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Icms 2015 burrows
The document summarizes the history and dispersal of the Phillipps manuscript collection. It was assembled by Sir Thomas Phillipps in the 19th century and grew to over 60,000 volumes, making it one of the largest medieval manuscript collections ever amassed by a private collector. After Phillipps' death, the collection was reorganized and portions were sold off over the subsequent decades to libraries and at auction. The document outlines the project to digitally represent the provenance of manuscripts in the collection using linked data approaches and visualize their transmission over time and between collectors.
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Icms 2015 burrows
- 1. The Provenance andHistory of the Manuscripts formerly in the Phillipps Collection Department of Digital HumanitiesToby Burrows
- 2. The Phillipps manuscriptcollection • Phillipps’ own printed catalogue (1837-1871) goes up to no. 23,837 • Thomas Fitzroy Fenwick (grandson, d. 1938) spent fifty years reorganizing and renumbering: up to no. 38,628 • Fenwick’s estimate of the total was close to 60,000 volumes and individual documents • Phillipps also owned 50,000 books, as well as many prints, photographs, drawings and paintings Sir Thomas Phillipps (1792-1872)
- 3. Assembling the collection Meerman 1824 Meerman 1824 Celotti 1825 Celotti 1825 Craven Ord 1829-32 Craven Ord 1829-32Page- Turner 1824 Page- Turner 1824 Lang 1828 Lang 1828 Drury 1827 Drury 1827 Guilford (North) 1830 Guilford (North) 1830 Heber 1836 Heber 1836 VanEss 1823-6 Van Ess 1823-6 PHILLIPPSPHILLIPPS Libri 1859-62 Libri 1859-62
- 4. Re-creation of Phillipps’shelves, Grolier Club
- 5. Dispersal of thecollection Fenwick family (1886-1945): • Sales to interested libraries and governments (Germany, Belgium, Netherlands, France, Ireland, Wales) – more than 2,500 items • Auctions at Sotheby’s, 1886 to 1938 – 22 auctions, more than 22,000 lots, raised £97,000 (over £30 million) • Residue (12,000 items) sold to the Robinson brothers in 1945 for £100,000 (£11-12 million) W.H. Robinson Ltd (1945-1958): •Series of sale catalogues, 1945-1954 •Donation to the Bodleian Library of the remaining materials, 1958 Sotheby’s (1946-1950, 1965-1977): •Series of sale catalogues
- 6. Data sources Source FormatComments Schoenberg Database of Manuscripts Relational database Incorporates other sources, esp. sales catalogues 6,000 Phillipps MSS; 20,000 Phillipps events Library catalogues (BL, KB etc.) Relational databases Generally MARC records Provenance in notes Export can be awkward Union catalogues Relational databases Printed bibliographies Formats vary Coverage varies Export can be awkward Sale catalogues Printed books (some digitized) Online sources (PDFs, Web sites) Many included in Schoenberg MSS in ABE, eBay etc. Phillipps catalogues and lists Printed book; Partly digitized Supplemented by handwritten notes Partly included in Schoenberg Handwritten notes not digitized Phillipps provenance indexes (BL, IRHT) Handwritten; Not digitized Arranged by Phillipps number No longer updated Annotated sales catalogues & printed catalogues Handwritten; Not digitized Researchers (Munby), owners (Phillipps), auctioneers (Sotheby’s) Held in Cambridge UL, Bodleian, BL
- 9. Project summary • Twomain research questions: – The history and significant characteristics of the transmission of a major group of European manuscripts between collections and collectors over the centuries (provenance) – The applicability and value of Linked Data technologies as a methodology for the large-scale analysis of the history of cultural objects and collections (“network archaeology”) • Project plan – Ingest the data; transform them to a common Data Model; represent them computationally; analyse and visualize them; make them available to other researchers • Tools – Excel, OpenRefine, Neo4j, Nodegoat, visualization tools
- 10. In 1862, SirThomas Phillipps bought Phillipps MS 16402 in London as part of the Sotheby’s sale of the collection of Guglielmo Libri. London 1862 MS16402 Libri Phillipps Sotheby’s
- 11. Neo4j: graph database •Nodes and relationships (each with properties) • Various tools for data import • Cypher query language for creating nodes, relationships and properties • Cypher is also used to run queries, analyse paths, count and list • No schema as such – develop and define as you go • Own visualization interface, but also works with others • Data export – JSON
- 12. Neo4j Data Model– nodes (entities) Node (entity: label) Type Properties AGENT Person Organization name OBJECT Manuscript id title foliation layout binding illustration WORK Text Description Exhibition title incipit language PUBLICATION Catalogue Book Article title
- 13. Neo4j Data Model– relationships Relationship Properties GAVE SOLD CONSIGNED OWNS ACQUIRED date id certitude price PRODUCED date certitude CONTAINS locus SAME_AS certitude Relationship Properties COMPOSED TRANSLATED COMPILED date certitude ANNOTATED INSCRIBED date locus certitude DESCRIBED_IN date item no. DESCRIBED_AS date item no.
