Researchers from Cardiff University are using artificial intelligence (AI) to automatically segment and capture information from museum specimens and perform data quality improvement without human input. The university has been working with museums from across Europe including the Natural History Museum, London. The AI is being used to refine and validate new methods and contribute to the mammoth task of digitizing hundreds of millions of specimens. There are more than 3 billion biological and geological specimens in natural history museums globally. Digitizing these specimens -- where the physical information is transformed into a digital format -- has become a new task for museums as the digital world become ubiquitous. The digitalization helps reduce the amount of manual handling of specimens, which are delicate and prone to damage.

Scientists are using cutting-edge artificial intelligence to help extract complex information from large collections of museum specimens. A team from Cardiff University is using state-of-the-art techniques to automatically segment and capture information from museum specimens and perform important data quality improvement without the need of human input. They have been working with museums from across Europe, including the Natural History Museum, London, to refine and validate their new methods and contribute to the mammoth task of digitizing hundreds of millions of specimens. With more than 3 billion biological and geological specimens curated in natural history museums around the world, the digitization of museum specimens, in which physical information from a particular specimen is transformed into a digital format, has become an increasingly important task for museums as they adapt to an increasingly digital world. A treasure trove of digital information is invaluable for scientists trying to model the past, present and future of organisms and our planet, and could be key to tackling some of the biggest societal challenges our world faces today, from conserving biodiversity and tackling climate change to finding new ways to cope with emerging diseases like COVID-19.

With coyote attacks on humans in cities and suburbs making headlines – coyotes injured two people in Chicago earlier this month – officials could tap into a data repository to get a better handle on what's bringing the area's animals into such close proximity to humans. Called eMammal, the tool has been around for several years in one form or another and has helped researchers manage camera-trapping projects. It uses a data pipeline that takes images and metadata from the field through a cloud-based review processes and into SIdora, a Smithsonian Institution data repository. To date, eMammal has data on more than 1 million detections of wildlife worldwide, including in cities. Smithsonian researchers collaborated with others at the North Carolina Museum of Natural Sciences, Conservation International and the Wildlife Conservation Society to develop an open standard for camera trap metadata -- the Camera Trap Metadata Standard -- as part of the eMammal project. Camera traps are ruggedized cameras that researchers place in forests, jungles, grasslands, cities and elsewhere to capture images of mammals.