Publications

23 pages, 1008 KB

Open AccessEditor’s ChoiceArticle

Experimenting with Grant Peer Review: A Mixed Methods Case Study of the Effects on Time Use and the Quality of Reviewing

by Peter van den Besselaar and Charlie Mom

Publications 2026, 14(2), 33; https://doi.org/10.3390/publications14020033 - 27 May 2026

Viewed by 1093

Abstract

Rejection of review invitations due to time constraints is putting pressure on the peer review system, showing that less time-consuming ways of reviewing are needed. This paper presents the results of a field experiment with a new format for grant peer review and [...] Read more.

Rejection of review invitations due to time constraints is putting pressure on the peer review system, showing that less time-consuming ways of reviewing are needed. This paper presents the results of a field experiment with a new format for grant peer review and answers the question of whether this new format is less time-consuming while still providing high-quality reviews. In the new approach, the Peer Circle (PC), a team of reviewers collectively evaluates several grant applications. The PC was applied to four fields and compared with four similar fields using conventional peer review. Qualitative and quantitative methods have been used to analyze heterogeneous data such as interviews with and a survey of the peer reviewers; text analysis of the review reports; and statistical analysis of bibliometric applicant data. The comparison suggests that the PC saves time and enlarges the reviewer population considerably. Most reviewers felt that the quality of the PC evaluations was at least as good as that of the conventional evaluations, if not better. Given these findings, the experiment is now continued on a much larger scale. Apart from that, the theoretical implication is that the way of organizing peer review has an important effect on the functioning of the system. Full article

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22 pages, 1449 KB

Open AccessFeature PaperEditor’s ChoiceArticle

On the Vulnerability of Citation Metrics in the Era of Generative Artificial Intelligence

by Kay Smarsly

Publications 2026, 14(2), 23; https://doi.org/10.3390/publications14020023 - 11 Apr 2026

Viewed by 2239

Abstract

Large language model (LLM) chatbots, as a widely used form of generative artificial intelligence, have reduced the marginal cost of producing publication-style manuscripts and have expanded feasible routes for manipulating citation metrics within the publishing ecosystem. Citation-based indicators (e.g., the h-index, the i10-index, [...] Read more.

Large language model (LLM) chatbots, as a widely used form of generative artificial intelligence, have reduced the marginal cost of producing publication-style manuscripts and have expanded feasible routes for manipulating citation metrics within the publishing ecosystem. Citation-based indicators (e.g., the h-index, the i10-index, and total citation counts) remain embedded in research evaluation and are sensitive to indexing practices of bibliographic databases, with Google Scholar providing broad coverage combined with comparatively limited curation. In this study, a systematic literature review is conducted to synthesize reported mechanisms of citation-metric manipulation and to examine limitations of citation-metric use, including evidence reported in civil engineering. A Google Scholar proof-of-concept case study examines whether the indexing of LLM-assisted, non-peer-reviewed documents with concentrated references to a target author is associated with changes in author-level citation metrics under platform-specific conditions. After indexing, a stepwise increase in author-level metrics is observed, demonstrating the feasibility of citation-metric manipulation under the platform-specific conditions. Finally, this paper discusses the implications for research integrity and citation manipulation in the era of generative artificial intelligence. It also presents recommendations for researchers, academic institutions and evaluation committees, publishers and editors, bibliographic database providers, and funding institutions and policymakers. Full article

(This article belongs to the Special Issue AI in Academic Metrics and Impact Analysis)

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24 pages, 2082 KB

Open AccessEditor’s ChoiceArticle

Research on Large Language Model-Based Bibliographic Cataloging Agent in the CNMARC Context

by Zhuoxi Tan, Xin Yang, Qinyu Chen and Tao Chen

Publications 2026, 14(1), 19; https://doi.org/10.3390/publications14010019 - 18 Mar 2026

Cited by 3 | Viewed by 2397

Abstract

To address the efficiency and cost limitations of traditional manual cataloging, this study proposes a large language model-driven automated cataloging workflow in which the Metadata Extraction Agent (MEA), Description Cataloging Agent (DCA), Subject Analysis & Indexing Agent (SAIA), and Quality Control Agent (QCA) [...] Read more.

To address the efficiency and cost limitations of traditional manual cataloging, this study proposes a large language model-driven automated cataloging workflow in which the Metadata Extraction Agent (MEA), Description Cataloging Agent (DCA), Subject Analysis & Indexing Agent (SAIA), and Quality Control Agent (QCA) collaborate to perform cataloging tasks. Experiments are conducted using a dataset of over 33,000 CNMARC bibliographic records from a University Library, together with data from the Chinese Library Classification (5th edition). Meanwhile, the agent-based workflow framework directly employs large language models without additional enhancement techniques, thereby providing a useful experimental benchmark for evaluating future AI-assisted cataloging systems. The results show that the framework performs well in metadata recognition, bibliographic description, and macro-level classification tasks, and can relatively stably generate standardized records. However, limitations remain in fine-grained semantic indexing and the interpretation of complex contexts. Therefore, in light of the capability limitations revealed by the experimental results, the study argues that fully automated end-to-end cataloging relying solely on generative AI is not yet entirely feasible. Future improvements should integrate techniques such as retrieval-augmented generation, supervised fine-tuning, and structured reasoning prompts, while establishing traceable mechanisms to enhance the reliability of intelligent cataloging. Full article

