the computing-and data-intensive aspects of their research, helping them to make effective use of Cyberinfrastructure (CI). The University of Oklahoma (OU) is leading a national "virtual residency" program to prepare ACI-REFs to provide CI facilitation to the diverse populations of Science, Technology, Engineering and Mathematics (STEM) researchers that they serve. Until recently, CI Facilitators have had no education or training program; the Virtual Residency program addresses this national need by providing: (1) training, specifically (a) summer workshops and (b) third party training opportunity alerts; (2) a community of CI Facilitators, enabled by (c) a biweekly conference call and (d) a mailing list.
The objective of this paper is to explore data privacy and sharing challenges associated with conducting regional data collection using large-scale unmanned aircraft systems (UAS) technologies. During the 2016 growing season, the North Dakota State University Extension Service and Elbit Systems of America conducted a proofof-concept UAS Research and Development (R&D) project to use large-scale UAS platforms to address some of the issues limiting UAS technology adoption. While the researchers initially thought that the high-resolution spatial and temporal field imagery collected would be well received by area farmers, the team learned that some had concerns over farm operations privacy and about who would have access to the data. The team conducted multiple stakeholder meetings to address the issues raised.While the flights were successfully executed, problems related to farmer interest in the data collected and how to get that data to farmers were identified. Though the project demonstrated how third party operation of large-scale UAS can remove the operational burden from farmers, the project introduced additional privacy concerns and highlighted the need for better rural broadband to make grower access to data in the cloud practical.
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.
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