Crossing the academic ocean? Judit Bar-Ilan's oeuvre on search engines studies

[EN] The main objective of this work is to analyse the contributions of Judit Bar-Ilan to the search engines studies. To do this, two complementary approaches have been carried out. First, a systematic literature review of 47 publications authored and co-authored by Judit and devoted to this topic. Second, an interdisciplinarity analysis based on the cited references (publications cited by Judit) and citing documents (publications that cite Judit's work) through Scopus. The systematic literature review unravels an immense amount of search engines studied (43) and indicators measured (especially technical precision, overlap and fluctuation over time). In addition to this, an evolution over the years is detected from descriptive statistical studies towards empirical user studies, with a mixture of quantitative and qualitative methods. Otherwise, the interdisciplinary analysis evidences that a significant portion of Judit's oeuvre was intellectually founded on the computer sciences, achieving a significant, but not exclusively, impact on library and information sciences.Orduña-Malea, E. (2020). Crossing the academic ocean? Judit Bar-Ilan's oeuvre on search engines studies. Scientometrics. 123(3):1317-1340. https://doi.org/10.1007/s11192-020-03450-4S131713401233Bar-Ilan, J. (1998a). On the overlap, the precision and estimated recall of search engines. A case study of the query “Erdos”. Scientometrics,42(2), 207–228. https://doi.org/10.1007/bf02458356.Bar-Ilan, J. (1998b). The mathematician, Paul Erdos (1913–1996) in the eyes of the Internet. Scientometrics,43(2), 257–267. https://doi.org/10.1007/bf02458410.Bar-Ilan, J. (2000). The web as an information source on informetrics? A content analysis. Journal of the American Society for Information Science and Technology,51(5), 432–443. https://doi.org/10.1002/(sici)1097-4571(2000)51:5%3C432:aid-asi4%3E3.0.co;2-7.Bar-Ilan, J. (2001). Data collection methods on the web for informetric purposes: A review and analysis. Scientometrics,50(1), 7–32.Bar-Ilan, J. (2002). Methods for measuring search engine performance over time. Journal of the American Society for Information Science and Technology,53(4), 308–319. https://doi.org/10.1002/asi.10047.Bar-Ilan, J. (2003). Search engine results over time: A case study on search engine stability. Cybermetrics,2/3, 1–16.Bar-Ilan, J. (2005a). Expectations versus reality—Search engine features needed for Web research at mid 2005. Cybermetrics,9, 1–26.Bar-Ilan, J. (2005b). Expectations versus reality—Web search engines at the beginning of 2005. In Proceedings of ISSI 2005: 10th international conference of the international society for scientometrics and informetrics (Vol. 1, pp. 87–96).Bar-Ilan, J. (2010). The WIF of Peter Ingwersen’s website. In B. Larsen, J. W. Schneider, & F. Åström (Eds.), The Janus Faced Scholar a Festschrift in honour of Peter Ingwersen (pp. 119–121). Det Informationsvidenskabelige Akademi. Retrieved 15 January 15, 2020, from https://vbn.aau.dk/ws/portalfiles/portal/90357690/JanusFacedScholer_Festschrift_PeterIngwersen_2010.pdf#page=122.Bar-Ilan, J. (2018). Eugene Garfield on the web in 2001. Scientometrics,114(2), 389–399. https://doi.org/10.1007/s11192-017-2590-9.Bar-Ilan, J., Mat-Hassan, M., & Levene, M. (2006). Methods for comparing rankings of search engine results. Computer Networks,50(10), 1448–1463. https://doi.org/10.1016/j.comnet.2005.10.020.Thelwall, M. (2017). Judit Bar-Ilan: Information scientist, computer scientist, scientometrician. Scientometrics,113(3), 1235–1244. https://doi.org/10.1007/s11192-017-2551-3

Attention profile: information to which we pay attention?

Technology, a tool for better completing our daily tasks, could, if not mastered properly, be converted into a work barrier. That could produce a narrow vision that prevents focus on the true purpose of work. This can be seen in that little attention is given to metadata schemes without structured bibliographic descriptions. This paper outlines the schemes based on Attention profile, providing a brief introduction to the concept and outlining the two main standards that are trying to structure it, Apml and the recent Open Taste initiative

