The document describes a visualization of PLoS data created by mapping PLoS thesaurus terms to an existing map of all science created from Scopus data. It discusses mapping terms directly and indirectly, analyzing coverage by journal and year, and using the maps and underlying data to answer questions posed by PLoS about trends, relationships between fields, and how PLoS coverage compares to other databases. Visualizations are created in software like Pajek and Gephi and are meant to facilitate communication around science structure and metrics.
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PLOS Visualization Project
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Visualizing PLoS Data
DHUG 2014
February 11, 2014
Kevin Boyack, Bob Kasenchak, Margie Hlava
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AGENDA
Background
Data
Science mapping
Mapping thesaurus directly
Mapping thesaurus indirectly
» SciTech S&T map
» Thesaurus map
Overlays
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BACKGROUND
To answer questions posed by PLoS using PLoS index (thesaurus)
terms, a map of the thesaurus space is desirable
» Where is the coverage thin, where is it dense?
» What areas are core and which are emerging trends? Which fields are growing
quickly?
» Which fields are interrelated?
» Which fields have low activity but are related to very active fields?
Some questions require more than PLoS data
» How does the coverage in PLoS journals relate to coverage in other databases?
A map of the thesaurus space can function as a template upon which
various distributions can be overlaid
Some questions are more amenable to tables than maps
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DATA
Thesaurus
» 10,551 unique terms in 15,164 locations
» 11 top level terms
» 296 second level terms in 305 locations
PLoS article data parsed / indexed by Access Innovations
» Article level data through mid-2013; 84,989 records
» 72,796 records with index (thesaurus) terms; 171,181 terms
Scopus data
» SciTech Strategies licenses use of Scopus data from Elsevier
» 1996-2011; 25M records
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THESAURUS STRUCTURE
Biology CS Earth Env Eng Medicine People Physical Methods SS
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SCIENCE MAPPING
A map is a visual representation of the relationships within a
» Classification system (e.g., journal categories, taxonomy)
» Document corpus
Science maps can/have been created using
» Documents, Journals, Authors, Terms, Taxonomies
30-40 year tradition of science mapping
» Well-established methodologies
» Current computing power and data availability enable large scale mapping and
analysis
Maps used for communication, strategy, planning, evaluation …
» Understanding of structure, relative location
» Basis for metrics
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GENERAL PROCESS
Select data source and objects (e.g., docs) of interest Scopus / WoS
PubMed
Patents
Your own data
Calculate similarity between objects (doc-doc) Citations (links)
Words (title/abstr)
Create a visual layout (map) of the objects / clusters Pajek / Gephi
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MAPPING PLOS TERMS DIRECTLY
Mapped second level terms
» All terms rolled up to the second level term at the top of the branch
Cosine similarity between terms based on co-occurrence in articles
Map created using Fruchterman-Reingold mapping routine in Pajek
» Based on only the top 3 links per term – strongest linkages
Each node (term) colored using top level terms
» Biology and life sciences (light green)
» Medicine and health sciences (salmon)
» Physical sciences (purple)
» Research and analysis methods (yellow)
» Social sciences (light orange)
» …
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MAPPING PLOS TERMS DIRECTLY
Resulting map is difficult to use
» Using second level terms leaves out needed detail
» Term counts span several orders of magnitude – hard to map well
» Terms within the same branch of the thesaurus don’t cluster well
Resulting map doesn’t account well for context (areas where PLoS has
low coverage)
Need a map that does a better job of providing context, and that better
matches our current picture of science
So … we decided to index Scopus data using the PLoS rule-base and
map based on that more inclusive context
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MAPPING PLOS TERMS INDIRECTLY
Use existing map of all of science
Triangulate term positions
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SCITECH S&T MAP
20 million articles (1996-2011)
3 million patents (1996-2011)
220,000 document clusters
Cluster contents using citation
Cluster positions using text
The visual is the final step
(last 10%); the full process is
needed for robust analysis
Template on which other info
can be overlaid; documents (and
related info) tied to map positions
We are the only ones in the
world who do contextual analysis
at this scale
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TRIANGULATION
Use full S&T map as basis
Based on Scopus 2007-2011
Locate each thesaurus term as the
average position of its documents
The resulting map contains 11,934
terms; full coverage; interpretable
Terms with multiple colors appear
multiple times
The term map becomes the basemap
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THESAURUS BASEMAP
The term map becomes the basemap
Circle size - # documents
Circle color – top level term
Many small circles – detailed thesaurus
and rule base that differentiates
Few large circles – broad terms and
rule base that doesn’t differentiate
Location/size of terms facilitates questions
- Is term well located in the thesaurus?
