Data integration in the life sciences continues to be important but challe- ing. The ongoing development of new experimental methods gives rise to an increasingly wide range of data sets, which in turn must be combined to allow more integrative views of biological systems. Indeed, the growing prominence of systems biology, where mathematical models characterize behaviors observed in experiments of di?erent types, emphasizes the importance of data integration to the life sciences. In this context, the representation of models of biological behavior as data in turn gives rise to challenges relating to provenance, data quality, annotation, etc., all of which are associated with signi?cant research activities within computer science. The Data Integration in the Life Sciences (DILS) Workshop Series brings together data and knowledge management researchers from the computer s- ence research community with bioinformaticians and computational biologists, to improve the understanding of how emerging data integration techniques can address requirements identi?ed in the life sciences.

Data integration in the life sciences continues to be important but challe- ing. The ongoing development of new experimental methods gives rise to an increasingly wide range of data sets, which in turn must be combined to allow more integrative views of biological systems. Indeed, the growing prominence of systems biology, where mathematical models characterize behaviors observed in experiments of di?erent types, emphasizes the importance of data integration to the life sciences. In this context, the representation of models of biological behavior as data in turn gives rise to challenges relating to provenance, data quality, annotation, etc., all of which are associated with signi?cant research activities within computer science. The Data Integration in the Life Sciences (DILS) Workshop Series brings together data and knowledge management researchers from the computer s- ence research community with bioinformaticians and computational biologists, to improve the understanding of how emerging data integration techniques can address requirements identi?ed in the life sciences.