(This article belongs to the Special Issue Overview on Today’s AI Tools for Authors)

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29 pages, 3634 KB

Open AccessEditor’s ChoiceArticle

Human–AI Complementarity in Peer Review: Empirical Analysis of PeerJ Data and Design of an Efficient Collaborative Review Framework

by Zhihe Yang, Xiaoyu Zhou, Yuxin Jiang, Xinjie Zhang, Qihui Gao, Yanzhu Lu and Anqi Yang

Publications 2026, 14(1), 1; https://doi.org/10.3390/publications14010001 - 19 Dec 2025

Cited by 4 | Viewed by 3609

Abstract

In response to the persistent imbalance between the rapid expansion of scholarly publishing and the constrained availability of qualified reviewers, an empirical investigation was conducted to examine the feasibility and boundary conditions of employing Large Language Models (LLMs) in journal peer review. A [...] Read more.

In response to the persistent imbalance between the rapid expansion of scholarly publishing and the constrained availability of qualified reviewers, an empirical investigation was conducted to examine the feasibility and boundary conditions of employing Large Language Models (LLMs) in journal peer review. A parallel corpus of 493 pairs of human expert reviews and GPT-4o-generated reviews was constructed from the open peer-review platform PeerJ Computer Science. Analytical techniques, including keyword co-occurrence analysis, sentiment and subjectivity assessment, syntactic complexity measurement, and n-gram distributional entropy analysis, were applied to compare cognitive patterns, evaluative tendencies, and thematic coverage between human and AI reviewers. The results indicate that human and AI reviews exhibit complementary functional orientations. Human reviewers were observed to provide integrative and socially contextualized evaluations, while AI reviews emphasized structural verification and internal consistency, especially regarding the correspondence between abstracts and main texts. Contrary to the assumption of excessive leniency, GPT-4o-generated reviews demonstrated higher critical density and functional rigor, maintaining substantial topical alignment with human feedback. Based on these findings, a collaborative human–AI review framework is proposed, in which AI systems are positioned as analytical assistants that conduct structured verification prior to expert evaluation. Such integration is expected to enhance the efficiency, consistency, and transparency of the peer-review process and to promote the sustainable development of scholarly communication. Full article

(This article belongs to the Special Issue AI in Academic Metrics and Impact Analysis)

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8 pages, 208 KB

Open AccessEditor’s ChoiceReview

The Epistemic Downside of Using LLM-Based Generative AI in Academic Writing

by Bor Luen Tang

Publications 2025, 13(4), 63; https://doi.org/10.3390/publications13040063 - 1 Dec 2025

Cited by 8 | Viewed by 5676

Abstract

There is now widespread use of large language (LLM)-based generative artificial intelligence (AI) tools in academic research and writing. While these are convenient, quick, and output enhancing, they also arguably incur ethical issues, such as questionable authenticity and plagiarism. Here, I explore epistemological [...] Read more.

There is now widespread use of large language (LLM)-based generative artificial intelligence (AI) tools in academic research and writing. While these are convenient, quick, and output enhancing, they also arguably incur ethical issues, such as questionable authenticity and plagiarism. Here, I explore epistemological aspects of AI use in academic writing and posit that there is evidence for three related pitfalls in AI use that should not be ignored. These include (1) epistemic detriment or harm in terms of illusions of understanding, (2) potential for cognitive dulling or impairment, and (3) AI dependency (both habitual and/or emotional). Thus, any potential infringements of academic ethics aside, AI use in academic writing incurs intrinsic problems that are epistemic in nature. These epistemic downsides call for restraint and moderation beyond regulatory measures to address ethical issues in AI use. Full article

23 pages, 1583 KB

Open AccessEditor’s ChoiceArticle

Bias in Citation Visibility: Temporal Dynamics and the Unequal Life Cycle of Academic Articles—Evidence from SME and Internationalization Research

by Reyner Pérez-Campdesuñer, Alexander Sánchez-Rodríguez, Rodobaldo Martínez-Vivar, Margarita De Miguel-Guzmán and Gelmar García-Vidal

Publications 2025, 13(4), 62; https://doi.org/10.3390/publications13040062 - 1 Dec 2025

Cited by 5 | Viewed by 2492

Abstract

This study analyzes the temporal evolution of citations received by academic articles in the field of micro, small, and medium-sized enterprises (SMEs) and internationalization processes, with the aim of identifying patterns of growth and decline in scientific visibility. Based on a dataset of [...] Read more.

