Citizen science (CS) has become a crucial form of public participation in science, helping to address global challenges and contribute to more democratic knowledge production. Despite its increasing presence (including literature reviews), uncertainties persist regarding the quality of data collected, the level of citizen participation, and ethical concerns. This editorial explores CS participation, aiming to provide a comprehensive analysis of engagement patterns, and challenges, and offering recommendations to enhance citizen involvement in scientific initiatives. A systematic review of existing literature indexed on Web of Science is conducted, following the PRISMA framework. The review covered 1,613 selected studies examining ‘participation’ across various scientific fields, identifying the methodologies used and the factors influencing engagement, such as benefits, challenges, and ethical concerns. The results reveal a growing trend in CS, particularly in the natural sciences, with citizen involvement mostly limited to data collection rather than higher-level engagement like analysis or decision-making. The study also highlights the underrepresentation of social sciences and the need for more interdisciplinary approaches. In conclusion, the article calls for increased interdisciplinarity, greater integration of citizens in decision-making processes, diversification of participatory methods, and context-specific strategies for participation. It emphasizes the importance of fostering public trust and awareness in science and proposes that these actions will improve the inclusivity and impact of citizen science initiatives., This work has been supported by a Ramón y Cajal grant funded by the Spanish Ministry of Science (Ref: RYC2022-037076-I), and by the PRIME TIMEproject (PID2022-142569NA-I00), funded by MCIN/AEI/10.13039/501100011033, and by “ERDF A way of making Europe”, Peer reviewed

This study examines content curation on the front pages of four Spanish digital newspapers: two legacy media outlets, El País and La Vanguardia, and two purely digital, elDiario.es and El Español. This exploratory and unprecedented research aims to understand the characteristics of front-page news curation in digital press in terms of themes, authorship and genre, origin and temporal range of curated content, types of sources used, the function of curated links, and techniques employed to add value to the presented information. The CAS (Curation Analysis System) methodology is used for content analysis of a selection of news items from different time slots and days of the week, following the constructed week method. The results provide a better understanding of the characteristics of curation on newspaper front pages, comparing them with other curation studies in journalistic products, such as media newsletters and live news.

Includes supplementary materials for the online appendix., ChatGPT Search was launched on October 31 by OpenAI as a new AI-powered search engine. Among its features, it stands out for its ability to retrieve information from various online sources, including scholarly databases, which potentially allows the use of this tool for academic tasks, both quantitative and qualitative. To test its features, five academic tasks are designed: two quantitative (collecting hit count estimates from Google Search and scraping bibliometric indicators from ResearchGate); two qualitative tasks (performing a narrative synthesis of an academic topic and generating a brief academic author profile), and a mixed task (identifying, collecting and describing a list of publications from Google Scholar Profiles). The results show the inability of ChatGPT Search to conduct quantitative tasks correctly, fabricating the results (hallucination). Qualitative tasks are performed with better results; however, errors are detected, which prevent recommending the tool without manual analysis and refinement. Finally, the ability to generate links to scientific publications can open up competition among academic sites to be mentioned in the ChatGPT Search responses, giving rise to Academic Generative Engine Optimization (A-GEO)., Study supported by project “Publicized research: new indicators to measure the effects of timely marketing on scholarly evaluation (PRIME TIME)” (PID2022-142569NA-I00), funded by MCIN/AEI/10.13039/501100011033,and by “ERDF A way of making Europe”, and by Project “Parameters and strategies to increase the relevance of media and digital communication in society: curation, visualiza-tion and visibility (CUVICOM)”, funded by MICIN/AEI/PID2021-123579OB-I00 and by FEDER/EU.

