The creation of digital representations of cultural heritage is sustainable and has continuing value only if the digital objects can be discovered by others and are seen as valuable in future research and interpretation and if that value is rewarded in professional terms (Harley et al. 2010). In the Hampson and Amarna Projects we have taken a number of steps that have been informed by prior studies of the ways in which digital resources generally and digital heritage specifically can be more effectively integrated into scholarship (Kansa 2005; Kintigh 2006; Snow et al. 2006). As we have noted, a key vehicle to ensure the discovery of digital heritage objects is the effective documentation of the object via metadata and its exposure to search engines and other content aggregation systems. Each of the formal archives to which these materials have been deposited has robust metadata and discovery mechanisms and the reader is referred to those sites for details.
In addition to the role of text metadata in discovery, substantial research is underway to develop computational methods for automatic recognition and object retrieval of 3-D digital objects (cf. Bustos et al. 2007b) especially those represented as point clouds. The entire contents of a recent issue of the International Journal of Computer Vision (Theoharis et al. 2010) were dedicated to this arena and much research is underway (cf. Tangelder and Veltkamp 2007). It is not a wild fantasy to predict that general-purpose tools to locate objects via a 3-D search engine will be widely available relatively soon (cf. Ferreira et al. 2010; Bustos et al. 2007a) and that even heritage-focused ones will be created (Kampel and Sablatnig 2006). To take steps to future-proof current digital object collections it is important to monitor this research and anticipate the formats and methods of exposure that may be forthcoming.
The comments facility has now been turned off.
The US National Science Foundation Advisory Committee for Cyberinfrastructure Task Force on Data and Visualization Final Report, (March 2011) specifically calls out the problems with citation of data as a key impediment to research. On page 19 of that report they say: Key Recommendation: Create new citation models in which data and software tool providers are credited with their data contributions and establish metrics that recognize open access policies and sharing. Encourage change in citation patterns to include a role for citations (e.g. to value activities such as 'data provider/curator' and/or 'software tool provider' alongside 'data analyzer' or 'computational modeler'), which can help create a credit market for data and software sharing. Encourage publication of data in a citable form before paper publication as advocated in initiatives such as SageCite (13). | Fred Limp |
© Internet Archaeology/Author(s)
University of York legal statements | Terms and Conditions
| File last updated: Tue Jun 28 2011