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While the publication of datasets in scientific repositories has becomebroadly recognised, the repositories tend to have increasing semantic-relatedproblems. For instance, they present various data reuse obstacles formachine-actionable processes, especially in biological repositories, hamperingthe reproducibility of scientific experiments. An example of these shortcomingsis the GenBank database. We propose GAP, an innovative data model to enhancethe semantic data meaning to address these issues. The model focuses onconverging related approaches like data provenance, semantic interoperability,FAIR principles, and nanopublications. Our experiments include a prototype toscrape genomic data and trace them to nanopublications as a proof of concept.For this, (meta)data are stored in a three-level nanopub data model. The firstlevel is related to a target organism, specifying data in terms of biologicaltaxonomy. The second level focuses on the biological strains of the target, thecentral part of our contribution. The strains express information related todeciphered (meta)data of the genetic variations of the genomic material. Thethird level stores related scientific papers (meta)data. We expect it willoffer higher data storage flexibility and more extensive interoperability withother data sources by incorporating and adopting associated approaches to storegenomic data in the proposed model.

GAP Enhancing Semantic Interoperability of Genomic Datasets and Provenance Through Nanopublications