The Canonical vs. The Contextual

Neatline’s Approach to Connecting Archives with Spatio-Temporal Interfaces

David McClure

Abstract: 

Neatline is a geo-temporal mapping tool that lets users create interpretive displays of archival collections by plotting records on maps and timelines. Developed by the Scholars’ Lab at the University of Virginia Library and supported by the National Endowment for the Humanities and the Library of Congress, Neatline is built as a suite of plugins for Omeka, a digital archive-building tool developed by the Roy Rosenzweig Center for History and New Media at George Mason University.

Omeka provides a content management system that lets users create archives consisting of collections of “items,” each backed by a Dublin Core metadata record. After installing the Neatline plugins, users can create interpretive "exhibits" that present and elaborate on a carefully curated subset of the background archival collection. Starting with geospatial and temporal data on the archival records, users can draw free-form spatial annotations on the map, define start and end dates on the timeline, drag out uncertainty gradients across time spans, add titles and descriptive prose, sort the records into nested hierarchies, and configure complex animations to show change over time.

One of the most challenging and interesting aspects of the Neatline development process has been an ongoing effort to achieve the correct level of integration between the interpretive, context-specific data created in the Neatline editing interface and the more standardized, archival data that exists behind the scenes in the Omeka collection. Should these two categories of data be fully integrated, or held somewhat at arm’s length?

In our case, this question was foregrounded by the application architecture of Neatline. But I argue that the basic opposition between "canonical" and "contextual" data is at play in most projects that create interfaces to display geospatial and temporal data. Because of the visual, often highly-stylized presentation of spatio-temporal information, we need to maintain a firm distinction between the canonical and the contextual or we risk creating structurally messy, single-use data sets tied to specific applications and interfaces, which are inherently short-lived.