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New Technology of Library and Information Service  2004, Vol. 20 Issue (3): 25-28    DOI: 10.11925/infotech.1003-3513.2004.03.08
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The Development,Implementation and Evaluation of EAD
Ye Peihua   Wang Ping
(Department of Information Management,School of Management, Jilin University,Changchun 130023,China)
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Abstract  

EAD is a standard that archivists have been developing to improve description of and access to their holdings. The development of Encoded Archival Description (EAD) began in 1990s. SGML is the encoded language of EAD, and the content and structure was specified by SGML DTD. The development and implement of EAD is very rapid and extensive. As a standard of archival description, EAD has great potentialities. This paper explores origin and structure of EAD, examines the current state of development and implementation for EAD, addresses positive and negative evaluations of EAD.

Key wordsFinding aids      Archival description      Encoding standard      EAD      MARC      SGML     
Received: 03 November 2003      Published: 25 March 2004
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G275

 
Corresponding Authors: Ye Peihua     E-mail: yepeihua9856@eyou.com
About author:: Ye Peihua,Wang Ping

Cite this article:

Ye Peihua,Wang Ping. The Development,Implementation and Evaluation of EAD. New Technology of Library and Information Service, 2004, 20(3): 25-28.

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https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2004.03.08     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2004/V20/I3/25

1JAMES M.ROTH, Serving Up EAD: An Exploratory Study on the Deployment and Utilization of Encoded Archival Description Finding Aids, AMERICAN ARCHIVIST, vol.64, Fall/Winter 2001
2王萍.国外档案编目编码技术.情报科学,2001
3张晓林.元数据研究与应用.北京图书馆出版社,2002
4A project of the Digital scriptorium. Rare Book, Manuscript, and Special Collections Library. http://odyssey.lib.duke.edu/findaid/(archival Finding Aids)
5Beta EAD Applications Guidelines,final draft,December,20,1996:121-122
6Encoded Archival Description Sites on the Web.December,31,1998. http://lcweb.loc.gov/ead/
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