Metilli, Daniele;
Vlachidis, Andreas;
Humbel, Marco;
Pickering, Victoria;
Carine, Mark;
Sloan, Kim;
Nyhan, Julianne;
(2022)
Towards a Network Analysis of Hans Sloane's Collection: A Preliminary Study.
Presented at: EUSN 2022 – 6th European Conference on Social Networks, London, UK.
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Abstract
“The Sloane Lab: Looking back to build future shared collections” is a 3-year project funded by the UKRI Towards a National Collection programme. The project aims to re-establish connections between Sloane’s collections and catalogues and to mend the broken links between the past and present of the UK's founding collection in the catalogues of the British Museum (BM), Natural History Museum (NHM) and the British Library (BL). Engaging with interested communities and employing digital technology, the project will integrate a fragmented cultural heritage collection and enable its unification through a participatory lens. The collection was amassed by Sir Hans Sloane (1660–1753), who gathered and, with his amanuenses, catalogued more than 70,000 disparate objects which formed the initial nucleus of the original British Museum (the collection was then dispersed across the present-day BM, BL, and NHM). The project will integrate disparate data sources, ranging from historical catalogues to contemporary records, and will enable a wide analysis of Sloane’s extensive social network. As shown by James Delbourgo, Sloane’s collection was “not the achievement of a single individual, but rather the result of exchanges involving countless people across the globe” (Collecting the World, 2017, p. 202). Sloane’s historical catalogues contain many references to people and places, but this data has never been studied extensively through network analysis methods. Our goal is to build a graph of Sloane’s social network by analysing mentions of people in the catalogues, to further understand the connections between them. In addition, we hope to devise computational approaches that can focalize the “data absences” that affect the collection, centring the biases that affected heritage description practices, and drawing attention to people who had an important role in the collection, but whose contribution has been historically overlooked or is now lost. At present, we have access to the digital versions of five of Sloane’s historical manuscript catalogues, which have been encoded in TEI-XML format in the Enlightenment Architectures project. We have started our study from the Miscellanea manuscript, which actually contains seven separate catalogues, plus two indices. We have built a parser to extract data about people and places from the catalogues from the XML files, and gathered more information about them through VIAF and Wikidata. We have then analysed the networks of people and places in the whole dataset, visualised the data, and compared the individual catalogues to understand how their social networks are linked, how they differ from each other, and how they relate to the places and to the objects themselves. In this presentation, we will show the results of our initial analysis, which will then be expanded to other historical catalogues and data sources, laying the foundations for a more complete understanding of the social network behind Sloane’s collection.
Type: | Conference item (Presentation) |
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Title: | Towards a Network Analysis of Hans Sloane's Collection: A Preliminary Study |
Event: | EUSN 2022 – 6th European Conference on Social Networks |
Location: | London, UK |
Dates: | 12 - 16 September 2022 |
Open access status: | An open access version is available from UCL Discovery |
Publisher version: | https://www.insna.org/events/6th-european-social-n... |
Language: | English |
Keywords: | network analysis, social network, hans sloane, museum collections, historical catalogues |
UCL classification: | UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of Arts and Humanities UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of Arts and Humanities > Dept of Information Studies UCL > Provost and Vice Provost Offices > UCL SLASH UCL |
URI: | https://discovery.ucl.ac.uk/id/eprint/10156173 |
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