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

4

+

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!