At the beginning of this century, the National Research Council identified eight grand challenges in environmental science, which focused on the importance of understanding and being able to predict trends in biogeochemical cycles, biodiversity, ecosystem functioning, climate variability, hydrology, infectious disease, and land-use dynamics. It is becoming increasingly clear that ecologists must consider connectivity as they develop the theory underpinning predictive models to tackle such challenges. In this context, connectivity may refer to (1) cross-system causal relationships between important ecosystem variables (e.g., teleconnections) or (2) the physical transfer of material (e.g., water, sediment, genes, organisms) or energy across boundaries. As FAIR (Findable, Accessible, Interoperable, and Reproducible) data principles gain traction in ecology, decades of observations of species counts, habitat characteristics, geochemical concentrations, meteorological measurements, and many other types of data from a diverse variety of ecosystems are becoming increasingly available, presenting new opportunities for ecologists to identify previously overlooked connections within and across ecosystems. Importantly, the increasing availability of open and FAIR data has the potential to democratize ecology and provide opportunities for a much more diverse pool of researchers to collaborate and identify novel relationships and connections in ecological data that are crucial for advancing theory and improving predictive models and/or forecasts. In this session, we invite contributions that highlight novel connections in ecology that have been identified through the synthesis of open and FAIR data, including data provided by one or multiple ecological observatories, experimental, and/or collaborative networks. We especially encourage speakers to highlight how FAIR data have provided unique opportunities to identify broad-scale ecological connections that would not have otherwise been detected.