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«Dr Mitchell Whitelaw Faculty of Arts and Design University of Canberra Our archives, libraries, and other cultural collections are increasingly ...»

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Visualising Archival Collections - The Visible Archive Project

Dr Mitchell Whitelaw

Faculty of Arts and Design

University of Canberra

Our archives, libraries, and other cultural collections are increasingly digital collections. With the

digitisation of historical material, and the collection of contemporary “born digital” records, archives

and other such collections have become custodians not only of objects, but of data, and responsible for

its structure, preservation, and presentation. In discussing the promises and perils of digitisation for cultural collections, Abby Smith vividly describes the “variability” and “plasticity” of data, and the challenges this poses for digital recordkeeping.1 However these dynamic qualities of data also provide opportunities for new approaches that can help address some of the challenges of the digital.

We deal with visual representations of data on a daily basis; every web site and computer interface, not to mention spreadsheet chart or graph, is essentially data in visible form. The plasticity of data, its computability, means that data can be represented visually in any number of different forms, some familiar and some not. Given a certain body of data then, the question is how best to display or present it; an entire field of research and practice - data visualisation - is founded on this question.

This paper presents a research project that essentially addresses the same question, in the context of cultural collections. If we consider an archival collection - in this case the collection of the National Archives of Australia - as a rich set of data: how best to display it? Supported by the National Archives under its 2008 Ian Maclean award, the Visible Archive project developed new techniques for the interactive display of archival data. The work presented here shows that visualisation can play a significant role in supporting our undertanding of, and access to, large archival collections, at levels from the whole collection, to the individual item.

Aims and Context As Friendly and Denis show, the visual display of quantitative information has a long history, spanning cartography and scientific diagrams, graphs and charts.2 More recent visualisation is characterised by the development of interactive computing, and the ability to manipulate visual representations directly.

Friendly and Denis also comment on the recent proliferation and interdisciplinarity of visualisation work. Friedman's 2007 survey of data visualisation provides a sense of this diversity, as well as the dominant influence of networked data sources and presentation techniques.3 Current visualisation practice is a broad and growing field spanning information technology, the digital humanities, design, and art; this interdisciplinarity informs the background and context of this project.

In tandem with this broadening of visualisation comes a growing recognition that science is not alone in generating ever-increasing volumes of data, or in needing to access and interpret that data effectively. Studies of history, society and culture make increasing use of digital materials and methodologies, including visualisation.4, 5 Researchers in the field have begun to recognise the potential of visualisation; Lev Manovich for example describes research into “visualising cultural patterns.”6 Examples of visualisations of large collections are scarce, but include George Legrady's 2005 Making Visible the Invisible, a dynamic visualisation of activity in the collection of the Seattle Central Library.7 Jeanne Kramer-Smyth's ArchivesZ project is more relevant to this project - an interactive tool using visualisation to support search and exploration, focusing on the scope and availability of records.8 These two examples also speak to the interdisciplinarity of approaches in this field: Legrady works in media arts and design, while Kramer-Smyth's approach is based in information management.

The approach in this project was informed by reflections on search, currently the dominant tool in the display and navigation of digital archival records. While search is a very effective technique for delivering records in response to a specific query, it has significant limitations. As an access tool, search assumes that a user is able to provide a query; but a user who is unfamiliar with the collection's scope, contents, or structure, may not be in a position to query it effectively. Personal experience suggests that such users (who are certainly in the majority) take a trial-and-error approach to search, using successive queries as a way to develop some sense of scope and context. This might be likened to using small, localised core samples to discover hidden geological features; except that in geology core samples are used because accessing those underground structures directly is difficult and expensive.

Data is, by comparison, easy and cheap to access. Visualisation enables us to literally show everything, to display large volumes of data in a way that reveals patterns and communicates context, but also provides access to the fine grain of individual elements. The work of visualisation studio Stamen Design, who make “show everything” their motto, is influential here.9 The central aims of this project were straightforward: to develop and trial new techniques for the visualisation of large archival collections; specifically, to create visualisations that complement (rather than compete with) search, focusing on providing large-scale, “show everything” views of the collection, and on revealing the relations and structures within it. Joanna Sassoon has argued that the push for the digitisation of cultural collections, and its focus on “content,” risks a decontextualised or superficial view of the archival record.10 A key hypothesis here was that visualisation can redress this tendency, and play a role in enhancing a sense of context in the digital collection The research process here was practical, experimental, and iterative. A large set of “sketch” visualisations were produced, each informing the next and developing in complexity. This process was documented in detail on the project blog, which also provided a useful platform to gather feedback from peers and collaborators.11 The final outcomes of the project – also available through the blog were two prototype visualisations: one displaying the whole Archives collection at Series level; the other focusing on the contents of a single Series - A1.