Includes supplementary materials for the online appendix., ChatGPT Search was launched on October 31 by OpenAI as a new AI-powered search engine. Among its features, it stands out for its ability to retrieve information from various online sources, including scholarly databases, which potentially allows the use of this tool for academic tasks, both quantitative and qualitative. To test its features, five academic tasks are designed: two quantitative (collecting hit count estimates from Google Search and scraping bibliometric indicators from ResearchGate); two qualitative tasks (performing a narrative synthesis of an academic topic and generating a brief academic author profile), and a mixed task (identifying, collecting and describing a list of publications from Google Scholar Profiles). The results show the inability of ChatGPT Search to conduct quantitative tasks correctly, fabricating the results (hallucination). Qualitative tasks are performed with better results; however, errors are detected, which prevent recommending the tool without manual analysis and refinement. Finally, the ability to generate links to scientific publications can open up competition among academic sites to be mentioned in the ChatGPT Search responses, giving rise to Academic Generative Engine Optimization (A-GEO)., Study supported by project “Publicized research: new indicators to measure the effects of timely marketing on scholarly evaluation (PRIME TIME)” (PID2022-142569NA-I00), funded by MCIN/AEI/10.13039/501100011033,and by “ERDF A way of making Europe”, and by Project “Parameters and strategies to increase the relevance of media and digital communication in society: curation, visualiza-tion and visibility (CUVICOM)”, funded by MICIN/AEI/PID2021-123579OB-I00 and by FEDER/EU.

[ES] Introducción. Este estudio caracteriza la producción de vídeos de divulgación
científica publicados por la empresa SciTube, que combina producción audiovisual
profesional con validación académica. Operando como canal público de divulgación
y servicio de comunicación científica, convirtiéndola en un caso relevante para
analizar estrategias profesionales en YouTube.
Método. Se diseñó un análisis mixto basado en 33 variables cuantitativas y
cualitativas, organizadas en seis dimensiones. Los metadatos de los 411 vídeos
publicados entre 2019 y 2025 se obtuvieron mediante la API de YouTube, mientras
que las variables narrativas, visuales, sonoras y de accesibilidad se analizaron
mediante observación directa de 50 vídeos recientes.
Análisis. Se realizó un análisis estadístico descriptivo de los metadatos y un análisis
de contenido sistemático de los vídeos seleccionados para identificar patrones de
producción y estrategias narrativas asociadas a mayor rendimiento y engagement.
Resultados. Se observa un crecimiento continuo, con una duración media cercana
a cuatro minutos y una concentración temática en Medicina y Salud (45,5%). El
contenido divulgativo, el tono urgente y la voz femenina se asocian con mayor
engagement.
Conclusiones. SciTube ha consolidado un modelo profesionalizado de divulgación
científica en YouTube, eficaz en términos narrativos, aunque limitado por la escasa
atención a la accesibilidad y los metadatos., Estudio financiado por el Proyecto Investigación publicitada: nuevos indicadores para medir los
efectos del marketing oportuno en la evaluación académica (PRIME TIME) (PID2022-142569NA-I00),
MCIN/AEI/10.13039/501100011033 y por FEDER Una manera de hacer Europa .

[EN] Purpose ¿ Google Scholar isincreasingly being used to conductscholarly information searches. Yet, the design
and features of this tool can have a critical impact on the publications retrieved and subsequently cited,
highlighting the need for models that can shed light on the scholarly search process. Existing informationseeking models, though, are often too abstract to capture the reality of how scientists engage with Google
Scholar. Here, we develop the Find-Use Model, a process-oriented analytical framework designed to describe,
classify and measure scholarly search behaviour in online environments.
Design/methodology/approach ¿ The Find-Use Model was developed through a literature review on scholarly
information-seeking and direct observation of Google Scholar in use. Following a Grounded Theory (GT)
approach, a constant comparative analysis was applied to iteratively refine categories derived from both data and
theory. The model comprises three stages (Open, Search, Close), 17 actions and seven contextual categories,
capturing both system functionalities and user characteristics.
Findings ¿ The model accounts for iterative, non-linear behaviours and explicitly links search actions
(e.g. Build) to use actions (e.g. Get). Evaluated against recognised quality criteria, the model demonstrates
strengths in accuracy, systematic organisation and explanatory capacity.
Originality/value ¿ The Find-Use Model is a useful tool for future observational research that linksinformation
search processes with scholarly visibility and impact. Its application opens new avenues to assess and enhance
the ways in which researchers discover and use scholarly information., This work was supported by MICIU/AEI/10.13039/501100011033 and by FSEþ (award
number: RYC2022-037076-I) and MCIN/AEI/10.13039/501100011033 and by ERDFAway of making
Europe (award number: PID2022-142569NA-I00).

