Timeline Tools

Timeline Tools, or TT, is a visualisation suite geared towards digital collections – though it may be used for other kinds of datasets as well. Its core feature is a novel timeline layout that is able to scale from datasets containing a few hundred records, to collections that span hundred thousands of items. Via zooming and panning it is possible to get a quick overview of an entire collection, but also to look closely at individual items.

I developed TT during my PhD on Visualising Cultural Data in collaboration with System Simulation, a software company specialised in data management for the heritage sector. I tested the library with a range of cultural collections, including the Tate and Cooper Hewitt, and evaluated it with curators and archivists from the Britten-Pears Archive, Science Museum, Victoria & Albert Museum, Oxford University’s Beazley Archive and many others.

Situation

Digital collections – the digital metadata produced and maintained by archives, libraries and museums – are becoming ever more valuable 1 for cultural institutions. However, curators and archivists currently lack the means to make use of their cultural data.

Through this visualisation tool curators are, often for the first time, able to see their entire digital collection, and visually analyse their digital assets.

Result
The tool has successfully been tested with and used by cultural institutions across the UK and has, on many occasions, allowed novel and surprising insights. An iPad version has been created by System Simulation to be used by visitors within a museum and allowing them to see exhibited items in the context of a museum’s collection.


More than 60,000 records of the Tate digital collection are accessible through the timeline visualisation tool.


The collection of the Geffrye Museum documents English homes from 1600 to the present day. Through the visualisation tool it is possible to see how the focus of the collection changed over time.

Process
Existing visualisation tools often rely on aggregation and summative views to visualise large datasets. The difficulty when visualising cultural data is that every ‘piece’ of data stands for an individual cultural artefact, represented by a uniquely crafted record description. In order to do justice to the uniqueness of every data point in cultural datasets, I wanted to somehow retain the ability to represent all of them individually. I played with the metaphor of a ‘heap of stuff’ that communicates the totality of a set of items, while retaining accessibility to individual items or subsets of a collection. I made prototypes in JavaScript and d3.js that implemented a force-directed layout, where individual dots arrange themselves to a contiguous shape. A pleasing experience for users, but one that did not scale well to more than a few hundred items. In addition, the constant motion of the visualisation proved impractical for visual analysis. The final implementation uses a novel diagram format I call Temporal Jittering. The diagram exploits the fact that temporal data in cultural collections is usually defined with a level of confidence. Records are represented as dots and positioned on a horizontal timeline somewhere within their allowed timeframe, in order to generate a compact layout. In higher zoom levels the diagram is represented as a single shape, only revealing individual records – along with their associated images – when the user zooms in. By clicking on a dot a user can then find out more about an individual record and perform various filtering actions on the entire dataset.

Notes:

  1. See for example: The British Library, 2015. Unlocking The Value – The British Library’s Collection Metadata Strategy 2015-2018. Available at: http://www.bl.uk/bibliographic/pdfs/british-library-collection-metadata-strategy-2015-2018.pdf.