Toward context metrics: citation classification on the Web of Science

[EN] The purpose of this note is to describe the new citation context classification feature provided by Web of Science, in which citations received by publications are classified into five categories (Background, Basis, Support, Differ, and Discuss). To exemplify the functionality, two case studies have been carried out: one involving a journal (Profesional de la información; 1,604 publications) and the other, an author (Loet Leydesdorff; 341 publications). Both cases reflect the still low coverage of classified citations, which currently limits the use of this functionality. Finally, some of the questions that arise with the use of these context metrics (precision, comprehension, simplificatio[EN] El objetivo de esta nota es describir la nueva funcionalidad de clasificación de citas por contexto proporcionada por Web of Science (WoS), en la que las citas recibidas por un trabajo se clasifican en cinco categorías (Background, Basis, Support, Differ, Discuss). Con el fin de testear las prestaciones de la funcionalidad se han llevado a cabo dos casos de estudio. Por un lado, una revista (Profesional de la información; 1,604 publicaciones) y, por otro lado, un autor (Loet Leydesdorff; 341 publicaciones). Ambos casos reflejan la todavía baja cobertura de citas clasificadas (inferior al 6%) que limitan el uso de esta funcionalidad actualmente. Finalmente se discuten algunos de los interrogantes que se abren con el uso de estas métricas de contexto, tales como la precisión, comprensión, simplificación, representatividad, comparabilidad, idioma, agregación, uso evaluativo, efectos y extrapolación.Orduña Malea, E. (2022). Hacia las métricas de contexto: clasificación de citas en Web of Science. Anuario ThinkEPI. 16(1):1-7. https://doi.org/10.3145/thinkepi.2022.e16a321716

Propuesta de un modelo de análisis redinformétrico multinivel para el estudio sistémico de las universidades españolas (2010)

La universidad, en tanto que institución milenaria, tiene una influencia y peso en la sociedad actual incuestionable. Una influencia tanto activa (en su vertiente formadora de futuros profesionales y ciudadanos, y de generación de nuevo conocimiento e investigación) como pasiva (debido a sus enormes necesidades de financiación). Este peso e influencia de las universidades en la sociedad marcan la necesidad de establecer mecanismos y procedimientos para analizar su rendimiento, eficiencia y eficacia como institución, así como de instrumentos para visualizar adecuadamente este rendimiento, todo ello en el contexto de una sociedad marcada por la gestión y transferencia masiva de información a través de las redes de comunicación. Se vislumbran por tanto 3 líneas de investigación complementarias: el análisis de la universidad, su rendimiento (explicitado por el rastro digital que ésta genera y las técnicas existentes para cuantificarlo), y la visualización de este rendimiento, donde la técnica de ranking es la más extendida dado el impacto que su formato genera en los usuarios. Además, la naturaleza sistémica de la universidad determina, complica y acota cada una de estas áreas. La presente tesis doctoral pretende por tanto explorar las capacidades que la cibermetría (renombrada en este trabajo como redinformetría) proporciona para analizar, desde un punto de vista sistémico, las universidades del sistema español, con el propósito de obtener nuevo conocimiento acerca del rendimiento de éstas que permita la construcción futura de rankings sistémicos de universidades. Para ello, en primer lugar se ofrece un estado de la cuestión enfocado en las 3 principales líneas de trabajo (universidad, ranking y cibermetría), con el objetivo de ofrecer un marco de trabajo exhaustivo y crítico. Tras la parte introductoria, se propone un modelo de análisis redinformétrico multinivel de universidades que facilite la obtención de información estructurada, y que permita su posterior utilización en el diseño y elaboración de rankings web de universidades. Este modelo de análisis se basa en el establecimiento de 3 niveles (institucional, externo y satélite) y dos subniveles (contorno e interno). Los resultados obtenidos muestran que el modelo de análisis propuesto, basado en niveles (institucional, externo y satélite) y subniveles (contorno y unidad) es sencillo, independiente de técnica y proporciona información estructurada que permite un análisis completo de cada institución.Orduña Malea, E. (2012). Propuesta de un modelo de análisis redinformétrico multinivel para el estudio sistémico de las universidades españolas (2010) [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/14420Palanci

¿Atraen las universidades latinoamericanas a la industria en la publicación científica?: una aproximación bibliométrica a través de "Scopus"

