Open Engineering Framework

• Open Engineering Framework is a systematic, open, and reproducible approach that integrates transparency, modularity, and collaboration across engineering domains.
• It employs standardized methodologies, modular processes, and open-source tools to facilitate rigorous evaluation and adapt to evolving research needs.
• The framework supports interdisciplinary collaboration by enabling consistent workflow management and data provenance for complex engineering analysis.

An Open Engineering Framework denotes an explicit, systematic approach—methodological, architectural, or software-based—for enabling open, collaborative, and reproducible research, analysis, and development within diverse engineering domains. These frameworks are distinguished by their openness (transparency, accessibility, and extensibility), support for complex workflows, and their facilitation of rigorous, trustworthy outcomes while addressing domain-specific needs, as exemplified across open access publishing, systems engineering, computational science, and socio-technical change management.

1. Core Principles and Scope

Open Engineering Frameworks share key philosophical and operational principles, including:

• Openness: All methods, processes, data, software, and results are accessible, inspectable, and reusable by the broader research community.
• Systematic Evaluation and Methodology: Frameworks provide structured sets of attributes, workflows, or process fragments (e.g., systematic 18-attribute analysis for OA journals (Graziotin et al., 2013), standardized project phases in openSE (Bonnal et al., 2018)) that enable transparent assessment, analysis, or engineering practice.
• Modularity and Extensibility: Components are designed for modular use and straightforward extension to accommodate evolving needs or new domains (e.g., method fragments in OPF (Fahmideh et al., 2020); services in open5Gcube (Horstmann et al., 20 May 2025)).
• Reproducibility and Traceability: Emphasis on documentation, provenance capture, and repeatable procedures, often employing persistent data storage, workflow templates, and configuration tracking (e.g., RCE’s central data storage (Boden et al., 2019); SpellBook export/import in Geomancer (Miranda et al., 2019)).
• Collaboration: Frameworks incorporate mechanisms for multidisciplinary or distributed collaboration, stakeholder management, and knowledge exchange.

2. Methodological Frameworks and Domain Adaptation

Several Open Engineering Frameworks originate from the codification of best practices and methodologies in specific fields, but are adaptable or generalizable:

• Systematic Analysis Frameworks: The 18-attribute framework for OA journals divides evaluation into bibliographic information, activity metrics, economics, accessibility, and predatory issues, with analytical methods (descriptive statistics, group comparisons) ensuring objective, transparent evaluation (Graziotin et al., 2013).
• Situational Method Engineering (SME): The OPEN Process Framework (OPF) and its service-oriented extensions (Fahmideh et al., 2020, Fahmideh et al., 2020) enable project-specific methodology construction by assembling and customizing method fragments, formalized under a standardized meta-model. This supports rapid adaptation to new paradigms (e.g., service-orientation) by systematically mining and integrating new process fragments.
• Requirements Engineering in Open Innovation: Frameworks for requirements engineering under open innovation synthesize practices from open source, stakeholder analysis, release planning, and prioritization—emphasizing continuous stakeholder monitoring and adaptive governance (Linåker et al., 2022).

3. Software Frameworks and Infrastructure

A significant class of Open Engineering Frameworks consists of open-source software platforms providing infrastructure for development, simulation, experimentation, or workflow management:

• Computational Science and Simulation: OpenFPM offers scalable, high-level abstractions for particle and mesh methods, leveraging template meta-programming, automatic domain decomposition, and dynamic load balancing to simplify parallel simulation codes while ensuring portability and performance (Incardona et al., 2018).
• Workflow and Integration Platforms: RCE facilitates multidisciplinary engineering collaborations via a graphical workflow editor, tool integration wizard, distributed orchestration, and centralized data provenance (Boden et al., 2019).
• Job and Parameter Management: OACIS manages large-scale parameter sweeps, automates job submissions to HPC clusters, and integrates post-processing analyzers, systematically supporting reproducible computational experiments (Murase et al., 2018).
• Mobile Network Experimentation: open5Gcube encapsulates RAN, CN, and auxiliary mobile network functions into containerized stacks, managed via simple orchestration, enabling reproducible lab setups with commercial devices across multiple generations (Horstmann et al., 20 May 2025).