[EN] ChatGPT Search was launched on October 31 by OpenAI as a new AI-powered
search engine. Among its features, it stands out for its ability to retrieve
information from various online sources, including scholarly databases, which
potentially allows the use of this tool for academic tasks, both quantitative and
qualitative. To test its features, five academic tasks are designed: two
quantitative (collecting hit count estimates from Google Search and scraping
bibliometric indicators from ResearchGate); two qualitative tasks (performing a
narrative synthesis of an academic topic and generating a brief academic
author profile), and a mixed task (identifying, collecting and describing a list of
publications from Google Scholar Profiles). The results show the inability of
ChatGPT Search to conduct quantitative tasks correctly, fabricating the results
(hallucination). Qualitative tasks are performed with better results; however,
errors are detected, which prevent recommending the tool without manual
analysis and refinement. Finally, the ability to generate links to scientific
publications can open up competition among academic sites to be mentioned
in the ChatGPT Search responses, giving rise to Academic Generative Engine
Optimization (A-GEO)., Study supported by project Publicized research: new indicators to measure the effects of timely marketing on scholarly evaluation (PRIME TIME) (PID2022-142569NA-I00), funded by MCIN/AEI/10.13039/501100011033, and by ERDF A way of making Europe , and by Project Parameters and strategies to increase the relevance of media and digital communication in society: curation, visualization and visibility (CUVICOM) , funded by MICIN/AEI/PID2021-123579OB-I00 and by FEDER/EU.

[ES] A pesar de la creciente integración de las herramientas de Deep Research en los flujos de trabajo
académicos, siguen siendo escasas las evidencias empíricas acerca del funcionamiento, estabilidad y posibles sesgos de sus sistemas de citación. Este estudio aborda este hueco en la literatura
evaluando la intensidad, consistencia y características bibliográficas de las referencias citadas
en los informes de literatura generados por Ai2 Asta, con el objetivo de comprender cómo funciona su sistema de citación y analizar sus implicaciones para la comunicación científica. Para
ello, se enviaron diez consultas específicas a través de la funcionalidad Summarise Literature de
Asta, y se llevaron a cabo dos rondas independientes de recogida de datos. De cada informe se
extrajeron y examinaron las citas embebidas en el texto, las referencias citadas, así como otras
métricas relacionadas con el proceso de respuesta. Los resultados revelan una alta intensidad
de citación, así como un conjunto de fuentes diverso pero concentrado en unas pocas fuentes.
Sin embargo, se observa una notable inestabilidad en la composición de las referencias citadas
en consultas idénticas, junto con una falta de concordancia entre los documentos recuperados
y los finalmente citados, lo que sugiere la existencia de mecanismos adicionales y opacos de
selección durante la generación del informe. Estos hallazgos indican que, aunque Ai2 Asta
produce informes bien estructurados y de calidad, su inestabilidad y falta de transparencia en
el proceso de citación, plantean desafíos de cara a su uso en estudios cuantitativos de la ciencia,
debido a su falta de reproducibilidad y trazabilidad. A pesar del número limitado de consultas
y del alcance disciplinar restringido, los resultados ofrecen información valiosa para investigadores, expertos en Bibliometría, desarrolladores y evaluadores científicos interesados en
comprender, utilizar o regular de forma responsable los asistentes académicos basados en IA., Grant PID2022-142569NA-I00, funded by MCIN/AEI/10.13039/501100011033
and by ERDF A way of making Europe

