Access to digital is accelerating almost exponentially.  Every year the number of open repositories/databases increases at a rate that feels like the publishing explosion - impossible to keep up with.  Yet, there is still a big issue with this data.  Even if it is available by an API and even if it is from a reputable source, how easy is it to understand (both from a schema and a knowledge level) and integrate into existing data?<br/><br/>It seems clear that in order to work with open data we need a bridge between data coming from disparate sources in a variety of formats and well structured semantic data that both humans and machines can understand.  In this regard, the JavaScript Object Notation for Linked Data (JSON-LD) format seems to be the obvious choice.  On one hand, it is a plain text (UTF-8) file that is human readable and easy to archive, and on the other is a named graph of semantic triples that is a machine actionable.<br/><br/>In this presentation we will discuss the SciFlow system for processing SciData framework formatted JSON-LD encoded data.  The system, built in the Django framework, allows managed migration of chemical data to be validated, normalized and optimized across a wide range of data types.

SciFlow: A Django/Python workflow system for processing scidata formatted JSON-LD

ACS Spring Meeting 2021

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