The service, called Dataset Search, launches yesterday, and it 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 Google’s search engine and combined with information from the Knowledge Graph. (So if dataset X was published by CERN, a little information about the institute will also be included in the search.)
Natasha Noy, a research scientist at Google AI who helped created 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.”
The initial release of Dataset Search will cover the environmental and social sciences, government data, and datasets from news organizations like ProPublica. However, if the service becomes popular, the amount of data it indexes should quickly snowball as institutions and scientists scramble to make their information accessible.
This should be helped by the recent flourishing of open data initiatives around the world. “I do think in the last several years the number of repositories has exploded,” says Noy.
She credits the increasing importance of data in scientific literature, which means journals ask authors to publish datasets, as well as “government regulations in the US and Europe and the general rise of the open data movement.”
Having Google involved should help make this project a success, says Jeni Tennison, CEO of the Open Data Institute (ODI). “Dataset search has always been a difficult thing to support, and I’m hopeful that Google stepping in will make it easier,” she says.
To create a decent search engine, you need to know how to build user-friendly systems and understand what people mean when they type in certain phrases, says Tennison. Google obviously knows what it’s doing in both of those departments.
In fact, says Tennison, ideally Google will publish its own dataset how Dataset Search gets used. Although the metadata tags the company is using to make datasets visible to its search crawlers are an open standard (meaning that any competitor like Bing or Yandex can also use them and build a competing service), search engines improve most quickly when a critical mass of users is there to provide data on what they’re doing.
“Simply understanding how people search is important... what kind of terms they use, how they express them,” says Tennison. “If we want to get to grips with how people search for data and make it more accessible, it would be great if Google opened up its own data on this.”
In other words: Google should publish a dataset about dataset search that would be indexed by Dataset Search. What could be more appropriate?
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