Module 18 · Part 6 · Using Your Data
Data Sharing
Develop a data sharing framework that maximizes the scientific value of your registry while protecting participant privacy and your organization's interests.
Patient Registries 101 · Dr. Danielle Boyce · EpilepsyLive
Why data sharing is important
- Rare disease registries hold irreplaceable data.
- At the same time, sharing without governance, without consent coverage, security protections, and use restrictions, can harm participants and expose your organization to legal and reputational risk.
- The goal is a principled sharing framework: maximally open within appropriate protections.
Federated analysis (no data transfer)
- Researchers submit an analysis code that runs on your data behind your firewall; only aggregate results are returned.
- Tools: OHDSI distributed network studies, PCORnet, TriNetX Best for: Large queries where individual data transfer would be impractical; reduces privacy risk
Deidentified data sharing
- Individual-level records, stripped of identifying information per HIPAA Safe Harbor or Expert Determination standards, are shared under a data use agreement.
- Best for: Most registry research uses; wide applicability Limitations: Some reidentification risk with rare diseases and rare variants; "deidentified" rare disease data is not as anonymous as commo…
Controlled access (identified data)
- Identified or potentially re-identifiable data shared only with approved researchers under strict DUA, subject to Data Access Committee review.
- Best for: Genomic data; linkage studies; longitudinal matching Implementation: Use access control systems like dbGaP or GA4GH Passport/Visa
NIH repositories
- dbGaP (Database of Genotypes and Phenotypes): NIH's primary controlled access repository for genomic and phenotypic data from human studies. NIH funded studies are increasingly required to deposit…
- dbGaP (Database of Genotypes and Phenotypes): NIH's primary controlled access repository for genomic and phenotypic data from human studies.
- NCBI BioProject / BioSample: For genomic sequence data
- ClinVar: For variant-disease assertions
Global repositories
- EGA (European Genome-phenome Archive): European equivalent of dbGaP; GDPR-native ega-archive.org
Open data platforms
- Synapse (Sage Bionetworks): Supports both open and controlled access sharing; widely used by participant led research synapse.org
- Zenodo: General open data repository; appropriate for fully deidentified summary data zenodo.org
Data sharing agreements
- Every data sharing arrangement needs a Data Use Agreement (DUA) covering
- Permitted uses of the data
- Prohibition on reidentification
- Data security requirements (encryption at rest and in transit, access controls)
- Prohibition on data redistribution without separate approval
- Publication notification requirements
Data sharing agreements (cont.)
- Data retention period and destruction upon expiration
- Reporting requirements (annual reports to your DAC)
FAIR data principles
- FAIR stands for Findable, Accessible, Interoperable, and Reusable, a framework for maximizing the value of shared scientific data.
- go-fair.org
Key resources
- NIH Data Sharing Policy
- dbGaP Submission Guide
- GA4GH Data Access Framework
- Synapse Data Sharing Platform
- FAIR Principles
- AHRQ Registry User's Guide Chapter 7: Dissemination
Key resources (cont.)
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