Mia Ridge’s article “Where next for open cultural data in museums” brings up the history and usage of the open cultural data. Ridge highlights events such as the US and UK launching open data sites in 2009 and the Brooklyn museum’s release of its data through an API as key moments in the wave towards digitizing and making cultural data available. As a short aside, API is an abbreviation of “application program interface” even this definition is not very clarifying however and it is difficult to find a practical definition online.
Ridges goes on to make the point that now while many museums have made data available, the data is not used as much as the institution or organization might expect. She points to murky licenses and inconsistent data as potential reasons for this underuse.
I resonate with this point as for my DH 101 project my group was working with data collected from a series of menus in the New York Public Library collection. The library has been working to digitize the menus in a series of ways they have, however, been relying on public volunteer support. This is problematic as it leads to inconsistent inputs such as in capitalization of names and the input of units. For my own group, this kept us from being able to take our research in particular directions.
We also experienced there being too much data available. While this may not seems like a problem it became overwhelming and the length of our project forced us to narrow our focus. This speaks directly to the museum’s sentiments on their data not being used as much as they would have thought. Museums house many objects, each containing its own extensive metadata and data contained within the object itself. When we move to digitize this, the data produced is extensive and this makes it difficult for it all to be used.
True! For museum data to be used in a helpful way it must be categorized with very specific vocabularies and groupings so people can easily access exactly what they need and only that.
I loved your project! The collection I had for my DH101 project had very clean data, and needed very little refining. We too had the same problem of having “too much data.” It’s quite unfortunate that this is often the case, where there are many things missing, bad record keeping, and inconsistency with massive archives.
I liked how you were able to directly apply this article to your personal experiences creating your DH101 project. I feel that many people underestimate the amount of sheer information a museum holds, and that archival is a strenuous and time-consuming process. It’s just unfortunate, however, that this hard word often goes unnoticed since as Ridges pointed out, they are rarely utilized.
I agree that there is a need for museums to better organize their extensive collection of data and metadata so that they are easier to work with. But I understand, from the perspective of museums, why it is so difficult to create a clean dataset free of inconsistencies and missing information. Sometimes, the information is just not available. And I assume that this is the case for a lot of historical artifacts or age-worn objects like century-old paintings for instance because it is highly unlikely that museums had a standardized meticulous system of recording data and even if they did, I assume many records failed to pass the test of time. That being said, when it comes to organizing data, museums should focus on how to work around these gaps and holes and provide reasonable justifications for their decisions because for any data visualization or data-related project to be meaningful, the data set itself has to be accurate, transparent and verifiable.