Chen, C., McCain, K., White, H., & Lin, X. (2002). Mapping Scientometrics (1981–2001). Proceedings of the American Society for Information Science and Technology, 39(1), 25-34.

Chen, C., McCain, K., White, H., & Lin, X. (2002). Mapping Scientometrics (1981–2001). Proceedings of the American Society for Information Science and Technology, 39(1), 25-34.

科學映射圖(science mapping)是整合資訊視覺化(information visualization)和科學計量學(scientometrics)的研究，藉由圖形呈現揭露科學文獻的結構與相關的專業(specialties)，科學映射圖的最基礎技術為詞語的共現分析和共被引分析，分別提供獨特的科學研究前沿結構洞察力，Braam, Moed, & Raan (1991a, 1991b)的研究發現結合這兩種技術能夠讓出版品的認知內容(cognitive content of publications)產生更為清楚的圖像。

科學計量學是測量科學或技術進展的研究 (Garfield, 1979b)。傳統上科學計量學有相當強烈的應用導向，針對科學或技術的輸入與輸出發展出許多測量方法與指標，許多知識工作者以這些測量方法與指標為工具應用於各種不同的研究：例如可以針對國家、地區和研究機構的研發能力進行政策與計畫的評估，或是對於研究領域的知識結構進行領域分析。van Raan (1997)和Persson (2000)都是以Scientometrics期刊論文做為研究資料的研究。van Raan (1997)分析科學計量學的最佳狀態(the state of the art)以及對它的應用導向傳統進行描述，van Raan建議科學計量學需要和知識發現(knowledge discovery)與資料探勘(data mining)整合來獲得明顯的效益。Persson (2000)以1978到1999年，44卷，1062篇論文資料進行分析，找出最常被引用的出版品，並且產生期刊共被引、國家間的直接引用連結、作者間的共被引以及直接引用等圖形，表現各種不同的結構。

本研究以1981到2001年間的Scientometrics期刊論文為研究資料，選擇被引用次數達五次以上的參考文獻，共計403筆文獻，根據這些文獻的共被引資訊，繪製網路圖做為科學映射圖的基本圖形，並以論文的引用速率產生動畫的效果。本研究首先以文獻的共被引次數計算Pearson 相關係數(Pearson's correlation coefficients)產生共被引矩陣(co-citation matrix)。並且利用主成分分析(principal component analysis)對共被引矩陣進行因素分析，以分析出的因素代表領域的專業。同時也利用共被引矩陣產生網路圖，經過尋徑網路縮放(pathfinder network scaling)保留較重要的共被引連結，以簡化圖形的複雜性。最後以VRML(virtual reality modeling language)呈現圖形，並且以動畫呈現文獻的被引用率增長情形。本研究共計找出25個因素，較大的三個因素所對應的專業分別命名為科學研究中的引用(citations in science studies)、全球與國家的科學表現(world and national science performance)、研究產出的評估(evaluation research outputs)。

The design of the visualization model adapts a virtual landscape metaphor with document cocitation networks as the base map and annual citation rates as the thematic overlay. The growth of citation rates is presented through an animation sequence of the landscape model.

Science mapping aims to reveal structures of scientific literature and underlying specialties using graphical representations. ... Co-word analysis (Callon, Law, & Rip, 1986) and co-citation analysis (Small, 1973) are among the most fundamental techniques for science mapping. ... Each offers a unique perspective on the structure of scientific frontiers. Researchers have found that a combination of co-word and co-citation analysis could lead to a clearer picture of the cognitive content of publications (Braam, Moed, & Raan, 1991a, 1991b).

As an integral part of our long-term research, our investigation emphasizes an interdisciplinary synergy that may involve fields of study such as information visualization and scientometrics.

Can we provide domain analysts, science performance evaluators, researchers, students, and other knowledge workers something tangible and meaningful that they can readily incorporate it into their work process? Can we improve the way we learn about a new subject matter, the way we explore a knowledge domain, and the way we trace the history and evolution of a specialty? And ultimately, can we augment our ability to judge the significance of scientific work more efficiently and more accurately?

