Suchergebnisse

This study aims to compare the geographical and disciplinary coverage of OA journals in three databases: OpenAlex, Scopus and the WoS. We used the ROAD database, managed by the ISSN International Centre, as a reference database which indexes 62,701 OA active resources (as of May 2024). Among the 62,701 active resources indexed in the ROAD database, the Web of Science indexes 6,157 journals, while Scopus indexes 7,351, and OpenAlex indexes 34,217. A striking observation is the presence of 25,658 OA journals exclusively in OpenAlex, whereas only 182 journals are exclusively present in WoS and 373 in Scopus.
The geographical analysis focusses on two levels: continents and countries. As for disciplinary comparison, we use the ten disciplinary levels of the ROAD database. Moreover, our findings reveal a striking similarity in OA journal coverage between WoS and Scopus. However, while OpenAlex offers better inclusivity and indexing, it is not without biases. WoS and Scopus predictably favor journals from Europe, North America and Oceania. Although OpenAlex presents a much more balanced indexing, certain regions and countries remain relatively underrepresented. Typically, Africa is proportionally as under-represented in OpenAlex as it is in WoS, and some emerging countries are proportionally less represented in OpenAlex than in WoS and Scopus.
These results underscore a marked similarity in OA journal indexing between WoS and Scopus, while OpenAlex aligns more closely with the distribution observed in the ROAD database, although it also exhibits some representational biases.

En este estudio se propone una metodología utilizando OpenAlex (OA) para monitorear el acceso abierto (AA) a las publicaciones científicas para el caso de Argentina, país donde rige el mandato de autoarchivo -Ley 26.899 (2013)-. Se conformó una muestra con 167.240 artículos de investigadores del Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) que se analizaron con técnicas estadísticas. Se estimó que OA puede representar entre 85-93% de los autores para todas las disciplinas, excepto Ciencias Sociales y Humanidades, donde solo alcanza al 47%. Se calculó que 41% de los artículos publicados entre 1953-2021 incluidos en la fuente están en AA, porcentaje que sube a 46% al considerar exclusivamente el periodo post ley (2014-2021). En ambos periodos es la vía dorada la que representa mayor proporción. Al comparar periodos iguales post y pre ley, se observó que la tendencia en alza de la vía dorada era preexistente a la legislación y la disponibilidad de artículos cerrados en repositorios aumentó un 5% a lo que se estima en base a tendencias existentes. Se concluye que si bien la vía verde ha tenido una evolución positiva, ha sido la publicación en revistas doradas lo que ha impulsado más rápidamente el acceso a la producción argentina. Asimismo, que la metodología basada en OA, piloteada aquí por primera vez, es viable para monitorear el AA en Argentina ya que arroja porcentajes similares a otros estudios nacionales e internacionales.

En este artículo se analiza la cobertura de OpenAlex y Scopus de las publicaciones científicas de autores/as de Argentina, tanto para el país completo como para un conjunto de 10 universidades nacionales seleccionadas, en el período 2019-2023. El objetivo es comparar el desempeño de ambas bases de datos en cuanto a volumen global de publicaciones indexadas, idiomas y áreas disciplinares. Los resultados arrojan que la cobertura de OpenAlex es más amplia y diversa, en particular para las publicaciones en español, en ciencias sociales y humanidades y con un único autor o autora. También se observa que la cobertura de OpenAlex es particularmente relevante para las universidades de menor tamaño y para los artículos con colaboración nacional o de autoría individual, modalidades tradicionalmente poco representadas en las bases de datos bibliométricos tradicionales. Estos resultados son particularmente relevantes en un contexto de ataque a la ciencia producida en Argentina de parte del Gobierno federal electo en el 2023 y del corte al acceso a Scopus como fuente para la investigación científica.