- 14. Neo4j Data Model– relationship statements Node Relationship Node AGENT: Person GAVE OBJECT: Manuscript AGENT: Organization SOLD OBJECT: Manuscript OBJECT: Manuscript CONTAINS WORK: Text AGENT: Person COMPOSED WORK: Text PUBLICATION: Catalogue CONTAINS WORK: Description OBJECT: Manuscript DESCRIBED_AS WORK: Description AGENT: Organization PRODUCED PUBLICATION: Catalogue WORK: Exhibition DESCRIBED_IN PUBLICATION: Catalogue
- 19. DATA MODEL –Nodegoat Object Sub-objects Related to: PERSON Nationality (country) Manuscript Text Catalogue ORGANIZATION Location (city; country) Manuscript Text Catalogue MANUSCRIPT Sold Donated Owned Described In Produced Contents Person/Organization: Agent, Owner, Buyer, Donor, Recipient, Scribe, Artist, Producer Location (city; country) Catalogue Text TEXT Person: Author Manuscript CATALOGUE Organization: Publisher Person: Compiler Manuscript
- 27. Current status ofthe project • Data imported: selections from Schoenberg data, other sample data • Data Model: theoretical work + working versions • Demo versions of Neo4j and Nodegoat databases • Tested and documented queries, analyses and visualizations • To come: – Adding much more data in a production environment (Nodegoat) – Carrying out more extensive visualizations and analyses • Across the whole collection • In relation to specific “use cases” – Exporting data for reuse by other researchers
- 28. Dr Toby Burrows MarieCurie Fellow Department of Digital Humanities King’s College London 26-29 Drury Lane London WC2B 5RL toby.burrows@kcl.ac.uk @tobyburrows tobyburrows.wordpress.com
Editor's Notes
- #2 I’m going to talk about the manuscript collection of Sir Thomas Phillipps, one of the great 19th-century collectors I’ll be reporting on a European Union project aimed at reconstructing the Phillipps collection, and about the ways in which I’m using new technologies to achieve this
- #3 The size of the collection – almost certainly the biggest private collection ever assembled; bigger than most (all?) public collections Phillipps was not just a collector of manuscripts
- #4 Phillipps was buying at a good time – many private libraries came on the market in 1820s and 1830s especially A period when prices for manuscripts rose quite sharply in Britain – Phillipps played a significant part in this price rise
- #5 Phillipps was the illegitimate son and sole heir of a wealthy Manchester mill owner After filling his stately home at Middle Hill (Gloucestershire) with his collection, he then moved it all to Thirlestaine House in Cheltenham in 1864 This gives some idea of the profusion of the collection Grolier Club, New York – these are actual documents from the Phillipps Collection
- #6 The collection was inherited by one of Phillipps’ daughters and her husband Most of the dispersal was managed by Phillipps’ grandson Thomas Fitzroy Fenwick (died in 1938) Robinson brothers’ sales were followed by a series of sales by other antiquarian dealers, especially Sotheby’s, through to the mid-1970s Still documents advertised for sale on sites like ABE Books today
- #7 Today, a wide variety of sources of information about the Phillipps manuscripts Produced for different purposes, in very varied formats – some in digital form, others not digitized No comprehensive list of Phillipps manuscripts; no consolidated source of information about their history
- #8 SDM includes almost 20,000 transactions relating to Phillipps manuscripts – about 6,000 of the manuscripts are covered Assembling the data for my project – started with a CSV export from the Schoenberg Database, filtered for Phillipps transactions
- #9 Other data sources are more difficult to make use of This is a page from a list of some of the Phillipps MSS, made for probate purposes – note the alterations and revisions, and the short descriptions There are two different hand-written versions of this list, with slightly different coverage This one is in the Grolier Club Library in New York (the other is in the Bodleian Library)
- #10 Two aspects of the project – firstly, the history of the Phillipps Collection: study of the provenance of the manuscripts on a large scale Looking particularly at the patterns of relationships between the manuscripts and the people and organizations involved in their history – both individually and collectively Secondly, the use of digital tools and data modelling methodologies to represent these patterns, and to serve as a basis for visualization and analysis
- #11 Look at data modelling first There are a variety of ways of representing provenance in a computational setting – none is entirely satisfactory I went back to a basic conceptual model Here is a typical provenance event statement Here is a simple conceptual model of this event showing entities (nouns in blue) linked by their actions or roles (verbs in red) + properties or attributes of these actions (orange) – places and dates when they occurred
