SciSchema.org: A Multidisciplinary Collection of Schemas for Structured Scientific Process Descriptions
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.
Keywords
Cite
@article{arxiv.2607.27955,
title = {SciSchema.org: A Multidisciplinary Collection of Schemas for Structured Scientific Process Descriptions},
author = {Jennifer D'Souza and Sameer Sadruddin and Anisa Rula and Ana Bossler and Andrés Fullana and Enric Bas and Syed Ather and Defne Circi and Anlan Chen and L. Catherine Brinson and Alyssa Columbus and George Demetriou and Dongjun Jeong and Tarun Kumar and Frank Krüger and Sascha Genehr and Kai Budde-Sagert and Anamaria Leonescu and Francesco Lodola and Chiara Florindi and Gagana Balasubramanya Murthy and Samson Oluwapelumi Olagbile and Nazia Riasat and Yan Sha and Kevin Shen and Shaokai Yang},
journal= {arXiv preprint arXiv:2607.27955},
year = {2026}
}
Comments
25 pages, 9 figures, Submitted for peer review to Nature Scientific Data