Using Google Scholar for journal impact factors and the h‐index in nationwide publishing assessments in academia – siren songs and air‐raid sirens

Mini-review: Rehabilitation engineering: Research priorities and trends

Rehabilitation Engineering is the use of engineering principles applied to rehabilitation, disability, and independent living. Google Scholar is a searchable resource that allows people from around the world to create profiles of their interests and collaborations, and it provides a means to search the broad scientific and technical literature. Google Scholar was used to identify the 150 most cited people who listed Rehabilitation Engineering in their profile. Research impact, characteristics, and areas of research of the most cited rehabilitation engineers were examined. Furthermore, gender and geographical differences in research metrics of the highest citied rehabilitation engineers were investigated. Consumer priorities in rehabilitation engineering were identified using a voice of consumer (VoC) survey and recent literature based on VoC studies. Gaps between research publication and activities and consumer priorities were identified to recommend seven areas of research with high demand and opportunity for growth and innovation. Implications.

Language Bias in the Google Scholar Ranking Algorithm

The visibility of academic articles or conference papers depends on their being easily found in academic search engines, above all in Google Scholar. To enhance this visibility, search engine optimization (SEO) has been applied in recent years to academic search engines in order to optimize documents and, thereby, ensure they are better ranked in search pages (i.e., academic search engine optimization or ASEO). To achieve this degree of optimization, we first need to further our understanding of Google Scholar’s relevance ranking algorithm, so that, based on this knowledge, we can highlight or improve those characteristics that academic documents already present and which are taken into account by the algorithm. This study seeks to advance our knowledge in this line of research by determining whether the language in which a document is published is a positioning factor in the Google Scholar relevance ranking algorithm. Here, we employ a reverse engineering research methodology based on a statistical analysis that uses Spearman’s correlation coefficient. The results obtained point to a bias in multilingual searches conducted in Google Scholar with documents published in languages other than in English being systematically relegated to positions that make them virtually invisible. This finding has important repercussions, both for conducting searches and for optimizing positioning in Google Scholar, being especially critical for articles on subjects that are expressed in the same way in English and other languages, the case, for example, of trademarks, chemical compounds, industrial products, acronyms, drugs, diseases, etc.

Ranking by Relevance and Citation Counts, a Comparative Study: Google Scholar, Microsoft Academic, WoS and Scopus

Search engine optimization (SEO) constitutes the set of methods designed to increase the visibility of, and the number of visits to, a web page by means of its ranking on the search engine results pages. Recently, SEO has also been applied to academic databases and search engines, in a trend that is in constant growth. This new approach, known as academic SEO (ASEO), has generated a field of study with considerable future growth potential due to the impact of open science. The study reported here forms part of this new field of analysis. The ranking of results is a key aspect in any information system since it determines the way in which these results are presented to the user. The aim of this study is to analyze and compare the relevance ranking algorithms employed by various academic platforms to identify the importance of citations received in their algorithms. Specifically, we analyze two search engines and two bibliographic databases: Google Scholar and Microsoft Academic, on the one hand, and Web of Science and Scopus, on the other. A reverse engineering methodology is employed based on the statistical analysis of Spearman’s correlation coefficients. The results indicate that the ranking algorithms used by Google Scholar and Microsoft are the two that are most heavily influenced by citations received. Indeed, citation counts are clearly the main SEO factor in these academic search engines. An unexpected finding is that, at certain points in time, Web of Science (WoS) used citations received as a key ranking factor, despite the fact that WoS support documents claim this factor does not intervene.

The lost academic home: institutional affiliation links in Google Scholar Citations

Purpose

Google Scholar Citations (GSC) provides an institutional affiliation link which groups together authors who belong to the same institution. The purpose of this paper is to ascertain whether this feature is able to identify and normalize all the institutions entered by the authors, and whether it is able to assign all researchers to their own institution correctly.

Design/methodology/approach

Systematic queries to GSC’s internal search box were performed under two different forms (institution name and institutional e-mail web domain) in September 2015. The whole Spanish academic system (82 institutions) was used as a test. Additionally, specific searches to companies (Google) and world-class universities were performed to identify and classify potential errors in the functioning of the feature.

Findings

Although the affiliation tool works well for most institutions, it is unable to detect all existing institutions in the database, and it is not always able to create a unique standardized entry for each institution. Additionally, it also fails to group all the authors who belong to the same institution. A wide variety of errors have been identified and classified.

Research limitations/implications

Even though the analyzed sample is good enough to empirically answer the research questions initially proposed, a more comprehensive study should be performed to calibrate the real volume of the errors.

Practical implications

The discovered affiliation link errors prevent institutions from being able to access the profiles of all their respective authors using the institutions lists offered by GSC. Additionally, it introduces a shortcoming in the navigation features of Google Scholar which may impair web user experience.

Social implications

Some institutions (mainly universities) are under-represented in the affiliation feature provided by GSC. This fact might jeopardize the visibility of institutions as well as the use of this feature in bibliometric or webometric analyses.

Originality/value

This work proves inconsistencies in the affiliation feature provided by GSC. A whole national university system is systematically analyzed and several queries have been used to reveal errors in its functioning. The completeness of the errors identified and the empirical data examined are the most exhaustive to date regarding this topic. Finally, some recommendations about how to correctly fill in the affiliation data (both for authors and institutions) and how to improve this feature are provided as well.

Un panorama académico de dos caras: retrato de los documentos altamente citados en Google Scholar (1950-2013)

El principal objetivo de este trabajo es identificar el conjunto de documentos altamente citados en Google Scholar y definir sus características nucleares (tipología documental, idioma, disponibilidad en abierto, fuentes y número de versiones), bajo la hipótesis de que la amplia cobertura del buscador podría proporcionar un retrato diferente de este conjunto documental a la ofrecida por las bases de datos tradicionales. Para ello, se ha realizado una consulta por año (desde 1950 hasta 2013) identificando los 1000 documentos más citados y obteniendo una muestra final de 64.000 registros (el 40% de los cuales proporcionaban un enlace al texto completo). Los resultados muestran que el documento altamente citado “promedio” es un artículo de revista o libro (éstos constituyen el 62% del top 1% de los documentos más citados de la muestra), escrito en inglés (92.5%) y disponible online en PDF (86% de la muestra). Aun así, se debe indicar la existencia de errores especialmente en la detección de documentos duplicados y en la correcta vinculación de citas. En todo caso, la muestra manejada (documentos altamente citados) minimiza los efectos de dichas limitaciones. Dada la alta presencia de libros (manuales) y, en menor medida, de otras tipologías documentales (como congresos o informes), se concluye que Google Scholar ofrece una visión original y diferente del conjunto de documentos académicos más influyentes (medidos desde la perspectiva de la contabilización de citas), conformado no sólo por material estrictamente científico (artículos en revistas), sino académico en sentido amplio.