Scientific investigations the way of the semantic web workgroup at Biohackathon 2014, introduction slides

1. DESIGNING AND RUNNING
SCIENTIFIC INVESTIGATIONS
THE WAY OF SEMANTIC WEB.
Jean-Luc Perret
Novartis Animal Health

2. WHY DO WE NEED
SEMANTIC WEB ON THE
BENCH?

3. やった！
Yes !

4. やった！
やった！
やった！

5. Oh Great !
Data…

6. But what did
they exactly do?

7. Oh no!

8. Can’t you
produce structured
data with your
article ?
Oh no!

9. Can I help you?
Hmm, maybe

10. WETLAB
STUDY
GOAL
Perform the study well to get valid
results
Draw conclusions
Produce good quality study report
so that experiment can be repeated
under the same conditions

11. WETLAB
STUDY
REALITY
Lot of overhead to insure
• compliance
• quality of plan
• ressource availability
• quality of execution
• quality and integrity of data
these overheads have a big negative
impact on productivity
GxP (GLP, GCP, GMP) / Biosafety / Ethics guidelines /
animal experimentation permits / Transportation and
storage regulations / Import and export permits…

12. Yes we can!

13. BENEFITS
• Automatic generation of draft text for
• study plans
• study reports (at least “material and methods “and “results” sections).
• Improved efficiency
• New ways to make
hypothesis
• Simplified reuse and sharing of study plans
• Assistance for ressource allocation, proposing alternatives (including instrument validation processes)
• Assistance for study plan approval processes
• Flexible way to capture and store raw data (including amendments and deviations, provenance data thereby insuring data
integrity -> GLP compliance gained almost for free).
• Translation of study plan to machine language for automation
• Assistance for study approval process (deviations impact qualification, data table extraction, quality control validation)
• Simultaneous search capabilities on
• planned processes (including amendments)
• performed processes (including deviations)
• results, including raw and processed data, maybe even hypothesis and conclusions
• Analysis of relationship between study plans and results can improve practices or detect interesting outliers

14. BENEFITS
• Structured data including process data
• New Meta-analysis tools for improved
reasoning capabilities on massive
experimental results
• Simultaneous search capabilities on
• planned processes (including amendments)
• performed processes (including deviations)
• results, including raw and processed data, maybe even hypothesis and
conclusions
• Structured data available for meta-analysis

16. ؤة׳׫צ欹נ׳ؚ׵