ORKG reborn
ORKG reborn extends ORKG workflows for structured, machine-actionable representation of publication content and claims.
It is aimed at making scholarly arguments more queryable and reusable than conventional narrative-only publishing formats.
This is especially relevant for synthesis-heavy fields where claims, methods, and evidence need to be compared across many papers.
Links
Advancing FAIR principles in scientific communication through reborn articles
- Speaker: Lauren Snyder
- Date: 2025-06-12
Key Points
- Machines cannot interpret most scientific data
- But what if we could produce scientific data in a machine-readable form?
- The Python package dtreg uses schemas for graphics
- ORKG Reborn yields machine-readable information about the figures and machine environments under which publications were produced.
- ORKG reborn also yields machine-readable information about single statements in publications.
- ORKG reborn focuses on the breakdown of scientific publications into single statements and aims to contextualize them with metadata and persistent identifiers such as DOI.
My notes
Link to original
- It seems to be an interesting approach to break down scientific publications into single statements.
See also: Open Research Knowledge Graph, FAIR, MOC Open Science Data and Knowledge Graphs