Mapping Late Hokusai Research: Digitizing and Publishing Bilingual Research Data

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Abstract

The initiative “Late Hokusai: Thought, Technique, Society” took place at the British Museum (BM) and SOAS, University of London (2016-2019). As part of its activities, it built a linked-data platform prototype on ResearchSpace. The prototype offers a redesigned process for how museum researchers and users find, research with, discuss and expand bilingual data about early modern Japanese artist Katsushika Hokusai (1760-1849) and instigated a discussion about what a collaborative research platform for the Hokusai research community could look like. While Japanese resource specialists have long recognized the complexity of Japanese script as a challenge for multilingual research and collection platforms, the processes for and results of integrating Japanese source data into bi- or multilingual museum databases remained unsatisfactory. This paper revisits the challenges posed by “non-Latin script” (NLS) in museum databases in the case of the Hokusai research platform at the British Museum, which integrated Japanese and English languages. It localizes the issues arising from working with Japanese source data in the Latin script project environment and accompanies the museum researchers' tasks regarding the correct input, rendering and display of the source script at each step: 1) object analysis, 2) registering NLS metadata, 3) processing NLS information and 4) visualizing LS and NLS information for general and specialist audiences. After assessing these practices, the paper critically reflects on selected approaches, successes, and shortcomings experienced while creating such a prototype. By sharing its experiences, the project hopes to aid prospective research projects on a similar path regarding project setup and documentation. Furthermore, it advocates the sustainability of research practices according to data reusability parameters.

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APA

Santschi, S. (2022). Mapping Late Hokusai Research: Digitizing and Publishing Bilingual Research Data. Digital Studies/ Le Champ Numerique, 12(1). https://doi.org/10.16995/dscn.8079

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