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Facilitating the discovery of public datasets
Tuesday, January 24, 2017
Posted by Natasha Noy, Google Research and Dan Brickley, Open Source Programs Office
There are many hundreds of data repositories on the Web, providing access to tens of thousands—or millions—of datasets. National and regional governments, scientific publishers and consortia, commercial data providers, and others
for fields ranging from
and more. Access to this data is critical to facilitating reproducibility of research results, enabling scientists to build on others’ work, and providing data journalists easier access to information and its provenance. For these reasons, many publishers and funding agencies now require that scientists make their research data available publicly.
However, due to the volume of data repositories available on the Web, it can be extremely difficult to determine not only where is the dataset that has the information that you are looking for, but also the veracity or provenance of that information. Yet, there is no reason why searching for datasets shouldn’t be as easy as searching for recipes, or jobs, or movies. These types of searches are often open-ended ones, where some structure over the search space makes the exploration and serendipitous discovery possible.
To provide better discovery and rich content for books, movies, events, recipes, reviews and a number of other content categories with Google Search, we
on structured data that content providers embed in their sites using
vocabulary. To facilitate similar capabilities for datasets, we have
recently published new guidelines
to help data providers describe their datasets in a structured way, enabling Google and others to link this structured metadata with information describing locations, scientific publications, or even
, facilitating data discovery for others. We hope that this metadata will help us improve the discovery and reuse of public datasets on the Web for everybody.
The schema.org approach for describing datasets is based on an effort recently standardized at W3C (the
Data Catalog Vocabulary
), which we expect will be a foundation for future elaborations and improvements to dataset description. While these industry
are evolving, we are confident that the standards that already exist today provide a solid basis for building a data ecosystem.
While we have released the guidelines on publishing the metadata, many technical challenges remain before search for data becomes as seamless as we feel it should be. These challenges include:
Defining more consistently what constitutes a dataset:
For example, is a single table a dataset? What about a collection of related tables? What about a protein sequence? A set of images? An API that provides access to data? We hope that a better understanding of what a dataset is will emerge as we gain more experience with how data providers define, describe, and use data.
Ideally, datasets should have permanent identifiers conforming to some well known scheme that enables us to identify them uniquely, but often they don’t. Is a URL for the metadata page a good identifier? Can there be multiple identifiers? Is there a primary one?
Relating datasets to each other
: When are two records describing a dataset “the same” (for instance, if one repository copies metadata from another )? What if an aggregator provides more metadata about the same dataset or cleans the data in some useful way? We are working on clarifying and defining these relationships, but it is likely that consumers of metadata will have to assume that many data providers are using these predicates imprecisely and need to be tolerant of that.
Propagating metadata between related datasets:
How much of the metadata can we propagate among related datasets? For instance, we can probably propagate provenance information from a composite dataset to the datasets that it contains. But how much does the metadata “degrade” with such propagation? We expect the answer to be different depending on the application: metadata for search applications may be less precise than, say, for data integration.
Describing content of datasets:
How much of the dataset content should we describe to enable support for queries similar to those used in
Explore for Docs, Sheets and Slides
, or other exploration and reuse of the content of the datasets (where license terms allow, of course)? How can we efficiently use content descriptions that providers already describe in a declarative way using
W3C standards for describing semantics of Web resources and linked data
In addition to the technical and social challenges that we’ve just listed, many remaining research challenges touch on longer term open-ended research: Many datasets are described in unstructured way, in captions, figures, and tables of scientific papers and other documents. We can build on
to extract this metadata. While we have a reasonable handle on ranking in the content of Web search, ranking datasets is often a
: we don’t know yet if the same signals that work for ranking Web pages will work equally well for ranking datasets. In the cases where the dataset content is public and available, we may be able to extract additional semantics about the dataset, for example, by learning the types of values in different fields. Indeed, can we understand the content enough to enable data integration and discovery of related resources?
A Call to Action
As any ecosystem, a data ecosystem will thrive only if a variety of players contribute to it:
For data providers, both individual providers and data repositories:
publishing structured metadata using
, and other community standards will make this metadata available for others to discover and use.
For data consumers (from scientists to data journalists and more):
citing data properly, much as we cite scientific publications (see, for example, a recently proposed
to contribute to
schema.org metadata for datasets, providing domain-specific vocabularies, as well as working on tools and applications that consume this rich metadata.
Our ultimate goal is to help foster an ecosystem for publishing, consuming and discovering datasets. As such, this ecosystem would include data publishers, aggregators (in the form of large data repositories that provide additional value by cleaning and reconciling metadata), search engines that enable data discovery of the data, and, most important, data consumers.
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