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1. There is increasing availability and use of unstructured and semi-structured citizen science data in biodiversity research and conservation. This expansion of a rich source of ‘big data’ has sparked numerous research directions, driving the development of analytical approaches that account for the complex observation processes in these datasets. 2. We review outstanding challenges in the analysis of citizen science data for biodiversity monitoring. For many of these challenges, the potential impact on ecological inference is unknown. Further research can document the impact and explore ways to address it. In addition to outlining research directions, describing these challenges may be useful in considering the design of future citizen science projects or additions to existing projects. 3. We outline challenges for biodiversity monitoring using citizen science data in four partially overlapping categories: challenges that arise as a result of (a) observer behaviour; (b) data structures; (c) statistical models; and (d) communication. Potential solutions for these challenges are combinations of: (a) collecting additional data or metadata; (b) analytically combining different datasets; and (c) developing or refining statistical models. 4. While there has been important progress to develop methods that tackle most of these challenges, there remain substantial gains in biodiversity monitoring and subsequent conservation actions that we believe will be possible by further research and development in these areas. The degree of challenge and opportunity that each of these presents varies substantially across different datasets, taxa and ecological questions. In some cases, a route forward to address these challenges is clear, while in other cases there is more scope for exploration and creativity.
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A Framework for Harnessing Citizen Scientists and Journalist Networks for Post-disaster Reconnaissance
Vast amounts of damage data exist following natural disasters; the difficulty is collecting these perishable data in a timely fashion and curating them in a way that facilitates their use in follow-on research. Traditional on-the-ground reconnaissance efforts tend to focus limited human and financial resources on collecting and documenting detailed data of the most severe damage, typically in relatively small geographic areas immediately following an event. To improve post-event loss predictions, it is imperative that we collect broad and robust damage datasets, including details on good and poor performance and performance in small-to-moderate events. This presentation will introduce a framework to proactively engage local journalists and citizen scientists to collect vast amounts of real-world observational data via social media networks, such that valuable time, money, and engineering expertise can be focused on interpreting the citizen science data and on investigating the most interesting observations of damage more fully. The presentation will discuss how parts of this framework are being implemented in a National Science Foundation RAPID Response Research project to collect and curate social media data from Hurricane Florence and what future work is necessary to fully realize this citizen scientist-enabled framework for developing robust damage datasets for the natural hazards research community.
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- Award ID(s):
- 1902460
- PAR ID:
- 10209512
- Date Published:
- Journal Name:
- 2019 Natural Hazards Workshop—Researchers Meeting
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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