The emphasis on multilevel modeling techniques in the Neurosciences has led to an increased need for large-scale databases containing neuroscientific data. Despite this, such databases are not being populated at a rate commensurate with their demand amongst Computational Neuroscientists. The reasons for this are common to scientific database curation in general–limitation of resources. Much of Neuroscience’s long tradition of research has been documented in computationally inaccessible formats, such as the pdf, making large-scale data extraction laborious and expensive. Here we present three sets of studies designed to construct automated tools for alleviating three bottlenecks in the workflow of a community-curated knowledge base of neuroscience-related information.