This study analyzes the temporal evolution of citations received by academic articles in the field of micro, small, and medium-sized enterprises (SMEs) and internationalization processes, with the aim of identifying patterns of growth and decline in scientific visibility. Based on a dataset of 1936 articles retrieved from Scopus, we constructed an article–year panel that enabled the application of multiple statistical approaches. Discrete-time survival models showed that the annual probability of receiving at least one citation is initially low, increases slightly until the fifth year, and then declines progressively thereafter. Negative binomial regression confirmed significant growth during the first five years, followed by a slowdown. Kaplan–Meier estimations reinforced this finding by showing that the cumulative proportion of articles receiving their first citation within a decade remains limited. These results confirm that citation dynamics are nonlinear and subject to early obsolescence, with most visibility concentrated in the short term. Importantly, this temporal bias in indexing and evaluation systems disproportionately favors recent publications while undervaluing older but still influential research. Such structural bias has profound implications for visibility and equity in scholarly communication, especially for disciplines and regions where citation cycles are longer. The findings thus validate the study’s propositions: first, that citation growth slows significantly after the fifth year, and second, that this slowdown represents a structural bias that amplifies inequities in research evaluation. Full article

(This article belongs to the Special Issue Bias in Indexing: Effects on Visibility and Equity)

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37 pages, 6437 KB

Open AccessEditor’s ChoiceArticle

A Novel Methodology for Identifying the Top 1% Scientists Using a Composite Performance Index

by Alexey Remizov, Shazim Ali Memon and Saule Sadykova

Publications 2025, 13(4), 55; https://doi.org/10.3390/publications13040055 - 2 Nov 2025

Cited by 2 | Viewed by 4894

Abstract

There is a growing need for comprehensive and transparent frameworks in bibliometric evaluation that support fairer assessments and capture the multifaceted nature of research performance. This study proposes a novel methodology for identifying top-performing researchers based on a composite performance index (CPI). Unlike [...] Read more.

There is a growing need for comprehensive and transparent frameworks in bibliometric evaluation that support fairer assessments and capture the multifaceted nature of research performance. This study proposes a novel methodology for identifying top-performing researchers based on a composite performance index (CPI). Unlike existing rankings, this framework presents a multidimensional approach by integrating sixteen weighted bibliometrics metrics, spanning research productivity, citation, publications in top journal percentiles, authorship roles, and international collaboration, into a single CPI, enabling a more nuanced and equitable evaluation of researcher performance. Data were retrieved from SciVal for 1996–2025. Two ranking exercises were conducted with Kazakhstan as the analytical unit. Subject-specific rankings identified the top 1% authors within different research areas, while subject-independent rankings highlighted the overall top 1%. CPI distributions varied markedly across disciplines. A comparative analysis with the Stanford/Elsevier global top 2% list was conducted as additional benchmarking. The results highlight that academic excellence depends on a broad spectrum of strengths beyond just productivity, particularly in competitive disciplines. The CPI provides a consistent and adaptable tool for assessing and recognizing research performance; however, future refinements should enhance data coverage, improve representation of early-career researchers, and integrate qualitative aspects. Full article

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14 pages, 1218 KB

Open AccessEditor’s ChoiceArticle

Geographic Variation in LLM DOI Fabrication: Cross-Country Analysis of Citation Accuracy Across Four Large Language Models

by Eungi Kim, Frankline Kipchumba and Sein Min

Publications 2025, 13(4), 49; https://doi.org/10.3390/publications13040049 - 1 Oct 2025

Cited by 4 | Viewed by 3149

Abstract

This study evaluates digital object identifier (DOI) hallucination in large language model (LLM)-generated scholarly citations, with a focus on systematic geographic disparities. To conduct this study, we systematically evaluated four LLMs (GPT-4o-mini, Claude-3-haiku, Gemini-2.0-flash-lite, and DeepSeek V3) using standardized information behavior prompts across [...] Read more.

This study evaluates digital object identifier (DOI) hallucination in large language model (LLM)-generated scholarly citations, with a focus on systematic geographic disparities. To conduct this study, we systematically evaluated four LLMs (GPT-4o-mini, Claude-3-haiku, Gemini-2.0-flash-lite, and DeepSeek V3) using standardized information behavior prompts across ten countries with diverse income levels. The models generated 3451 citations, which we validated using the CrossRef API. The results showed that DOI hallucination follows systematic patterns influenced by model choice, geographic context, and publication recency. Hallucination rates exceeded 80% in lower-income countries and increased sharply for publications from the 2020s across all regions. Fabricated citations—citations that appear structurally complete but contain invalid DOIs—were especially prevalent in countries such as India and Bangladesh. Model-specific factors showed the strongest association with hallucination, followed by income level and publication period. These findings raise concerns about the epistemic reliability of LLM-generated scholarly references and underscore the need for region-aware training, real-time DOI validation, and robust verification protocols in academic contexts. Full article

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28 pages, 644 KB

Open AccessEditor’s ChoiceArticle

Reckoning with Retractions in Research Funding Reviews: The Case of China

by Shaoxiong Brian Xu and Guangwei Hu

Publications 2025, 13(3), 41; https://doi.org/10.3390/publications13030041 - 4 Sep 2025

Cited by 5 | Viewed by 11165

Abstract

China’s retraction crisis has raised concerns about research integrity and accountability within its scientific community and beyond. To address this issue, we proposed in an earlier publication that Chinese research funders incorporate retraction records into the evaluation of research funding applications by establishing [...] Read more.

