1 to 10 of 97 Results
Jul 29, 2024 -
Herbal Gold: Chinese medicine, COVID and the CCP
MP3 Audio - 40.7 MB -
MD5: daeeb7f8cbc20018396e1d1a8f388ce3
mp3 audio file of podcast |
Mar 14, 2023 -
Mapping Drugs in Han Dynasty Excavated Texts
Tabular Data - 207.3 KB - 8 Variables, 2700 Observations - UNF:6:XWZgSQBtNkNxJO/kDhJwbQ==
Etymon Transcription of Drug Terms in Han Excavated Texts,
GIS plot points for regional sources for Drugs in Tao Hongjing's Bencaojing jizhu 本草經集注
GIS Locations of the Four Excavations sites.
These worksheets form the basis for the Map titled "Mapping Drugs in Han Dynasty Excava... |
Mar 14, 2023 -
Mapping Drugs in Han Dynasty Excavated Texts
MS Excel Spreadsheet - 36.0 KB -
MD5: f73c1ef771d6245639d9e9faaa1f7218
Distributions of Drugs in the Mingyi bielu, according to kingdoms. Also includes a list of regional drug sources in the Wu Pu bencao, extracted using MARKUS. |
Mar 14, 2023 -
Mapping Drugs in Han Dynasty Excavated Texts
Tabular Data - 6.8 KB - 6 Variables, 97 Observations - UNF:6:IBVwfl1Pgv9mQUWTXmving==
Distributions of Drugs in the Tao zhu, according to 3 kingdoms geography.
Also includes summative statistics about the distribution in both Taozhu and Mingyi bielu. |
Mar 14, 2023 -
Mapping Drugs in Han Dynasty Excavated Texts
Tabular Data - 191 B - 4 Variables, 4 Observations - UNF:6:48PJMTDYnrH7RaMKAV+4cw==
GIS Latlongs for sites for the four excavated text |
Dec 15, 2021 -
葛仙翁肘後備急方 Fulltext Database
Tabular Data - 2.2 MB - 10 Variables, 10951 Observations - UNF:6:YjQ1tb2XPtKA/cQF1jOY1A==
Includes all tags and their values, calculated according to how many terms occur in a given paragraph.
TermsCount=how many different terms in a passage
TotalOccur=how many times these occur in a passage. (If one term is repeated, then the TotalOccur will be greater than the Ter... |
Dec 15, 2021 -
葛仙翁肘後備急方 Fulltext Database
Tabular Data - 330.0 KB - 4 Variables, 7201 Observations - UNF:6:/s0KpglP7gQBwFNxDK5rIQ==
Includes all tags and their values, calculated according to overall frequency in the complete database. TF=Term frequency (how many total instances of the term)
DF=Document frequency (how many different passages in which the term appears). |
Adobe PDF - 471.9 KB -
MD5: 53fe63e908d1b15d87c18e936ce26c76
Conference Proceedings Paper |
Feb 23, 2021 -
Markus encoded 葛仙翁肘後備急方
HTML - 232.8 KB -
MD5: 886c453f3d4b7fa42f3640650d6b2782
Markups |
Feb 23, 2021 -
Markus encoded 葛仙翁肘後備急方
HTML - 289.6 KB -
MD5: 183f3ac81630d5f55c90c83737acc1b1
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