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.
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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 taxonomy, which is the set of Latin names that classify it.
All together, that information adds up to a mind-boggling amount of data.
But combining data across domains with different standards is really tricky. Fortunately, collection managers have been working to standardize their processes across disciplines and for many types of samples. Grants have helped science communities build tools for standardization.
In biological collections, the tool Specify allows managers to quickly classify specimens with drop-down menus prepopulated with standards for taxonomy and other parameters to consistently describe the incoming specimens.
A common metadata standard in biology is Darwin Core. Similar well-established metadata and tools exist across all the sciences to make the workflow of taking real items and putting them into a machine as easy as possible.
Special tools like these and metadata help collection managers make data from their objects reusable for research and educational purposes.
All the small things
My team and I conducted 10 focus groups, with a total of 32 participants from several physical sample communities. These included collection managers across disciplines, including anthropology, archaeology, botany, geology, ichthyology, entomology, herpetology and paleontology.
Each participant answered questions about how they accessed, organized, stored and used data from their collections in an effort to make their materials ready for AI to use. While human subjects need to provide consent to be studied, most species do not. So, an AI can collect and analyze the data from nonhuman physical collections without privacy or consent concerns.
We found that collection managers from different fields and institutions have lots of different practices when it comes to getting their physical collections ready for AI. Our results suggest that standardizing the types of metadata managers record and the ways they store it across collections could make the items in these samples more accessible and usable.
Additional research projects like our study can help collection managers build up the infrastructure they’ll need to make their data machine-ready. Human expertise can help inform AI tools that make new discoveries based on the old treasures in museum collections.