[EN] Citizen science (CS) has become a crucial form of public participation in science, helping to address global challenges and contribute to more democratic knowledge production. Despite its increasing presence (including literature reviews), uncertainties persist regarding the quality of data collected, the level of citizen participation, and ethical concerns. This editorial explores CS participation, aiming to provide a comprehensive analysis of engagement patterns, and challenges, and offering recommendations to enhance citizen involvement in scientific initiatives. A systematic review of existing literature indexed on Web of Science is conducted, following the PRISMA framework. The review covered 1,613 selected studies examining `participation¿ across various scientific fields, identifying the methodologies used and the factors influencing engagement, such as benefits, challenges, and ethical concerns. The results reveal a growing trend in CS, particularly in the natural sciences, with citizen involvement mostly limited to data collection rather than higher-level engagement like analysis or decision-making. The study also highlights the underrepresentation of social sciences and the need for more interdisciplinary approaches. In conclusion, the article calls for increased interdisciplinarity, greater integration of citizens in decision-making processes, diversification of participatory methods, and context specific strategies for participation. It emphasizes the importance of fostering public trust and awareness in science and proposes that these actions will improve the inclusivity and impact of citizen science initiatives., This work has been supported by a Ramón y Cajal grant funded by the Spanish Ministry of Science (Ref: RYC2022-
037076-I), and by the PRIME TIME project (PID2022-142569NA-I00), funded by MCIN/AEI/10.13039/501100011033,
and by ERDF A way of making Europe .

[EN] Given the growing importance of academic search engine optimization (A-SEO) in enhancing research visibility, it is critical to understand how different SEO analytics tools impact quantitative research findings. This study compares four leading SEO analytics tools ¿ Ahrefs, SEMrush, Serpstat, and Ubersuggest ¿ using data from two well-known academic open-access mega publishers (MDPI and Frontiers) to assess the similarities of the web metrics provided, specifically organic keyword counts, keyword search volumes, and URL-based web traffic. Results show significant variation in the data, with a low overlap in organic keywords and URL-based web traffic across tools, suggesting that tool selection significantly determines findings. The study also highlights methodological challenges when comparing SEO tools, most notably differences in filtering procedures that jeopardize a rigorous comparison of tools. This work provides valuable insights for researchers and reviewers, offering guidelines to improve research designs and enhance A-SEO research transparency., The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Grant PID2022-142569NA-I00, funded by MCIN/AEI/ 10.13039/501100011033 and by "ERDF A way of making Europe" and Grant PID2021-123579OB-I00, funded by MCIN/AEI/ 10.13039/501100011033 and by "ERDF A way of making Europe."

[EN] Purpose To describe the characteristics of research outputs using persistent identifiers generated by ResearchGate to gain insight into what publications are shared and disseminated through this functionality, revealing their academic and non-academic impact.Design/methodology/approach A total of 1,092,934 RG-DOIs were collected, using the DataCite API, along with bibliographic metadata for the associated registered output (RG-DOI publications). The subsequent analysis evaluated the publication date, document type, and language. These values were crossreferenced against the full text of a random sample of 666 records to verify accuracy.Findings RG-DOIs have served primarily to identify and make accessible scholarly gray literature, including posters, presentations, conference papers, and theses, with notable emphasis on publications in Spanish and Portuguese. Around 41,000 citations from Web of Science indexed publications to RG publications are evidence of their infrequent but perceptible use in scholarly discourse. The declining number of registrations of RG-DOIs observed may indicate a shift in researcher preferences to alternative platforms for DOI generation.Research limitations The study uncovered substantial inconsistencies in DataCite metadata, which can be attributed to the automated DOI registration process and internal changes in the available document types on ResearchGate.Practical implications The study encountered challenges in conducting a quantitative analysis due to inconsistencies in the metadata. These have potential implications for researchers, practitioners, and librarians relying on RG-DOIs to conduct bibliometric or bibliographic analysis.Originality/value This study is the first comprehensive analysis of RG-DOIs and, as such, provides a unique perspective into academic gray literature. It also sheds light on the quality of ResearchGate data transmitted to DataCite when registering DOIs., This study was supported by Grant PID2022-142569NA-I00, funded by MCIN/AEI/10.13039/501100011033 and by the European Union through "ERDF A way of making Europe."

