“How iNaturalist can correctly recognize (most of the time, at least) different living organisms is thanks to a machine-learning model that works off of data collected by its original app, which first debuted in 2008 and is simply called iNaturalist. Its goal is to help people connect to the richly animated natural world around them.
The iNaturalist platform, which boasts around 2 million users, is a mashup of social networking and citizen science where people can observe, document, share, discuss, learn more about nature, and create data for science and conservation. Outside of taking photos, the iNaturalist app has extended capabilities compared to the gamified Seek. It has a news tab, local wildlife guides, and organizations can also use the platform to host data collection “projects” that focus on certain areas or certain species of interest.
When new users join iNaturalist, they’re prompted to check a box that allows them to share their data with scientists (although you can still join if you don’t check the box). Images and information about their location that users agree to share are tagged with a creative commons license, otherwise, it’s held under an all-rights reserved license. About 70 percent of the app’s data on the platform is classified as creative commons. “You can think of iNaturalist as this big open data pipe that just goes out there into the scientific community and is used by scientists in many ways that we’re totally surprised by,” says Scott Loarie, co-director of iNaturalist. …
But with an ever-growing amount of data, our ability to wrangle these numbers and stats manually becomes virtually impossible. “You would only be able to handle these quantities of data using very advanced computing techniques. This is part of the scientific world we live in today,” Durant adds….
Another problem that researchers have to consider is maintaining the quality of big datasets, which can impinge on the effectiveness of analytics tools. This is where the peer-review process plays an important role….”