China’s retraction crisis has raised concerns about research integrity and accountability within its scientific community and beyond. To address this issue, we proposed in an earlier publication that Chinese research funders incorporate retraction records into the evaluation of research funding applications by establishing a retraction-based review system. This review system would debar researchers with retraction records from applying for funding for a specified period. However, our earlier proposal lacked practical guidance on how to operationalize such a review system. In this article, we expand on our proposal by fleshing out the proposed ten debarment determinants and offering a framework for quantifying the duration of funding ineligibility. Additionally, we outline the critical steps for implementing the retraction-based review system, address the major challenges to its effective and sustainable adoption, and propose viable solutions to these challenges. Finally, we discuss the benefits of implementing the review system, emphasizing its potential to strengthen research integrity and foster a culture of accountability in the Chinese academic community. Full article

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32 pages, 4415 KB

Open AccessEditor’s ChoiceReview

Disinformation in the Digital Age: Climate Change, Media Dynamics, and Strategies for Resilience

by Andrea Tomassi, Andrea Falegnami and Elpidio Romano

Publications 2025, 13(2), 24; https://doi.org/10.3390/publications13020024 - 6 May 2025

Cited by 21 | Viewed by 15192

Abstract

Scientific disinformation has emerged as a critical challenge at the interface of science and society. This paper examines how false or misleading scientific content proliferates across both social media and traditional media and evaluates strategies to counteract its spread. We conducted a comprehensive [...] Read more.

Scientific disinformation has emerged as a critical challenge at the interface of science and society. This paper examines how false or misleading scientific content proliferates across both social media and traditional media and evaluates strategies to counteract its spread. We conducted a comprehensive literature review of research on scientific misinformation across disciplines and regions, with particular focus on climate change and public health as exemplars. Our findings indicate that social media algorithms and user dynamics can amplify false scientific claims, as seen in case studies of viral misinformation campaigns on vaccines and climate change. Traditional media, meanwhile, are not immune to spreading inaccuracies—journalistic practices such as sensationalism or “false balance” in reporting have at times distorted scientific facts, impacting public understanding. We review efforts to fight disinformation, including technological tools for detection, the application of inoculation theory and prebunking techniques, and collaborative approaches that bridge scientists and journalists. To empower individuals, we propose practical guidelines for critically evaluating scientific information sources and emphasize the importance of digital and scientific literacy. Finally, we discuss methods to quantify the prevalence and impact of scientific disinformation—ranging from social network analysis to surveys of public belief—and compare trends across regions and scientific domains. Our results underscore that combating scientific disinformation requires an interdisciplinary, multi-pronged approach, combining improvements in science communication, education, and policy. We conducted a scoping review of 85 open-access studies focused on climate-related misinformation and disinformation, selected through a systematic screening process based on PRISMA criteria. This approach was chosen to address the lack of comprehensive mappings that synthesize key themes and identify research gaps in this fast-growing field. The analysis classified the literature into 17 thematic clusters, highlighting key trends, gaps, and emerging challenges in the field. Our results reveal a strong dominance of studies centered on social media amplification, political denialism, and cognitive inoculation strategies, while underlining a lack of research on fact-checking mechanisms and non-Western contexts. We conclude with recommendations for strengthening the resilience of both the public and information ecosystems against the spread of false scientific claims. Full article

(This article belongs to the Special Issue Open Science: Developments and Disinformation Regarding Scientific Information)

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10 pages, 1499 KB

Open AccessEditor’s ChoiceArticle

Evaluation of Content Quality of Online Health Information by Global Quality Score: A Case Study of Researchers Misnaming It and Citing Secondary Sources

by Andy Wai Kan Yeung

Publications 2025, 13(2), 23; https://doi.org/10.3390/publications13020023 - 1 May 2025

Cited by 20 | Viewed by 5578

Abstract

The Global Quality Score (GQS) is one of the most frequently used tools to evaluate the content quality of online health information. To the author’s knowledge, it is frequently misnamed as the Global Quality Scale, and occasionally secondary sources are cited as the [...] Read more.

The Global Quality Score (GQS) is one of the most frequently used tools to evaluate the content quality of online health information. To the author’s knowledge, it is frequently misnamed as the Global Quality Scale, and occasionally secondary sources are cited as the original source of the tool. This work aimed to reveal the current situation especially regarding the citations among published studies. Web of Science, Scopus, and PubMed were queried to identify papers that mentioned the use of the GQS. Among a total of 411 analyzed papers, 45.0% misnamed it as Global Quality Scale, and 46.5% did not cite the primary source published in 2007 to credit it as the original source. Another 80 references were also cited from time to time as the source of the GQS, led by a secondary source published in 2012. There was a decreasing trend in citing the primary source when using the GQS. Among the 12 papers that claimed that the GQS was validated, half of them cited the primary source to justify the claim, but in fact the original publication did not mention anything about its validation. To conclude, future studies should name and cite the GQS properly to minimize confusion. Full article

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21 pages, 1577 KB

Open AccessEditor’s ChoiceArticle

Does Publisher Volume Matter? A Cross-Sectional Analysis of Scopus Journal Publishing Patterns

by Eungi Kim

Publications 2025, 13(2), 17; https://doi.org/10.3390/publications13020017 - 1 Apr 2025

Cited by 3 | Viewed by 8117

Abstract

The objective of this study is to examine the relationship between publisher volume—the number of journals a publisher produces—and journal publishing patterns in Scopus, including various journal metrics such as the h-index, SCImago Journal Rank (SJR), and journal quartiles. The SCImago database, which [...] Read more.

