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Data Management in Human Imaging
- 1. Data Management in Human Imaging Dominic Job, David Rodriguez, Elizabeth McDowell, Joanna Wardlaw Centre for Clinical Brain Sciences – Neuroimaging Sciences The University of Edinburgh, October 2014
- 2. Requirements for Research Data Management & BRIC, CRIC and WTCRF •Significant risks to data longevity – Ever changing nature of research – Reliance on ‘Human infrastructure’ For data linking, provenance, support and documentation (Whyte A. et al., 2008 – Digital Curation Centre)
- 4. Data & risks • IImmaaggiinngg: • MR scanners, PET, Ultrasound, Optical (retinal) –5+ Scanners in the University, up to 500k 2D images/hour (Mostly DDIICCOOMM format)? • CClliinniiccaall//ootthheerr ddaattaa: • Formal interviews by professionals: Psychiatrist, Neonatologist, Psychologist, Researcher • AAnnoonnyymmiissaattiioonn & sseeccuurriittyy • PPrroocceessssiinngg ppiippeelliinneess && AAnnaallyyssiiss • PPuubblliiccaattiioonn && AArrcchhiivviinngg//PPrreesseerrvvaattiioonn??
- 5. Data Preservation and Reuse – Data is of high value as it is expensive to collect (BRIC, CRIC) Current imaging assets > £100 million > 100TB Much of this data is very ‘rare’ Collected and Analysed over past 20 years Multiple P.I’s/groups/sites/universities/hospitals/IT teams WTCRF: 30,000 + documents – Cost of curation vs. reuse value –Old data often reused » Huge cost savings £200 - £5,000 per subject –Enables future discoveries in basic and clinical neuroscience –Reused as new methods and theories develop
- 6. Data sharing & security • Medical data is highly sensitive – DPA1988, NHS R&D, Caldicott, MHRA, GCP • Must be held securely Catalogued & Anonymised • DICOM Confidential, (Rodríguez González D. et al., 2010); • Scan Manager, CRF Manager® ( UoE, WTCRF software) • Secure, auditable access control and sharing – 2 factor verification under deployment (CMVM)
- 7. Solutions – CRF Imaging Core application & CRF Manager ® – Data management Planning - Online – Scan Manager & DICOM Confidential – Standard Provenance templates (to capture research -> Electronic lab note books) – BRAINS database – Research Data Management Service (UoE) • MANTRA, Data Vault, Data Store, Data Share, w3
- 8. Alzheimer's disease (AD) vs normal control group
Editor's Notes
- New requirements for Research Data Management The ever changing nature of research, in combination with our reliance on ‘human infrastructure’ for provenance support and documentation (Whyte A. et al., 2008), create a significant risk to the longevity of our data sets. To ameliorate these risks several applications have been developed that catalogue the imaging data at source, support documentation of lifecycle provenance data collection, and collate the data in a sustainable infrastructure and format: (DICOM Confidential, (Rodríguez González D. et al., 2010); Scan Manager (UoE in-house software); and standardised provenance templates (e.g. http://www.sbirc.ed.ac.uk/documents/templateREADME.pdf).
- Initial Enquiry & CRF Imaging Core application: Green - Investigator responsibility Blue - R&D/Ethics responsibility Orange - BRIC responsibility - IRAS : Integrated Research Application System - Ethics approval, subject consent, appropriate anonymisation - Costs - CRF Imaging Core application (online - includes Data management Plan) - covers assessment of ‘reuse potential’ - is being updated e.g. UoE Data management Planning: http://www.ed.ac.uk/schools-departments/information-services/research-support /data-management/data-management-home (or Data management Planning Online (DCC) https://dmponline.dcc.ac.uk/)
- Imaging - progress towards data longevity is getting better, but will always be evolving with technology and methods. Clinical - progress towards data longevity is medium, often dependent on the discipline of the person (s) involved in acquisition. - Working towards providing a database linking service and ‘complete study baseline data’ archiving service. Processing and Analysis – longevity is poor and variable. - difficult to document and record provenance. Software and methods/parameters change daily. Varying levels of discipline. - working on infrastructure to record pipelines and parameter sets.
- To date, we have not collected study data that we can confidently classify as ‘no longer useful’. Approximately 50% of processed data is reused. Analysis and Reuse generates 6-10 times more data than the ‘baseline’ data A critical function of the human brain imaging research lifecycle is that of data linking. Without correctly linking imaging data to its corresponding clinical and biological data, the data is of little use. One solution is the BRAINS database, which aims to provide a long term solution to this issue, including a standardised data schema, and a standard routine for collection of non-imaging data for prospective studies. BRAINS database – Currently a fully anonymised database of 3D MR imaging and link related data for sharing study data. (Brain Images of Normal Subjects)
- Software: old software and some old data no longer meet industry standards or run on current OS’s difficult to maintain/update with age. Data Vault, Data Store, Data Share
- Templates enable us to capture any information with great flexibility. MANTRA – Data management courses for students and staff. Electronic lab note books are currently being developed (in house) for data acquisition in the initial phases of data collection, but not yet within the processing and analysis phases. http://www.sinapse.ac.uk/research-resources/brains-project W3 http://www.w3.org/ - migrating & unifying ‘labs’ to DATA Store, centralised resources etc
- Example of brain image data sharing improving basic science (from the BRAINS project). This image shows that the appearance of brain structure in a group of subjects diagnosed with Alzheimer's disease (AD) versus a normal control group changes based on the method used. The image on the left shows voxel-wise parametric effect size and the image on the right shows voxel-wise NONparametric effect size (red areas = higher effect sizes > 0.75). If the voxel-wise data were Normally distributed then the parametric effect size would equal the NONparametric effect size. However, these voxel distributions are not Normally distributed therefore the parametric method appears to be artificially increasing effect size between the AD and normal control groups. References: BRAINS: http://www.sinapse.ac.uk/research-resources/brains-project Caldicott Guardians: http://systems.hscic.gov.uk/data/ods/searchtools/caldicott/index_html CMVM: College of Medicine and Veterinary Medicine Data Protection Act 1988: http://www.legislation.gov.uk/ukpga/1998/29/contents Good Clinical Practice: http://www.mhra.gov.uk/Howweregulate/Medicines/Inspectionandstandards/GoodClinicalPractice/ MHRA: http://www.mhra.gov.uk/ NHS R & D: http://www.rdforum.nhs.uk/content/ Rodríguez González D, Carpenter T, van Hemert JI, Wardlaw J. An open source toolkit for medical imaging de-identification. Eur Radiol 2010; 20:1896?1904. http://dx.doi.org/10.1007/s00330-010-1745-3. Sourceforge is: http://sourceforge.net/projects/privacyguard/ Wellcome Trust Clinical Research Facility, Edinburgh University: (CRF Manager software): https://www.wtcrf.ed.ac.uk/ Whyte A., Job D., Giles S., Lawrie S. Meeting Curation Challenges in a Neuroimaging Group. International Journal of Digital Curation. 2008, Vol. 3, No. 1, pp. 171-181 XNAT https://wiki.xnat.org/