- #12 I then looked for software which managed data in a way which was similar to that kind of conceptual model A graph database like Neo4j can show provenance events as a series of nodes and relationships which correspond to the nouns and verbs in that model Neo4j enables path analysis and pattern matching – not just quantitatively, but also by looking for specific chains of relationships
- #13 I then had to try and develop a detailed Data Model using the Neo4j notation These are the basic entities (nodes/labels) + types + some key properties “Object” is primarily a physical entity: the manuscript volume itself “Work” is a conceptual entity – the text carried by a manuscript, or the description of a manuscript contained in a catalogue (like FRBR’s Work)
- #14 These are the basic relationships (verbs) + some key properties The properties relate to the action, not to the entities involved in the action Not just ownership-related transactions – also want to include description-related transactions
- #15 Here are some sample relationship statements expressed in Neo4j notation
- #16 This shows Phillipps MSS now owned by Columbia University, the Morgan Library and some other US libraries, together with their donors: George Plimpton, William S. Glazier and others Data from the Schoenberg Database
- #17 And this shows some manuscripts which were once owned by both Phillipps and Chester Beatty, and sold by Sotheby’s I’ve expanded the network for Phillipps 12283, so it also shows the works contained in this manuscript, and their authors, as well as a catalogue description for it (Data from the Schoenberg Database)
- #18 A few screen shots from my small Neo4j graph database using Neo4j’s own visualization interface This shows the Phillipps MSS now owned by the Royal Library in The Hague, with their Phillipps numbers and the titles of the works they contain (data from the KB catalogue)
- #19 But I ran into significant limitations with Neo4j – both in the way it handles data modelling, and in its capacity for visualization and analysis So I’ve also been testing an alternative approach using software called Nodegoat Developed in the Netherlands at the University of Amsterdam
- #20 Nodegoat uses a structure based on types of objects, which can each have sub-objects The sub-objects can serve as event clusters, as you can see from my data model “Manuscript” is the central object; its sub-objects are mostly event types The sub-objects can include links to related objects – especially people and organizations, who play different roles depending on the type of event
- #21 I currently have a test data collection in Nodegoat, involving 100 Phillipps manuscripts and about 250 provenance transactions (data from the Schoenberg Database) Here is an example of a manuscript object – its description is the top half Its associated sub-objects are summarized in the lower half This MS has three “Sold” sub-objects, as well as “Owned”, “Produced” and “Contents” (a link to the text it contains) Produced in 1580 in the UK, then sold three times between 1815 and 1967, owned by Yale University in 2010
- #22 Nodegoat has interesting visualization interfaces – geographical, social networks, and chronological This is the geographical visualization interface, showing how manuscripts have moved over the centuries, both around Europe and to the United States This is for the whole of the sample dataset (200 MSS) – you can also limit the visualization to specific manuscripts or groups of manuscripts
- #23 A closer look at this map - you can see the cities where manuscripts were produced (in purple) The lines reflect movements due to sales (in blue) or donations (in orange) or other changes in ownership (red)
- #24 You can also use the time slider to see how the pattern of movement changed over time This is the picture up to 1937 – only a couple of MSS in the dataset had moved to the United States before that date
- #25 Here is the chronological visualization – showing the relative numbers of sales since 1750 (in blue/purple)
- #26 And finally, the social visualization showing the network of connections in the sample dataset of 100 Phillipps manuscripts Shows the centrality of Sir Thomas Phillipps (big red circle), Sotheby’s (green) and France (white) as a place of origin Also a time slider to view the changes in the network over time
- #27 Can zoom in to inspect each node and each relationship Here’s a magnification of part of the graph The three major nodes are Sotheby’s (green: 54 sales transactions), Phillipps (red: 47 sales transactions) and France (white: where 32 of the manuscripts were originally produced)
- #28 Conclude with a summary of the project to date My aim is to ingest as much data as possible during the project, and to make the platform available for addition of further data in the future by other researchers Effectively building an information system about the Phillipps manuscripts, while also developing generic models for the provenance of cultural heritage objects