Intellectual structure of stem cell research: a comprehensive author co-citation analysis of a highly collaborative and multidisciplinary field.

Autor/inn/en	Zhao, Dangzhi; Strotmann, Andreas
Titel	Intellectual structure of stem cell research: a comprehensive author co-citation analysis of a highly collaborative and multidisciplinary field.
Quelle	In: Scientometrics, (2010) 1, S.115-131 PDF als Volltext Verfügbarkeit
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0138-9130
DOI	10.1007/s11192-010-0317-2
Schlagwörter	Citation analysis; Author co-citation analysis; Scholarly communication; Intellectual structure; Research policy; Stem cell research; Biomedical research
Abstract	Abstract This study is an attempt to approach the intellectual structure of the stem cell research field 2004–2009 through a comprehensive author co-citation analysis (ACA), and to contribute to a better understanding of a field that has been brought to the forefront of research, therapy and political and public debates, which, hopefully, will in turn better inform research and policy. Based on a nearly complete and clean dataset of stem cell literature compiled from PubMed and Scopus, and using automatic author disambiguation to further improve results, we perform an exclusive all-author ACA of the 200 top-ranked researchers of the field by fractional citation count. We find that, despite the theoretically highly interdisciplinary nature of the field, stem cell research has been dominated by a few central medical research areas—cancer and regenerative medicine of the brain, the blood, the skin, and the heart—and a core of cell biologists trying to understand the nature and the molecular biology of stem cells along with biotechnology researchers investigating the practical identification, isolation, creation, and culturing of stem cells. It is also remarkably self-contained, drawing only on a few related areas of cell biology. This study also serves as a baseline against which the effectiveness of a range of author-based bibliometric methods and indicators can be tested, especially when based on less comprehensive datasets using less optimal analysis methods.
Erfasst von	OLC
Update	2023/2/05