The study of dynamical systems has long focused on the characterization of their asymptotic dynamics such as fixed points, limit cycles and other types of attractors and how these invariant sets change their properties as systems parameters are varied. More recently, however, the importance of transient dynamics, especially of long transients and sequential transitions between them, has been increasingly recognized in various fields including ecology, neuroscience and cell biology. Among several possible origins of long transients, ghost attractors have received particular attention due to interesting dynamical properties in non-autonomous systems, new theoretical developments, and an increasing number of systems that empirically show dynamics consistent with ghost attractors. In this talk, I will present a new formal definition for ghost attractors of fixed points which allows us to not only develop an algorithm for finding ghosts (available as open-source python package: PyGhostID) but also to identify novel types of ghosts with unique properties, leading to surprising phenomena such as bifurcations involving ghosts. I will further present how our framework can be used to gain new insights into the transient dynamics of a wide range of systems, ranging from information processing and decision making in neuronal circuits, tipping of coupled climate elements and potentially also tissue regeneration following a heart attack.