CDIF - a unified framework for the integration of data from different research domains

To elucidate some of the key cross-disciplinary research questions of today such as mitigating environmental crises or adapting to climate change, we need cross-disciplinary data analysis, as well as policy integration.  This requires that data can be seamlessly blended from environmental and social science domains, along with citizen science and other sources.   Traditionally, there have been many serious challenges with the integration of these data:  structural, semantic, organisational, and legal, among others.  Relevant data may be of different types, use wildly different formats, leveraging different measurement units, including geospatial.  Examples of such heterogeneity include large environmental data spaces like Copernicus, official statistics from national agencies, sub-national social surveys, and various individual research projects.

To build systems for integrating and accessing such blended, cross-disciplinary data, a new and robust approach to cross-domain metadata is urgently needed. Rather than creating yet another standard, the Cross-Domain Interoperability Framework (CDIF) provides an implementation framework for how this can be done in practice, based on the re-use of existing common standards working together coherently.  CDIF builds on the FAIR principles but is a concrete implementation framework for data and infrastructure practitioners and, by design, provides comprehensive coverage of the most critical areas in the research data lifecycle: Discovery, Data Structure, Semantics, Provenance, Universals, and Access.  This presentation will introduce the concept and culture of CDIF, the suite of existing standards that are leveraged by CDIF, and how it can be implemented in concrete use-cases related to climate change adaptation and managing effects of the green transition.