In the evolving landscape of scientific research, ORCID identifiers are essential for maintaining academic integrity and enhancing research visibility. This study proposes a methodological framework for identifying and analyzing the presence and characteristics of researchers in a specific country in ORCID, using complementary data from OpenAlex to improve results. Our objectives were to develop a reliable method to identify ORCID records of researchers in Spain, study the frequency of profile updates, and investigate the most frequently filled metadata sections and common profile characteristics across disciplines. We identified 190,455 ORCID records of Spanish researchers and found high engagement with some metadata, particularly in the 'Employment' (73%) and 'Publications' (83%) sections. Additionally, 73% of records were actively maintained. The most common metadata combination included 'Works', 'Employment', and 'Education and Qualifications'. However, we noted significant variability and incomplete records across disciplines. The study highlights the need for more complete and frequently updated profiles, improved institutional integration, and strategic efforts to enhance ORCID adoption. These improvements could enhance the reliability of ORCID for tracking researcher mobility and academic trajectories.

This paper aims to analyze the extent to which inventive activity relies on open science. In other words, it investigates whether inventors utilize Open Access (OA) publications more than subscription-based ones, especially given that some inventors may lack institutional access. To achieve this, we utilized the (Marx, 2023) database, which contains citations of patents to scientific publications (Non-Patent References - NPRs). We focused on publications closely related to invention, specifically those cited solely by inventors within the body of patent texts. Our dataset was supplemented by OpenAlex data. The final sample comprised 961,104 publications cited in patents, of which 861,720 had a DOI. Results indicate that across all disciplines, OA publications are 38% more prevalent in patent citations (NPRs) than in the overall OpenAlex database. In biology and medicine, inventors use 73% and 27% more OA publications, respectively, compared to closed-access ones. Chemistry and computer science are also disciplines where OA publications are more frequently utilized in patent contexts than subscription-based ones.

The main objective of this study is to evaluate the coverage and information quality of
research entities (authors, organizations, venues, and disciplines) in eight new academic databases.
Design/methodology/approach: A random Crossref sample of over 115k DOIs was chosen and
subsequently searched across seven databases. Dimensions and OpenAlex are the best products processing authors because they have the lowest percentage of authors with one publication (Dimensions, 88.1%; OpenAlex, 89.9%), and the lowest slope coefficient (old OpenAlex, α=3.25; Dimensions, α=3.46). They also show low average author variation (Dimensions, .12; OpenAlex, .17). Microsoft Academic is the database that detects the most affiliations (87%) and organizations (71.2%). Crossref-based products such as Dimensions (98.1%), Scilit (99.3%), and The Lens (96.4%) identify more venues and publishers than other products. Semantic Scholar is highlighted as the database that thematically classifies the most publications (94.1%). Regarding document types, the study also
identifies transversal problems in the extraction and identification of entities in books and book
chapters. This is the first study that compares the largest number of free-access scholarly
databases, exploring the completeness degree and quality of the research entity information
(authors, organizations, disciplines and venues). The results of this study have important implications for selecting different databases when it comes to search literature for reviews, meta-analyses and
other studies.

The Streetlight Effect represents an observation bias that occurs when individuals search for something only where it is easiest to look. Despite the significant development of Post-Publication Peer Review (PPPR) in recent years, facilitated in part by platforms such as PubPeer, existing literature has not examined whether PPPR is affected by this type of bias. In other words, if the PPPR mainly concerns publications to which researchers have direct access (eg to analyze image duplications, etc.). In this study, we compare the Open Access (OA) structures of publishers and journals among 51,882 publications commented on PubPeer to those indexed in OpenAlex database (#156,700,177). Our findings indicate that OA journals are 33% more prevalent in PubPeer than in the global total (52% for the most commented journals). This result can be attributed to disciplinary bias in PubPeer, with overrepresentation of medical and biological research (which exhibits higher levels of openness). However, after normalization, the results reveal that PPPR does not exhibit a Streetlight Effect, as OA publications, within the same discipline, are on average 16% less prevalent in PubPeer than in the global total. These results suggest that the process of scientific self-correction operates independently of publication access status.

