Scholarly reference trees

In this paper, we propose, explain and implement bibliometric data analysis and visualization model in a web environment. We use NLP syntactic grammars for pattern recognition of references used in scholarly publications. The extracted information is used for visualizing author egocentric data via tree like structure. The ultimate goal of this work is to use the egocentric trees for comparisons of two authors and to build networks or forests of different trees depending on the forest’s attributes. We have stumbled upon many different problems ranging from exceptions in citation style structures to optimization of visualization model in order to achieve an optimal user experience. We will give a summary of our grammars’ restrictions and will provide some ideas for possible future work that could improve the overall user experience. The proposed trees can function by themselves, or they can be implemented in digital repositories of libraries and different types of citation databases

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

This article is (c) Emerald Group Publishing and permission has been granted for this version to appear here (please insert the web address here). Emerald does not grant permission for this article to be further copied/distributed or hosted elsewhere without the express permission from Emerald Group Publishing Limited[EN] 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.Orduña Malea, E.; Ayllón, JM.; Martín-Martín, A.; Delgado-López-Cózar, E. (2017). The lost academic home: institutional affiliation links in Google Scholar Citations. Online Information Review. 41(6):762-781. doi:10.1108/OIR-10-2016-0302S76278141

Standardization problem of author affiliations in citation indexes

Academic effectiveness of universities is measured with the number of publications and citations. However, accessing all the publications of a university reveals a challenge related to the mistakes and standardization problems in citation indexes. The main aim of this study is to seek a solution for the unstandardized addresses and publication loss of universities with regard to this problem. To achieve this, all Turkey-addressed publications published between 1928 and 2009 were analyzed and evaluated deeply. The results show that the main mistakes are based on character or spelling, indexing and translation errors. Mentioned errors effect international visibility of universities negatively, make bibliometric studies based on affiliations unreliable and reveal incorrect university rankings. To inhibit these negative effects, an algorithm was created with finite state technique by using Nooj Transducer. Frequently used 47 different affiliation variations for Hacettepe University apart from “Hacettepe Univ” and “Univ Hacettepe” were determined by the help of finite state grammar graphs. In conclusion, this study presents some reasons of the inconsistencies for university rankings. It is suggested that, mistakes and standardization issues should be considered by librarians, authors, editors, policy makers and managers to be able to solve these problems

O problema da padronização das afiliações de autores na base de dados Web of Science: o caso Embrapa e sua solução

Scientific and technological production is essential in the innovation process of a country or scientific institution. However, bibliometric evaluation of this performance has been facing for many years a major challenge: the lack of accuracy of the information recorded in scientific documents and databases. This article has as main objective to verify to what extent this problem still persists in Brazilian scientific production, through literature and bibliometric investigations. In this second case, it was done, for example, a basic study on the visibility of the Brazilian Agricultural Research Corporation in the database Web of Science .The results showed a high incidence of standardization issues, reflected in the percentage of 11.93% of variations related to the original name and acronym Embrapa. Given this result, the institution issued an internal normative resolution regulating the membership of its employees in national and international publications.A produção científica e tecnológica é fundamental no processo de inovação de um país ou instituição científica. No entanto, a avaliação bibliométrica desse desempenho enfrenta há muitos anos um grande desafio: o problema da falta de exatidão das informações registradas em documentos científicos e bases de dados. Este artigo possui como principal objetivo verificar em que medida esse problema ainda persiste na produção científica brasileira, por meio de investigações bibliográfica e bibliométrica. Nesse segundo caso, foi realizado, como exemplo, um estudo básico sobre a visibilidade da Empresa Brasileira de Pesquisa Agropecuária na base de dados Web of Science. O resultado demonstrou uma grande incidência de problemas de padronização, refletidos no percentual de 11,93% de variações relacionadas ao nome e sigla originais da empresa. Diante desse resultado, a instituição publicou uma resolução normativa interna regulamentando a afiliação de seus empregados em publicações nacionais e internacionais