identify gaps and promote equitable access to knowledge. This study investigates differences in the informational complexity of Wikipedia articles across three language editions: English, Spanish and Portuguese. By analysing key quantitative features such as word count, number of topics, figures and references in articles from five thematic categories, we provide empirical evidence of significant variation in content depth. Our results reveal that both language and thematic category significantly affect Wikipedia article complexity across variables. In addition, a significant interaction between language and category was observed for number of topics. These findings highlight the linguistic disparities in knowledge representation on Wikipedia and underscore the importance of promoting linguistic diversity and inclusivity in global knowledge dissemination.

The study aims to determine whether Chatman’s concept of the ‘small world’ applies to the information-seeking behaviour of the Nogai Turks, an ethnic community. This study employed a qualitative research methodology. The data were collected through semi-structured, scheduled interviews and analysed using thematic content analysis. The participants’ opinions were examined using four themes based on the main factors of Chatman’s small world concept (information behaviour, social norms, worldview, and behavioural context/social type) and 119 subcategories. These findings indicate that the information-seeking behaviour of Nogai Turks aligns with Chatman’s theory that living within the boundaries of the small world limits information behaviour. The results show that, consistent with Chatman’s assumption, the social norms, behavioural context, and worldview of Nogai Turks influence their information behaviour.

This study presents a large-scale scientometric analysis of 35,514 retracted publications indexed in Scopus between 2001 and 2024, focusing on temporal trends, citation dynamics and global disparities in retraction patterns. The primary goal was to investigate how retractions evolve over time and how retracted articles continue to influence academic discourse through ‘lost citations’ – citations to works no longer considered valid. Using generalised additive models, we identified two significant surges in retraction volume, in 2010–2011 and 2020–2022, and revealed that these were largely driven by bulk retractions in conference proceedings. Citation analysis showed that while early retracted papers were often highly cited, most retracted documents in the last decade have negligible scholarly impact. However, severe inequality persists: a small subset of retracted papers accumulates the majority of citations. At the author level, we found that citation influence is only weakly correlated with the number of retracted articles, indicating reputational distortions. Country-level trends revealed that China leads retraction counts, followed by India and the United States, while publisher-level data showed the dominance of conference-oriented outlets such as the IEEE Computer Society. Our findings raise important concerns about the persistence of invalidated knowledge in citation metrics and the structural vulnerabilities of certain publishing formats. This study calls for enhanced retraction labelling, improved transparency in databases and retraction-aware bibliometric evaluations to preserve academic integrity.

The aim of this article is to compile and expose the different methods of analysis of emotions and feelings from textual information. A Systematic Literature Review (SLR) was conducted to answer the following questions: What are the methods of analysis of emotions and feelings based on a text that predominates in academic literature? A narrative analysis approach was applied in order to comprehensively understand, locate and synthesise the studies related to the methods used in the analysis of feeling and emotion. The selection of the sample articles to be reviewed was made by means of inclusion and exclusion criteria. The results obtained show that there are several methodologies for sentiment and emotion analysis from digital textual information. Among these, Machine Learning (ML) emerges as particularly well-suited for analysing unstructured data, outperforming other approaches in its ability to manage large data sets and uncover complex patterns. Its application, both in supervised and unsupervised frameworks, has proven highly effective in integrating psychological constructs into textual analysis.

Open research data refer to the practice of sharing research data with others to enhance science innovations and discoveries. Despite the great potentials of open research data, it comes with certain limitations, especially with regards to the privacy of research participants’ data. In this article, we examine the tension between public data repository policy and the General Data Protection Regulation (GDPR). To achieve that, we draw on privacy as contextual integrity theory. We further enrich our research by interviewing 12 researchers from European institutions to examine their perception of whether open research data have privacy challenges or not. Our findings reveal that according to the heuristics steps of privacy as contextual integrity and the GDPR requirements, open research data may entail a violation to research participants’ informational privacy. Moreover, data repository’s policy is geared towards protecting the confidentiality of research participants’ data rather than their privacy. We further reveal that researchers conflate privacy and anonymity and lack knowledge of sharing research data practices.