COS 131-8
A snapshot of environmental sensor network use and management in academia
Many ecological research groups focus on generating accurate, useful, and impactful models of the past, current, and possible future states of ecosystem structure and function. Model development can be a complex endeavor that may require many large, complex, and/or multifarious data sets that are increasingly sourced from an expanding variety of automated environmental sensors. While research networks and institutions have been using sensor networks for decades, there appears to be an increasing reliance by academic research groups on smaller, private sensor networks. However, it has been noted in the literature that the loss of data by ecological research groups may be limiting the growth of the body of knowledge that can be used to address among the most urgent ecological problems. An online survey of academic ecological research groups in the US was used to investigate the extent to which academic ecological research groups use sensors, manage data, and share their research with the broader research community via publications and data repositories. Results from 136 ecologists representing research groups at 92 universities are presented.
Results/Conclusions
The research groups that participated in the survey participate in a broad range of ecological disciplines, including community and landscape ecology, marine and aquatic ecology, and biogeochemistry. They represent >1,300 personnel supported directly by >$24 million annually in research funding, utilize >7,000 sensors on >1,700 ‘platforms’ (towers, soil plots, etc.) and collect approximately 7.2-10.5 terabytes of data per year. Data analysis, management, and documentation mostly utilizes traditional tools such as spreadsheets. Respondents identified that they would like to acquire more skillsets, trained personnel, and information management tools to ensure data longevity and re-use. The acquisition, storage, and desired re-use of data by this respondent pool, a small percentage of the entire academic ecological research community, is comparable with large, well-funded networks. This suggests that research funding granted toward the development and infusion of better information management tools that link data from independent academic research groups could be an efficient investment in the future of ecological science. A short synopsis of current cyberinfrastructure development by the authors, specifically catering to small research groups, is presented as an example of directions that such future work might go.