1. OIA Key Attributes + DRAFT
May 18, 2011
Comment: focus on medical molecular imaging; pathology in a later effort

2. 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
Brandon Whitcher, Mango Solutions
Alden Dima

3. Key Attributes Contribution Support User Support General
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

4. Contribution Support: Quality of the data curation process
de-identification support
Validate/verify that de-identification was successful
Example: BIRN DUP application that de-identifies
Support for de-identification standards DICOM suppl. 142
Metadata preparation tools – clear definition needed if used in final document; split (experiment description; clinical data  non-image data)
Tools for efficient capture and organization of metadata
Utilization of common nomenclature
Example: OSA ISP metadata tool, Ontologies: BRIDG, imaging biomarker ontology, UMLS? …
NLM: Numerous ontologies being developed – this must be considered carefully.
For recommendation document: Appropriate domain specific (clin) metadata; those would come from relevant sources specific to the domain; always provide a specific example illustrating the recommendation
Distinguish between bulk data and individual data upload (e.g. eCRF)
Revision control (depending on use case e.g. data sharing)
Apply revision control concepts to data elements
Examples: Commercial institutions do this routinely. EHRs. NBIA may have this capability (Eliot Siegel).
Capturing provenance
Capturing important information on the acquisition process is needed
Examples: Perhaps “data papers” will help
There are also goal specific requirements

5. Contribution Support: Speed to post datasets
Avoid Limits on data upload size and speed
Protocols to load the data
FTP, DICOM, SOAP based interfaces, webDAV
Re-de-identify using automated methods (needs example and explanation); usage based de-identification
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 (? Rick); data model flexibility and context (?); HealthVault example

6. Data Upload Attributes Continued
DICOM conformance checks
Automated methods are preferred (UID replacement, integrity check etc.); limit manual interaction steps (NBIA)
ADNI is performing automated qa (specific use cases)
Metadata Expectations
Utilize a standard information model
Example: Use AVT to
Store results of computation/analysis
Manual annotations
Computational algorithms
Summary statistics
Utilize emerging data models (e.g. AIM)
Definition : Ontologies vs information models
Ontology – standard terminology (e.g. RADLex, SNOMed, …)
Information Model – the syntax for making statements (DICOM structured reporting, NBIA has proprietary XML format)

7. 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
Revision control and review on datasets
Query on computation/analysis results
Have multiple indices for the same dataset

8. User Support Continued
Download Support
Shopping carts are good for certain situations
Use of standard protocols (e.g. rsync, FTP)
Web services
Support for portable hard drive
Annotated manifest (images, text descriptions summarizing the metadata)
Computational Support (nice to have)

9. User Support Contributor Agreement User License Least restrictive
Standard license

10. General Long-term integrity Support Backup /mirror solutions
Handle.net type solution
Local copies of format specification for non-standard data
Not just images, but meta-data as well
Crypto-graphic hashes as a unique identifier, check sums
Hash is helpful for data analysis and retrieval
Support
Check de-identification (service?)
Need to encourage and we prefer automated systems

11. Encouragement & Credit
Making data available as a requirement of:
Funding
Publication
Data papers
Very early yet
Infrastructure should support this if adopted by the community

12. Recognition
Major Problem: medical imaging field started off not sharing
Open Data Papers
Open Data Awards
Requirement for Tenure?
Allow/encourage users/readers to contribute to a body of knowledge, similar to papers
Other Ideas?

13. Next Step for OIA
Present recommendations to the RSNA radiology informatics committee
Prepare manuscript on OIA Committee recommendations
NIH Movement to make funded data available (e.g. datamedcentral)
Publishers
OSA
BiomedCentral
Elsevier: Media