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Developments in datamanagement


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Arie Kaizer van Philips over datamanagement op de SURFnet Relatiedagen 2012

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Developments in datamanagement

  1. 1. Data Management in Research: Your data is an assetPhilips Researche-Science Support groupSeptember, 2012
  2. 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. 2
  3. 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 3
  4. 4. Example: Home Health Care + 4
  5. 5. Example: Embedded Neonatal MonitoringDevelop and validate embedded neonatal monitoring targeted at the NICUworkstation 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 5 Courtesy: Martijn Schellekens, Patient Care Solutions, Philips Research
  6. 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 6
  7. 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 7
  8. 8. Example: Data Acquisition and Analysis WorkflowReusable 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 8
  9. 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 9
  10. 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 10
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