Meta (formerly Sciencescape), “a scientific knowledge network powered by machine intelligence,” recently launched 20 new platform services designed to help scientific industries address challenges related to scientific literature discovery and overload. All of Meta’s services are created around its machine intelligence platform, which, according a Nov. 2, 2015, press release, reads “more than 19 million full-text articles, the entirety of PubMed, and continuously crawls the web to identify all of the people and entities mentioned in the literature. It then analyzes the number and quality of citations associated with each article, and uses that information to rank the papers.”
Furthermore, “Meta examines the average impact of the papers associated with every entity in its universe to determine a value for each entity. The result is the largest scientific knowledge graph in the world—one so expansive, it knows almost every paper, person and entity that make up the universe of science, and how they relate to each other.”
The result is a powerful literature discovery engine and other services that are designed to change the way researchers interact with scientific literature and the ecosystem around publications. The Knowledge Graph is described by the company as “an extensive and evolving network of researchers and entities that together form the universe of scientific knowledge.”
Sam Molyneux, Meta’s CEO and co-founder, explains that the company’s mission “is to organize and deliver all of the world’s scientific and technical information.” Meta is working with publishers to consume all of their content, and it will combine that content with various ontologies to build up Meta’s Knowledge Graph. Additional services are then built on top of that layer.
Meta has started this process by focusing on the biomedical sciences to index papers, researchers, and citations. It plans to expand into physics, chemistry, and patents, among other areas.
Molyneux explained that the search results will comb through these “huge swaths of science” to look for relevant people, topics, and articles; results will “push things you need to read” to the top of a queue in an effort to help scientists prioritize what to follow.
One of the most striking aspects of the platform services is their appearance—the data visualization services are quite elegant. Molyneux explains Meta’s philosophy about their look and feel:
We’re focused on the factors that enable researchers to identify key papers to read within huge lists. We’ve studied how researchers prioritize what papers to read when presented with a huge list. We’ve taken that information and stripped it back to information about articles and entities. We try to present a minimum of information [to researchers so they can] prioritize at a single glance. Imagery is a key part for contextualization.
He also explained, “We spent a lot of time thinking about the information/publication overload problem from a machine learning perspective, but also from a design perspective. We wanted to create an experience that is beautiful and scientific and precise.”
About the Company
At the helm of the organization is a brother-sister team based in Canada. Sam Molyneux’s sister, Amy Molyneux, is the co-founder and EVP. Sam’s background is in cancer genomics research, while Amy is a software engineer. As Sam Molyneux explains, “I spent a lot of years at the leading edge of cancer research. … Amy spent a lot of years working on large-scale data sets. … [Working together] was a natural fit. We complemented each other well.”
The company was founded in 2010 as Sciencescape. It has grown into a large team of engineers and data scientists from all over the world. Half of the company focuses on Meta Science, the literature and discovery search engine, while “the other half focuses on information services to open up our machine learning and data for industries/companies that use data in other ways [beyond] just reading articles,” Molyneux says.
For instance, some of its services are tailored toward specific industries. One offers Entity Horizon Scanning (tracking emerging areas of science), Technology and Product Emergence (tracking tools and technology), and Concept Emergence (tracking nascent fields), which are targeted to life science companies. Other tools are aimed at publishers to help “manuscripts find the right journals.”
Partnerships With Publishers
Because scientific literature underpins all of Meta’s services, it has strong partnerships with many of the key players working in biomedical sciences—the American Medical Association (AMA), BioMed Central, The BMJ, Cambridge University Press, De Gruyter, eLife, Elsevier, Hindawi Publishing Corp., MDPI, Oxford University Press, PeerJ, PLOS (Public Library of Science), SAGE Publications, Taylor & Francis Group, and many others. As of early December, the Meta website indicates that it has partnerships with publishers of more than 18,000 journals and books, and additional partnership announcements are expected in the coming weeks.
Benefits for Libraries
“One of the concepts we’re really focused on … is helping everyone get more value from their subscriptions to full-text journals from publishers,” says Molyneux. “The core of [the] model is analyzing and making the information that lives in scientific articles discoverable so researchers are more aware of articles published in the past [or of other entities in the same space]. At the heart of all of our partnerships are publishers … but it’s also about driving value to the library’s patrons, increasing the value [organizations] get out of their current subscriptions.”
Access to the literature discovery platform will be available through the Meta website (first as a public beta), and it will always be free to use. Interested individuals can register now to reserve an account.
View the screen shots in the upper-right corner of this article to get a sneak peek at Meta.