Easy-MODA: Simplifying Standardised Registration of Scientific Simulation Workflows

How NovaMechanics built the first automated tool for generating standardised MODA documentation of scientific simulation workflows — automating complex model registration to enhance reproducibility and FAIR compliance across physics-based and data-driven simulations.

Computational and Structural Biotechnology Journal • 2024
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The Challenge

Bridging the Reproducibility Gap in Scientific Simulations

Scientific simulation workflows are fundamental to computational research in materials science, nanoinformatics, drug discovery, and environmental modelling. Yet reproducibility remains a critical problem: models are complex, hardware and software dependencies are numerous, and documentation is fragmented or absent. Researchers cannot reliably reproduce published computational results, hindering trust in predictions and slowing innovation.

The MODA (Modelling Data) framework was created to standardise simulation workflow documentation per CEN CWA 17284:2018, but adoption has been minimal because manual documentation is error-prone, time-consuming, and requires deep knowledge of interdependent metadata fields. A fully automated solution was needed to democratise MODA documentation for both physics-based and data-driven models.

CEN CWA
Standards-compliant (CWA 17284:2018) MODA documentation
FAIR
Enhanced FAIR principles for simulation reproducibility
Multi-Model
Physics-based and data-driven workflows fully supported

Our Approach

An automated workflow for MODA documentation from project metadata to exportable standards-compliant files

Enter project overview and metadata

Input project title, DOI, publication details, and workflow description. Easy-MODA stores these as foundational metadata and automatically links them to all subsequent models and data transformations in the workflow.

Add models and data transformations

Define the workflow structure by adding individual models and data transformations. Each element is tagged with type, purpose, and dependencies, supporting both simple linear workflows and complex scenarios (linked, iterative, tightly coupled multi-model systems).

Select model type and fill structured forms

For each model, select whether it is physics-based or data-based. Easy-MODA presents type-specific forms with pre-filled compatible options, guided suggestions, and field validation to eliminate incompatible combinations and reduce manual entry errors.

Auto-map fields and export documentation

Easy-MODA automatically maps fields between QMRF (QSAR Model Reporting Format) and MODA templates, generates standardised DOC format documentation, and exports as JSON for reuse and versioning. All interdependencies are resolved automatically.

Store and retrieve via cloud platform

Save MODA documents to cloud-based storage via the Enalos Cloud Platform, enabling retrieval, modification, and sharing. JSON exports allow version control and integration with reproducibility frameworks across the research community.

Results at a Glance

First
Automated MODA Tool
First fully automated tool for MODA documentation generation and workflow registration
CEN
Standards Compliant
CWA 17284:2018 compliant documentation for physics-based and data-driven models
FAIR
Data Principles Supported
Enhanced FAIR compliance for scientific simulation findability and reusability
Physics + Data
Both Model Types
Full support for physics-based simulations and AutoML data-driven models in workflows
JSON
Reusable Exports
Export workflows as JSON for version control and reproducibility across platforms
Free
Web Access
Freely available on the Enalos Cloud Platform with cloud-based storage and retrieval

Related Publication

Peer-Reviewed Paper

Easy-MODA: Simplifying standardised registration of scientific simulation workflows through MODA template guidelines powered by the Enalos Cloud Platform

Kolokathis P.D., Sidiropoulos N.K., Zouraris D., Varsou D.-D., Mintis D.G., Tsoumanis A., Dondero F., Exner T.E., Sarimveis H., Chaideftou E., Paparella M., Nikiforou F., Karakoltzidis A., Karakitsios S., Sarigiannis D., Friis J., Goldbeck G., Winkler D.A., Peijnenburg W., Serra A., Greco D., Melagraki G., Lynch I., Afantitis A. — Computational and Structural Biotechnology Journal, 2024, 25:256–268 — DOI: 10.1016/j.csbj.2024.10.018