Scientists and consumers of scientific knowledge can struggle to synthesize the quickly evolving state of empirical research. Even where recent, comprehensive literature reviews and meta-analyses exist, there is frequent disagreement on the criteria for inclusion and the most useful partitions across the studies. To address these problems, the Solomon Hsiang and James Rising created an online tool, called the Distributed Meta-Analysis System (DMAS), for collecting, combining, and communicating a wide range of empirical results.
DMAS is divided into multiple “databases”, each of which contains the statistical relationships between a key independent and dependent variable. Within each database, an “estimate” is either a parameter estimate or a function estimate, which maps one or more dimensions to an uncertain value. The tool also handles the mechanical aspects of combining estimates within a meta-analyses: pooling, hierarchical Bayesian mean, and hierarchical Bayesian spread.
DMAS contributes to a growing body of tools which make research faster and easier. While other projects provide repositories for publishing methods and data, such as curatescience.org and socialscienceregistry.org, DMAS takes the outputs of individual research studies and makes them accessible and comparable.
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