What can be learned by applying big data tools to scholarly information? What are some real world applications? How does this benefit various stakeholders in our space? This presentation will answer these questions.

1. Greg Tananbaum
Strategic Partnerships
Leveraging Big Data in the
Scholarly Communication Space

2. 800 K
1 M
600 K
400 K
200 K
1900 1950
Power
2010
Science has scaled up
400x
since 1950
26,560,336
papers since 1811
4,000+
new every day
Graph: ReLX Group

3. These papers represent
a global conversation that is
happening among researchers
across science.

4. It’s a paper-by-paper
conversation that charts the
progress of ideas and
discoveries across every major
field of knowledge.

5. But given the volume of papers
and their complexity,
deep engagement increasingly
requires new classes of
products and services.

6. AI-enabled systems that are able
to read and understand scientific
literature at scale.

7. This is no easy task, considering
we’re dealing with the the densest
and most information-rich
documents on Earth.

8. When the term “big data” is applied
to scientific literature, big doesn’t
just mean volume – it also means
complexity.

9. That is what Meta was created to
accomplish. To read, understand,
and create structured connections
across all of scientific knowledge.

10. Our Mission – Unlock the world’s scientific and
technical insights using artificial intelligence.
Founded in 2010 • Team of 25+ • Venture Backed
Toronto (HQ) • San Francisco

11. Our model is built on content that
we’ve obtained from a variety of
sources. We have direct indexing
partnerships with 40 of the world’s
leading publishers, with more being
added all the time.

12. We use the information within papers
to generate the world’s largest scientific
knowledge graph. We identify, map, and rank
the connections between more than 37 million
different scientific entities, mapping the
entirety of science today and how we got here.

13. 3.5B
recommendations
1B
paper-concept
matches
422M
citations
26M
papers
16M
genetic elements
20M
concepts
17M
researchers
2M
antibodies
407K
drugs
427K
institutes
234K
bacteria
96K
diseases
85K
products
36K
journals
4.6B
Knowledge Graph
connections
The World’s Largest Scientific Knowledge Graph

14. The Knowledge Graph is the base data set that
powers our big data applications – including
Meta Science, Bibliometric Intelligence,
and Horizon Scanning.

15. Literature Discovery
Meta Science
Literature Discovery
Meta Science
Meta Science is a free AI-enabled literature
discovery engine for researchers to stay
apprised of the latest developments in their
areas of research, explore the evolution of
certain topics, and follow high-impact
journals and authors.

16. Literature Discovery
Meta Science
• 44 million pages representing virtually every person
and entity in biomedicine
• Used by researchers at over 1,200 institutions
• Industries include academia, publishing, pharma, life
science tools, government
• Benefit to the scientific ecosystem: article discovery
Literature Discovery
Meta Science

19. Bibliometric Intelligence
Predictive Insights
Bibliometric Intelligence helps journal
editors manage the flow of manuscripts
they are tasked with evaluating by helping
them pinpoint subject-appropriate and high
impact manuscripts at the moment of first
submission.

20. Bibliometric Intelligence
Predictive Insights
It is a quantitative set of tools
that complement the qualitative
expertise that editors bring to
the task of evaluating
manuscripts.

21. Bibliometric Intelligence
Predictive Insights
• Pre-ranks incoming manuscripts based on deep
predictive profiling
• Intelligently cascades rejected manuscripts to more
appropriate sister journals within a portfolio
• Currently integrated into Aries Systems’ Editorial
Manager, with more integrations to be announced

27. Horizon Scanning
Predictive Insights
Horizon Scanning is a predictive
intelligence engine for making
sense of emerging topics,
disciplinary intersections, and the
next "what's hot" areas in science,
years in advance.

28. Horizon Scanning
Predictive Insights
The system scans scientific and
patent literature to identify every
entity mentioned in the texts.
Those concepts are then analyzed
based on a number of semantic
patterns within the article sets.

29. Horizon Scanning
Predictive Insights
Based on those patterns,
the system measures their current
global prominence and predicts
their future prominence,
three years from now.

32. Our understanding of research developments is
like a visible light spectrum. There is so much
going on that are unable to discern.

33. What big data tools can do is extend our field
of vision. Meta is providing tools to process
these signals.

34. Thank You
For more information visit meta.com
Greg Tananbaum · greg@meta.com