Developments in datamanagement
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Transcript of Developments in datamanagement
Philips Researche-Science Support groupSeptember, 2012
Data Management in Research:Your data is an asset
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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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Example: Clinical Decision Support
(data generation)
(data augmentation/ improvement)
(knowledge creation)
(evidence integration)
Imaging physics
Image processing
Clinical science
Imaging informatics
• CT and PET scanners
• MRI magnet design and pulse sequences
• high resolution / contrast
• segmentation• registration• modeling• visualization
• clinical trials• medical literature• evidence-based
medicine
• computer-aided detection• computer-aided quantification• computer-aided diagnosis• intelligent image retrieval• therapy planning
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+
Example: Home Health Care
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Example: Embedded Neonatal Monitoring
Contactless Core and Peripheral Temperature
Capacitive ECG sensing
Reflective SpO2
Mechanical sensors for Heart Rate and Breathing Rate
Develop and validate embedded neonatal monitoring targeted at the NICU workstation that will improve the workflow and increase patient comfort.
Courtesy: Martijn Schellekens, Patient Care Solutions, Philips Research
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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
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
- 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
Impr
ove
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Example: Data Acquisition and Analysis WorkflowReusable implementation for time series
Data Acquisition
Analysis(Real-time)
Local Storage
On-site Data Acquisition
Data Analysis
(Offline)
Data Vault
Off-site Storage and Data Analysis
Standard data format
(e.g. tdms, edf, bdf, wfdb)
Standard data format
e.g. (tdms, edf, bdf, wfdb)
Viewere
.g.
La
bv
iew A
PI
Central catalogue of data sets
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Example: CTMM TraIT data flowsHospital (IT) Translational Research (IT)
Research (IT)
LIMS
data domains
clinical
imaging
experimental
biobanking
integrated data
translational research
workspace
Public Data
…
e.g. tranSMART
e.g.caTissue
NBIA
e.g. R
TTP
OpenClinica
Varioussolutions
HIS
PACS
LIS
Courtesy: Wim van der Linden, Henk Obbink, Philips Research and CTMM TraIT
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Recommendations
• Think end-to-end: from data acquisitionto 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
Your data is an asset!