The China Biographical Database Project
The approach studied in this chapter for transforming historical records into biographical data has been attested by a large-scale digital humanities project, one of the most well known in the Chinese studies discipline. An effort to organize the numerous biographies in the Chinese historical record, CBDB is a relational database with biographical information about approximately 422,600 individuals (as of September 2018) that is meant to be useful for statistical, social network, spatial, and other kinds of analyses. The database is jointly developed by Harvard University, Academia Sinica, and Peking University since 2005 (Harvard University, 2018) and the team members include humanities and computer experts mostly based in Cambridge, Massachusetts and Beijing. The project employs computational techniques to locate and encode biographical records in Chinese texts and makes such data available to researchers. The data can be accessed online (at http://projects.iq.harvard.edu/cbdb) and as a standalone offline database in Microsoft Access format. An Application Programming Interface (API) for system interoperability is also available (Xu, 2018). Collaborators of the project have also transformed the database entries into a file for the iOS built-in dictionary (Harvard University, 2018). CBDB project team members systematically work through texts of a given format to mine vast amounts of data about large numbers of historical persons, versus doing research on each individual. Because CBDB records information about where people lived, where they studied, where they served in office, what offices they held, who their parents were, who they married, and who they knew etc., all these aspects of life can be correlated for very large groups of people in history.
CBDB is by nature a relational database. In other words, in contrast to a single table into which all data is loaded, it consists of many different tables that are linked together, allowing categorization and coding of many different aspects of the life histories of people in China’s past. As opposed to an unstructured text-only database, a relational database for historical people benefits the end-user in discovering and locating information about individual historical figures, and also in analyzing them systematically at an aggregate level. Through the wide range of data it collects, CBDB offers many ways to examine the lives of past individuals and groups. Its approach towards the generation and making use of data is transforming the historical research method of prosopography (Stone, 1972). As a collective biography, prosopography inquires into the common characteristics of a group of people. Through collecting data on phenomena that involve common aspects of people’s lives, it sheds light on questions about social groups in history and allows scholars to make much better sense of relationships between individuals and groups. Effective deployment of the prosopographical method supports discovery in ancient texts and the relationships that they record (Verboven et al. 2007), especially with computer-aided analysis. When populating a biographical database such as CBDB, it not only provides scholars with larger and more comprehensive data, but also data that is smarter (Zeng, 2017).
There have been similar efforts to build and analyze datasets and databases not only for studying imperial Chinese history, but also for Australia (Biographical Database of Australia, 2018), Anglo-Saxon England (Prosopography of Anglo-Saxon England, 2010), the Byzantine Empire (Martindale, 2014), Egyptian Middle Kingdom (Persons and Names of the Middle Kingdom, 2018), Islamic history (Romanov, 2017), Jaina history (Flügel, 2017), Japan (Japanese Biographical Database, 2018; Born, 2018), and Taiwanese history (Sie et al., 2017), to list some examples. These projects on history elsewhere, especially those dealing with non-Latin scripts, often face similar issues and potentials in processing and analyzing hard-copy historical sources.