Table 5 Interoperability and data sharing measures in AI-based genomics and automated microscopy image analysis for high-throughput screening studies
Themes | Core concepts | Authors |
|---|---|---|
Data management frameworks | Adoption of GA4GH DRS standard, Dockstore support, and cloud-agnostic access | Schatz et al., 2022 |
Data archives and sharing | Establishment of formalized genomic data archives, and responsible sharing practices within consortia like the H3Africa consortium | Fadlelmola et al., 2021 |
Laboratory data management | Development of the iLAP system for lab data management, integration with repositories, and LIMS for enhanced interoperability | Stocker, Fischer et al., 2009 |
Data sharing policies | Emphasis on clear data sharing policies and establishment of data access committees | Wright, Koornhof et al., 2013 |
Access control models | Proposal of Attribute-Based Access Control (ABAC) model for secure large dataset sharing | Reddick et al., 2022 |
Data integration frameworks | Development of the BRISK framework for data integration and collaboration in genetics research, including automated permissions systems | Tan et al., 2011 |
Federated data platforms | Promotion of federated data platform interoperability, and support for GA4GH and GO FAIR initiatives | Alvarellos M. et al., 2023 |
Standards adoption | Advocacy for GA4GH standards adoption and cloud-based workflows for data sharing and analysis | Rehm et al., 2021 |
Standardized data outputs | Utilization of platforms like GeneWeaver and Ontological Discovery Environment to ensure standardized data outputs and promote interoperability | Baker et al., 2012 |