Integrating research performance analysis and science mapping

Noyons, Ed; Moed, Henk F.; Raan, Anthony F. J. van

doi:10.1007/bf02459614

Cited by 211 publications

(132 citation statements)

References 10 publications

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“…Performance analysis aims at evaluating groups of scientific actors (countries, universities, departments, researchers) and the impact of their activity (Noyons, Moed, & van Raan, 1999;van Raan, 2005a) on the basis of bibliographic data. Science mapping aims at displaying the structural and dynamic aspects of scientific research (Börner, Chen, & Boyack, 2003;Noyons, Moed, & Luwel, 1999).…”

Section: Introductionmentioning

confidence: 99%

An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory field

Cobo

López-Herrera

Liu

et al. 2011

Journal of Informetrics

1,757

1,256

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Section: Introductionmentioning

confidence: 99%

An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory field

Cobo

López-Herrera

Liu

et al. 2011

Journal of Informetrics

1,757

1,256

View full text Add to dashboard Cite

“…More internal or external knowledge structures have not been used. For example, sophisticated evolutional models studied in past scientometrics literature [BRUCKNER, & AL., 1990] or citation information among identified topics [NOYONS & AL., 1999] are of this kind. Application of these models to predict future trends may result in better performance.…”

Section: Implications and Conclusionmentioning

confidence: 99%

A comparison of methods for detecting hot topics

Tseng

Lin²,

Lee³

et al. 2009

Scientometrics

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In scientometrics for trend analysis, parameter choices for observing trends are often made ad hoc in past studies. For examples, different year spans might be used to create the time sequence and different indices were chosen for trend observation. However, the effectiveness of these choices was hardly known, quantitatively and comparatively. This work provides clues to better interpret the results when a certain choice was made. Specifically, by sorting research topics in decreasing order of interest predicted by a trend index and then by evaluating this ordering based on information retrieval measures, we compare a number of trend indices (percentage of increase vs. regression slope), trend formulations (simple trend vs. eigen-trend), and options (various year spans and durations for prediction) in different domains (safety agriculture and information retrieval) with different collection scales (72500 papers vs. 853 papers) to know which one leads to better trend observation. Our results show that the slope of linear regression on the time series performs constantly better than the others. More interestingly, this index is robust under different conditions and is hardly affected even when the collection was split into arbitrary (e.g., only two) periods. Implications of these results are discussed. Our work does not only provide a method to evaluate trend prediction performance for scientometrics, but also provides insights and reflections for past and future trend observation studies.

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“…Under the former, researchers in the same department, research partners or co-authors of scientific papers, regardless of their affiliation, are regarded to be members of a research group. In this case the research population is defined on the grounds of co-author details or citations (Noyons et al 1999).…”

Section: Definition Of a Research Groupmentioning

confidence: 99%

Detecting, identifying and visualizing research groups in co-authorship networks

2009

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The present paper proposes a method for detecting, identifying and visualizing research groups. The data used refer to nine Carlos III University of Madrid departments, while the findings for the Communication Technologies Department illustrate the method. Structural analysis was used to generate co-authorship networks. Research groups were identified on the basis of factorial analysis of the raw data matrix and similarities in the choice of co-authors. The resulting networks distinguished the researchers participating in the intra-departmental network from those not involved and identified the existing research groups. Fields of research were characterized by the Journal of Citation Report subject category assigned to the bibliographic references cited in the papers written by the authorfactors. The results, i.e., the graphic displays of the structures of the socio-centric and coauthorship networks and the strategies underlying collaboration among researchers, were later discussed with the members of the departments analyzed. The paper constitutes a starting point for understanding and characterizing networking within research institutions.

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Integrating research performance analysis and science mapping

Cited by 211 publications

References 10 publications

An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory field

An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory field

A comparison of methods for detecting hot topics

Detecting, identifying and visualizing research groups in co-authorship networks

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