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To describe PCORnet, a clinical research network developed for patient-centered outcomes research on a national scale. Descriptive study of the current state and future directions for PCORnet. We conducted cross-sectional analyses of the health systems and patient populations of the 9 Clinical Research Networks and 2 Health Plan Research Networks that are part of PCORnet. Within the Clinical Research Networks, electronic health data are currently collected from 337 hospitals, 169,695 physicians, 3,564 primary care practices, 338 emergency departments, and 1,024 community clinics. Patients can be recruited for prospective studies from any of these clinical sites. The Clinical Research Networks have accumulated data from 80 million patients with at least one visit from 2009 to 2018. The PCORnet Health Plan Research Network population of individuals with a valid enrollment segment from 2009 to 2019 exceeds 60 million individuals, who on average have 2.63 years of follow-up. PCORnet’s infrastructure comprises clinical data from a diverse cohort of patients and has the capacity to rapidly access these patient populations for pragmatic clinical trials, epidemiological research, and patient-centered research on rare diseases. •PCORnet is a national network-of-networks developed to conduct patient-centered outcomes research.•There are nine Clinical Research Networks and two Health Plan Research Networks within PCORnet.•The Clinical Research Networks have collected EHR data for a cohort of 80 million individuals, and the Health Plan Network have collected enrollment and claims files on over 60 million individuals.•PCORnet infrastructure can support large-scale pragmatic clinical trials and observational research using its distributed data network.

Studies conducted at sites across ecological research networks usually strive to scale their results to larger areas, trying to reach conclusions that are valid throughout larger enclosing regions. Network representativeness and constituency can show how well conditions at sampling locations represent conditions also found elsewhere and can be used to help scale-up results over larger regions. Multivariate statistical methods have been used to design networks and select sites that optimize regional representation, thereby maximizing the value of datasets and research. However, in networks created from already established sites, an immediate challenge is to understand how well existing sites represent the range of environments in the whole area of interest. We performed an analysis to show how well sites in the USDA Long-Term Agroecosystem Research (LTAR) Network represent all agricultural working lands within the conterminous United States (CONUS). Our analysis of 18 LTAR sites, based on 15 climatic and edaphic characteristics, produced maps of representativeness and constituency. Representativeness of the LTAR sites was quantified through an exhaustive pairwise Euclidean distance calculation in multivariate space, between the locations of experiments within each LTAR site and every 1 km cell across the CONUS. Network representativeness is from the perspective of all CONUS locations, but we also considered the perspective from each LTAR site. For every LTAR site, we identified the region that is best represented by that particular site—its constituency—as the set of 1 km grid locations best represented by the environmental drivers at that particular LTAR site. Representativeness shows how well the combination of characteristics at each CONUS location was represented by the LTAR sites’ environments, while constituency shows which LTAR site was the closest match for each location. LTAR representativeness was good across most of the CONUS. Representativeness for croplands was higher than for grazinglands, probably because croplands have more specific environmental criteria. Constituencies resemble ecoregions but have their environmental conditions “centered” on those at particular existing LTAR sites. Constituency of LTAR sites can be used to prioritize the locations of experimental research at or even within particular sites, or to identify the extents that can likely be included when generalizing knowledge across larger regions of the CONUS. Sites with a large constituency have generalist environments, while those with smaller constituency areas have more specialized environmental combinations. These “specialist” sites are the best representatives for smaller, more unusual areas. The potential of sharing complementary sites from the Long-Term Ecological Research (LTER) Network and the National Ecological Observatory Network (NEON) to boost representativeness was also explored. LTAR network representativeness would benefit from borrowing several NEON sites and the Sevilleta LTER site. Later network additions must include such specialist sites that are targeted to represent unique missing environments. While this analysis exhaustively considered principal environmental characteristics related to production on working lands, we did not consider the focal agronomic systems under study, or their socio-economic context.

