[Paper] Towards a Metadata Schema for Energy Research Software

Source: arXiv - 2601.09456v1

Overview

The paper tackles a practical bottleneck in the energy research community: the lack of a standardized way to describe research software. By designing and testing a domain‑specific metadata schema, the authors aim to make energy‑related software more discoverable, interoperable, and reusable—key goals of the FAIR4RS (Findable, Accessible, Interoperable, Reusable for Research Software) initiative.

Key Contributions

• Requirement‑driven schema design – a systematic analysis of what energy researchers actually need from software metadata.
• A concrete metadata schema – a lightweight yet expressive set of fields tailored to energy‑research software (e.g., model type, simulation scale, energy domain, licensing, provenance).
• User‑centered evaluation – usability testing with domain experts to validate the schema’s completeness and ease of use.
• Guidelines for presentation – practical recommendations on how to surface metadata fields in tools and repositories to encourage adoption.
• Open discussion of FAIR4RS trade‑offs – insights into balancing formal standards with the day‑to‑day workflow of scientists and engineers.

Methodology

1. Requirement Analysis – The authors surveyed energy researchers, examined existing software repositories, and mapped FAIR4RS principles to the specific needs of the energy domain.
2. Schema Drafting – Using the gathered requirements, they iteratively defined metadata elements (core, optional, and domain‑specific) and aligned them with existing standards (e.g., CodeMeta, schema.org).
3. Prototype Implementation – A simple web‑form and a JSON‑LD template were built to let participants enter metadata for their own tools.
4. User Testing – 12 energy researchers from academia and industry filled out the form for real software projects. The team collected quantitative usability metrics (completion time, error rate) and qualitative feedback (clarity, perceived usefulness).
5. Refinement – Based on the test results, the schema and its UI presentation were tweaked to reduce cognitive load and improve consistency.

Results & Findings

• Balanced Scope – The final schema includes ~20 fields, covering essential technical details (e.g., input/output formats, computational resources) without overwhelming users.
• High Completion Rate – 92 % of participants could fill out the entire form without external help, indicating good understandability.
• Time Efficiency – Average completion time dropped from 7 minutes (first iteration) to 3.5 minutes after UI refinements.
• Metadata Quality Improves Reusability – Participants reported that the schema helped them think critically about licensing, versioning, and documentation, which are often neglected.
• Presentation Matters – Clear grouping, inline help text, and example values were identified as the most decisive factors for successful metadata entry.

Practical Implications

• Easier Discovery in Repositories – Energy‑focused software portals (e.g., OpenEnergyPlatform, Zenodo collections) can ingest the schema to power richer search filters (by model type, geographic scope, etc.).
• Automation Friendly – The JSON‑LD representation enables CI/CD pipelines to auto‑generate citation files, dependency graphs, and compliance reports.
• Cross‑Project Interoperability – Standardized metadata makes it simpler to chain together simulation tools, data pipelines, and visualization modules in large‑scale energy system studies.
• Reduced Onboarding Overhead – New team members can quickly understand a codebase’s purpose and requirements just by reading the metadata, accelerating collaborative development.
• Compliance with Funding Mandates – Many grant agencies now require FAIR‑compliant software; the schema provides a ready‑made checklist for researchers to meet those obligations.

Limitations & Future Work

• Domain Scope – The schema was designed with a focus on conventional energy system modeling; extensions may be needed for emerging areas like quantum‑grid simulations or renewable‑hardware control.
• Sample Size – User testing involved a relatively small, mostly academic cohort; broader industry validation could uncover additional usability challenges.
• Tool Integration – While a prototype UI was built, embedding the schema into popular development environments (e.g., VS Code extensions, GitHub Actions) remains future work.
• Evolution Governance – The authors note the need for a community‑driven maintenance process to keep the schema aligned with evolving standards and software practices.

Bottom line: By delivering a pragmatic, researcher‑tested metadata schema, this work paves the way for more searchable, interoperable, and reusable energy research software—benefiting developers, data scientists, and policy makers alike.

Authors

• Stephan Ferenz
• Oliver Werth
• Astrid Nieße

Paper Information

• arXiv ID: 2601.09456v1
• Categories: cs.SE, cs.DL
• Published: January 14, 2026
• PDF: Download PDF