Ecologists often study processes operating at continental to global scales using amalgams of disparate data sources. Ecological data are, increasingly big data characterized by large data volumes and analysis; high heterogeneity of data types, spatiotemporal scales, and methods (variety); multiple potential sources of uncertainty and error (veracity); and, increasingly, high rates of data generation and analysis (velocity) (LaDeau et al. 2017). Challenges of variety and veracity, in particular, have been long-standing informatic barriers to achieving global-scale syntheses in biodiversity science and remain major frontiers in ecoinformatics research. In general, ecologists (and scientists in general) vastly underleverage the collective power of the data they generate. In response to these trends and challenges, multiple groups have begun to build and coalesce around multiple nodes of ecological data. Big data in ecology seems to be emerging as a federated ecosystem, or network of networks. Multiple communities are building and curating multiple kinds of data resources. Other scientists are building systems to interlink these data and build analytical systems that span multiple nodes. Ecoinformatics seeks to build the informatic resources that enable better science: larger data volumes brought to bear on the question of interest; the ability to gather and synthesize multiple heterogeneous data streams; the ability for specialists to quickly add, annotate, and improve existing data resources; start-to-finish workflows that maximize transparency and reproducibility. Ecoinformatics is often thought of narrowly, as a solely technological pursuit, but good ecoinformatics rests on three pillars: 1) strong scientific drivers and motivating questions (which guides priorities for allocating scarce resources), 2) a strong emphasis on building community-based platforms (because ecological expertise is highly distributed among scientists and institutions), and 3) the technological infrastructure that supports open data, linked data, and linked data-model loops. In this session, we seek to review the state of the art in the emerging research field of ecoinformatics, bringing together leaders in building the data, computational, and community resources that enable global-scale big-data ecology.