TITLE: GAP Enhancing Semantic Interoperability of Genomic Datasets and Provenance Through Nanopublications 

ABSTRACT: While the publication of datasets in scientific repositories has become
broadly recognised, the repositories tend to have increasing semantic-related
problems. For instance, they present various data reuse obstacles for
machine-actionable processes, especially in biological repositories, hampering
the reproducibility of scientific experiments. An example of these shortcomings
is the GenBank database. We propose GAP, an innovative data model to enhance
the semantic data meaning to address these issues. The model focuses on
converging related approaches like data provenance, semantic interoperability,
FAIR principles, and nanopublications. Our experiments include a prototype to
scrape 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 first
level is related to a target organism, specifying data in terms of biological
taxonomy. The second level focuses on the biological strains of the target, the
central part of our contribution. The strains express information related to
deciphered (meta)data of the genetic variations of the genomic material. The
third level stores related scientific papers (meta)data. We expect it will
offer higher data storage flexibility and more extensive interoperability with
other data sources by incorporating and adopting associated approaches to store
genomic data in the proposed model.
While the publication of datasets in scientific repositories has become
broadly recognised, the repositories tend to have increasing semantic-related
problems. For instance, they present various data reuse obstacles for
machine-actionable processes, especially in biological repositories, hampering
the reproducibility of scientific experiments. An example of these shortcomings
is the GenBank database. We propose GAP, an innovative data model to enhance
the semantic data meaning to address these issues. The model focuses on
converging related approaches like data provenance, semantic interoperability,
FAIR principles, and nanopublications. Our experiments include a prototype to
scrape 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 first
level is related to a target organism, specifying data in terms of biological
taxonomy. The second level focuses on the biological strains of the target, the
central part of our contribution. The strains express information related to
deciphered (meta)data of the genetic variations of the genomic material. The
third level stores related scientific papers (meta)data. We expect it will
offer higher data storage flexibility and more extensive interoperability with
other data sources by incorporating and adopting associated approaches to store
genomic data in the proposed model.
GAP Enhancing Semantic Interoperability of Genomic Datasets and Provenance Through Nanopublications
http://arxiv.org/abs/2111.08739v2