| description abstract | The strength and speed of modeling software has increased drastically in recent years. As it does so, researchers across a variety of fields work to determine how most effectively to utilize this strength and speed. Many of them have turned to interdisciplinarity as a means of creating more representative models. This is the case for disaster research as this area of interest involves many intricately interdependent systems. In working on interdisciplinary projects, past research has noted several barriers to complete integration, including differences in language and methodology, institutional structures not conducive to interdisciplinary collaboration, and nuanced tension between disciplines. Many solutions to these issues have been presented: facilitated conversation, increased institutional support, and several others. However, one area of difficulty for which comprehensive solutions have not yet been realized is data integration. This is indeed a challenge that lays at the heart of meaningful interdisciplinarity. The data are frequently telling an intricately interwoven story, and the more these data can be analyzed in a cohesive manner the more likely it is that researchers will be able to harness their predictive power to reduce disasters. In order to understand what efforts have been made at data integration, 29 papers are systematically reviewed in order to extract the nature of previous attempts, reasons for integration, challenges, shortcomings, and recommendations for future work. The papers analyzed most commonly referenced the different syntax and data types as a challenge of integration. Regarding shortcomings of integration efforts, the most common concern was that of model parameterization bias and substantial uncertainties. As a recommendation for future work, the papers most commonly suggested more standardization of data and methods across collaborating disciplines and from one project to the next in order to avoid these shortcoming and challenges. | |
