4. Slide 4
Cutting edge in R&D
Global partner
Independent
Digital Lab Informatics
Innovation
Active network
Open collaboration
Customer orientation
Trust
Who we are
5. Slide 5
Who we are
Focus on value Concepts and
methodology
Approach &
committment
7. Slide 7
Life science data
Scientific data are
Valuable assets to NGO, academic and industries
Domain/context specific
Only interpreted by experts
Scientific data are subject of continuous change:
Growth
Formats, standards, and technology
Concept extensions
Context changes
8. Slide 8
Change of concepts
Phenomenological based concept Gene-based concept
Pharmacology example: Ion channels taxonomy
9. Slide 9
Painpoints
Data standardization, data curation, master data management,
data migration, ….
Are complex endeavor's
Are labor, and alignment-intensive
Need expert input (technical and scientific)
Are highly iterative
Are difficult to frame in time-lines or costs
How to address this challenge?
11. Slide 11
Reference architecture
Data migration
Manage
Curation runs
Manage
Results
Analysis
I
II
III
IV
…...
Manage
Dictionary
Data
Source
Sources
Copy
Copy of targetWorking area
Transformation Glossary and VocabularyProperty Mapping
Extraction &
Loading
Data Concept
Target
Data
SourceGlossary
Vocabulary
Annotation
Rules
Mapping
Rules
Transformation
Rules
Run
Configuration
Data
partitioning
Data
Processing
Filtering
Monitoring &
Audit
Logs & Observ.
Exceptions
Comments
Dashboard
Calculate
Properties
Data
Comparison
Visual
Analytics
Tag
Data
List
Management
CDC
SQL to Load
Audit Trails
16. Slide 16
Benefits
Benefits are
Modular set up
All functions available within one integrated framework
Separate components for technical and scientific experts alike
Data curation – part of a process not of individual data editing
Easy-to-use
Configurable toolbox tailored to any program
Integrated visual / comparative analysis between source and target data
Reduction of technical issues
Error propagation contained, roll backs possible
Focus on data, not on technology