Abstract
The migration of artificial intelligence (AI) researchers from academia to industry has recently sparked concerns about its implications for scientific progress. Can academia retain enough talent to shape AI advancements and counterbalance the growing influence of corporate AI labs? Analyzing OpenAlex data, we find a significant transition of premier talent to industry roles over the past decade, particularly to major tech firms. Young, highly cited scholars from leading institutions are the most likely to make this move. Following the transition, their research tends to show reduced novelty and impact. This industry-dominant shift in AI research highlights worries of an "AI brain drain", the sidelining of exploratory science for commercial interests, and the potential misalignment with societal goals.

Wikipedia talk pages serve as democratic forums for editors, fostering discussions on article content and policies, which markedly differ from the formal content of main articles. While existing research predominantly focuses on the articles themselves, this study explores the unique citation dynamics within talk pages. Utilising data from the Wikipedia Knowledge Graph, Crossref Event Data, and OpenAlex, we examine the characteristics and patterns of citations within these collaborative discussions. Preliminary findings suggest that talk pages, although less frequented than main articles, engage deeply with scholarly outputs, often discussing them without necessarily transferring these citations to the main article content. This underscores the distinct consumption patterns on talk pages and their importance in shaping public and scholarly discourse on Wikipedia.

Modern scientific research evokes ecological imagery and metaphors, given that it is global, interdependent, and diverse. Ecological network structures—like matrices of species inhabiting islands across an archipelago—can be reordered to form nested patterns. These patterns describe the overall health of ecosystems, place species on a spectrum between being described as generalists (foxes) or specialists (hedgehogs), and which of these interactions might appear or disappear. Using the number of citations universities receive for work published in a particular subfield taken from over 20 million scientific publications in OpenAlex, we construct and analyze yearly nested ecological networks of a dozen academic fields between 1990 to 2017. We find increasingly nested structures across fields, indicating a robust global research ecology that also infers future acknowledgment in different subfields. We argue that this framework can inform policy on scientific research and university funding and evaluation.

This research note presents evidence of persistent disparities in naming practices in social science research between the global North and South from 1960 to the present. The analysis relied on bibliometric records of 11.9 million articles sourced from the OpenAlex collection. By covering the 1960–2024 period and including six social science and humanities subdisciplines—Economic, Geography, History, Political Sciences, Psychology, and Sociology—this study replicates and expands an existing study that used Scopus data (0.5 million articles). Results show that scientific articles about global North countries and populations are systematically less likely to mention the country name in their titles compared to global South. For example, compared to the US, articles about China are between 1.8 and 2.7 times more likely to include the country name in the title. Significant variations across subdisciplines are observed, with Psychology displaying the lowest localization rates and greatest geographical gaps, and History showing seemingly converging trends over time across geographies. Continued research is needed to warrant diversity and inclusivity in social science research. A country-year dataset with country-mention counts in titles and abstracts is made available.

AbstractEducational outcomes are heavily reliant on student engagement, yet this concept is complex and subject to diverse interpretations. The intricacy of the issue arises from the broad spectrum of interpretations, each contributing to the understanding of student engagement as both complex and multifaceted. Given the emergence and increasing use of Learning Analytics (LA) within higher education to provide enhanced insight into engagement, research is needed to understand how engagement is conceptualised by LA researchers and what dimensions and indicators of engagement are captured by studies that use log data. This systematic review synthesises primary research indexed in the Web of Science, Scopus, ProQuest, A + Education, and SAGE journals or captured through snowballing in OpenAlex. Studies were included if they were published between 2011 and 2023, were journal articles or conference papers and explicitly focused on LA and engagement or disengagement within formal higher education settings. 159 studies were included for data extraction within EPPI Reviewer. The findings reveal that LA research overwhelmingly approaches engagement using observable behavioural engagement measures, such as clicks and task duration, with very few studies exploring multiple dimensions of engagement. Ongoing issues with methodological reporting quality were identified, including a lack of detailed contextual information, and recommendations for future research and practice are provided.

