Center for Climate Change and Transformation - Data Integration and Management
Data Integration and Management
To better understand the evolution of climate and the exacerbating impacts on our society and ecosystems, we need to provide observations and climate data models at a variety of spatial and temporal scales so to build meaningful information and future scenarios for both the scientific community and stakeholders at national or local-level, and thus have a tangible effect in the policies for climate adaptation. In order to efficiently store and share these heterogenous sources of data, integrating sociological, economic, climatic, and ecological characteristics altogether, a transdisciplinary approach is required, in addition to an accurate set of organizational and maintenance rules to keep this complex Spatial Data Infrastructure (SDI) coherent and operational.
FAIRness
FAIR data principles are crucial to climate change studies to collaborate with the international research community. Beyond appropriate data stewardship, these principles underpin truly replicable and trustworthy research, and Open Science in general. They enable better collaboration among scientists, publish research outcomes, promote transparency in research practices, inspire innovative ideas and empower policymakers to make informed decisions. Reaching adequate FAIRness is of foremost importance and thus it is necessary to stay up-to-date with the continuously evolving panorama of tools and technologies that help accomplish it in our data management practice.
Contacts: Andrea Vianello, Piero Campalani
Semantic and ontology
Ontologies have been successfully employed as computational artifacts to support knowledge reuse and sharing. They help scientists achieve semantic interoperability by allowing them to bring together climate data from different resources in a meaningful way for further processing and analysis. In this way, researchers can access and combine climate-related data such as temperature, precipitation, and greenhouse gas emissions without uncertainty or loss of context, with shared usage of formalized terms and predicates, fostering knowledge reasoning applications to operate on our catalogues and solve complex tasks. Through semantic interoperability, we aim to harmonize data coming from different agencies and research institutions and stored in different data formats.
Contacts: Cristine Griffo, Ekaterina Chuprikova
Data processing and pipelines
Cost-effective data processing as a service is of vital importance for our infrastructure, particularly when managing very large datasets. The implementation of flexible and efficient data processing chains requires a high level of expertise from the ingestion of new data to the access to the end users of complex data workflows in support of climate and environmental analysis tools and higher-level services on top of it.
Contacts: Peter Zellner
Metadata management
Metadata standards and schemas are clearly an essential component of our SDI, and the key factor for optimal findability of our data to the outside communities. Rich and exhaustive metadata are necessary for describing and truly replicating research outputs, going from isolated towards a network of FAIR Data Objects (FDO). The European Commission encourages sharing metadata and data openly to optimize data acquisition costs and facilitate collaboration. While international metadata standards and catalogue federation capabilities are already in place our data catalogue, this constantly evolving topic requires a dedicated and continuous effort in order to keep up with emerging technologies.
Contacts: Bartolomeo Ventura, Andrea Vianello
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