The Linked Data paradigm involves practices to publish, share, and connect data on the Web. Thus, it is a compelling approach for the dissemination and re-use of scientic data, realizing the vision of the so-called Linked Science. However, by just converting legacy scientic data as Linked Data, we do not fully meet the requirements of data re-use. Scientic data is evolving data. To ensure re-use and allow exploitation and validation of scientic results, several challenges related to scientic data dynamics should be tackled. In this paper, we deal with the publication of diachronic life science linked data. We propose a change model based on RDF to capture versioned entities. Based on this model we convert legacy data from biological databases as diachronic linked data. Our linked data server can assist biologists to explore biological entities and their evolution by either using SPARQL queries or navigating among entity versions. All services are publicly available at http://diana.imis.athena-innovation.gr/lod