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Abstract This manuscript shares the lessons learned from providing scientific computing support to over 600 researchers and discipline experts, helping them develop reproducible and scalable analytical workflows to process large amounts of heterogeneous data.When providing scientific computing support, focus is first placed on how to foster the collaborative aspects of multidisciplinary projects on the technological side by providing virtual spaces to communicate and share documents. Then insights on data management planning and how to implement a centralized data management workflow for data‐driven projects are provided.Developing reproducible workflows requires the development of code. We describe tools and practices that have been successful in fostering collaborative coding and scaling on remote servers, enabling teams to iterate more efficiently. We have found short training sessions combined with on‐demand specialized support to be the most impactful combination in helping scientists develop their technical skills.Here we share our experiences in enabling researchers to do science more collaboratively and more reproducibly beyond any specific project, with long‐lasting effects on the way researchers conduct science. We hope that other groups supporting team‐ and data‐driven science (in environmental science and beyond) will benefit from the lessons we have learned over the years through trial and error. 

Riverine silicon (Si) plays a vital role in governing primary production, water quality, and carbon sequestration. The Global Aggregation of Stream Silica (GlASS) database was constructed to assess changes in riverine Si concentrations and fluxes, their relationship to available nutrients, and to evaluate mechanisms driving these patterns. GlASS includes dissolved Si (DSi), dissolved inorganic nitrogen, and dissolved inorganic phosphorus concentrations at daily to quarterly time steps, daily discharge, and watershed characteristics for rivers with drainage areas ranging < 1 km2 to 3 million km2 and spanning eight climate zones, mainly in the northern hemisphere. Data range between years 1963 and 2023. GlASS uses publicly available datasets, ensuring transparency and reproducibility. Original data sources are cited, data quality assurance workflows are public, and input files to a common load estimator are provided. 

Timely and reliable sensing of infrastructure conditions is critical in disaster management for planning effective infrastructure restorations. Social media, a near real-time information source, has been widely used in disasters for forming timely situational awareness. Yet, using social media to sense electricity infrastructure conditions has not been explored. This study aims to address the research gap through mining public topics from social media. To achieve this purpose, we proposed a systematic and customized approach wherein (1) electricity-related social media data is extracted by the classifier developed based on Bidirectional Encoder Representations from Transformers (BERT); and (2) public topics are modeled with unigrams, bigrams, and trigrams to incorporate the formulaic expressions of infrastructure conditions in social media. Electricity infrastructures in Florida impacted by Hurricane Irma are studied for illustration and demonstration. Results show that the proposed approach is capable of sensing the temporal evolutions and geographic differences of electricity infrastructure conditions. 

As droughts become longer and more intense, impacts on terrestrial primary productivity are expected to increase progressively. Yet, some ecosystems appear to acclimate to multiyear drought, with constant or diminishing reductions in productivity as drought duration increases. We quantified the combined effects of drought duration and intensity on aboveground productivity in 74 grasslands and shrublands distributed globally. Ecosystem acclimation with multiyear drought was observed overall, except when droughts were extreme (i.e., ≤1-in-100-year likelihood of occurrence). Productivity losses after four consecutive years of extreme drought increased by ~2.5-fold compared with those of the first year. These results portend a foundational shift in ecosystem behavior if drought duration and intensity increase, from maintenance of reduced functioning over time to progressive and profound losses of productivity when droughts are extreme. 

ABSTRACT Mast seeding, the synchronous and highly variable production of seed crops by perennial plants, is a population‐level phenomenon and has cascading effects in ecosystems. Mast seeding studies are typically conducted at the population/species level. Much less is known about synchrony in mast seeding between species because the necessary long‐term data are rarely available. To investigate synchrony between species within communities, we used long‐term data from seven forest communities in the U.S. Long‐Term Ecological Research (LTER) network, ranging from tropical rainforest to boreal forest. We focus on cross‐species synchrony and (i) quantify synchrony in reproduction overall and within LTER sites, (ii) test for relationships between synchrony with trait and phylogenetic similarity and (iii) investigate how climate conditions at sites are related to levels of synchrony. Overall, reproductive synchrony between woody plant species was greater than expected by chance, but spanned a wide range of values between species. Based on 11 functional and reproductive traits for 103 species (plus phylogenetic relatedness), cross‐species synchrony in reproduction was driven primarily by trait similarity with phylogeny being largely unimportant, and synchrony was higher in sites with greater climatic water deficit. Community‐level synchrony in masting has consequences for understanding forest regeneration dynamics and consumer‐resource interactions. 

Abstract Plants display a range of temporal patterns of inter‐annual reproduction, from relatively constant seed production to “mast seeding,” the synchronized and highly variable interannual seed production of plants within a population. Previous efforts have compiled global records of seed production in long‐lived plants to gain insight into seed production, forest and animal population dynamics, and the effects of global change on masting. Existing datasets focus on seed production dynamics at the population scale but are limited in their ability to examine community‐level mast seeding dynamics across different plant species at the continental scale. We harmonized decades of plant reproduction data for 141 woody plant species across nine Long‐Term Ecological Research (LTER) or long‐term ecological monitoring sites from a wide range of habitats across the United States. Plant reproduction data are reported annually between 1957 and 2021 and based on either seed traps or seed and/or cone counts on individual trees. A wide range of woody plant species including trees, shrubs, and lianas are represented within sites allowing for direct community‐level comparisons among species. We share code for filtering of data that enables the comparison of plot and individual tree data across sites. For each species, we compiled relevant life history attributes (e.g., seed mass, dispersal syndrome, seed longevity, sexual system) that may serve as important predictors of mast seeding in future analyses. To aid in phylogenetically informed analyses, we also share a phylogeny and phylogenetic distance matrix for all species in the dataset. These data can be used to investigate continent‐scale ecological properties of seed production, including individual and population variability, synchrony within and across species, and how these properties of seed production vary in relation to plant species traits and environmental conditions. In addition, these data can be used to assess how annual variability in seed production is associated with climate conditions and how that varies across populations, species, and regions. The dataset is released under a CC0 1.0 Universal public domain license.