Abstract
This paper reports results on a bibliometric case study of the long-term development of research organizations, using an internationally leading biomedical institute as example. Using scientometric concepts, small group theory, organizational ecology, and process-based organizational theory, we developed a life cycle based theoretical model for analyzing long-term development of research groups and institutes. Three bibliometric indicators are proposed for growth, activity profile stability, and focus. With these, the research dynamics of the case institute are described. First, overall output growth matches developments internationally in developmental biology and stem cell research, and, in line with this, journal article output increasingly dominates the institute’s activity profile. Second, superposed on the overall growth curve, a stepwise development is observed, consisting of long phases of growth and stabilisation. These steps reflect local conditions and events. Historical sources from the Institutes’ archive and interviews with the current staff of the institute suggest that the pattern of life cycles reflects a strong influence of pioneering individuals. But once settled, pioneering directors who remain in function for many years delay adaptation of the institutes’ mission to field developments. Furthermore, national science policies on PhD training, and on priority areas have influenced the life cycles, as did merging with other institutes. As in a social science case, also in this case study stabilized local conditions lead to adaptation to research field dynamics in a delayed fashion. In the present case stable output periods lasted at most 15 years, when local impulses led to new growth of research output and thus prevented onset of a lifecycle decline. The continued growth in the larger field both promoted and legitimized these local impulses.
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Notes
Hubrecht Institute for Developmental Biology and Stem Cell Research, Utrecht, The Netherlands.
We use the concept of ‘group’ here interchangeable with ‘laboratory’, and ‘institute’.
A domain is a relatively secluded social context wherein a research group performs activities. Larédo and Mustar (2000) distinguish five such domains: the scientific arena; the education system; the economic system; government; public media.
\( {\text{Sim }}\left( {{\text{APyr}}_{t} ,{\text{ APyr}}_{t - 1} } \right) = \frac{{\sum\limits_{i = 1}^{n} {\left( {{\text{Ai}}_{\text{yrt}} } \right)*\left( {{\text{Ai}}_{{{\text{yrt}} - 1}} } \right) } }}{{\surd \sum\limits_{i = 1}^{n} {\left( {{\text{Ai}}_{\text{yrt}} } \right)^{ 2} *\surd \sum\limits_{i = 1}^{n} {\left( {{\text{Ai}}_{{{\text{yrt}} - 1}} } \right)^{ 2} } \, } }} = \left( {0, 1} \right) \)
where APyr t is the Activity Profile: items on activities of category i to n, in year t;
Aiyrt is the output items in Activity category i (e.g. journal publication) in Year t.
This ‘Cosine’ formula: Salton and McGill (1983), and Jones and Furnas (1987); the approach is similar to one earlier introduced to compare term profiles of document clusters (Braam, 1991).
We use integer counts of items per year as for all output categories. At the item level, differences may exist in time and efforts to produce them, both between and within categories, but data to correct for this are lacking. In our analysis of long term patterns in output production this is not a large problem: each output category can be followed, and local fluctuations, due to the production of ‘rare’ items consuming much time and effort, are captured if other output decreases as a result. For performance comparison, it can be more sensitive (Moed 2005).
Sources: www.hubrecht.eu/information/history.html; www.developmental-biology.org/about/about.html, Gerhart (1997).
The KNAW has as its other main tasks science policy advice, promoting scientific cooperation, and quality assessment. In this latter task, the KNAW closely cooperates with the research council (NWO) and the association of Netherlands universities (VSNU).
Web of Science, 16 September 2009; Topic = (development * biology); Timespan = All Years. Databases = SCI-EXPANDED, SSCI, A&HCI, CPCI-S, CPCI-SSH. 28,218 records. The documents are classified in subject areas as biochemistry & molecular biology (3.192), cell biology (2.406), oncology (1.681), biotechnology and applied microbiology (1.397), genetics & heredity (1.386), plant sciences (1.383), biology (1.215), entomology (1.184), developmental biology (1174), and others. Of the 28.218 found documents, 15.177 were articles; 7.590 reviews and 3.909 proceedings papers. Of the found documents, 26.970 were in the English language. Most were from the USA (12.594), whereas the Netherlands came 11th with 646 records counted.
Web of Science, 16 September 2009; TI = (stem AND cell*); Timespan = All Years. Databases = SCI-EXPANDED, SSCI, A&HCI, CPCI-S, CPCI-SSH. 67.043 records. Note: As Topic search was larger than 100.000, we used a TI search, so we underestimate the number of papers here. The documents were classified in many different subject areas, most in haematology (29.187), then in oncology (13.499), immunology (11.286), transplantation (10.495), cell biology (8.408), biophysics (7.912) medicine (6.168), biochemistry & molecular biology (4.167), and in biotechnology & applied microbiology (3.991), all others subject areas were below 5.000 documents for this search. Most records were from USA (24.674), the Netherlands was at 9th place with 2.052 records counted. Document types were: meeting abstracts (28.509), articles (25.875), reviews (3.645) and proceedings papers (3.103). The specific terms used in the search may underestimate activities worldwide, and for stem cell research even more (see above). Still, the general pattern is quite clear: a continuous growth of output in these particular research areas.
And of course contributed to these trends. Several directors were internationally leading pioneers in the field, and e.g., Nieuwkoop founded the International Society of Developmental Biologists.
KNAW Yearbook 1971, p. 157.
Hubrecht Laboratory, Progress Report 1993.
Hubrecht Laboratory, Progress Report 1994.
Hubrecht Institute/Laboratory Progress Report 2007, 2006, 2005, 2004.
Hubrecht Laboratory Progress Report 2003.
Stepwise, as we observe periods without output growth of the HI, whereas global output steadily increases.
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Braam, R., van den Besselaar, P. Indicators for the dynamics of research organizations: a biomedical case study. Scientometrics 99, 949–971 (2014). https://doi.org/10.1007/s11192-014-1235-5
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DOI: https://doi.org/10.1007/s11192-014-1235-5