Arie Kaizer van Philips over datamanagement op de SURFnet Relatiedagen 2012

1. Data Management in Research:
Your data is an asset
Philips Research
e-Science Support group
September, 2012

2. Your data is an asset
Observations
• Science is getting data-centric/intensive
• Many Research projects are data-intensive
• Upcoming business models are data-intensive
• Data are expensive assets: re-use of data is needed
• Data analytics combines information from very heterogeneous data sets
Examples of Data
• Data from clinical trials, captured by instruments, generated by
simulations and generated by sensor networks.
• Data are medical images, patient records, physiological data, laboratory
data, genetic data, logging data, surveys, etc.
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3. Example: Clinical Decision Support
(data generation) (knowledge
creation)
Imaging physics Clinical science
• CT and PET • clinical trials
scanners • medical literature
• MRI magnet design • evidence-based
and pulse sequences medicine
• high resolution /
contrast
(data augmentation/ improvement) (evidence
integration)
Image processing Imaging informatics
• computer-aided detection
• segmentation
• computer-aided quantification
• registration
• computer-aided diagnosis
• modeling
• intelligent image retrieval
• visualization
• therapy planning
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4. Example: Home Health Care
+
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5. Example: Embedded Neonatal Monitoring
Develop and validate embedded neonatal monitoring targeted at the NICU
workstation that will improve the workflow and increase patient comfort.
Contactless
Core and Peripheral
Temperature
Mechanical Capacitive ECG
sensors for Heart sensing
Rate and
Breathing Rate
Reflective SpO2
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Courtesy: Martijn Schellekens, Patient Care Solutions, Philips Research

6. Your data is an asset
Challenges
• Legal requirements like protecting sensitive data (privacy)
• End-to-end solutions: from data acquisition to analytics
• The very large heteroginity of data
• Need to re-use of data sets which requires to largely improve the data
management maturity level
• Preservation: archiving for long term use and retrieval
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7. Data Management Maturity Level
Level 4:
- Integration of workflows and data management
- Frameworks that handle data, workflows and applications
Level 3:
- Data standards in place, (e.g. from naming conventions to interfaces)
- High level data interfaces
Improve
- Data can be used across projects
Level 2:
- Handling Data privacy is in place
- Data about the data is available (metadata)
Level 1:
- Disaster recovery (backup, archive).
- Access control: Authentication and authorization
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8. Example: Data Acquisition and Analysis Workflow
Reusable implementation for time series
Central catalogue
of data sets
Viewer
Data
Acquisition
Data Vault Data
e.g. Labview
Local
API
Storage Analysis
Standard (Offline)
Analysis Standard data format
(Real-time) e.g. (tdms, edf, bdf, wfdb)
data format
(e.g. tdms, edf, bdf, wfdb)
On-site Data Acquisition Off-site Storage and Data Analysis
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9. Example: CTMM TraIT data flows
Hospital (IT) Translational Research (IT)
data domains
HIS
clinical integrated translational
data research
Open
Clinica workspace
PACS
imaging
T
LIS T NBIA
e.g.
P biobanking
tranSMART
Research (IT)
e.g.
e.g. R
LIMS caTissue
experimental
Public Data
Various
solutions
…
Courtesy: Wim van der Linden, Henk Obbink, Philips Research and CTMM TraIT
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10. Your data is an asset!
Recommendations
• Think end-to-end: from data acquisition
to data analytics
• Enable and support re-use of data
– Mature data management in the data lifecycle is a pre-requisite
– Add meta data and annotations, Use ontologies
– Manage data privacy
– Provide catalogue of available data sets
• Introduce standard data management solutions
– Use what is out there!
• Provide dedicated expertise and support
– Surf eScience Center
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