Google launches new search engine to help scientists find the datasets they need
Original post on: https://www.theverge.com
By James Vincent
Dataset Search could be a scientist’s best friend
Google’s goal has always been to organize the world’s information, and its first target was the commercial web. Now, it wants to do the same for the scientific community with a new search engine for datasets.
The service, called Dataset Search, launches today, and will be a companion of sorts to Google Scholar, the company’s popular search engine for academic studies and reports. Institutions that publish their data online, like universities and governments, will need to include metadata tags in their webpages that describe their data, including who created it, when it was published, how it was collected, and so on. This information will then be indexed by Dataset Search and combined with input from Google’s Knowledge Graph. (That’s the name for those boxes that pop up for common searches. So if dataset X was published by CERN, some info about the institute will also be included in the results.)
Speaking to The Verge, Natasha Noy, a research scientist at Google AI who helped create Dataset Search, says the aim is to unify the tens of thousands of different repositories for datasets online. “We want to make that data discoverable, but keep it where it is,” says Noy.
At the moment, dataset publication is extremely fragmented. Different scientific domains have their own preferred repositories, as do different governments and local authorities. “Scientists say, ‘I know where I need to go to find my datasets, but that’s not what I always want,’” says Noy. “Once they step out of their unique community, that’s when it gets hard.”
Noy gives the example of a climate scientist she spoke to recently who told her she’d been looking for a specific dataset on ocean temperatures for an upcoming study but couldn’t find it anywhere. She didn’t track it down until she ran into a colleague at a conference who recognized the dataset and told her where it was hosted. Only then could she continue with her work. “And this wasn’t even a particularly boutique depository,” says Noy. “The dataset was well written up in a fairly prominent place, but it was still difficult to find.”