Ice cores in freezers, dinosaurs on display, fish in jars, birds in boxes, human remains and ancient artifacts from long gone civilizations that few people ever see – museum collections are filled with all this and more.
These collections are treasure troves that recount the planet’s natural and human history, and they help scientists in a variety of different fields such as geology, paleontology, anthropology and more. What you see on a trip to a museum is only a sliver of the wonders held in their collection.
Museums generally want to make the contents of their collections available for teachers and researchers, either physically or digitally. However, each collection’s staff has its own way of organizing data, so navigating these collections can prove challenging.
Creating, organizing and distributing the digital copies of museum samples or the information about physical items in a collection requires incredible amounts of data. And this data can feed into machine learning models or other artificial intelligence to answer big questions.
Currently, even within a single research domain, finding the right data requires navigating different repositories. AI can help organize large amounts of data from different collections and pull out information to answer specific questions.
But using AI isn’t a perfect solution. A set of shared practices and systems for data management between museums could improve the data curation and sharing necessary for AI to do its job. These practices could help both humans and machines make new discoveries from these valuable collections.
As an information scientist who studies scientists’ approaches to and opinions on research data management, I’ve seen how the world’s physical collection infrastructure is a patchwork quilt of objects and their associated metadata.
AI tools can do amazing things, such as make 3D models of digitized versions of the items in museum collections, but only if there’s enough well-organized data about that item available. To see how AI can help museum collections, my team of researchers started by conducting focus groups with the people who managed museum collections. We asked what they are doing to get their collections used by both humans and AI.
Museums can have vast collections – everything from samples from archeological sites to preserved insects to dinosaur bones. And huge collections means lots of data to collect and organize.
Justin Pumfrey/The Image Bank via Getty Images
Collection managers
When an item comes into a museum collection, the collection managers are the people who describe that item’s features and generate data about it. That data, called metadata, allows others to use it and might include things like the collector’s name, geographic location, the time it was collected, and in the case of geological samples, the epoch it’s from. For samples from an animal or plant, it might include its…
