Synthesising multiple sources of data and information is beneficial in biostatistical applications because it can provide more precise estimates and reduces the risk of biases introduced by using only partial data. However, this can be challenging when combining different types, quality or scales of data. I will give a brief overview of my contributions to three Bayesian methodological aspects. First, if for each data source we have a separate Bayesian model, but these have quantities in common, how can we combine these in a single Bayesian analysis? Second, if some parts of the data/model are regarded as less reliable, how can we formulate "cut posteriors" that prevent unreliable components contaminating the reliable components? Third, if some information is in the form of prior information about an observable quantity, how can we "translate" this into an informative parameter prior?