Amongst museums and libraries, data formats and exchange standards have not been universally accepted, which has inhibited the free sharing and aggregation of information between institutions. While there have been many advances in standards and sharing methods over the last 50 years, the Mellon study from 2007 (Museum Data Exchange: Learning How to Share) shows there are still many challenges with searching for the same types of items in different art museums. The metadata is fairly well standardized for the Creator name, nationality of the Creator, role of the Creator and Object Work Type, though data could still be better standardized in small ways to make big improvements in interoperability. In addition, integrating data between museums, libraries and archives or, within a museum’s own collection, is not often attempted or successful. The information tends to stay in data silos or in some cases acts as what Dorothea Salo describes as the “roach motel” problem, were data goes in to institutional repositories but never comes out (Good Things Come to Those Who Share).
In 2007, the Mellon foundation provided funds for nine museums to model effective museum data exchange. The mission for the project was as follows: defining a common data structure, extracting data from collections management systems in that structure, defining a protocol for exchanging collections data, and creating a dialogue surrounding the topic of shared collections data. As someone interested in the education field of museums, one of the results from the Mellon study that I found most exciting were the opportunities to expose the collection to both early and higher education learning. For example, ARTstor, which was an early co-creator of CDWA-lite, welcomes contributions from OAI (Open Archives Initiative) contributors to routinely update and add to the sites collection.
In my exercise, I wanted students to understand the collaboration and communication necessary for efficient museum data exchange. Museums must use similar vocabulary in order for collections to be searchable by both visitors and scholars. In my activity, I used a 48-piece puzzle to represent different pieces of information necessary to complete a whole “picture”. Each student acted as one of the nine institutions used in the 2007 Museum Data Exchange experiment, including: Harvard Art Museum, Metropolitan Museum of Art, National Gallery of Art, Princeton University Art Museum, Yale University Art Gallery, the Cleveland Museum of Art, Minneapolis Institute of Art, Victoria & Albert, and the National Gallery of Canada. Students were then given additional pieces of the puzzle with terms written on the back. They had to match their puzzle pieces to another students corresponding piece in order to finish the puzzle. For example, one student’s puzzle piece may have read “artist” while another student’s read “John Waters”. I used a photograph that was catalogued and added to the digitized collection at the Walker Art Center in Minneapolis titled, “Blue Plate Special”. Once the puzzle was complete, we searched the photograph on the museum’s website where students were able to visualize the picture they had put together through effective communication and teamwork.
Hailey Helmerich, Graduate Student, Museum Science & Management, Education Track
University of Tulsa, April 2017
Museum Data Exchange: Learning How to Share
Case Example: Museum Data Exchange Good Things Come to Those Who Share
On the River Bure