[EN] The digital age, driven by advancements in data processing and internet technologies, has seen virtual social networks, particularly Instagram, become integral to daily life, influencing both leisure and professional activities. Instagram, with over two billion users in 2023, emphasizes visual storytelling, making it a pivotal platform for media outlets to engage audiences through photos and videos. This study quantitatively analyses the Instagram engagement and content strategies of five prominent legacy news outlets from the United States and the United Kingdom¿The New York Times, The Guardian, USA Today, The Independent, and The Washington Post. Through an examination of over 9000 posts from January to December 2023, this research evaluates posting frequency, user engagement (likes and comments), hashtag usage, profile mentions, and geographic coverage. Findings indicate significant differences in posting strategies and engagement levels, highlighting The Washington Post¿s intensive posting and The Guardian¿s selective engagement strategy. This study emphasizes tailored strategies, thematic consistency, and careful use of mentions and hashtags as crucial for maximizing audience interaction., This work is part of the Project "Parameters and strategies to increase the relevance of media and digital communication in society: curation, visualisation and visibility (CUVICOM)" funded by MICIU/AEI/PID2021-123579OB-I00 and by "ERDF/EU". This publication is part of the Project PID2022-142569NA-I00 funded by MCIN/AEI/10.13039/501100011033 and by "ERDF A way of making Europe".

[EN] Given the importance of Google Search in generating visits to institutional repositories (IR), a lack of visibility in search engine results pages can hinder the possibility of their publications being found, read, downloaded, and, eventually, cited. To address this, institutions need to evaluate the visibility of their repositories to determine what actions might be implemented to enhance them. However, measuring the search engine optimization (SEO) visibility of IRs requires a highly accurate, technically feasible method. This study constitutes the first attempt to design such a method, specifically applied here to measuring the IR visibility of Spain's national university system in Google Search based on a set of SEO-based metrics derived from the Ubersuggest SEO tool. A comprehensive dataset spanning three months and comprising 217,589 bibliographic records and 316,899 organic keywords is used as a baseline. Our findings show that many records deposited in these repositories are not ranked among the top positions in Google Search results, and that the most visible records are mainly academic works (theses and dissertations) written in Spanish in the Humanities and Social Sciences. However, most visits are generated by a small number of records. All in all, our results call into question the role played by IRs in attracting readers via Google Search to the institutions' scientific heritage and serve to underscore the prevailing emphasis within IRs on preservation as opposed to online dissemination. Potential improvements might be achieved using enhanced metadata schemes and normalized description practices, as well as by adopting other actionable insights that can strengthen the online visibility of IRs. This study increases understanding of the role played by web indicators in assessing the web-based impact of research outputs deposited in IRs, and should be of particular interest for a range of stakeholders, including open access and open science advocates, research agencies, library practitioners, repository developers, and website administrators., Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. Research funded by Grant GV/2021/141 (Regional Government of Valencia, Spain) and Grant PID2022-142569NA-I00, funded by MCIN/AEI/10.13039/501100011033 and by "ERDF A way of making Europe".

[EN] Press releases covering novel scientific developments play an important role in science communication. Their number has increased in the past years, making them a valuable object of quantitative analysis in science communication research. However, large-scale research on academic press releases is limited, likely due to the lack of comprehensive and open datasets. In this data paper, we describe how we created a dataset of press releases published on EurekAlert!, an online science news service. The dataset consists of the metadata of a total of 566,566 press releases. We describe how we extracted the data, transformed it into a relational database, and made the code and data openly available to the research community. Next to that, we explain the structure of the dataset and how it can be accessed and propose potential integrations into other research information systems., We would like to thank Brian Lin, Director, Editorial Content Strategy at EurekAlert! for providing advice on our work with press releases from EurekAlert! and approving our data availability approach. We also thank Andrew Hoffman, data steward at Leiden University, for advising us on the process of openly publishing the dataset. Rodrigo Costas and Jonathan Dudek are partially funded by the South African DSI-NRF Centre of Excellence in Scientometrics and Science, Technology and Innovation Policy (SciSTIP). Jingwen Zhang is financially supported by the China Scholarship Council (Grant No.202206190142). Enrique Orduna-Malea is partially funded by PID2022-142569NA-I00, granted by MCIN/AEI/ 10.13039/501100011033 and by "ERDF A way of making Europe."

