SciSchema.org: A Multidisciplinary Collection of Schemas for Structured Scientific Process Descriptions

2026-07-30

AI Summary

Scientific process descriptions are often embedded in unstructured text, hindering reproducibility, comparison, and automation. To address this challenge, this work presents the first cross-disciplinary, expert-driven repository of structured scientific process schemas, encompassing 16 expert-annotated patterns across five domains. Through a human-in-the-loop workflow, candidate schemas generated by large language models were iteratively refined via domain expert feedback, yielding reusable fields such as inputs, outputs, steps, and parameters. The resulting schemas are formalized in both JSON Schema and SHACL formats and accompanied by an integrated toolchain. The project also releases a comprehensive dataset—including schemas, intermediate artifacts, review records, and analysis scripts—to support knowledge graph construction, semantic publishing, and cross-study comparison.

Abstract

Scientific processes are often described in heterogeneous article discourse, with details needed for comparison, reproducibility, reuse, and automation dispersed across prose, tables, figures, protocols, and supplementary files. We present the first release of SciSchema.org, a multidisciplinary collection of 16 expert-annotated schemas spanning Biology & Biotechnology, Materials & Chemistry, Imaging & Measurement, Physics, and Psychology. Each schema defines reusable fields for describing process instances, including inputs, outputs, materials, instruments or software, parameters, conditions, procedural steps, measurements, and provenance-related information. The schemas were created through a human-in-the-loop schema-mining workflow in which large language models generated candidate structures from process specifications, scientific articles, and expert feedback, followed by domain-expert construction of final master schemas. The dataset contains final schemas in JSON Schema and SHACL formats, intermediate model-generated schemas, expert-feedback records, source-paper metadata, community-development materials, and analysis scripts. Technical validation assessed schema structure, development provenance, expert review, and syntactic conformance. The collection supports structured annotation, metadata enrichment, scientific knowledge graphs, information extraction, semantic publishing, and cross-study comparison.

Problem

• scientific process description
• structured representation
• reproducibility
• heterogeneous data
• schema

Innovation

• structured scientific process
• schema mining
• human-in-the-loop
• large language models
• scientific knowledge graphs