El principal objetivo de este trabajo es determinar el nivel de colaboración científica entre universidades latinoamericanas y empresas, en términos de co-autoría, así como identificar las principales instituciones involucradas en esas colaboraciones. Para ello, se extrajeron todas las publicaciones entre 2009 y 2018 que dispusieran de un/a autor/a afiliado/a a una universidad de un país latinoamericano (de un total de 20 analizados), y otro co-autor/a afiliado/a a una empresa, utilizando para ello Scival, producto de Elsevier alimentado con datos de Scopus. Se obtuvieron 22,469 registros, de los que se identificaron 1531 empresas y 428 universidades latinoamericanas. Los resultados evidencian unos porcentajes bajos de colaboración Universidad-Industria durante el período analizado. Sin embargo, estas publicaciones se caracterizan por lograr un alto impacto en citas. A pesar del alto número de empresas identificadas, solamente unas pocas (principalmente de las industrias farmacéuticas, tecnológicas y Petróleo) han establecido conexiones robustas con un conjunto pequeño de universidades, principalmente brasileñas, cuyo rendimiento enmascara el resto de colaboraciones de menor intensidad identificadas en otros países. Por otro lado, la presencia de empresas públicas (por ejemplo, Petrobras, Agrosavia, Embrapa, YPF, Petróleos Mexicanos, etc.) es igualmente destacable. Se recomiendan el establecimiento de políticas públicas estables orientadas a fomentar y potenciar las relaciones Universidad-Industria en la región, basadas en la integración y regulación de estas acciones en las actividades del investigador.The main objective of this work is to determine the collaboration level of Latin American universities with companies in terms of scientific co-authorship, and to identify the main institutions involved in these collaborations. To do this, all publications from 2009 to 2018 with at least one co-author belonging to each of 20 Latin American countries, and another co-author affiliated to a company, were extracted from Elsevier’s Scival (powered by Scopus data), obtaining a set of 22,469 records, from which 1,531 companies (both of public and private nature) and 428 Latin American universities were identified. Despite publications co-authored by universities and companies are highly-cited, results evidence low percentages of academic collaboration between Latin American universities and companies over the period. Just few firms (mainly from Pharmacy, Technology and Petroleum markets) have established strong connections with few universities, mainly from Brazil, whose performance masks the remaining minor linkages established in other countries. Otherwise, the presence of publicly-traded companies (e.g., Petrobras, Agrosavia, Embrapa, YPF or Petróleos Mexicanos) is also remarkable. The establishment of stable public policies aimed at promoting and strengthening University-Industry relations in the region, and based on the integration and regulation of these actions in the researcher's activities, is recommended.Dossier: Estudios métricos de la información: abordajes teóricos, metodológicos y empíricosFacultad de Humanidades y Ciencias de la Educació

De la aldea pública de los repositorios institucionales a la metrópoli privada de ResearchGate

[ES] ResearchGate es una plataforma de compartición de contenidos académicos que incluye servicios de valor añadido (mensajería, redes sociales, métricas de impacto, alertas informativas, búsqueda de empleo, etc.). Su facilidad de uso, gratuidad y utilidad ha llevado a la comunidad científica a depositar (en ocasiones de forma exclusiva en esta plataforma) sus publicaciones, en detrimento del propio repositorio institucional Este breve artículo repasa algunos de los errores funcionales que los repositorios institucionales han cometido y plantea los efectos que el uso de ResearchGate puede suponer para la reputación online de las universidades si no se planifica un rediseño de los repositorios institucionales.Orduña-Malea, E. (2020). De la aldea pública de los repositorios institucionales a la metrópoli privada de ResearchGate. Revista PH (Online). (100):102-104. https://doi.org/10.33349/2020.100.4659S10210410

Publication guidelines: editorial efficiency or professional despair?