- Is the rule base acting as desired?
- Should term be broken up?
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NOT JUST PRETTY PICTURES
All visualizations are based on tabular data
Pictures help form hypotheses
Detailed analysis provides answers
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ANALYSES
Biology and life sciences
Computer and information sciences
Earth sciences
Ecology and environmental sciences
Engineering and technology
Medical and health sciences
Physical sciences
Research and analysis methods
People and places
Social sciences
Science policy
Circuit models
Chromatin immunoprecipitation
Evolutionary modeling
Electron transport chain
Intelligence
Language
Cognition
Decision making
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ANALYSES
Biology and life sciences
Computer and information sciences
Earth sciences
Ecology and environmental sciences
Engineering and technology
Medical and health sciences
Physical sciences
Research and analysis methods
People and places
Social sciences
Science policy
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ANALYSES
Biology and life sciences
Computer and information sciences
Earth sciences
Ecology and environmental sciences
Engineering and technology
Medical and health sciences
Physical sciences
Research and analysis methods
People and places
Social sciences
Science policy
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ANALYSES
Biology and life sciences
Computer and information sciences
Earth sciences
Ecology and environmental sciences
Engineering and technology
Medical and health sciences
Physical sciences
Research and analysis methods
People and places
Social sciences
Science policy
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ANALYSES
Biology and life sciences
Computer and information sciences
Earth sciences
Ecology and environmental sciences
Engineering and technology
Medical and health sciences
Physical sciences
Research and analysis methods
People and places
Social sciences
Science policy
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ANALYSES
Biology and life sciences
Computer and information sciences
Earth sciences
Ecology and environmental sciences
Engineering and technology
Medical and health sciences
Physical sciences
Research and analysis methods
People and places
Social sciences
Science policy
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ANALYSES
Biology and life sciences
Computer and information sciences
Earth sciences
Ecology and environmental sciences
Engineering and technology
Medical and health sciences
Physical sciences
Research and analysis methods
People and places
Social sciences
Science policy
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ANALYSES
Biology and life sciences
Computer and information sciences
Earth sciences
Ecology and environmental sciences
Engineering and technology
Medical and health sciences
Physical sciences
People and places
Social sciences
Science policy
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ANALYSES
Biology and life sciences
Computer and information sciences
Earth sciences
Ecology and environmental sciences
Engineering and technology
Medical and health sciences
Physical sciences
Research and analysis methods
People and places
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ANALYSES
PLoS – All journals and years
PLoS – All journals (2008)
PLoS – All journals (2009)
PLoS – All journals (2010)
PLoS – All journals (2011)
PLoS – All journals (2012)
PLoS – All journals (2013)
PLoS Biology
PLoS Computational Biology
PLoS Genetics
PLoS Medicine
PLoS NTD
PLoS Pathogens
PLoS One
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ANALYSES
PLoS – All journals and years
PLoS – All journals (2008)
PLoS – All journals (2009)
PLoS – All journals (2010)
PLoS – All journals (2011)
