This road map is the outcome of the collaboration between Stuart Wheater, Demetris Avraam, Paul Burton, Alex Westerberg, Leire Abbarategui, Olly Butters, Becca Wilson and Patricia Ryser-Welch. It shows planned development and timeline of DataSHIELD architecture and functionalities.
We have included satellite activities to demonstrates our commitments to the sustainability of the project as well as our commitments to develop further DataSHIELD as a community of developers, analysts and data scientists. Â
Releases
Testing and maintaining quality
Doc. / training res.
Govervance
Infrastructure
User Interaction
Omics
Machine learning
Non-param. statistics
Visualisation
Data governance
Data
Prepare v5.0
v6.0 - DSI compatibility
v6.1
Update training material
Prepare v5.1
v6.2
v6.3
v6.4
v7 - Explainable and interpretatable IA -virtually-joined and server-level analysis
Develop testing framework
Continuous integration and monitoring with new updates and software technologies
Write training material
New Bar
Testing v6.0
Testing v.6.1
Testing v6.2
Testing v6.3
Testing v6.4
Testing v7.0
v6.1 documentation
v6.0 training material update
v6.2 documentation
v6.4 documentation
v6.3 Documentation
Improving DataSHIELD Analyst document and teaching material
Documentating and developing training material for v7.0
Initiate steering committee - Paul Burton and Andrei Morgan
First steering committee focus on governance and future of the project
Developers collaboration
Steering committee governance
Developers collaboration
Steering committee governance and future functionality
Developers planning development of v.7
Steering committee source of funding and long term planning
Developers monitoring of version 7 development
Engaging with users for release functionality
Engaging with users for release of functionality
Engaging with users for release functionality
Engaging with users for additional functionality
Engaging with users for release functionality
Training and workshop needs
Training and workshop review
Training a workshop review
Statistical methodologies and ML review
Statistical methodologies and ML review
Statistical methodologies and ML review
Develop DS Resources (Back end)
Server reporting, monitoring, benchmarks
Develop and implement containerisation
Integration on the DataSHIELD server of well-established R libraries
Development tools to integrate DS results with institutions systems (Front end)
Defining user interaction needs - with UI department Newcastle University
Develop GUI
Reduce learning curve for DataSHIELD analysts and wider scientific audience (passing results)
Develop dsOmics with IS Global
Medical Faculty Newcastle University - deploying DataSHIELD Fatty liver dataset to other scientists
Working toward release with IS Global
Further development of Fatty liver datasets
DataSHIELD using Bioconductor - IS Global and DataSHIELD core team
Integration and development machine learning imaging techniques
AI: hyper-parametrisation of ML algorithms for virtually-joined analysis and server-level tools
AI: integration of ML techniques to prevent individual-level disclosure
Proof of concepts
Development
Development of non-disclosive graphs using R libraries
Proof of concepts
Review of proof of concepts
Development
Synthetic data generation and validation with DataSHIELD server
