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RevAIse Data Model Documentation

An open standard for describing, sharing, and reproducing AI-assisted systematic literature review workflows

Welcome to the official documentation for the RevAIse Data Model.

Overview

RevAIse provides a comprehensive, standardized way to document complete AI-assisted systematic review workflows, ensuring:

  • Transparency - Full documentation of AI usage, parameters, and workflow decisions throughout the review process
  • Reproducibility - Complete capture of workflow methods, review stages, and research environments
  • Interoperability - Standard format for sharing and comparing systematic review processes
  • Traceability - Detailed provenance for all workflow stages and methodological decisions

Quick Start

Access the Schema

The RevAIse schema is available in multiple formats:

  • LinkML YAML - The source schema definition
  • JSON Schema - For validation in applications
  • JSON-LD Context - For linked data applications

Current Version

Version Information

You are viewing documentation for: stable

Latest Stable Release

ReadTheDocs automatically maintains documentation for all tagged releases. Use the version selector at the bottom of the page to switch between versions.

Key Components

Review Core Objects

These are the fundamental objects that characterize a systematic review workflow:

  • Review - The root container for systematic reviews and their workflow documentation
  • Author - Review authors and workflow contributors
  • Protocol - Review protocol and workflow methodology specifications
  • Literature Record - Individual literature items flowing through the review workflow

Shared Infrastructure Objects

These objects are imported in review_core.yaml for sharing across workflow stages:

Review Workflow Stages

These represent the sequential phases of a systematic review workflow:

  • Scoping - Optional exploratory planning and question refinement
  • Registration - Protocol registration and workflow pre-specification
  • Search - Literature search strategy execution and documentation
  • Screening - Title/abstract and full-text screening workflows
  • Extraction - Data extraction processes from included studies
  • Synthesis - Data synthesis workflows and analysis procedures
  • Reporting - Optional reporting, checklist, PRISMA flow, and publication documentation

Features

Workflow-Based Organization

Systematic reviews are structured as documented workflows with discrete stages, each capturing: - Execution metadata (timing, participants, methodologies) - Input/output specifications and data flow between stages - AI usage documentation and human-AI collaboration patterns - Quality control measures and validation procedures

AI Workflow Documentation

Comprehensive capture of AI assistance within systematic review workflows including: - Model specifications and versions used in each stage - Prompts and parameters for AI-assisted review steps - Human oversight points and intervention patterns - Performance metrics and quality assessment data

Workflow Provenance Tracking

Complete traceability of systematic review processes with: - Temporal information for all workflow activities - Attribution of decisions (human reviewers and AI systems) - Tool and environment specifications for reproducibility - Decision rationale and methodological justifications

Quality Assurance Workflows

Built-in support for systematic review quality processes: - Review checklists and quality assessment tools integrated into workflows - Inter-rater agreement metrics and conflict resolution procedures - Quality control checkpoints and validation workflows - Amendment tracking and workflow versioning

Schema Formats

Format Description Use Case
LinkML YAML Source schema definition Schema development and workflow template creation
JSON Schema JSON validation schema Application validation and data checking
JSON-LD Context Linked data context Semantic web applications and metadata

Version Support

This documentation system maintains all versions:

  • Latest - The most recent stable release
  • Dev - Current development version from main branch
  • Tagged Releases - All historical versions (e.g., v0.1.0, v0.2.0)

Use the version selector to access documentation for any version.

Getting Started

  1. Explore the Schema - Start with the main schema documentation
  2. Review Workflow Examples - Check the schema for example systematic review instances
  3. Map Your Review Workflow - Document your systematic review process using RevAIse stages
  4. Validate Your Data - Use the JSON Schema for workflow validation
  5. Contribute - Visit our GitHub repository

Use Cases

Research Teams

  • Document and share complete systematic review workflows and methodologies
  • Enable workflow replication and adaptation across different research contexts
  • Build institutional libraries of proven systematic review processes

Journal Publishers

  • Require standardized workflow documentation for AI-assisted systematic reviews
  • Enable compliance checking with systematic review reporting guidelines (PRISMA, etc.)
  • Support reproducibility initiatives through transparent workflow documentation

Tool Developers

  • Understand standard systematic review workflow patterns to build compatible tools
  • Enable data exchange between different systematic review platforms
  • Support standardized documentation of AI assistance in review tools

Research Methodologists

  • Develop and refine systematic review workflow standards and best practices
  • Compare and analyze different systematic review methodological approaches
  • Create training materials and templates for systematic review workflows

License

The RevAIse Data Model is released under the CC0 1.0 Universal license, making it freely available for any use.

Citation

If you use RevAIse in your work, please cite:

@software{revaise_model,
  title = {RevAIse: A Data Model for AI-Assisted Systematic Literature Review Workflows},
  author = {Boero, Riccardo},
  year = {2025},
  url = {https://github.com/open-and-sustainable/revaise-model},
  doi = {10.5281/zenodo.17054435}
}