PLoS – All journals (2012)
PLoS – All journals (2013)
PLoS Biology
PLoS Computational Biology
PLoS Genetics
PLoS Medicine
PLoS NTD
PLoS Pathogens
PLoS One
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ANALYSES
PLoS – All journals and years
PLoS – All journals (2008)
PLoS – All journals (2009)
PLoS – All journals (2010)
PLoS – All journals (2011)
PLoS – All journals (2012)
PLoS – All journals (2013)
PLoS Biology
PLoS Computational Biology
PLoS Genetics
PLoS Medicine
PLoS NTD
PLoS Pathogens
PLoS One
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ANALYSES
PLoS – All journals and years
PLoS – All journals (2008)
PLoS – All journals (2009)
PLoS – All journals (2010)
PLoS – All journals (2011)
PLoS – All journals (2012)
PLoS – All journals (2013)
PLoS Biology
PLoS Computational Biology
PLoS Genetics
PLoS Medicine
PLoS NTD
PLoS Pathogens
PLoS One
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ANALYSES
PLoS – All journals and years
PLoS – All journals (2008)
PLoS – All journals (2009)
PLoS – All journals (2010)
PLoS – All journals (2011)
PLoS – All journals (2012)
PLoS – All journals (2013)
PLoS Biology
PLoS Computational Biology
PLoS Genetics
PLoS Medicine
PLoS NTD
PLoS Pathogens
PLoS One
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ANALYSES
PLoS – All journals and years
PLoS – All journals (2008)
PLoS – All journals (2009)
PLoS – All journals (2010)
PLoS – All journals (2011)
PLoS – All journals (2012)
PLoS – All journals (2013)
PLoS Biology
PLoS Computational Biology
PLoS Genetics
PLoS Medicine
PLoS NTD
PLoS Pathogens
PLoS One
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ANALYSES
PLoS – All journals and years
PLoS – All journals (2008)
PLoS – All journals (2009)
PLoS – All journals (2010)
PLoS – All journals (2011)
PLoS – All journals (2012)
PLoS – All journals (2013)
PLoS Biology
PLoS Computational Biology
PLoS Genetics
PLoS Medicine
PLoS NTD
PLoS Pathogens
PLoS One
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ANALYSES
PLoS – All journals and years
PLoS – All journals (2008)
PLoS – All journals (2009)
PLoS – All journals (2010)
PLoS – All journals (2011)
PLoS – All journals (2012)
PLoS – All journals (2013)
PLoS Biology
PLoS Computational Biology
PLoS Genetics
PLoS Medicine
PLoS NTD
PLoS Pathogens
PLoS One
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ANALYSES
PLoS – All journals and years
PLoS – All journals (2008)
PLoS – All journals (2009)
PLoS – All journals (2010)
PLoS – All journals (2011)
PLoS – All journals (2012)
PLoS – All journals (2013)
PLoS Biology
PLoS Computational Biology
PLoS Genetics
PLoS Medicine
PLoS NTD
PLoS Pathogens
PLoS One
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ANALYSES
PLoS – All journals and years
PLoS – All journals (2008)
PLoS – All journals (2009)
PLoS – All journals (2010)
PLoS – All journals (2011)
PLoS – All journals (2012)
PLoS – All journals (2013)
PLoS Biology
PLoS Computational Biology
PLoS Genetics
PLoS Medicine
PLoS NTD
PLoS Pathogens
PLoS One
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ANALYSES
PLoS – All journals and years
PLoS – All journals (2008)
PLoS – All journals (2009)
PLoS – All journals (2010)
PLoS – All journals (2011)
PLoS – All journals (2012)
PLoS – All journals (2013)
PLoS Biology
PLoS Computational Biology
PLoS Genetics
PLoS Medicine
PLoS NTD
PLoS Pathogens
PLoS One
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ANALYSES
PLoS – All journals and years
PLoS – All journals (2008)
PLoS – All journals (2009)
PLoS – All journals (2010)
PLoS – All journals (2011)
PLoS – All journals (2012)
PLoS – All journals (2013)
PLoS Biology
PLoS Computational Biology
PLoS Genetics
PLoS Medicine
PLoS NTD
PLoS Pathogens
PLoS One
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ANALYSES
