Evidence mapping

Converting stacks of published papers into research databases

Evidence Mapping and Computable Research

Evidence maps are a novel evidence synthesis product which allow systematic identification of gaps and gluts in decision-critical evidence. We began speculating as to their application to environmental health in 2018 (Wolffe et al. 2019), before we saw rapid uptake and integration of the methods by the US EPA (NASEM 2021).

Eventually, if we can make evidence maps comprehensive, interoperable, and detailed enough, they will allow automation of many aspects of evidence synthesis. Unfortunately, this seems some way off, according to our study of database practices in evidence mapping (Wolffe et al. 2020).

The problem can be solved, with opportunities presented by knowledge organisation systems such as ontologies (Whaley et al. 2020). However, successful deployment of ontologies requires technological development of its own, for which I set up a "meta-data enhanced study templates" (MDESTs) discussion group.

The ultimate solution is to replace the PDF as the primary product of research with structured labelled data. Manuscripts will still be an essential means of communicating about research, but they should be derived from a comprehensive and interoperable record of data about a research project, not vice-versa.

This is leading in the direction of how semantic authoring tools might finally be successfully integrated into research planning and reporting workflows, for which I am currently working on basic prototypes and writing funding proposals.

Why we need computable research for "truly" systematic reviews