Traditionally, ecological theory and resulting concepts for research and application capitalize on observational data and have driven the design of environmental observation systems. Given the complexity and number of ecosystems the amount of available data has always been a bottleneck to confirm the truth or rational justification of hypotheses on ecosystem functioning. Meanwhile, society and policy makers urge environmental scientists to provide more information for decision making and large amounts of resources are provided for observation systems on ecosystem integrity. Together with recent developments in observation methods, data processing and storage, and computational analytics, this provides extraordinary opportunities to ecosystem science. New analytical approaches and model-data fusion will for sure boost innovation in theoretical ecology and resulting concepts. However, this also leads to the need for interoperability and harmonization of data and standardization of methods. Globally, there is a lack of interoperability that is partly also due to different concepts. This culminates into different research infrastructures’ (RI) designs, each with their own strengths and benefits. Bringing together data providers from large RIs and their users this session aims to analyse ecological theories and conceptual frameworks with respect to the common aspects of scaling, ecosystem processes and patterns, cause-effect relations and required methods like observation or experimentation. The discussions may reveal new aspects that help to further develop continental and global environmental RI and overcome fragmentation into individual conceptual frameworks or methodological approaches, resulting in improved provision of Big Data in environmental science. Insights from modelling are welcome.