Sustainable intensification is an emerging model for agriculture designed to reconcile accelerating global demand for agricultural products with long-term environmental stewardship. Defined here as increasing agricultural production while maintaining or improving environmental quality, sustainable intensification hinges upon decision-making by agricultural producers, consumers, and policy-makers. The Long-Term Agroecosystem Research (LTAR) network was established to inform these decisions. Here we introduce the LTAR Common Experiment, through which scientists and partnering producers in US croplands, rangelands, and pasturelands are conducting 21 independent but coordinated experiments. Each local effort compares the outcomes of a predominant, conventional production system in the region ('business as usual') with a system hypothesized to advance sustainable intensification ('aspirational'). Following the logic of a conceptual model of interactions between agriculture, economics, society, and the environment, we identified commonalities among the 21 experiments in terms of (a) concerns about business-as-usual production, (b) 'aspirational outcomes' motivating research into alternatives, (c) strategies for achieving the outcomes, (d) practices that support the strategies, and (e) relationships between practice outreach and adoption. Network-wide, concerns about business as usual include the costs of inputs, opportunities lost to uniform management approaches, and vulnerability to accelerating environmental changes. Motivated by environmental, economic, and societal outcomes, scientists and partnering producers are investigating 15 practices in aspirational treatments to sustainably intensify agriculture, from crop diversification to ecological restoration. Collectively, the aspirational treatments reveal four general strategies for sustainable intensification: (1) reducing reliance on inputs through ecological intensification, (2) diversifying management to match land and economic potential, (3) building adaptive capacity to accelerating environmental changes, and (4) managing agricultural landscapes for multiple ecosystem services. Key to understanding the potential of these practices and strategies are informational, economic, and social factors-and trade-offs among them-that limit their adoption. LTAR is evaluating several actions for overcoming these barriers, including finding financial mechanisms to make aspirational production systems more profitable, resolving uncertainties about trade-offs, and building collaborative capacity among agricultural producers, stakeholders, and scientists from a broad range of disciplines.

\"Network research has recently been adopted as one of the tools of the trade in archaeology, used to study a wide range of topics: interactions between island communities, movements through urban spaces, visibility in past landscapes, material culture similarity, exchange, and much more. This Handbook is the first authoritative reference work for archaeological network research, featuring current topical trends and covering the archaeological application of network methods and theories. This is elaborately demonstrated through substantive topics and case studies drawn from a breadth of periods and cultures in world archaeology. It highlights and further develops the unique contributions made by archaeological research to network science, especially concerning the development of spatial and material culture network methods and approaches to studying long-term network change. This is the go-to resource for students and scholars wishing to explore how network science can be applied in archaeology through an up-to-date overview of the field.\"--Publisher's website.

The benefits and challenges of electronic health records (EHRs) as data sources for clinical and epidemiologic research have been well described. However, several factors are important to consider when using EHR data to study novel, emerging, and multifaceted conditions such as postacute sequelae of SARS-CoV-2 infection or long COVID. In this article, we present opportunities and challenges of using EHR data to improve our understanding of long COVID, based on lessons learned from the National Institutes of Health (NIH)–funded RECOVER (REsearching COVID to Enhance Recovery) Initiative, and suggest steps to maximize the usefulness of EHR data when performing long COVID research.

During the worldwide shutdown in response to the COVID‐19 pandemic, many reports emerged of urban wildlife sightings. While these images garnered public interest and declarations of wildlife reclaiming cities, it is unclear whether wildlife truly reoccupied urban areas or whether there were simply increased detections of urban wildlife during this time. Here, we detail key questions and needs for monitoring wildlife during the COVID‐19 shutdown and then link these with future needs and actions with the intent of improving conservation within urban ecosystems. We discuss the tools ecologists and conservation scientists can use to safely and effectively study urban wildlife during the shutdown. With a coordinated, multicity effort, researchers and community scientists can rigorously investigate the responses of wildlife to changes in human activities, which can help us address long‐standing questions in urban ecology, inspire conservation of wildlife, and inform the design of sustainable cities.