[EN] Las guías de publicación (GP) son documentos elaborados por las revistas con el fin de instruir a los autores a la hora de enviar un manuscrito para su publicación. A tal fin incluyen desde aspectos formales que deben cumplir los documentos para su envío (formato de las referencias bibliográficas, extensión, estructura, etc.) hasta información relativa a aspectos éticos del trabajo científico o políticas editoriales de las revistas. Pese a la importancia de estos documentos para la gestión de la investigación, su claridad y calidad son muy desiguales entre publicaciones, generando frustración al personal investigador y gastos económicos a las editoriales. El objetivo de este trabajo es proponer un decálogo de recomendaciones genéricas para la elaboración de guías de publicación, así como establecer una taxonomía de elementos informativos a incluir en estos documentos.Orduña Malea, E. (2021). Guías de publicación: ¿eficiencia editorial o desesperación profesional?. Anuario ThinkEPI. 15:1-7. https://doi.org/10.3145/thinkepi.2021.e15e05S1715Cabrera-Nguyen, Peter (2010). “Author guidelines for reporting scale development and validation results in the Journal of the society for social work and research”. Journal of the Society for social work and research, v. 1, n. 2, pp. 99-103. https://doi.org/10.5243/jsswr.2010.8Fecyt (2020). Bases de la séptima convocatoria de evaluación de la calidad editorial y científica de las revistas científicas españolas. Fecyt. https://calidadrevistas.fecyt.es/sites/default/files/noticias/report_2020_12_10bases7conv_def_2.pdfFennell, Catriona (2016). “‘Your paper, your way’ has made submission easier for more than 1 million authors”. Elsevier connect [blog post], 6 septiembre. https://www.elsevier.com/connect/editors-update/your-paper,-your-way-has-made-submission-easier-for-more-than-1-million-authorsKent, Anderson (2018). “Interpreting Elsevier’s acquisition of Aries systems”. The scholarly kitchen [blog post], 6 agosto. https://scholarlykitchen.sspnet.org/2018/08/06/interpreting-elseviers-acquisition-aries-systemsLiu, Jianxin (2021). “Video or perish? An analysis of video abstract author guidelines”. Journal of librarianship and information science [online first]. https://doi.org/10.1177/09610006211006774Nambiar, Remya; Tilak, Priyanka; Cerejo, Clarinda (2014). “Quality of author guidelines of journals in the biomedical and physical sciences”. Learned publishing, v. 27, n. 3, pp. 201-206. https://doi.org/10.1087/20140306Oermann, Marilyn H.; Nicoll, Leslie H.; Chinn, Peggy L.; Conklin, Jamie L.; McCarty, Midory; Amarasekara, Sathya (2018). “Quality of author guidelines in nursing journals”. Journal of nursing scholarship, v. 50, n. 3, pp. 333-340. https://doi.org/10.1111/jnu.12383Sun, Yu-Chih (2021). “Do journals’ author guidelines tell us what we need to know about plagiarism?”. Journal of scholarly publishing, v. 52, n. 3, pp. 156-172. https://doi.org/10.3138/jsp.52.3.03Wu, Shiyou; Wyant, Diane C.; Fraser, Mark W. (2016). “Author guidelines for manuscripts reporting on qualitative research”. Journal of the Society for Social Work and Research, v. 7, n. 2, pp. 405-425. https://doi.org/10.1086/68581

