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Abstract The National Ecological Observatory Network (NEON) provides over 180 distinct data products from 81 sites (47 terrestrial and 34 freshwater aquatic sites) within the United States and Puerto Rico. These data products include both field and remote sensing data collected using standardized protocols and sampling schema, with centralized quality assurance and quality control (QA/QC) provided by NEON staff. Such breadth of data creates opportunities for the research community to extend basic and applied research while also extending the impact and reach of NEON data through the creation of derived data products—higher level data products derived by the user community from NEON data. Derived data products are curated, documented, reproducibly‐generated datasets created by applying various processing steps to one or more lower level data products—including interpolation, extrapolation, integration, statistical analysis, modeling, or transformations. Derived data products directly benefit the research community and increase the impact of NEON data by broadening the size and diversity of the user base, decreasing the time and effort needed for working with NEON data, providing primary research foci through the development via the derivation process, and helping users address multidisciplinary questions. Creating derived data products also promotes personal career advancement to those involved through publications, citations, and future grant proposals. However, the creation of derived data products is a nontrivial task. Here we provide an overview of the process of creating derived data products while outlining the advantages, challenges, and major considerations. 

Abstract Vascular plants direct large amounts of carbon to produce the aromatic amino acid phenylalanine to support the production of lignin and other phenylpropanoids. Uniquely, grasses, which include many major crops, can synthesize lignin and phenylpropanoids from both phenylalanine and tyrosine. However, how grasses regulate aromatic amino acid biosynthesis to feed this dual lignin pathway is unknown. Here we show, by stable-isotope labeling, that grasses produce tyrosine >10-times faster than Arabidopsis without compromising phenylalanine biosynthesis. Detailed in vitro enzyme characterization and combinatorialin plantaexpression uncovered that coordinated expression of specific enzyme isoforms at the entry and exit steps of the aromatic amino acid pathway enables grasses to maintain high production of both tyrosine and phenylalanine, the precursors of the dual lignin pathway. These findings highlight the complex regulation of plant aromatic amino acid biosynthesis and provide novel genetic tools to engineer the interface of primary and specialized metabolism in plants. 

The measurement of the charge asymmetry for highly boosted top quark pairs decaying to a single lepton and jets is presented. The analysis is performed using 138 fb−1 of data collected in pp collisions at s√=13 TeV with the CMS detector during Run 2 of the Large Hadron Collider. The selection is optimized for top quark-antiquark pairs produced with large Lorentz boosts, resulting in non-isolated leptons and overlapping jets. The top quark charge asymmetry is measured for events with tt⎯⎯ invariant mass larger than 750 GeV and corrected for detector and acceptance effects using a binned maximum likelihood fit. The measured top quark charge asymmetry is in good agreement with the standard model prediction at next-to-next-to-leading order in perturbation theory with next-to-leading order electroweak corrections. Differential distributions for two invariant mass ranges are also presented. 

Abstract Climate change models often assume similar responses to temperatures across the range of a species, but local adaptation or phenotypic plasticity can lead plants and animals to respond differently to temperature in different parts of their range. To date, there have been few tests of this assumption at the scale of continents, so it is unclear if this is a large‐scale problem. Here, we examined the assumption that insect taxa show similar responses to temperature at 96 sites in grassy habitats across North America. We sampled insects with Malaise traps during 2019–2021 (N = 1041 samples) and examined the biomass of insects in relation to temperature and time of season. Our samples mostly contained Diptera (33%), Lepidoptera (19%), Hymenoptera (18%), and Coleoptera (10%). We found strong regional differences in the phenology of insects and their response to temperature, even within the same taxonomic group, habitat type, and time of season. For example, the biomass of nematoceran flies increased across the season in the central part of the continent, but it only showed a small increase in the Northeast and a seasonal decline in the Southeast and West. At a smaller scale, insect biomass at different traps operating on the same days was correlated up to ~75 km apart. Large‐scale geographic and phenological variation in insect biomass and abundance has not been studied well, and it is a major source of controversy in previous analyses of insect declines that have aggregated studies from different locations and time periods. Our study illustrates that large‐scale predictions about changes in insect populations, and their causes, will need to incorporate regional and taxonomic differences in the response to temperature. 

Abstract Many measurements at the LHC require efficient identification of heavy-flavour jets, i.e. jets originating from bottom (b) or charm (c) quarks. An overview of the algorithms used to identify c jets is described and a novel method to calibrate them is presented. This new method adjusts the entire distributions of the outputs obtained when the algorithms are applied to jets of different flavours. It is based on an iterative approach exploiting three distinct control regions that are enriched with either b jets, c jets, or light-flavour and gluon jets. Results are presented in the form of correction factors evaluated using proton-proton collision data with an integrated luminosity of 41.5 fb -1 at  √s = 13 TeV, collected by the CMS experiment in 2017. The closure of the method is tested by applying the measured correction factors on simulated data sets and checking the agreement between the adjusted simulation and collision data. Furthermore, a validation is performed by testing the method on pseudodata, which emulate various mismodelling conditions. The calibrated results enable the use of the full distributions of heavy-flavour identification algorithm outputs, e.g. as inputs to machine-learning models. Thus, they are expected to increase the sensitivity of future physics analyses.