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Phylogenetic trees underpin the reconstruction of evolutionary history and tests of evolutionary hypotheses. Advances in the acquisition and analysis of genetic sequence data have led to an increasing emphasis and reliance on molecular phylogenies, yet phenotypic evidence (morphology) remains vital. It enables us to make the link between organisms and their environment, and thus demonstrate the mechanisms of evolutionary change. It is also the only way to include extinct taxa, and therefore provide a deep time perspective. Although morphology is acknowledged to be essential for phylogeny, it is also widely recognized as intrinsically problematic. Developmental and functional linkage can result in suites or modules of non-independent morphological characters and thus misleading patterns with respect to phylogeny reconstruction. These problematic phenomena are acknowledged to exist, but they have been largely ignored; morphological data are routinely taken at 'face value' and are treated as equivalent by both molecular and palaeontological studies. Furthermore, we have no idea about the distribution or influence of these phenomena. As such, our understanding of a range of evolutionary events is undermined, and our ability to reconstruct evolutionary history is limited. To directly address the limitations of morphology, this project aims to: 1) Build a database of existing morphological datasets of extant clades from across the Arthropoda (i.e. crustaceans, insects, arachnids and myriapods); 2) Use meta-analysis to quantify the extent and distribution of the non-independent and incongruent characters across arthropod clades and across morphological modules; 3) develop methodological toolkits for reliable phylogenetic inference using morphology, with independent molecular data acting as benchmarks; 4) apply those methods to important evolutionary events that rely on interpretations of morphology but have proved contentious, for example, the origin of arthropods. The outcomes will address specific evolutionary hypotheses, and provide powerful tools, workflows and guides for future analyses by providing new ways of working.