The parameter Houlihan: A solution to high-throughput identifiability indeterminacy for brutally ill-posed problems

•Data assimilation using sparse data and complex models with many parameters can lead to non-unique or non-convergent parameter estimates.•When identifiability failure arises it can be difficult to decide which parameters to estimate from among the 10s to 100s of potential parameters.•The parameter Houlihan is a framework for selecting which parameters to estimate using data assimilation in the context of sparse data and identifiability failure to minimize non-uniqueness of parameter estimates and error.