由美國大學與研究圖書館學會（Association of College & Research Libraries, ACRL）善本與手稿部門RDA編輯群（RBMS RDA Editorial Group）所編製之供評審目的使用的《珍善資料記述編目(RDA版)》（Descriptive Cataloging of Rare Materials (RDA edition), DCRMR）草案，已於2021年11月16日完成，並且邀請全球圖書館同道進行評審及回饋意見。

DCRMR為包括善本、手稿、特藏等各種不同珍善資料格式之編目規則，且是搭配RDA元素集（RDA element set）的 RDA 相容手冊。DCRMR於其序言中述及「DCRMR完成後，將成為包括對多種格式（例如：音樂、連續出版品）之珍善資料編目說明的一個整合性和綜合性手冊，並且適用於善本書RDA相容之編目」。

DCRMR與2011年出版之DCRM (B)（Descriptive Cataloging of Rare Materials (Books)）的差異為 DCRMR 中所有說明都對應至 RDA 元素、用詞改採 RDA 之用詞（例如：「出版品」改為「資源」、「副本」改為「單件」或「具體呈現範例」（exemplar of manifestation）…）、具體反映RDA通用規則。

此外，DCRMR有別於ISBD之描述原則，將題名及著者敘述分為兩個章節來說明；與特定元素有關的附註（例如：題名註），不放在附註之章節，而是納入題名元素的章節；有關標點符號規定已自各章節中移除，改於引言（introduction）內說明；增加資料來源（Data Provenance）的介紹；刪除部分附錄或將之與正文內容結合。

DCRMR之PDF檔，自2021年11月15日起開放下載，下載網址為 https://bsc.rbms.info/assets/pdfs/DCRM%20RDA%20edition.pdf。

更新 國家圖書館學位論文學校及系所（新增4校；修改1校）

The aim of this paper is to present a prototype cataloging interface, which provides easier data entry, follows the cataloger’s thought process and is based on the advantages of the IFLA LRM model. The paper summarizes all stages of the conceptual design and shows how the LRM was implemented. The main purpose of the cataloging interface design is to show how a LRM-based cataloging module might look like and how it could improve the existing cataloging process.

The dynamicity of RDF data has motivated the development of solutions for archiving, i.e., the task of storing and querying previous versions of an RDF dataset. Querying the history of a dataset finds applications in data maintenance and analytics. Notwithstanding the value of RDF archiving, the state of the art in this field is under-developed: (i) most existing systems are neither scalable nor easy to use, (ii) there is no standard way to query RDF archives, and (iii) solutions do not exploit the evolution patterns of real RDF data. On these grounds, this paper surveys the existing works in RDF archiving in order to characterize the gap between the state of the art and a fully-fledged solution. It also provides RDFev, a framework to study the dynamicity of RDF data. We use RDFev to study the evolution of YAGO, DBpedia, and Wikidata, three dynamic and prominent datasets on the Semantic Web. These insights set the ground for the sketch of a fully-fledged archiving solution for RDF data.