Before the advent of computers and digital storage, scientific data was represented via human-readable, text and image-based articles, much as it is today. Wide adoption of computers and digital storage revolutionized the accessibility of scientific information for humans, but the revolution stopped short of making the information accessible to computers. Though distribution of computer-accessible versions of scientific data should have coincided with the adoption of digital data publication, a widely adopted system for storing and distributing computer-accessible scientific data is still absent. A data model and ontology for semantic representation of scientific data, SciData, has been developed as a means of storing and distributing data for computer accessibility and machine actionability. Wide adoption of SciData would permit aggregation and reconciliation of data from heterogenous datasets by computers to elucidate knowledge that may not otherwise be derived. The largest hurdle to adoption of a computer-accessible data model, such as SciData, is finding a way to seamlessly incorporate generation of SciData documents into the standard scientific data publication process. The rapid growth and adoption of the Python programming language made it a clear choice as the backbone for generating SciData documents. Though knowledge of Python programming is required at this stage, the ultimate goal is to make the python library accessible to all scientists so that SciData documents can be produced along with any publication of data. This talk will therefore describe our efforts to develop and implement the SciData python library as well as how other scientists can adopt the python library for production of SciData documents from any type of scientific data.

Development and application of a Python library for the semantic representation of scientific data

Macromolecular Chemistry: The Second Century

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ACS Spring 2020 National Meeting & Expo

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