Visualising the Collection

The first phase of the project focused on visualising the Archives' collection at the largest scale, working with data describing some 57,500 Series. This dataset reflects the Commonwealth Records System, providing a highly structured and descriptive representation of each Series, as well as recording relationships between Series, and between Series and the Agencies that control and record to them. Visualisation of this data was a stepwise process of experiment and exploration. Due partly to the sheer scale of the dataset – millions of lines of text - visualisation itself provided the most effective way to develop an understanding of the data. With each step in the process, new aspects of the dataset became apparent, which in turn informed the next iteration of the visualisations. A short tour of these developmental stages shows how this process unfolded, as well as illustrating key features of the data and the potential role of visualisation.

The first visualisation made was a simple graph exploring the size and historical distribution of the collection (Figure 1). It is a histogram, showing the number of Series with contents commencing in each year since 1800. The dominant feature is an overall increase in the number of Series commencing per year through the course of the twentieth century. The sharp fall-off in Series after the mid-1970s can be explained by the “30 year rule”, under which most recent records would be unavailable. Also notable is the small but significant distribution of Series with contents commencing before 1900.

Overall, the numbers involved show the magnitude of the collection, with many years registering over one thousand commencing Series. However in its detail, this graph begins to suggest that visualisation may be able to reveal more than the broad features of the collection. Three of the sharpest spikes in the graph - years with a dramatic increase in Series contents commencing - occur in 1901, 1914, and 1939.

It would seem that major historical events underlying these records can be reflected in the data; and that even the most basic visualisation can reveal traces of these events.

Figure 1: Histogram showing number of Series commencing per year Subsequent experiments with the histogram form all faced a single basic problem: scale. The aim here was to create an interactive visualisation of the entire collection, at Series level; but with over 57,000 Series, the pragmatics of the “show everything” approach posed a significant challenge. The following set of sketches respond to this simply by allocating each Series its own small piece of the visual territory.

In the following sketches each Series is represented as a small coloured square on a grid (Figure 2).

Series are sorted by contents start date, and layed out left-to-right, top-to-bottom. The spacing of the date labels on the vertical axis reflects the distribution shown in the first histogram, with roughly as many Series commencing between 1800 and 1900, as commenced between 1910 and 1920, and very large numbers of Series commencing in the post-War period. In Figure 2(a) colour - or more specifically hue - represents year span, with short-span Series coloured red, and long-span Series coloured purple. What results is another revealing view on the whole collection, showing for example a very large number of short-span Series, and relatively few with long spans; we can also observe features including a rapid increase in short-span Series after 1940.

Figure 2 Grid-based visualisations showing (a) Series spans and (b) items per Series In Figure 2(b) brightness is linked to the number of registered items in a Series; Series with few items are dim, those with many items are bright. This starry sky immediately reveals that the vast majority of Series in the collection have relatively few registered items. Its most striking features are the bands of bright red Series - short-span collections with many registered items - around 1950. Simple interaction was introduced here to browse Series details; browsing these bands reveals a group of Series documenting arrivals under the post-War Displaced Persons Program. These records stand out here not only because they contain many items, but because they are chronologically sequential, forming visual groups in the grid. This again shows that visualisation can reveal real structures within an archival collection. The addition of interaction also begins to show how a fine-grained, dynamic approach can support exploration and discovery, enabling us to quickly investigate and verify structures in the visualisation.

The key limitation of these grids is their inherent spatial constraint; despite the wide diversity of Series in the collection, each is represented here with a single, spatially uniform element. The next challenge was to use size more effectively and represent like with like - that is, link the size of a Series, to its size in the visualisation - while working within the constraints of a single-screen, whole-collection display.

The solution is a spatial optimisation process, otherwise known as “packing”. In the final wholecollection visualisations Series are represented as squares whose area is proportional to both the number of registered items it contains, and the shelf area it occupies (Figure 3). The chronological ordering of Series by year is maintained, as in the earlier grids, while the packing process re-orders Series commencing in the same year, packing them more efficiently into place. The result is a display where Series size and historical distribution are both immediately apparent. The inner square of each Series represents its number of registered items, while the outer band corresponds to the shelf space it occupies. These measures alone are revealing; for example many Series with small shelf areas but high item counts, contain items that are physically small (such as photographs or index cards).

Figure 3: Packed-square visualisation

The Commonwealth Records System represents relationships between Series, and between Series and the Agencies that record to and control them. In these visualisations Agencies are shown initially as colour; the hue of each square is linked to the first listed recording Agency. Interaction enables us to add further layers of contextual data to the display (Figure 4). The user can select a Series to focus on, revealing a detailed caption with Series attributes, and a set of lines showing relationships with other Series. These lines are colour-coded according to the CRS link types: related Series links are yellow;

succession links, referring to either preceding or successive Series, are blue; controlled by links are purple, leading from one Series to another that controls (or indexes) it; and controlling links, leading in the opposite direction, are red. Browsing these links, a user rapidly gains a sense of the relationships between Series and thus the context in which a given Series sits. The recording Agencies listed in the Series caption also contribute to this sense of context; selecting an Agency highlights all its recorded Series, providing further prompts for exploration and discovery. While this final sketch has several limitations - its user interface is basic, and it lacks any ability to refine or zoom the display – it demonstrates how interactive visualisation can provide context, and enable exploration, within a very large archival collection.

Figure 4: Interactive display of Series links and Agencies

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