AbstractAlthough the field of Artificial Intelligence in Education (AIEd) has a substantial history as a research domain, never before has the rapid evolution of AI applications in education sparked such prominent public discourse. Given the already rapidly growing AIEd literature base in higher education, now is the time to ensure that the field has a solid research and conceptual grounding. This review of reviews is the first comprehensive meta review to explore the scope and nature of AIEd in higher education (AIHEd) research, by synthesising secondary research (e.g., systematic reviews), indexed in the Web of Science, Scopus, ERIC, EBSCOHost, IEEE Xplore, ScienceDirect and ACM Digital Library, or captured through snowballing in OpenAlex, ResearchGate and Google Scholar. Reviews were included if they synthesised applications of AI solely in formal higher or continuing education, were published in English between 2018 and July 2023, were journal articles or full conference papers, and if they had a method section 66 publications were included for data extraction and synthesis in EPPI Reviewer, which were predominantly systematic reviews (66.7%), published by authors from North America (27.3%), conducted in teams (89.4%) in mostly domestic-only collaborations (71.2%). Findings show that these reviews mostly focused on AIHEd generally (47.0%) or Profiling and Prediction (28.8%) as thematic foci, however key findings indicated a predominance of the use of Adaptive Systems and Personalisation in higher education. Research gaps identified suggest a need for greater ethical, methodological, and contextual considerations within future research, alongside interdisciplinary approaches to AIHEd application. Suggestions are provided to guide future primary and secondary research.

Balancing speed and quality during crises pose challenges for ensuring the value and utility of data in social science research. The COVID-19 pandemic in particular underscores the need for high-quality data and rapid dissemination. Given the importance of behavioural measures and compliance with measures to contain the pandemic, social science research has played a key role in policymaking during this global crisis. This study addresses two key research questions: How FAIR (findable, accessible, interoperable and reusable) are social science data on the COVID-19 pandemic? Which study features are related to the level of FAIRness scores of datasets? We assess the FAIRness of n = 1131 articles, retrieved through a keyword search in the Web of Science database, employing both automated and manual coding methods. Our study inclusion criteria encompass empirical studies on the COVID-19 pandemic published between 2019 and 2023 with a social science focus and explicit reference to the underlying dataset(s). Our analysis of n = 45 datasets reveals substantial differences in FAIRness for different types of research on the COVID-19 pandemic. The overall FAIRness of data is acceptable, although particularly Reusability scores fall short, in both the manual and the automatic assessment. Further, articles explicitly linked to the Social Science concept in the OpenAlex database exhibit a higher mean overall FAIRness value. Based on these results, we derive recommendations for balancing ethical obligations and the potential tradeoff between speed and data (sharing) quality in social-scientific crisis research.

Balancing speed and quality during crises pose challenges for ensuring the value and utility of data in social science research. The COVID-19 pandemic in particular underscores the need for high-quality data and rapid dissemination. Given the importance of behavioral measures and compliance with measures to contain the pandemic, social science research has played a key role for policymaking during this global crisis.

This study addresses two key research questions: How FAIR (findable, accessible, interoperable, and reusable) are social science data on the COVID-19 pandemic? Which study features are related to the level of FAIRness scores of datasets? We assess the FAIRness of n=1,131 articles, retrieved through a keyword search in the Web of Science database, employing both automated and manual coding methods. Our study inclusion criteria encompass empirical studies on the COVID-19 pandemic published between 2019-2023 with a social science focus and explicit reference to the underlying dataset(s). Our analysis of n=45 datasets reveals substantial differences in FAIRness for different types of research on the COVID-19 pandemic. The overall FAIRness of data is acceptable, although particularly Reusability scores fall short, in both the manual and the automatic assessment. Further, articles explicitly linked to the Social Science concept in the OpenAlex database exhibit a higher mean overall FAIRness value. Based on these results, we derive recommendations for balancing ethical obligations and the potential tradeoff between speed and data (sharing) quality in social-scientific crisis research.

The replication data contains the manual and automatic coded values for FAIR criteria and the complete code to re-produce the results for the article.