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  • × author_ss:"Jiménez-Contreras, E."
  1. Moneda Corrochano, M. de la; López-Huertas, M.J.; Jiménez-Contreras, E.: Spanish research in knowledge organization (2002-2010) (2013) 0.01
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    Abstract
    This study analyzes Spanish research on Knowledge Organization from 2002 to 2010. The first stage involved extraction of records from national and international databases that were interrogated. After getting the pertinent records, they we re normalized and processed according to the usual bibliometric procedure. The results point to a mature specialty follow ing the path of the past decade. There is a remarkable increase of male vs. female authors per publication, although the gender gap is not big. It is also evident that ther e is a remarkable internationalization in publication and that the content map of the specialty is more varied than in the previous decade.
    Date
    22. 2.2013 12:10:07
    Type
    a
  2. Pino-Díaz, J.; Jiménez-Contreras, E.; Ruíz-Baños, R.; Bailón-Moreno, R.: Strategic knowledge maps of the techno-scientific network (SK maps) (2012) 0.01
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    Abstract
    Knowledge engineering and information mapping are two recent scientific disciplines in constant development where mathematics, linguistics, computer science, and information visualization converge. Their main focus is to discover and display new knowledge in large document databases. They have broad and innovative fields of application for strategic scouting in science and technology, knowledge management, business intelligence, and scientific and technological evaluation. This article presents a new method for mapping the strategic research network and illustrates its application to the strategic analysis of the knowledge domain "Spanish Research in Protected Areas for the Period 1981-2005." This strategic knowledge is displayed through a set of two-dimensional cartographic maps and three-dimensional images of two networks: the international network WoS_KWAJ (1981-2005) and the national network IEDCYT_KWAJ (1981-2005). These maps can be very useful in decision-making processes for science and technology policy.
    Source
    Journal of the American Society for Information Science and Technology. 63(2012) no.4, S.796-804
    Type
    a
  3. Robinson-García, N.; Jiménez-Contreras, E.; Torres-Salinas, D.: Analyzing data citation practices using the data citation index : a study of backup strategies of end users (2016) 0.01
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    Abstract
    We present an analysis of data citation practices based on the Data Citation Index (DCI) (Thomson Reuters). This database launched in 2012 links data sets and data studies with citations received from the other citation indexes. The DCI harvests citations to research data from papers indexed in the Web of Science. It relies on the information provided by the data repository. The findings of this study show that data citation practices are far from common in most research fields. Some differences have been reported on the way researchers cite data: Although in the areas of science and engineering & technology data sets were the most cited, in the social sciences and arts & humanities data studies play a greater role. A total of 88.1% of the records have received no citation, but some repositories show very low uncitedness rates. Although data citation practices are rare in most fields, they have expanded in disciplines such as crystallography and genomics. We conclude by emphasizing the role that the DCI could play in encouraging the consistent, standardized citation of research data-a role that would enhance their value as a means of following the research process from data collection to publication.
    Source
    Journal of the Association for Information Science and Technology. 67(2016) no.12, S.2964-2975
    Type
    a
  4. Torres-Salinas, D.; Robinson-García, N.; Jiménez-Contreras, E.; Herrera, F.; López-Cózar, E.D.: On the use of biplot analysis for multivariate bibliometric and scientific indicators (2013) 0.01
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    Abstract
    Bibliometric mapping and visualization techniques represent one of the main pillars in the field of scientometrics. Traditionally, the main methodologies employed for representing data are multidimensional scaling, principal component analysis, or correspondence analysis. In this paper we aim to present a visualization methodology known as biplot analysis for representing bibliometric and science and technology indicators. A biplot is a graphical representation of multivariate data, where the elements of a data matrix are represented according to dots and vectors associated with the rows and columns of the matrix. In this paper, we explore the possibilities of applying biplot analysis in the research policy area. More specifically, we first describe and introduce the reader to this methodology and secondly, we analyze its strengths and weaknesses through 3 different case studies: countries, universities, and scientific fields. For this, we use a biplot analysis known as JK-biplot. Finally, we compare the biplot representation with other multivariate analysis techniques. We conclude that biplot analysis could be a useful technique in scientometrics when studying multivariate data, as well as an easy-to-read tool for research decision makers.
    Source
    Journal of the American Society for Information Science and Technology. 64(2013) no.7, S.1468-1479
    Type
    a