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The Arctic environment is experiencing profound and rapid changes that will have far-reaching implications for resilient and sustainable development at the local and global levels. To achieve sustainable Arctic futures, it is critical to equip policymakers and global and regional stake- and rights-holders with knowledge and data regarding the ongoing changes in the Arctic environment. Community monitoring is an important source of environmental data in the Arctic but this research argues that community-generated data are under-utilized in the literature. A key challenge to leveraging community-based Arctic environmental monitoring is that it often takes the form of large, unstructured data consisting of field documents, media reports, and transcripts of oral histories. In this study, we integrated two computational approaches—topic modeling and network analysis—to identify environmental changes and their implications for resilience and sustainability in the Arctic. Using data from community monitoring reports of unusual environmental events in the Arctic that span a decade, we identified clusters of environmental challenges: permafrost thawing, infrastructure degradation, animal populations, and fluctuations in energy supply, among others. Leveraging visualization and analytical techniques from network science, we further identified the evolution of environmental challenges over time and contributing factors to the interconnections between these challenges. The study concludes by discussing practical and methodological contributions to Arctic resiliency and sustainability. 

Abstract The year 2022 marks the ten‐year anniversary of the White House's Big Data Research and Development Initiative. While this initiative, and the others it spawned,  helped to advance the many facets of data intensive research and discovery, obstacles and challenges still exist. If left unaddressed these obstacles will persist and at a minimum limit the potential of what can be achieved by harnessing the many new ways to collect, analyze, and share data and the insights that can be drawn from them. The opportunities and challenges related to Big Data in agriculture touch on all aspects of the general research data lifecycle; from instruments used to gather data, to advanced digital platforms used to store, analyze, and share data, and the innovative insights from using advanced computational methods. The eight papers included in this special issue were chosen in part because they highlight both the challenges and the opportunities that come from all stages of the data lifecycle common across agricultural research and development. These papers grew out of several workshops made possible by the support of the Midwest Regional Big Data Hub, which is sponsored by the National Science Foundation.