The objective of this study is to examine the relationship between publisher volume—the number of journals a publisher produces—and journal publishing patterns in Scopus, including various journal metrics such as the h-index, SCImago Journal Rank (SJR), and journal quartiles. The SCImago database, which is derived from Scopus data, serves as a proxy for journal impact and influence. The analysis also considered factors such as Open Access (OA) status, geographical location, and subject areas. Using the 2023 SJR dataset, publishers were classified into four categories: V1 (single journal), V2 (2–9 journals), V3 (10–99 journals), and V4 (100+ journals). The findings showed that V4 publishers accounted for 44.5% of Scopus-indexed journals despite comprising only 0.3% of all publishers, whereas V1 publishers represented 78.6% of all publishers but contributed only 21.3% of journals. High-volume publishers had more journals ranked in Q1 and Q2, while lower-volume publishers were more concentrated in Q3 and Q4. Results from the linear mixed-effects model indicated that publisher volume was associated with journal metrics, with higher-volume publishers generally achieving higher h-index and SJR scores. Western Europe and North America had the highest number of V4 publishers, whereas China, Spain, and Italy exhibited strong journal production but had fewer publishers in the highest-volume category. These results illustrate the dominance of a small group of high-volume (V4) publishers and the challenges smaller publishers face in gaining visibility and impact. They also underscore the need to consider policies that foster a more balanced and equitable scholarly publishing environment, particularly for underrepresented regions and subject areas. Full article

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12 pages, 247 KB

Open AccessEditor’s ChoiceArticle

From Fees to Free: Comparing APC-Based and Diamond Open Access Journals in Engineering

by Luís Eduardo Pilatti, Luiz Alberto Pilatti, Gustavo Dambiski Gomes de Carvalho and Luis Mauricio Martins de Resende

Publications 2025, 13(2), 16; https://doi.org/10.3390/publications13020016 - 1 Apr 2025

Cited by 5 | Viewed by 17761

Abstract

This study analyzes the impact of different Open Access (OA) publication models in engineering, comparing journals that charge Article Processing Charges (APCs) with those operating under the Diamond OA model. A total of 757 engineering OA journals, comprising 504 APC-based and 253 Diamond [...] Read more.

This study analyzes the impact of different Open Access (OA) publication models in engineering, comparing journals that charge Article Processing Charges (APCs) with those operating under the Diamond OA model. A total of 757 engineering OA journals, comprising 504 APC-based and 253 Diamond OA journals, were examined using bibliometric data from 2020 to 2023. The analysis focused on four key metrics: CiteScore, total citations, number of published articles, and the percentage of cited articles. The results indicate that APC-based journals dominate the upper quartiles (Q1 and Q2) regarding absolute citation counts, primarily driven by high-volume mega-journals such as IEEE Access. However, Diamond OA journals exhibit a higher proportion of cited articles (88.8% compared to 83.4% in APC-based journals) within the top 10% category. Despite their benefits in providing cost-free dissemination, Diamond OA journals account for only 8.4% of the 3012 active engineering journals indexed in Scopus, highlighting sustainability and visibility challenges. The findings suggest that, while APC-based journals achieve higher absolute citation counts, editorial reputation and visibility strategies significantly influence citation performance. This study contributes to the ongoing discussion on the financial sustainability and equity of OA publishing in engineering. Full article

(This article belongs to the Special Issue Diamond Open Access)

17 pages, 1917 KB

Open AccessEditor’s ChoiceArticle

Forecasting the Scientific Production Volumes of G7 and BRICS Countries in a Comparative Analysis

by Tindaro Cicero

Publications 2025, 13(1), 6; https://doi.org/10.3390/publications13010006 - 7 Feb 2025

Cited by 6 | Viewed by 5211

Abstract

This study applies ARIMA models to forecast scientific production trends among G7 (Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States) and BRICS (Brazil, Russia, India, China, and South Africa) countries using Scopus data from 1996 to 2023. The analysis [...] Read more.

This study applies ARIMA models to forecast scientific production trends among G7 (Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States) and BRICS (Brazil, Russia, India, China, and South Africa) countries using Scopus data from 1996 to 2023. The analysis shows that G7 countries maintain steady growth driven by established research infrastructures, while BRICS nations, particularly China, display accelerated growth due to substantial investments in R&D. The forecasts indicate that China could reach over 2,000,000 indexed scientific publications annually by 2030, potentially reshaping the global research landscape. These findings provide valuable insights for policymakers and research institutions, highlighting the shifting dynamics of global scientific leadership and emphasizing the importance of sustained investment in research to remain competitive. Full article

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10 pages, 476 KB

Open AccessEditor’s ChoiceOpinion

It Takes a Village! Editorship, Advocacy, and Research in Running an Open Access Data Journal

by Mandy Wigdorowitz, Marton Ribary, Andrea Farina, Eleonora Lima, Daniele Borkowski, Paola Marongiu, Amanda H. Sorensen, Christelle Timis and Barbara McGillivray

Publications 2024, 12(3), 24; https://doi.org/10.3390/publications12030024 - 13 Aug 2024

Cited by 6 | Viewed by 5941

Abstract

Partaking in the editorial process of an academic journal is both a challenging and rewarding experience. It takes a village of dedicated individuals with a vested interest in the dissemination and sharing of high-quality research outputs. As members of the editorial team of [...] Read more.