[ES] Objetivos: Analizar comparativamente cómo seis herramientas de búsqueda de patentes ¿Lens, Google Patents, Espacenet, Dimensions, Perplexity Patents y PQAI¿ median el acceso, la interpre-tación, la trazabilidad y la comunicación del conocimiento patentado en contextos académicos.Metodología: Se aplicó un diseño exploratorio-comparativo en dos fases: análisis documental y fun-cional de las herramientas, y prueba manual con una consulta común en lenguaje natural sobre un dispositivo wearable para detectar riesgo de deshidratación mediante sensores cutáneos y alertas móviles.Resultados: Los resultados muestran que ninguna herramienta combina plenamente accesibilidad, trazabilidad, integración ciencia-patente, explicabilidad algorítmica y gratuidad. Lens destaca por su equilibrio entre cobertura y relación ciencia-patente; Espacenet por su autoridad documental; Di-mensions por su integración académica; Perplexity Patents por su facilidad conversacional; y PQAI por su búsqueda semántica abierta. El estudio concluye que la IA facilita el acceso al conocimiento patentado, pero introduce riesgos de opacidad, baja reproducibilidad y dependencia interpretativa, por lo que se requiere alfabetización patentaria y algorítmica crítica.

[EN] This editorial explores the significance of research visibility within the evolving landscape of academic communication, mainly focusing on the role of search engines as online meta-markets shaping the impact of research. With the rapid expansion of scientific output and the increasing reliance
on algorithm-driven platforms such as Google and Google Scholar, the online visibility of scholarly
work has become an essential factor in determining its reach and influence. The need for more rigorous research into academic search engine optimization (A-SEO), a field still in its infancy despite
its growing relevance, is also discussed, highlighting key challenges in the field, including the lack
of robust research methodologies, the skepticism within the academic community regarding the
commercialization of science, and the need for standardization in reporting and measurement techniques. This editorial thus invites a multidisciplinary dialogue on the future of research visibility,
with significant implications for academic publishing, science communication, research evaluation,
and the global scientific ecosystem., [CA] Aquest editorial explora la importància de la visibilitat de la recerca en el context canviant de la
comunicació acadèmica, centrant-se principalment en el paper dels motors de cerca com a metamercats en línia que modelen l¿impacte de la recerca. Amb l¿expansió ràpida de la producció científica
i la creixent dependència de plataformes impulsades per algoritmes, com ara Google i Google Scholar,
la visibilitat en línia del treball acadèmic s¿ha convertit en un factor essencial per determinar el seu
abast i influència. També es discuteix la necessitat d¿una investigació més rigorosa sobre l¿optimització de motors de cerca acadèmics (A-SEO), un camp que encara es troba en les seves primeres
etapes malgrat la seva rellevància creixent. Es destaquen els principals reptes del camp, com ara la manca de metodologies d¿investigació sòlides, l¿escepticisme dins de la comunitat acadèmica respecte a la comercialització de la ciència i la necessitat d¿estandardització en les tècniques de mesura
i report. Aquest editorial convida així a un diàleg multidisciplinari sobre el futur de la visibilitat de
la recerca, amb implicacions significatives per a l¿edició acadèmica, la comunicació científica, l¿avaluació de la recerca i l¿ecosistema científic global., Grant PID2022-142569NA-I00, funded by MCIN/AEI/10.13039/501100011033 and by ERDF A way of making Europe .

[ES] Se presenta un análisis funcional de Scholar
Labs, la propuesta, todavía en fase beta, de inteligencia artificial integrada en Google Scholar. Se examina su interfaz
de búsqueda con el propósito de clarificar sus funciones
e identificar los recursos que pone a disposición del usuario. Para ello, la herramienta se pone a prueba mediante
la aplicación de una serie de peticiones (prompts) que permiten describir su comportamiento y valorar su grado de
eficacia a fecha de su día de lanzamiento. A pesar de las
limitaciones actuales y la previsible evolución de la herramienta, se vislumbran dos grandes oportunidades para la
comunidad científica: nuevas formas de interactuar con Google Scholar para la recuperación de documentos académicos y nuevos desafíos para incrementar la visibilidad de nuestra producción científica., Trabajo realizado en el marco del proyecto PID2022-142569NA-I00, financiado por el MCIN/AEI/
10.13039/501100011033 y por ERDF A way of making Europe .