JOURNAL ARTICLE
Counting when, who and how: Visualizing the British Museum's history of acquisition through collection data, 1753–2019.
Published In: Journal of the History of Collections, 2023, v. 35, n. 2. P. 305 1 of 3
Database: Historical Abstracts with Full Text 2 of 3
Authored By: MacDonald, Isobel 3 of 3
Abstract
This article critically explores the use of the British Museum's collection database, MuseumIndex+ (mi+), as a research tool to analyze the institution's acquisition history from 1753 to 2019 through quantitative methods. It reveals large-scale patterns in the growth and composition of the collection, highlighting significant acquisition spikes in the mid-nineteenth century—driven largely by purchases of Western prints and drawings—and in the late twentieth century, notably due to British archaeological material from rescue excavations and museum-led digs. The study also examines acquisition processes, showing that while donations and bequests account for the majority of objects, active purchasing dominated between the 1830s and 1870s, and that the collection comprises both large named collections and numerous smaller acquisitions from a wide range of sources. The paper emphasizes the strengths and limitations of mi+ as a data source, noting its Eurocentric biases and incomplete records, and advocates for combining such data-driven approaches with archival research to gain a more comprehensive understanding of the museum's complex collection history.
Additional Information
- Source:Journal of the History of Collections. 2023/07, Vol. 35, Issue 2, p305
- Document Type:Article
- Subject Area:Social Sciences and Humanities
- Publication Date:2023
- ISSN:0954-6650
- DOI:10.1093/jhc/fhac034
- Accession Number:167382525
- Copyright Statement:Copyright of Journal of the History of Collections is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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