Daoist, Buddhist, and Medical Sources from the Six Dynasties

“Religious communities used materia medica in many different ways. During the early years of the Chinese Empire, religious actors were among the most prolific medical writers, producing hundreds of texts…”

This database of primary sources is built in DocuSky, and collects 3830 chapters of Buddhist, Daoist and medical writings into a single place, and enables users to search them, and analyse them. The full-text corpus can be examined through the Docusky Viewer here. The corpus is drawn from the Buddhist and Daoist canons, but also from a boutique collection of medical literature, which contains a number of elsewhere unpublished medical manuscripts, including Mawangdui 馬王堆, Shuihudi 睡虎地, and Wuwei 武威, and Zhoujiatai 周家臺.

Screenshot of DocuSky Text Reader, showing source text, metadata and filtering.

The database is more than a simple text corpus. The text-reader alows you to search for multiple terms, and sort the search results according to time period, genre of writing, geographical site of production of the text, andmore. Each text is enriched with detailed bibliographic data based on traditional Buddhist ascriptions, Schipper’s Daoist Canon 道藏統考, and the author’s research. This metadata can be downloaded from here.

12,000 Drug Terms

Search Return on Drug Terms in the Corpus, listing
Title, Chapter Number, Frequency of Unique Terms, Sum of All Term, the Terms themselves, and the Terms According to Frequency
Medical texts are blue, Daoist green, Buddhist yellow.

Docusky offers much more than filtered text search. The corpus can also be analysed en masse, using the TermStatsTools function. It enables you to search for thousands of terms at once, and the search returns. I used it to search for 12,000 drug terms throughout the corpus. The returns show which terms appear in which chapter, and their frequency. They include detailed bibliographic data for each result, more than can be shown in this image. These can be used to filter the results, giving powerful flexibility for analysing the results. The image here shows medical texts in blue, Daoist texts in green, and Buddhist texts in yellow. We can immediately see the proportion of medical knowledge known by different communities.

Graphic Visualisation of Knowledge in Communities of Practice

This data can be studied in many different ways. In this graph, we can see the distribution of drug knowledge in the Daoist Canon according to genre of writing. It clearly shows that the Supreme Clarity (Shangqing 上清) Daoist sect possesed quantitatively the most drug knowledge. Alchemy is also high in drug lore, as would be expected. But notice the strange anomaly, in philosophy.

Interactive Data Exploration

Search Results showing distribution according to Genre.

If we want to know more, we can explore the data in fine detail. This table shows results from Daoist texts, sorted according to genre of writing. Each genre category can be further unfolded to reveal more detail. Here, Philosophical texts (in brown) are shown to contain 443 drug terms. Opening up the genre reveals that only three texts are making up this total, and one of them, the Baopuzi neipian 包朴子內篇contains the lion’s share, at 342 drugs. We can further open up the individual chapters. Chapter 4 contains 61 drugs, which are predmoninantly alchemical drugs: Cinnabar, Mercury, Verdigris, Arsenic, Mica and others.

Comparison of Textual Proximity

Comparison of Materia Medica Terms and their uniqueness or sharedness. 善見律毘婆沙
. 善見律毘婆沙

Social Network Analysis allows us to compare the contents of different chapters, and show degrees of proximity between texts. We can visualise clusters of terms, to show which are unique to a given text, or which are shared by multiple texts. In this image below, we see five Buddhist texts, fifth century translations of the Monastic Codes (skt. Vinaya, Ch. 律) on the right hand side. On the left hand side is the fourth-century, Chinese Recipes to Keep Close at Hand 周後方. Texts are indicated by darker grey dots. Terms that are unique to those texts cluster around the texts, and shared terms are connected between the texts with lines. From this, we can now pick out which terms are most commonly used across all medical literatures, and which terms are specialist to specific genres.

Plotting Text Data onto Historical Maps

DocuGIS allows us to display historical placenames in a text on GIS maps and visualise them against dynastic maps. I have marked up Annotated and Collated Materia Medica 本草經集注, a 3rd-5th century text, and it can be viewed online here. The image below plots the geographic origins of drugs as small blue triangles against a map of 3rd-century political boundaries. The red line shows a high concentration of drug producing zones. Looking more closely, we also see that this line follows along the contours of the Yellow river. This allows us to observe the influence of littoral trade routes on the drug economy, despite there being few, if any, textual sources describing this economy at the time.

Daoist and Buddhist texts come from https://Kanripo.org, medical texts from a variety of sources, including rare unpublished transcriptions of Han and pre-Han excavated texts.

Instructions for term distribution analysis, as well as the Daoist and Buddhist metadata, can be downloaded from here.


Stanley-Baker, Michael, Chen Shih-pei 陳詩沛, and Tu Hsieh-chang 杜拹昌 (2018) Daoist Buddhist and Medical Corpus for Six Dynasties DaoBudMed6d], Ed. Hong Yimay 洪一梅. National Taiwan University Press, 2018.
DOI: 10.6681/NTURCDH.DB_DocuSkyDaoBudMed6D/Text


Stanley-Baker, Michael, and William Eng Keat Chong (2019) “Materia Medica in Chinese Religious Sources:Towards a Critical Digital Philology for Modelling Knowledge Distribution in Early Chinese Texts” Conference Paper from 2019 Pacific Neighborhood Consortium Annual Conference and Joint Meetings (PNC), IEEE. Ed.: IEEE.
Link to Article

Stanley-Baker, Michael (2020) “Daoing Medicine: Practice Theory for Considering Religion and Medicine in Early Imperial China,” East Asian Science Technology and Medicine 50: 21-66.
Link to article


In addition to generous funding from the Max Planck Institute for the History of Science and support from many others (see here), recent development has been provided by Nanyang Technological University and the Ministry of Education, Singapore.