4. Evaluation, Transparency, and Assurance

Frameworks typically embed formal evaluation and validation practices to ensure trustworthiness and quality:

• Attribute-based Evaluation: In OA publishing, transparent criteria (e.g., DOI provision, archiving, fee justifications) are systematically verified and contrasted; group-wise and within-group analyses reveal inconsistencies in value versus cost (Graziotin et al., 2013).
• Micro/Macro-Process Distinction: Frameworks like POE-$\Delta$ (Markov et al., 3 Apr 2025) distinguish between fine-grained engineering transformations (“micro-process”) and stakeholder-driven validation/delegation (“macro-process”), applying sequent calculus formalism for change management and assurance.
• Artifact Evaluation: In open science, robust artifact review and provisioning (code, data, precise environments using infrastructure-as-code) are proposed as necessary for transparency, reproducibility, and reward alignment (OliveiraJr et al., 20 May 2024).

5. Challenges, Limitations, and Recommendations

Commonly identified obstacles and actionable recommendations include:

• Transparency Gaps: Many OA publishers lack clear fee justification or preservation policies; open frameworks call for mandatory disclosure of evaluation attributes and peer-verified indexing (Graziotin et al., 2013).
• Reproducibility and Preservation: Sustainability requires persistent, accessible storage, standardized licensing, and workflow automation with detailed provenance (Fernández et al., 2019, OliveiraJr et al., 20 May 2024).
• Stakeholder Complexity: Open innovation and multidisciplinary domains require dynamic stakeholder identification, continuous reprioritization, and governance adaptation, supported by social network analysis and value chain modeling (Linåker et al., 2022).
• Tool Support and Scalability: Manual management of complex workflows (e.g., change tracking in POE-$\Delta$ (Markov et al., 3 Apr 2025)) or parameter sweeps (e.g., OACIS (Murase et al., 2018)) is inefficient, motivating investments in automation, API accessibility, and graphical interfaces.
• Cultural and Policy Incentives: A shift in assessment criteria and institutional recognition for open artifacts, coupled with policy mandates, is identified as necessary for systemic change (OliveiraJr et al., 20 May 2024).

6. Implications for Research and Practice

Open Engineering Frameworks serve as the backbone for cumulative science and innovation across diverse engineering subfields. Their systemic, modular, and transparent approaches enable:

• Cumulative Knowledge Building: By formalizing best practices and encoding them into fragments or modules, knowledge is preserved, easily updated, and transferred.
• Rigorous Evaluation and Oversight: Standardized metrics and transparent methodologies increase trust in research outputs and engineering decisions.
• Reproducibility and Collaboration: Centralized provenance, shareable scripts/configurations, and artifact standardization lower barriers to interdisciplinary research and industrial uptake.
• Adaptability to New Paradigms: The modular nature of method repositories and frameworks ensures rapid accommodation of new technologies, methods, and societal challenges.

7. Representative Frameworks and Comparative Overview

Framework	Domain	Key Mechanisms/Attributes
Systematic OA Analysis (Graziotin et al., 2013)	Scholarly Publishing	18-attribute evidence-driven evaluation, group/statistical analysis
OPF & Extensions (Fahmideh et al., 2020, Fahmideh et al., 2020)	Software/SOSD Method Engineering	Method fragments, meta-model, situational assembly
openSE (Bonnal et al., 2018)	Particle Accelerator Systems Engineering	Lean, participative SE lifecycle, radiation safety, modularity
OpenFPM (Incardona et al., 2018)	Particle/mesh simulation	Distributed template abstractions, load balancing, scalability
OACIS (Murase et al., 2018)	HPC/Simulation Management	Automated job/data manager, web APIs, analyzer plug-ins
RCE (Boden et al., 2019)	Multidisciplinary Engineering	Workflow editor, remote tool orchestration, central data
Geomancer (Miranda et al., 2019)	Geospatial Feature Engineering	Python API, warehouse integrations, Spell/SpellBook abstractions
POE-$\Delta$ (Markov et al., 3 Apr 2025)	Change Engineering	Phenomena-centric modeling, sequent calculus, stakeholder-centric validation

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Open Engineering Frameworks operationalize openness, transparency, traceability, rigor, and adaptability in engineering, positioning them as foundational constructs for the future of collaborative, trustworthy, and reproducible engineering research and practice.