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Abstract It is a critical time to reflect on the National Ecological Observatory Network (NEON) science to date as well as envision what research can be done right now with NEON (and other) data and what training is needed to enable a diverse user community. NEON became fully operational in May 2019 and has pivoted from planning and construction to operation and maintenance. In this overview, the history of and foundational thinking around NEON are discussed. A framework of open science is described with a discussion of how NEON can be situated as part of a larger data constellation—across existing networks and different suites of ecological measurements and sensors. Next, a synthesis of early NEON science, based on >100 existing publications, funded proposal efforts, and emergent science at the very first NEON Science Summit (hosted by Earth Lab at the University of Colorado Boulder in October 2019) is provided. Key questions that the ecology community will address with NEON data in the next 10 yr are outlined, from understanding drivers of biodiversity across spatial and temporal scales to defining complex feedback mechanisms in human–environmental systems. Last, the essential elements needed to engage and support a diverse and inclusive NEON user community are highlighted: training resources and tools that are openly available, funding for broad community engagement initiatives, and a mechanism to share and advertise those opportunities. NEON users require both the skills to work with NEON data and the ecological or environmental science domain knowledge to understand and interpret them. This paper synthesizes early directions in the community’s use of NEON data, and opportunities for the next 10 yr of NEON operations in emergent science themes, open science best practices, education and training, and community building.

Abstract Biological invasions are a leading cause of rapid ecological change and often present a significant financial burden. As a vibrant discipline, invasion biology has made important strides in identifying, mapping, and beginning to manage invasions, but questions remain surrounding the mechanisms by which invasive species spread and the impacts they bring about. Frequent, multiscalar ecological monitoring such as that provided through the National Ecological Observatory Network (NEON) can be an important tool for addressing some of these questions. We articulate a set of major outstanding questions in invasion biology, consider how NEON data science is positioned to contribute to addressing these questions, and provide suggestions to help equip a growing contingent of NEON data users in solving invasion biology problems. We demonstrate these ideas through four case studies examining the mechanisms of plant invasions in the U.S. Intermountain West. In Case Study I, we evaluate the relationships between native species richness, non‐native species richness, and probability of invasion across scales. In Case Studies II and III, we explore the relationship between environmental factors and non‐native species presence to understand invasion mechanisms. Case Study IV outlines a method for improving the ability to distinguish invasive plants from native vegetation in remotely sensed data by leveraging temporal patterns of phenology. There are many novel elements in the NEON sampling design that make it uniquely poised to shed light on the mechanisms that can help us understand invasibility, prediction, and progression, as well as on the variability, longevity, and interactions of multiple invasive species’ impacts. Thus, knowledge gained through analysis of NEON data is expected to inform sound decision‐making in unique ways for managers of systems experiencing biological invasions.