Partaking in the editorial process of an academic journal is both a challenging and rewarding experience. It takes a village of dedicated individuals with a vested interest in the dissemination and sharing of high-quality research outputs. As members of the editorial team of an open access data journal, we reflect on the emergence of data-driven open research, a new journal genre (data paper), and a new journal type (data journal) in the Arts, Humanities, and Social Sciences (AHSS). Access to data—the currency of empirical research—is valuable to the research community, crucial to scientific integrity, and leads to cumulative advancements in knowledge. It therefore requires significant investment and appropriate venues for dissemination. We illustrate the necessity of raising awareness about data-driven open research and best practices in data-driven publishing. We discuss how it involves building a community of authors and readers, establishing a company of editors, reviewers, and support staff, and passing on the practice, which has been challenging the status quo in research and publishing. Potential future directions are considered, including data peer review and reward, recognition, and funding structures for data sharing. Full article

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11 pages, 1610 KB

Open AccessEditor’s ChoiceArticle

Bibliometric Analysis of Papers Dealing with Dental Videos on YouTube

by Andy Wai Kan Yeung, Maima Matin, Michel Edwar Mickael, Sybille Behrens, Dalibor Hrg, Michał Ławiński, Fabian Peter Hammerle and Atanas G. Atanasov

Publications 2024, 12(3), 22; https://doi.org/10.3390/publications12030022 - 25 Jul 2024

Cited by 3 | Viewed by 3809

Abstract

The aim of this study was to perform a bibliometric analysis to discover what topics of dental YouTube videos have been investigated by the scientific literature, and evaluate how video characteristics were related to citation count. The Scopus electronic literature database was accessed [...] Read more.

The aim of this study was to perform a bibliometric analysis to discover what topics of dental YouTube videos have been investigated by the scientific literature, and evaluate how video characteristics were related to citation count. The Scopus electronic literature database was accessed to identify relevant papers. After screening, a total of 128 papers entered the analysis. The bibliographic data were provided by Scopus, whereas content evaluations were manually performed. Most papers evaluated videos recorded in English (85.9%). Each of the 128 papers analyzed a mean (±SD) of 79.2 ± 61.6 videos. Mean journal impact factor was 1.8 ± 1.4, and mean citation count was 13.0 ± 22.4. The preference for publication of papers was inclined towards dental journals (80.5%), with the majority (54.7%) being published without open access. Papers dealing with videos targeting patients/public had higher citations than those targeting dental professionals only (14.1 ± 23.4 vs. 4.0 ± 6.3, p < 0.001). The most represented as well as the most highly cited specialty of the dental YouTube publications was oral and maxillofacial surgery/oral medicine. Some twin or triplet studies published in the same year covering the same topic were identified, but they often covered a different number of videos. Full article

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19 pages, 3527 KB

Open AccessEditor’s ChoiceArticle

Research on Disinformation in Academic Studies: Perspectives through a Bibliometric Analysis

by Nuria Navarro-Sierra, Silvia Magro-Vela and Raquel Vinader-Segura

Publications 2024, 12(2), 14; https://doi.org/10.3390/publications12020014 - 7 May 2024

Cited by 13 | Viewed by 6327

Abstract

Disinformation is a phenomenon of concern to all political systems, as it poses a threat to freedom and democracy through the manipulation of public opinion aimed at eroding institutions. This paper presents a bibliometric and systematized study which allows the establishment of a [...] Read more.

Disinformation is a phenomenon of concern to all political systems, as it poses a threat to freedom and democracy through the manipulation of public opinion aimed at eroding institutions. This paper presents a bibliometric and systematized study which allows the establishment of a comprehensive view of the research and current state of academic investigations on disinformation. To this end, a content analysis of the scientific articles indexed in Scopus up to 31 December 2023 has been carried out based on three categories of analysis: journals, authors and investigations. Similarly, a systematic study of the 50 most cited articles in this sample was performed in order to gain a deeper understanding of the nature, motivations and methodological approaches of these investigations. The results indicate that disinformation is a research topic which has gained great interest in the academic community since 2018, with special mention to the impact of COVID-19 and the vaccines against this disease. Thus, it can be concluded that disinformation is an object of study which attracts significant attention and which must be approached from transdisciplinarity to respond to a phenomenon of great complexity. Full article

(This article belongs to the Special Issue Disinformation in Science Communication: Threats, Challenges and Potential Solutions)

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10 pages, 1810 KB

Open AccessEditor’s ChoiceArticle

The Time from Submission to Publication in Primary Health Care Journals: A Cross-Sectional Study

by Tsung-An Chen, Ming-Hwai Lin, Yu-Chun Chen and Tzeng-Ji Chen

Publications 2024, 12(2), 13; https://doi.org/10.3390/publications12020013 - 28 Apr 2024

Cited by 8 | Viewed by 14498

Abstract

Background: The time from submission to publication can significantly impact the speed of knowledge dissemination and is influenced by multiple factors. This research aims to investigate the time from submission to publication of journals of primary health care and to explore the factors [...] Read more.

