Judith Brouwer
in a different time period.43 Despite these obstacles, academies tend to lend them
selves quite well for this kind of research. It is thus surprising that this type of
research has hardly been conducted in the context of academies. Through an
egocentric approach, meaning that the network is developed departing from one
actor (in this case the ZGW), SNA has the potential to visualise a large part of the
scholarly community of the 18th century.44 Not only would this type of research
focus on the leading scientists of that time, but also on the non-professionals who
participated in the academies' essay competitions or sent in ideas through cor
respondence. These individuals might be overlooked in other types of analyses.
Thus, it will provide historians with a broader understanding not only on how
knowledge was created but also on who contributed to the creation of knowledge.
Moreover, many academies, including the ZGW, have archived their interac
tions with individuals and other societies. Examples of these primary sources are
registers of membership, correspondence, responses to essay questions, and min
utes of meetings. Thus, there is a wide variety of primary sources from which a
dataset can be established. This paper starts this kind of analysis on a small scale,
by only using correspondence of the ZGW between 1768-1770 and the Member
ship Register, which contains information on each individual regarding his loca
tion and work. From these two primary sources a dataset was created from which
an online map was developed using the online network visualization tool Kumu.45
Using this dataset a small start was made to employ SNA techniques to get a
better understanding of who were the most important people for the functioning
of the ZGW's correspondence network. Two metrics were run through the data
using the online network visualization tool Kumu. First, the metric of Degree Cen-
trality was applied, which simply counts the number of direct connections one
element has. The general assumption of this metric is that the more connections
an element has, the more important the element is for the network.46 The other
metric is the Closeness Centrality of an element, which can be understood as the
103
43 Davidson, Early Modern Social Networks, 479; Wetherell, Historical Social Network Analysis,
125.
44 Cornell, Using social network, 338; Wetherell, Historical Social Network Analysis, 127.
45 Kumu, ZGW 1766-1771 Correspondence and Registry, https://kumu.io/JudithB/zgw-1768-
1770#zgw-1766-1771-correspondence-and-registry (last modified July 16, 2020). This map
visual can be found in Appendix I, Figure 1.
46 Esin Ergün Yasemin Kogak Usluel, An Analysis of Density and Degree-Centrality According
to the Social Networking Structure Formed in an Online Learning Environment. In: Educatio
nal Technology Society 19, no. 4 (2016), 37.