The present study is based on articles published in Scientometrics between 1981 and 2001, drawn from the Web of Science.

Scientometrics is “the study of the measurement of scientific and technological progress” (Garfield, 1979b). Its origin is in the quantitative study of science policy research, or the science of science, which focuses on a wide variety of quantitative measurements, or indicators, of science at large.

Input indicators include the amount of research grants awarded to institutions and the number of people receiving scientific degrees; output indicators include the number of scientific articles published, the number of citations to each article, and the number of patents granted.

Science policy and program evaluation studies have used such indicators to measure the scientific strength of various countries, regions, or research institutions.

Domain analysts have used such indicators to describe the intellectual structure of a knowledge domain.

Scientometric research has a strong application-oriented tradition (Garfield, 1979b; Raan, 1997).

Garfield (Garfield, 1979b) identified several publications appeared in the 1970s and contributed to the development of scientometrics, namely, the first Science Indicators published by the National Science Board in 1972 (Board, 1977), the Evaluative Bibliometrics: The Use of Publication and Citation Analysis in the Evaluation of Scientific Activity by Francis Narin and Computer Horizons, Inc. (CHI) in 1976 (Narin, 1976), which has been regarded as a good review source for anyone interested in scientometrics (Garfield, 1979b).

Derek Price’s 1965 article ‘Network of Scientific Papers’ (Price, 1965) has been also regarded as a key event in the development of the field of scientometrics.

Michael Moravcslk (1977) presented a review of scientometric literature (Moravcslk, 1977).

Anthony van Raan (1997) analyzed the state of the art of scientometrics and characterized its application-oriented tradition. He envisaged that scientometrics could benefit significantly from a greater integration with knowledge discovery and data mining.

Loet Leydesdorff (2001) identified some challenges of scientometrics and suggested that: “the state of the art of science studies is ‘preparadigmatic:’ it is an interdisciplinary area integrated only at the level of its subject matter, and an applicational area for various contributing disciplines.”

A directly related study of Scientometrics was done by Olle Persson (2000). He retrieved 1,062 articles published in the journal from volume 1 to volume 44 between 1978 and 1999. Top-10 most cited publications include (Garfield, 1979a; Schubert, Glanzel, & Braun, 1989; Small, Sweeney, & Greenlee, 1985). He generated several maps to show a variety of structures, including journal co-citation, direct citation links among countries, shared citations among authors, and direct citations among authors.

This study is based on bibliographc data retrieved from the Web of Science. The data contain all types of documents published in Scientometrics between 1981 and 2001. ... Each article must be cited for 5 times or more in order to be included in this integrated analysis. This threshold resulted in a total of 403 articles.

In this study we have adapted an integrated procedure of citation analysis and information visualization, including Pathfinder network scaling, Principal Component Analysis (PCA), and visual-spatial models rendered in Virtual Reality Modeling Language (VRML).

The cocitation strength is computed as Pearson’s correlation coefficients to form a co-citation matrix. ... The co-citation matrix forms the basis of a base map, a terminology commonly used in cartography.

Factor analysis, namely PCA, is subsequently applied to the co-citation matrix in order to produce a thematic overlay. The purpose of such a thematic overlay is to highlight the density distribution of various specialties. Factor loadings are used to color code each publication in the thematic overlay.

We simplify the cocitation matrix using Pathfinder network scaling, which retains the strongest co-citation links with reference to the so-called triangle inequality condition (Chen, 1997, 1998; Schvaneveldt, 1990).

Finally, the growth of citation rates is animated across the entire Pathfinder network to facilitate the identification of trends. The visualization-animation model is made available in VRML 2.0 for easy access on the Internet.

PCA identified 25 factors from the 403 by 403 co-citation matrix. In theory, each factor should correspond to a specialty. ... The large number of factors reflects the diversity of scientometrics.

In our analysis, we focus on the three largest factors of significant specialties of the field.
Specialty 1: Citations in Science Studies.
Specialty 2: World and national science performance.
Specialty 3: Evaluation research outputs.