美國國會圖書館（Library of Congress, LC）於 2025 年 5 月 9 日發布《LC Marva Quartz 使用手冊》（Library of Congress Marva Quartz User Manual）。

《LC Marva Quartz 使用手冊》之編製與發布，係有鑑於 LC 已開始轉移到 BIBFRAME 來建立編目資料，這是在圖書館目錄中替換 MARC 的第一步，並將在未來幾年內逐步推進，此種遷移稱為「BFProd」（BIBFRAME in Production），意指「生產中的 BIBFRAME」。

「BFProd」有三個重要組成部分：

1. 能夠在 MARC 和 BIBFRAME 之間輕鬆切換，因為 LC 內部和外部的雙重環境將持續一段時間；
2. 對 MARC 環境的調整將促進資料轉移，並帶來源自 BIBFRAME 所描述的「現代的機讀格式」(“Modern”MARC) 紀錄的新概念；
3. 訓練 LC 編目人員，並使其融入 BIBFRAME 環境。

《LC Marva Quartz 使用手冊》是每週更新的整合性資源，隨著 BFProd 的進展，Marva、BIBFRAME 和當前的 LC 政策也在不斷變化和審視。

該手冊內容除了介紹符合 BIBFRAME 框架的 Marva 如何進行「作品描述」（Work Des_cription）與「實例描述」（Instance Des_cription）外，也強調了 MARC 書目紀錄是不同實體模型應用的「平面」展現（“flat” representations）。採用 RDA 編目，MARC 書目雖然在一筆「平面」紀錄中會包含 RDA 作品屬性、RDA 表現形式屬性、RDA 具體呈現屬性、以及 RDA 單件屬性，然而 MARC 的局限性，也阻礙了 RDA 的完全可視覺化。

BIBFRAME 是另一種具有不同實體的模型，包含作品、實例和單件等實體。在 Marva 的 BFProd 中工作時，這些不同的 BIBFRAME 實體被分成各自獨立的區域，當 MARC 書目紀錄被帶入 Marva 進行編輯時，不同的 BIBFRAME 實體將從 MARC 紀錄中提取並放入相應的 BIBFRAME 實體中。

目前，每個 MARC 紀錄都會有一個 BIBFRAME 作品和一個 BIBFRAME 實例，在某些情況下，將產生 BIBFRAME 單件，但是大多數情況下，單件層級資訊將繼續使用 MARC 館藏格式和 Voyager 單件，並以MARC進行著錄。

更新 國家圖書館學位論文學校及系所（新增3校）

新增 國家圖書館學位論文系所名稱或代碼新增、修訂一覽表（114年5月底修訂表）

This study examines the perspectives of Indian librarians on the use of publisher-assigned classification numbers. The research aims to understand the current methods of obtaining classification numbers, the challenges librarians face, and the potential benefits of having uniform classification numbers provided by publishers. Data were collected from 314 library professionals. Key findings indicate significant support for publisher-assigned classification numbers, perceived benefits in terms of time savings and improved cataloging efficiency, and strong advocacy for the involvement of Indian library associations in promoting standardized practices. The study concludes with policy recommendations to enhance cataloging consistency and efficiency in Indian libraries.

This study evaluates an AI-powered Dewey Decimal Classification assistant the Dewey Decoder, a custom tool built on OpenAI GPT-4 for its effectiveness in academic libraries. A task-based experiment involved 61 purposively selected Sri Lankan university librarians who classified sample resources using the Dewey Decoder and their normal manual workflow. Data were gathered on (a) accuracy (agreement with an expert gold standard), (b) efficiency (time per classification task), and (c) usability (5-point Likert survey and open-ended feedback). Results show the Dewey Decoder achieved a mean accuracy rating of 4.32 / 5, correctly identifying broad classes in 93 % of cases while revealing occasional errors with nuanced or culturally specific works. Eighty-five per cent of participants reported time savings; 69 % completed each classification in under three minutes, compared with over five minutes manually. Usability was rated 4.52 / 5, with participants praising the tool’s step-by-step guidance but noting limits on Sinhala/Tamil support and the five-query cap in free GPT-4 accounts. Although purposive sampling ensured expert input, it constrains generalisability beyond similar academic settings. Overall, findings indicate that GPT-4-driven assistants can substantially enhance cataloguing speed and consistency, provided language coverage and integration with library systems are improved. Future research should test the tool across more diverse collections and librarian populations to validate these gains.