OIA Key Attributes DRAFT March 16 2011 Contributors

OIA Key Attributes DRAFT March 16, 2011

Contributors • • Michael Ackerman, NLM Rick Avila, Kitware Andy Buckler, Buckler Biomedical Terry Yoo, NLM David Clunie, Core Lab Partners James Luo, NIBIB Tony Reeves, Cornell University Daniel Rubin, Stanford University

Key Attributes • Contribution Support – Quality of the data curation process – Speed to post datasets – Support for imaging data types and metadata • User Support – Robust querying and ease of performing a download – Advanced computing services • General – Long-term integrity and support

Contribution Support: Quality of the data curation process • Anonymization support – – • Metadata preparation tools – – • Apply revision control concepts to data elements Examples: Commercial institutions do this routinely. EHRs. NBIA may have this capability (Eliot Siegel). Capturing provenance – – • Tools for efficient capture and organization of metadata Utilization of common nomenclature Example: OSA ISP metadata tool, Ontologies: BRDG, imaging biomarker ontology, AIM, … NLM: Numerous ontologies being developed – this must be considered carefully. Revision control – – • Validate/verify that de-identification was successful Example: BIRN DUP application that de-identifies Capturing important information on the acquisition process is needed Examples: Perhaps “data papers” will help There also goal specific requirements

Contribution Support: Speed to post datasets • Avoid Limits on data upload size and speed • Protocols to load the data – – FTP, SCP Re-de-identify using automated methods Retain certain fields for potential future purposes Automated methods to check that the data complies with expectations • Example: PET SUV, need to know patient weight and height • Goal is to try to obtain high quality data, but we would not throw away data if not conforming • Some expectations we may know in advance, others not • Organization – David: Try to be agnostic on data organization

Data Upload Attributes Continued • DICOM conformance checks – Automated methods are preferred – ADNI is performing automated qa • Metadata Expectations – Utilize a standard information model – Example: Use AVT to • Definition : Ontologies vs information models • Ontology – standard terminology • Information Model – the syntax for making statements (DICOM structured reporting, NBIA has proprietary XML format)

User Support • Query Capabilities – More generic than web page queries – More sophisticated query methods will drive database design • Outside applications can access and perform queries and get a response using a service model – – Flexibility to support a range of use cases Support both plain text search and a structured query One day support content based retrieval If we were to support data papers, there would be additional content and terms we could use