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Emergent Researcher
Challenges in Web
Content Solutions
December 2015
Jaqui.hodgkinson@elsevier.com
Agenda
- Introduction to our
solutions at Elsevier
- Emerging needs around
content metrics
- Emerging trends in
visualizat...
OUR CUSTOMERS OPERATE IN AN INTEGRATED ECOSYSTEM WITH
DRUG R&D AT ITS CORE
Academia &
Government
Medical
Devices
Diagnosti...
Target ID &
Valid
Pre-clinical Clinical
Post-
Launch
Lead ID &
Valid
Target Lead (drug)
Questions
addressed
How do we
moni...
| 5
SCIENTIFIC LITERATURE IS EXPLODING
• Rapidly approaching 1M new citations/year in Medline – HOW TO KEEP UP?
0
100000
2...
EMERGING TRENDS - CASE STUDY FROM BIOLOGY
Research facts
(literature)
Collated
facts
(extracted)
Quality
Metrics to
select...
EMERGING TRENDS - CASE STUDY FROM BIOLOGY
Research facts
(literature)
Collated
facts
(extracted)
Quality
Metrics to
select...
EXAMPLE USE CASE: BIOMARKERS
Perform
search for
all relevant
facts (text
mining)
Select
criteria to
sort or
facet data
fur...
WHICH METHODS WERE USED – BIOMARKER EXAMPLE
• Gaphically view
research trends on
biomarkers in the
universe of facts
• Sum...
KNOWLEDGE MAPS TO FIND THE RIGHT COLLABORATORS
Identified potential collaborators
in specific scientific
technologies by m...
VISUALIZATION FACETS TO NAVIGATE DATA
VISUALIZING THE MASS OF DATA – HEATMAPS
13
BRINGING VISUALISATION TO POINT OF FIRST USE
THANKYOU
Contact:
jaqui.hodgkinson @elsevier.com
jaquinl
jaquimason
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December 2, 2015: NISO/NFAIS Virtual Conference: Semantic Web: What's New and Cool

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Pushing from Behind: Expectations and the Semantic Web
Dr. Jaqui Hodgkinson, Vice President of Product Development, Elsevier

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December 2, 2015: NISO/NFAIS Virtual Conference: Semantic Web: What's New and Cool

  1. 1. Emergent Researcher Challenges in Web Content Solutions December 2015 Jaqui.hodgkinson@elsevier.com
  2. 2. Agenda - Introduction to our solutions at Elsevier - Emerging needs around content metrics - Emerging trends in visualization needs
  3. 3. OUR CUSTOMERS OPERATE IN AN INTEGRATED ECOSYSTEM WITH DRUG R&D AT ITS CORE Academia & Government Medical Devices Diagnostics Companies Pharma & Biotech TARGET ID & VALIDATION LEAD ID & VALIDATION PRE-CLINICAL / CLINICAL POST- MARKET Characterize & understand disease Design effective approach & validate lead Cull leads more quickly for safety & efficacy Manage risk & compliance & improve patient care
  4. 4. Target ID & Valid Pre-clinical Clinical Post- Launch Lead ID & Valid Target Lead (drug) Questions addressed How do we monitor the commercialized leads for adverse events? How do we assess and prioritize the drug ideas for safety, delivery and efficacy? What drugs can hit the identified targets? What are the potential targets related to the disease? BUT FIRST SOME BACKGROUND: OUR CUSTOMER WORKFLOW Leveraging capability - Text and data mining - Integration - Informatics
  5. 5. | 5 SCIENTIFIC LITERATURE IS EXPLODING • Rapidly approaching 1M new citations/year in Medline – HOW TO KEEP UP? 0 100000 200000 300000 400000 500000 600000 700000 800000 900000 1000000 1940 1950 1960 1970 1980 1990 2000 2010 2020 Yearly Citation Count Totals from 2014 MEDLINE (Publication Date Used for Categorization)
  6. 6. EMERGING TRENDS - CASE STUDY FROM BIOLOGY Research facts (literature) Collated facts (extracted) Quality Metrics to select  EVERY YEAR MILLIONS OF PAPERS ARE PUBLISHED  WE EXTRACT 2-3 MILLION “FACTS” PER YEAR  UP TO 70% OF RESEARCH CANNOT BE REPRODUCED  HOW DO WE HELP OUR RESEARCHERS DECIDE WHAT DATA TO TRUST?
  7. 7. EMERGING TRENDS - CASE STUDY FROM BIOLOGY Research facts (literature) Collated facts (extracted) Quality Metrics to select • EVERY YEAR MILLIONS OF PAPERS ARE PUBLISHED • WE EXTRACT 2-3 MILLION “FACTS” PER YEAR • UP TO 70% OF RESEARCH CANNOT BE REPRODUCED How can I decide which facts to believe? Can you tell me which methods were used to get to this fact? I need the raw data so I can re run the statistics I need to know who the author worked with I need to know how well respected this author is
  8. 8. EXAMPLE USE CASE: BIOMARKERS Perform search for all relevant facts (text mining) Select criteria to sort or facet data further Re mine data for new criteria Visualize and select for best output Metrics commonly requested in this approach are • number of times cited • methods used • h-index of an author • Cluster of publication year and tend *
  9. 9. WHICH METHODS WERE USED – BIOMARKER EXAMPLE • Gaphically view research trends on biomarkers in the universe of facts • Summarizes method used, and publication and number of references • Further allows user to dig down into overlapping publications and verify utility of the biomarker
  10. 10. KNOWLEDGE MAPS TO FIND THE RIGHT COLLABORATORS Identified potential collaborators in specific scientific technologies by mapping research landscape
  11. 11. VISUALIZATION FACETS TO NAVIGATE DATA
  12. 12. VISUALIZING THE MASS OF DATA – HEATMAPS
  13. 13. 13 BRINGING VISUALISATION TO POINT OF FIRST USE
  14. 14. THANKYOU Contact: jaqui.hodgkinson @elsevier.com jaquinl jaquimason

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