Background: The time from submission to publication can significantly impact the speed of knowledge dissemination and is influenced by multiple factors. This research aims to investigate the time from submission to publication of journals of primary health care and to explore the factors that influence this duration. Methods: We selected journals of primary health care and extracted their impact factors, annual publication frequencies, and open access status. The time from submission to acceptance (SA lag), acceptance to publication (AP lag), and submission to publication (SP lag) were calculated. Additionally, we conducted statistical analyses to determine whether impact factors, annual publication frequencies, and journal open access status had an influence on publication time. Results: This study revealed the average SP lag was 243.4 days (interquartile range, IQR 159–306), the average SA lag was 177.8 days (IQR 99–229.3), and the average AP lag was 65.6 days (IQR 14–101). Variations were observed in SP lag, SA lag, and AP lag among different journals. SP lag generally decreased with higher impact factors. Journals with open access had longer SA lag but shorter AP lag. There was a general trend of decreasing SP lag and SA lag with an increasing number of annual publications, but no clear trend was observed for AP lag. Conclusions: Improvements are needed in reducing the duration from submission to publication for primary health care journals. Significant variation exists among journals. Additionally, factors such as the impact factor, open access status, and the number of annual publications may influence publication speed. Full article

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30 pages, 1479 KB

Open AccessEditor’s ChoiceArticle

Coping with the Inequity and Inefficiency of the H-Index: A Cross-Disciplinary Empirical Analysis

by Fabio Zagonari and Paolo Foschi

Publications 2024, 12(2), 12; https://doi.org/10.3390/publications12020012 - 22 Apr 2024

Cited by 5 | Viewed by 6360

Abstract

This paper measures two main inefficiency features (many publications other than articles; many co-authors’ reciprocal citations) and two main inequity features (more co-authors in some disciplines; more citations for authors with more experience). It constructs a representative dataset based on a cross-disciplinary balanced [...] Read more.

This paper measures two main inefficiency features (many publications other than articles; many co-authors’ reciprocal citations) and two main inequity features (more co-authors in some disciplines; more citations for authors with more experience). It constructs a representative dataset based on a cross-disciplinary balanced sample (10,000 authors with at least one publication indexed in Scopus from 2006 to 2015). It estimates to what extent four additional improvements of the H-index as top-down regulations (∆H_h = H_h − H_h+1 from H₁ = based on publications to H₅ = net per-capita per-year based on articles) account for inefficiency and inequity across twenty-five disciplines and four subjects. Linear regressions and ANOVA results show that the single improvements of the H-index considerably and decreasingly explain the inefficiency and inequity features but make these vaguely comparable across disciplines and subjects, while the overall improvement of the H-index (H₁–H₅) marginally explains these features but make disciplines and subjects clearly comparable, to a greater extent across subjects than disciplines. Fitting a Gamma distribution to H₅ for each discipline and subject by maximum likelihood shows that the estimated probability densities and the percentages of authors characterised by H₅ ≥ 1 to H₅ ≥ 3 are different across disciplines but similar across subjects. Full article

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14 pages, 1435 KB

Open AccessEditor’s ChoiceArticle

Application of ChatGPT in Information Literacy Instructional Design

by Jelena Madunić and Matija Sovulj

Publications 2024, 12(2), 11; https://doi.org/10.3390/publications12020011 - 15 Apr 2024

Cited by 23 | Viewed by 9564

Abstract

Recent developments in generative artificial intelligence tools have prompted immediate reactions in the academic library community. While most studies focus on the potential impact on academic integrity, this work explored constructive applications of ChatGPT in the design of instructional materials for courses in [...] Read more.

Recent developments in generative artificial intelligence tools have prompted immediate reactions in the academic library community. While most studies focus on the potential impact on academic integrity, this work explored constructive applications of ChatGPT in the design of instructional materials for courses in academic information literacy. The starting point was the use of openly licenced information resources or content infrastructure as facilitators in the creation of educational materials. In the first phase, course teaching material was developed using a prompt engineering strategy, predefined standards, and a prompt script. As a second step, we experimented with designing a custom chatbot model connected to a pre-defined corpus of source documents. The results demonstrated that the final teaching material required careful revision and optimisation before use in an actual instructional programme. The experimental design of the custom chatbot was able to query specific user-defined documents. Taken together, these findings suggest that the strategic and well-planned use of ChatGPT technology in content creation can have substantial benefits in terms of time and cost efficiency. In the context of information literacy, the results provide a practical and innovative solution to integrate the new technology tool into instructional practices. Full article

(This article belongs to the Special Issue 10th Anniversary Special Issue "PUBMET2023 Conference on Scholarly Communication in the Context of Open Science")

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16 pages, 5472 KB

Open AccessEditor’s ChoiceArticle

Bibliometric Overview of ChatGPT: New Perspectives in Social Sciences

by Marian Oliński, Krzysztof Krukowski and Kacper Sieciński

Publications 2024, 12(1), 9; https://doi.org/10.3390/publications12010009 - 21 Mar 2024

Cited by 22 | Viewed by 9760

Abstract

This study delves into a bibliometric analysis of ChatGPT, an AI tool adept at analysing and generating text, highlighting its influence in the realm of social sciences. By harnessing data from the Scopus database, a total of 814 relevant publications were selected and [...] Read more.

