CZI's mission is to cure, prevent, or manage all diseases by 2100 through supporting science and technology. They accelerate biomedical research by developing tools and technologies and supporting open, collaborative models. Meta provides a knowledge graph integrating data from various sources like PubMed, bioRxiv, publishers, and more to enable recommendations and predictions. Specifically, Meta predicts the impact of new papers using a machine learning model trained on prior publications' citations after 3 years, since new papers have few citations. This predicted Eigenfactor is used to rank papers until proper citations accrue over time. Meta will soon launch publicly and seeks community feedback and partnerships.

1. Alex D Wade (@alexwade) 19 September 2019
CZI Science, Open Science, & Meta

3. Presentation Name Date
CZI’s mission
Supporting science and technology
that will make it possible to
cure, prevent, or manage all
diseases by the end of the century
Accelerating biomedical science
by developing new tools and
technologies and supporting open,
collaborative models of research
10 years81 years

4. we collaborate

6. ●
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7. preprints protocols

8. next deadline:
december 2019!
https://chanzuckerberg.com/rfa/
essential-open-source-software-for-science/
program officer: dario taraborelli

9. Projects request between
$50K and $250K for one year
Not limited to writing code
Docs, management, and
community engagement
— the “invisible work” —
can all be supported!
graphic by @deniseyu21 from a talk by karthik ram (@_inundata)

11. Meta

12. PubMed (Papers)
bioRxiv (preprints)
+ full text (Publishers)
+ full text (crawled)
Author
Disambiguation
Entity
Recognition
Affiliation
Disambiguation
Ringgold
(Institutions)
UMLS
Ontologies
ORCID
(Authors)
Paper
Disambiguation
Journal
Mapping
Citation
Mapping
PubMed
venues
Crossref
Eigenfactor
- Calculation
Recommendations
- Paper-to-Paper
- Cross-Entity Recs
Meta Knowledge Graph
Inputs
Knowledge Graph
Construction
Calculations

17. Predicting Impact
● Eigenfactor (EF) originally developed to assess journal importance.
● Extended to article-level (ALEF)
● Meta calculates an EF value
for every node in the graph
Bergstrom (2007); West el al (2010); Wesley-Smith, West, Bergstrom (2016)

18. Our approach: Predict the impact for new papers
● Problem: recent papers have zero/few citation edges so EF=0 but our users engage
almost exclusively with these new (uncited) papers
● Solution: infer an Eigenfactor value for recent papers
○ ML model trained on prior publications citations at 3 years
○ Validated on withheld dataset
● Predicted Eigenfactor (EFP) calculated on all new papers
○ EFP values used to rank/sort papers in feeds
○ EFP replaced with proper EF over time as citations begin to accrue

19. Meta: next steps
● Meta pubic launch: soon!
● We would love feedback & suggestions!
● Please come curate & share your own feeds!
● How can we partner with the community?

20. Thanks! | Questions?
@alexwade