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Adaptive Acquisition of VGI to Fill Out Gaps in Biological Observation Metadata
Abstract
Biological observation databases store data on species observations, being used in many kinds of research. Such observations are often queried and/or correlated primarily using metadata parameters (e.g., spatial queries on metadata concerning regions where observations were performed). However, metadata are often missing - either blank attributes, or lack of metadata records - which hampers the use of the observations databases. Filling these gaps is challenging because metadata requirements change as researchers acquire new knowledge about their problems. Related work is limited because it does not take this knowledge evolution into consideration. This chapter presents an approach to acquire missing metadata records, which fully supports dynamic on-the-fly evolution of metadata requirements. As proof of concept, we implemented a configurable software platform to collect data from “human sensors” and other sensors. Among its many dynamic characteristics, it allows deployment of context-sensitive forms to be filled by volunteers, according to a location and a research target.
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