Revealing the online network between university and industry: the case of Turkey

The present paper attempts to explore the relationship between the Turkish academic and industry systems by mapping the relationships under web indicators. We used the top 100 Turkish universities and the top 10 Turkish companies in 10 industrial sectors in order to observe the performance of web impact indicators. Total page count metric is obtained through Google Turkey and the pure link metrics have been gathered from Open Site Explorer. The indicators obtained both for web presence and web visibility indicated that there are significant differences between the group of academic institutions and those related to companies within the web space of Turkey. However, this current study is exploratory and should be replicated with a larger sample of both Turkish universities and companies in each sector. Likewise, a longitudinal study rather than sectional would eliminate or smooth fluctuations of web data (especially URL mentions) as a more adequate understanding of the relations between Turkish institutions, and their web impact, is reached.Orduña Malea, E.; Aytac, S. (2015). Revealing the online network between university and industry: the case of Turkey. Scientometrics. 105(3):1849-1866. doi:10.1007/s11192-015-1596-4S184918661053Aguillo, I. F., Granadino, B., Ortega, J. L., & Prieto, J. A. (2006). Scientific research activity and communication measured with cybermetrics indicators. Journal of the American Society for Information Science and Technology, 57(10), 1296–1302.Arslan, M. L., & Seker, S. E. (2014). Web based reputation index of Turkish Universities. International Journal of E-Education E-Business E-Management and E-Learning, 4(3), 197–203.Aytac, S. (2010). International scholarly collaboration in science, technology and medicine and social science of Turkish scientists. The International Information & Library Review, 42(4), 227–241.Bahçıvan, E. (ed.) (2013). Turkey’s Top 500 Industrial enterprises 2012. The Journal of the Istanbul Chamber of Industry, 48(569), 1–124 (special issue).Barabasi, A. L., & Albert, R. (1999). Emergence of Scaling in Random Networks. Science, 286(5439), 509–512.Cankir, B., Arslan, M. L., & Seker, S. E. (2015). Web Reputation Index for XU030 Quote Companies. Journal of Industrial and Intelligent Information, 3(2), 110–113.Faba-Fernández, C., Guerrero-Bote, Vicente P., & Moya-Anegón, F. (2003). Data mining in a closed web environment. Scientometrics, 58(3), 623–640.Fruchterman, T. M., & Reingold, E. M. (1991). Graph drawing by force‐directed placement. Software: Practice and experience, 21(11), 1129–1164.García-Santiago, L., & De Moya-Anegón, F. (2009). Using co-outlinks to mine heterogeneous networks. Scientometrics, 79(3), 681–702.Jolliffe, I. (2002). Principal component analysis. New York: Springer.Khan, G. F., & Park, H. W. (2011). Measuring the triple helix on the web: Longitudinal trends in the university–industry–government relationship in Korea. Journal of the American Society for Information Science and Technology, 62(12), 2443–2455.Leydesdorff, L., & Etzkowitz, H. (1996). Emergence of a triple helix of university–industry–government relations. Science and public policy, 23(5), 279–286.Leydesdorff, L., & Park, H. W. (2014). Can synergy in triple-helix relations be quantified? A review of the development of the triple-helix indicator. arXiv preprint arXiv:1401.2342.Meyer, M. (2000). What is special about patent citations? Differences between scientific and patent citations. Scientometrics, 49(1), 93–123.Meyer, M., Siniläinen, T., & Utecht, J. T. (2003). Towards hybrid triple helix indicators: A study of university-related patents and a survey of academic inventors. Scientometrics, 58(2), 321–350.Minguillo, D., & Thelwall, M. (2012). Mapping the network structure of science parks: An exploratory study of cross-sectoral interactions reflected on the web. Aslib Proceedings, 64(4), 332–357.Montesinos, P., Carot, J. M., Martínez, J. M., & Mora, F. (2008). Third mission ranking for world class universities: Beyond teaching and research. Higher Education in Europe, 33(2/3), 259–271.Orduna-Malea, E., & López-Cózar, E. D. (2014). Google scholar metrics evolution: An analysis according to languages. Scientometrics, 98(3), 2353–2367.Ortega, J. L., Orduna-Malea, E., & Aguillo, I. F. (2014). Are web mentions accurate substitutes for inlinks for Spanish universities? Online Information Review, 38(1), 59–77.Priem, J., & Hemminger, B. H. (2010). Scientometrics 2.0: New metrics of scholarly impact on the social Web. First Monday, 15(7). http://firstmonday.org/ojs/index.php/fm/article/viewArticle/2874 . Accessed 31 December 2014.Romero-Frías, E. (2011). Googling companies-a webometric approach to business studies. Leading Issues in Business Research Methods, 7(1), 93–106.Romero-Frías, E., & Vaughan, L. (2010). Patterns of web linking to heterogeneous groups of companies: The case of stock exchange indexes. Aslib Proceedings, 62(2), 144–164.Stuart, D., & Thelwall, M. (2006). Investigating triple helix relationships using URL citations: A case study of the UK West Midlands automobile industry. Research Evaluation, 15(2), 97–106.Thelwall, M. (2004). Link analysis: An information science approach. San Diego: Academic Press.Thelwall, M. (2014). A brief history of altmetrics. Research trends, 37, 3–4. http://www.researchtrends.com/issue-37-june-2014/a-brief-history-of-altmetrics/ . Accessed 31 December 2014.Thelwall, M., & Harries, G. (2003). The connection between the research of a university and counts of links to its Web pages: An investigation based upon a classification of the relationships of pages to the research of the host university. Journal of the American Society for Information Science and Technology, 54(7), 594–602.Vaughan, L. (2004). Exploring website features for business information. Scientometrics, 61(3), 467–477.Vaughan, L. (2006). Visualizing linguistic and cultural differences using Web co-link data. Journal of the American Society for Information Science and Technology, 57(9), 1178–1193.Vaughan, L., & Yang, R. (2012). Web data as academic and business quality estimates: A comparison of three data sources. Journal of the American Society for Information Science and Technology, 63(10), 1960–1972.Vaughan, L., Gao, Y., & Kipp, M. (2006). Why are hyperlinks to business Websites created? A content analysis. Scientometrics, 67(2), 291–300.Vaughan, L., & Romero-Frías, E. (2012). Exploring web keyword analysis as an alternative to link analysis: A multi-industry case. Scientometrics, 93(1), 217–232.Vaughan, L., & Thelwall, M. (2003). 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University rankings in the European Union: a multidimensional approach to a complex reality

The increasing complexity of the university, augmented by its commoditization, means that the information about its knowledge management is also more complex, whether we consider obtaining, contextualizing or redirecting it to various users. University rankings, despite the criticism they receive, appear as contextualized information tools for each type of user. The main goals of the creation and implementation of the multidimensional global ranking design and testing project, funded by the European Commission on Education, are described

The architectural visa project: documental description, characterization and normalization

Architectural documentation is a key element of knowing our cultural heritage. The heterogeneity and complexity of this documentation causes problems in their proper description and subsequent retrieval in databases. This work focuses on a specific typology within this type of documentation: the visa project. First, this typology is characterized as a document, defining, describing and analyzing its constituent parts according to the variety of laws and standards that govern it. Second, a conceptual design is proposed as a basis for construction of a project database system. Finally, the Inpav (National identifier of visa architectural projects) is suggested as standard identification