What We Offer
What data can we give you in return?
In return for your collection information, we will give you structured, cleaned and enriched data. Indeed, while processing your information, we will use cutting-edge standards and best practice models of the cultural heritage domain. These include controlled vocabularies, such as the Getty Union List of Artist Names (ULAN) and the Linked.Art data model, which were developed by museum professionals, based on ICOM’s CIDOC-CRM. Furthermore, if you tell us your specific needs, we would be happy to map the structured data according to your collection management system.
How can our data help with your own provenance research and cataloging?
Analyzing your collection information within our cross-institutional dataset will allow us to compare it with that of other museums, in regard to, for example, historical dealers and collectors, object paths, and research material. Such cross-referencing can enable synergy effects not only in provenance research, which is often time-consuming, but also in detailed cataloging. As we process the data, we are more than happy to share with you any relevant research discoveries, from common practices to inconsistencies across museums.
How can our research help your curatorial mission?
Have you ever wondered which US museums have developed similar or, indeed, dissimilar collecting patterns and strategies over time? If yes, then we might be able to help you! Focusing on Impressionist, Post-Impressionist and Modern paintings, we hope to locate your collection in the US museum landscape and thereby give you a sense of where it stands in relation to other institutions. We would be happy to share our findings with you along the way and may even surprise you—with new stories for specific works or unknown connections within your collection—helping you to develop new narratives for your curatorial practice, not to mention education and outreach.
How can our tools, models and methods help you in the future?
Have you ever wanted to search your collection for objects that might have been in Paris in c. 1913? Well, as it stands, most museum collection management systems and data models do not facilitate the recording of provenance data with its vagueness (e.g. circa 1913, near Paris) or uncertainty (e.g. probably or possibly) in a fully machine-readable way. Vagueness and uncertainty are, however, not only common aspects of provenance research, but also crucial to its accurate documentation. By testing, refining, and, where appropriate, developing tools, models, and methods specifically needed for producing historically accurate and fully machine-readable provenance data, then, our project hopes to enable both cross-object and cross-museum queries.
When should you expect to reap the above rewards?
While getting all museums on board may take some time, we are more than happy to share with you any research findings that we think might be helpful for your collection stewardship in the meantime. And while processing, analyzing, visualizing and contextualizing the cross-institutional dataset will also take a great deal of time and effort—not to mention testing, refining and, where appropriate, developing the tools, models and methods needed for doing just that—we nevertheless hope to share our results by 2024 at the latest.