PLoS – All journals and years
PLoS – All journals (2008)
PLoS – All journals (2009)
PLoS – All journals (2010)
PLoS – All journals (2011)
PLoS – All journals (2012)
PLoS – All journals (2013)
PLoS Biology
PLoS Computational Biology
PLoS Genetics
PLoS Medicine
PLoS NTD
PLoS Pathogens
PLoS One
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ANALYSES
PLoS – All journals and years
PLoS – All journals (2008)
PLoS – All journals (2009)
PLoS – All journals (2010)
PLoS – All journals (2011)
PLoS – All journals (2012)
PLoS – All journals (2013)
PLoS Biology
PLoS Computational Biology
PLoS Genetics
PLoS Medicine
PLoS NTD
PLoS Pathogens
PLoS One
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ANALYSES
PLoS One – All years
PLoS One (2008)
PLoS One (2009)
PLoS One (2010)
PLoS One (2011)
PLoS One (2012)
PLoS One (2013)
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ANALYSES
PLoS One – All years
PLoS One (2008)
PLoS One (2009)
PLoS One (2010)
PLoS One (2011)
PLoS One (2012)
PLoS One (2013)
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ANALYSES
PLoS One – All years
PLoS One (2008)
PLoS One (2009)
PLoS One (2010)
PLoS One (2011)
PLoS One (2012)
PLoS One (2013)
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ANALYSES
PLoS One – All years
PLoS One (2008)
PLoS One (2009)
PLoS One (2010)
PLoS One (2011)
PLoS One (2012)
PLoS One (2013)
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ANALYSES
PLoS One – All years
PLoS One (2008)
PLoS One (2009)
PLoS One (2010)
PLoS One (2011)
PLoS One (2012)
PLoS One (2013)
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ANALYSES
PLoS One – All years
PLoS One (2008)
PLoS One (2009)
PLoS One (2010)
PLoS One (2011)
PLoS One (2012)
PLoS One (2013)
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ANALYSES
PLoS One (2007-2011)
PLoS One (2008)
PLoS One (2009)
PLoS One (2010)
PLoS One (2011)
PLoS One (2012)
PLoS One (2013)
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ANALYSES
2007-2011
PLoS One
PNAS
JACS
J Mat Chem
Phys Rev Lett
J Nanosci Nanotechn
LNCS
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ANALYSES
2007-2011
PLoS One
PNAS
JACS
J Mat Chem
Phys Rev Lett
J Nanosci Nanotechn
LNCS
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ANALYSES
2007-2011
PLoS One
PNAS
JACS
J Mat Chem
Phys Rev Lett
J Nanosci Nanotechn
LNCS
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ANALYSES
2007-2011
PLoS One
PNAS
JACS
J Mat Chem
Phys Rev Lett
J Nanosci Nanotechn
LNCS
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ANALYSES
2007-2011
PLoS One
PNAS
JACS
J Mat Chem
Phys Rev Lett
J Nanosci Nanotechn
LNCS
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ANALYSES
2007-2011
PLoS One
PNAS
JACS
J Mat Chem
Phys Rev Lett
J Nanosci Nanotechn
LNCS
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ANALYSES
2007-2011
PLoS One
PNAS
JACS
J Mat Chem
Phys Rev Lett
J Nanosci Nanotechn
LNCS
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SUMMARY
Mapping the PLoS thesaurus directly (using co-occurrence) gave maps
that were only partially useful.
Indirect mapping of the PLoS thesaurus using a comprehensive map of
the scientific literature (based on Scopus data) was very successful.
The resulting visualization of the PLoS thesaurus became a stand-alone
basemap, a template that can be used to:
» Examine the thesaurus structure, content, and level of detail.
» Show coverage and trends for various entities such as journals, institutions, authors,
etc.
This thesaurus mapping effort helped visualize which areas of the
PLOS thesaurus were covered well, or not well enough, as well as
which fields were emerging or well-established.
All overlays based on tabular data, so detailed analysis is possible.
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QUESTIONS
Thank-you for your attention !