Neuroscience Requirements and Existing Solutions
Participants
- Giorgio Ascoli
- Ted Carnevale
- Mark Ellisman
- Jeff Grethe
- Mike Hirsch
- Joni Nissanov
- Adrian Robert
- Ken Smith
- Shiro Usui
Our Charge (from last year)
- Catalog information management and analysis needs for neuroscience subdisciplines
- Identify already available solutions
- Discuss organizational framework for this effort
Future Work (from last year)
- Populate the databases
- Design integrated views and query interfaces for the new data types
- Integrate the Smart Atlas and mediator to allow spatial queries of protein distribution data
Roadblocks to NS Data Sharing
- Data heterogeneity (scale, type, et cetera)
- Lack of data standards
- Subfield-specific issues (e.g. HIPAA, cost, quality control, annotation difficulty …)
- Technical & social problems
- Cultural gap requiring a paradigm shift
Several data types & subfields are mature
Potential for translation is great
Optimal leveraging of funding
The Problem in Practice
- Few available repositories for willing owners
- Long-term stability / persistence
- Limited "outside" deposition into existing DBs
- Lack of incentive
- Information sparseness
Populating and Exploiting Ready-To-Use Databases
- Identification of well defined data types
- Compliance with NIH data sharing mandate
- Society endorsement / certification
- Clear use, needs, communities
- Potential for dense coverage
Potential Certification Criteria
- Minimum standards for quality / annotation
- Reports on contributions and utilization
- Convenient user access
- Inclusion of legacy and prospective data
Candidate Data Types
- Structural, functional, molecular neuroimaging
- Computational models
- Neuronal morphology
- In situ hybridization
- Microarray
- Electrophysiology, time series
Next Steps / Action Plan
- NIH-Initiated "Call for Candidates"
- SfN Establishment of Criteria
- Enrolling Journals
Future Issues
- Duplication / mirroring
- Terminology / Ontology / CVs
- Machine readibility
- Adding new seeds
- Maintenance
