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 years 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 years of NEON operations in emergent science themes, open science best practices, education and training, and community building.

This data includes the data used to make Figures 1 and 2 in the manuscript. 

For Figure 1, we downloaded the titles from publications listed on the NEON publication website (https://www.neonscience.org/impact/papers-publications). Then, we imported the .csv (NEON_Word_Cloud_Data.csv) with the publication titles to the wordart.com software program which created the word cloud from this text. Settings such as word choice, spacing, font, and color were chosen manually. Only the most frequently appearing words (top ~⅓ of the list) were included in the figure. In addition to connecting words such as “and”, words part of NEON’s title (“National”, “Ecological”, “Observatory”, “Network”) were excluded from the final figure. 

For Figure 2, we obtained the perimeter data (Chimney_Tops_2_fire.shp) for the Chimney Tops 2 fire from MTBS perimeter data (https://www.mtbs.gov/direct-download). We downloaded the NEON AOP flight boundary for the Great Smoky Mountains NEON site (GRSM_NEON_AOP.shp) from the NEON spatial data and maps website (https://www.neonscience.org/data-samples/data/spatial-data-maps). We downloaded the NEON AOP canopy height (.tif) and Biomass (.tif) data products (DP3.30015.001) for the Great Smoky Mountains (GRSM) NEON site from the NEON data portal (https://data.neonscience.org/data-products/DP3.30015.001). We used the canopy height products of before (2016; CH_2016.tif) and after (2018; CH_2018.tif) the fire to create the difference in canopy heights (m). The biomass data (2018; Biomass_2018.tif) shows the biomass (Mg/ha) within the Chimney Tops 2 fire perimeter reflecting the lowest biomass within the area burned with high severity.

Please refer to the original, individual data sources for Figure 1 (NEON publications, https://www.neonscience.org/impact/papers-publications) and Figure 2 (MTBS fire data, https://www.mtbs.gov/direct-download; NEON canopy height and biomass data, https://data.neonscience.org/data-products/DP3.30015.001; NEON AOP flight boundaries, https://www.neonscience.org/data-samples/data/spatial-data-maps) for more information on data usage and limitations.