This study delves into a bibliometric analysis of ChatGPT, an AI tool adept at analysing and generating text, highlighting its influence in the realm of social sciences. By harnessing data from the Scopus database, a total of 814 relevant publications were selected and scrutinised through VOSviewer, focusing on elements such as co-citations, keywords and international collaborations. The objective is to unearth prevailing trends and knowledge gaps in scholarly discourse regarding ChatGPT’s application in social sciences. Concentrating on articles from the year 2023, this analysis underscores the rapid evolution of this research domain, reflecting the ongoing digital transformation of society. This study presents a broad thematic picture of the analysed works, indicating a diversity of perspectives—from ethical and technological to sociological—regarding the implementation of ChatGPT in the fields of social sciences. This reveals an interest in various aspects of using ChatGPT, which may suggest a certain openness of the educational sector to adopting new technologies in the teaching process. These observations make a contribution to the field of social sciences, suggesting potential directions for future research, policy or practice, especially in less represented areas such as the socio-legal implications of AI, advocating for a multidisciplinary approach. Full article

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14 pages, 740 KB

Open AccessEditor’s ChoiceReview

Benefits of Citizen Science for Libraries

by Dolores Mumelaš and Alisa Martek

Publications 2024, 12(1), 8; https://doi.org/10.3390/publications12010008 - 8 Mar 2024

Cited by 15 | Viewed by 8146

Abstract

Participating in collaborative scientific research through citizen science, a component of open science, holds significance for both citizen scientists and professional researchers. Yet, the advantages for those orchestrating citizen science initiatives are often overlooked. Organizers encompass a diverse range, including governmental entities, non-governmental [...] Read more.

Participating in collaborative scientific research through citizen science, a component of open science, holds significance for both citizen scientists and professional researchers. Yet, the advantages for those orchestrating citizen science initiatives are often overlooked. Organizers encompass a diverse range, including governmental entities, non-governmental organizations, corporations, universities, and institutions like libraries. For libraries, citizen science holds importance by fostering heightened civic and research interests, promoting scientific publishing, and contributing to overall scientific progress. This paper aims to provide a comprehensive understanding of the specific ways in which citizen science can benefit libraries and how libraries can effectively utilize citizen science to achieve their goals. The paper is based on a systematic review of peer-reviewed articles that discuss the direct benefits of citizen science on libraries. A list of the main benefits of citizen science for libraries has been compiled from the literature. Additionally, the reasons why it is crucial for libraries to communicate the benefits of citizen science for their operations have been highlighted, particularly in terms of encouraging other libraries to actively engage in citizen science projects. Full article

(This article belongs to the Special Issue 10th Anniversary Special Issue "PUBMET2023 Conference on Scholarly Communication in the Context of Open Science")

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12 pages, 717 KB

Open AccessFeature PaperEditor’s ChoiceArticle

FAIRness of Research Data in the European Humanities Landscape

by Ljiljana Poljak Bilić and Kristina Posavec

Publications 2024, 12(1), 6; https://doi.org/10.3390/publications12010006 - 5 Mar 2024

Cited by 7 | Viewed by 5741

Abstract

This paper explores the landscape of research data in the humanities in the European context, delving into their diversity and the challenges of defining and sharing them. It investigates three aspects: the types of data in the humanities, their representation in repositories, and [...] Read more.

This paper explores the landscape of research data in the humanities in the European context, delving into their diversity and the challenges of defining and sharing them. It investigates three aspects: the types of data in the humanities, their representation in repositories, and their alignment with the FAIR principles (Findable, Accessible, Interoperable, Reusable). By reviewing datasets in repositories, this research determines the dominant data types, their openness, licensing, and compliance with the FAIR principles. This research provides important insight into the heterogeneous nature of humanities data, their representation in the repository, and their alignment with FAIR principles, highlighting the need for improved accessibility and reusability to improve the overall quality and utility of humanities research data. Full article

(This article belongs to the Special Issue 10th Anniversary Special Issue "PUBMET2023 Conference on Scholarly Communication in the Context of Open Science")

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16 pages, 1987 KB

Open AccessEditor’s ChoiceArticle

Tracing the Evolution of Reviews and Research Articles in the Biomedical Literature: A Multi-Dimensional Analysis of Abstracts

by Stefano Guizzardi, Maria Teresa Colangelo, Prisco Mirandola and Carlo Galli

Publications 2024, 12(1), 2; https://doi.org/10.3390/publications12010002 - 12 Jan 2024

Cited by 1 | Viewed by 4197

Abstract

We previously examined the diachronic shifts in the narrative structure of research articles (RAs) and review manuscripts using abstract corpora from MEDLINE. This study employs Nini’s Multidimensional Analysis Tagger (MAT) on the same datasets to explore five linguistic dimensions (D1–5) in these two [...] Read more.

We previously examined the diachronic shifts in the narrative structure of research articles (RAs) and review manuscripts using abstract corpora from MEDLINE. This study employs Nini’s Multidimensional Analysis Tagger (MAT) on the same datasets to explore five linguistic dimensions (D1–5) in these two sub-genres of biomedical literature, offering insights into evolving writing practices over 30 years. Analyzing a sample exceeding 1.2 million abstracts, we observe a shared reinforcement of an informational, emotionally detached tone (D1) in both RAs and reviews. Additionally, there is a gradual departure from narrative devices (D2), coupled with an increase in context-independent content (D3). Both RAs and reviews maintain low levels of overt persuasion (D4) while shifting focus from abstract content to emphasize author agency and identity. A comparison of linguistic features underlying these dimensions reveals often independent changes in RAs and reviews, with both tending to converge toward standardized stylistic norms. Full article

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