Translation of chemical data into a computer actionable form is essential to catalyze the discovery of complex relationships between datasets. Traditional practices of publishing chemical data in non-interoperable forms (e.g. PDF files) make it difficult to achieve the goal of integrating diverse/heterogeneous datasets. In addition, traditional publication practices have not evolved into the digital era as there are no clear standards for interoperable publishing of chemical data. This means there are no obvious incentives for traditional chemists to modify their data publication practices.<br/>The SciData framework is proposed as an approach to improving data interoperability in a FAIR way. Encoding chemical data using the framework and using the JavaScript Object Notation for Linked Data (JSON-LD) encoding, produces data that is both an archivable text based file and a machine actionable encoding of the Resource Description Framework (RDF).<br/>In this presentation we will discuss SciDataLib, a Python library for converting data into JSON-LD. The current version of the library will be discussed, in addition to the development roadmap that will extend functionality. An example dataset from the CHEMBL database focusing on the human Ether- a-go-go-Related-Gene (hERG) gene encoded as SciData JSON-LD will be used to show how this data can be ingested into a graph database for semantic querying.

SciDataLib: A python library for generation of SciData framework files in JSON-LD

ACS Spring Meeting 2021

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.