The heliophysics and space weather communities have entered the era of big data for almost a decade now. Research analysis and modelling efforts to understand system-level heliophysical processes and to enable forecast of space weather events rely largely on the preservation, accessibility and usability of diverse data products (space-based and ground-based). Information architecture, including standards for data models, metadata and data access protocols, can enhance data distribution and support data mining and data-model comparison. For this session, we invite submissions describing how current or planned usage of data standards may support space weather or heliophysics data and services needs. We also welcome contributions on standardised data access and exchange, and analysis tools. Applications of these tools in supporting data-model comparison, data mining and machine learning are strongly encouraged. Suggestions on how data science or machine learning dataset can be described, archived, and made available to the community are welcome.