Checklists

Checklist for individual benchmarks
Checklist for benchmarking frameworks
Checklist for community engagement / social factors

Checklist for individual benchmarks

When setting up individual benchmarks, design them for reproducibility, robustness, and clear interpretation. Here’s a checklist to follow:

1. Define Objectives

• Clearly articulate the scientific question or problem the benchmark will address.
• Define the goals for the benchmark or the benchmarking framework. See above for more details on: version control, accessibility, automation, independence, testing, documentation, and sharing.
• Identify the outcomes and metrics to evaluate success (e.g., accuracy, runtime, statistical measures).

2. Select Input Data

• Use diverse and representative datasets to avoid bias.
• Ensure datasets are publicly available or properly documented for reuse.
• Validate data quality and pre-process it for consistency.

3. Design Protocols

• Specify all commands, parameters, and options to execute the benchmark.
• Define standardized workflows to minimize variability in runs.
• Document any assumptions or dependencies in the process.

4. Establish Quality Metrics

• Choose appropriate metrics to measure performance (e.g., RMSD, correlation coefficients, or other domain-specific values).
• Include thresholds for pass/fail criteria or statistical benchmarks to interpret results meaningfully.

5. Incorporate Reproducibility Features

• Use version-controlled code and data to track changes.
• Ensure protocols are system-independent and compatible across various environments.
• Consider using containerized environments (e.g., Docker) for consistent runtime configurations.

6. Automate Execution

• Integrate the benchmark with automated testing frameworks (e.g., Jenkins, GitHub Actions).
• Schedule regular runs, especially when updates to the system occur.
• Monitor for anomalies or changes in results to quickly detect regressions.

7. Document the Benchmark

• Provide detailed readme files or user guides covering:
  • Objectives and expected outcomes.
  • Step-by-step instructions to run the benchmark.
  • Explanation of output metrics and how to interpret them.
• Use templates to ensure documentation is comprehensive and uniform across benchmarks.

8. Test and Validate

• Run the benchmark in various settings to validate reproducibility.
• Conduct a trial to verify that results align with scientific expectations.
• Debug any issues that arise and refine the protocol accordingly.

9. Ensure Maintenance

• Assign maintainers or observers to monitor and update the benchmark as needed.
• Regularly review and update benchmarks to account for:
  • New datasets or methods.
  • Changes in software or dependencies.
  • Evolving scientific questions.

10. Share Results

• Publish results and protocols to encourage transparency and community engagement.
• Enable other researchers to run the benchmark, compare results, and contribute improvements.

Checklist for benchmarking frameworks

This checklist ensures the benchmarking framework is robust, sustainable, and widely applicable, enabling continuous validation and improvement of scientific methods.

1. Define Goals and Objectives

• Articulate the primary purpose of the framework (e.g., scientific reproducibility, continuous testing, protocol evaluation).
• Establish specific goals such as simplicity, generalization, automation, documentation, and maintenance.

2. Plan Framework Architecture

• Design a modular architecture to support flexibility and scalability.
• Ensure compatibility with multiple interfaces (e.g., command-line tools, scripts, APIs).
• Incorporate virtualization tools (e.g., Docker, Singularity) for environment consistency across systems.

3. Integrate Automation

• Implement automated testing workflows to minimize manual intervention.
• Use a centralized server with high-performance computing (HPC) integration to handle computationally intensive tasks.
• Enable automated scheduling and load balancing to optimize resource usage.

4. Ensure System Independence

• Develop the framework to work across diverse systems, including local machines, cloud services, and HPC clusters.
• Maintain a virtualization layer for seamless portability between different computational environments.

5. Establish Data and Protocol Standards

• Use standardized datasets and protocols to minimize bias and ensure reproducibility.
• Maintain version-controlled inputs, configurations, and code.
• Include complete protocol captures with all options, variables, and commands used in testing.

6. Implement Comprehensive Documentation

• Provide template-based documentation to guide users in setting up and maintaining benchmarks.
• Document:
  • Framework setup.
  • Individual benchmark objectives, inputs, outputs, and quality metrics.
  • Steps for troubleshooting and extending the framework.
• Embed documentation into the web interface and code repository for easy access.

7. Develop a User-Friendly Interface

• Create a web-based dashboard for:
  • Viewing benchmark results.
  • Monitoring pass/fail statuses.
  • Debugging failed tests.
• Allow users to visualize trends and access detailed reports.

8. Set Up Maintenance Protocols

• Assign maintainers or observers for each benchmark to handle test failures.
• Define procedures for addressing:
  • Technical failures (e.g., compilation or script errors).
  • Stochastic failures (e.g., due to algorithm randomness).
  • Scientific failures (e.g., unexpected deviations in results).
• Encourage community involvement through hackathons or workshops to address widespread issues.

9. Foster Community Collaboration

• Publicize the framework and benchmarks to the community to encourage contributions.
• Establish communication channels (e.g., mailing lists, forums) to discuss framework updates and improvements.
• Host regular community announcements about new benchmarks and their maintenance status.

10. Monitor and Evaluate Performance

• Continuously run benchmarks to track software performance over time.
• Use statistical measures to evaluate reproducibility and identify trends.
• Ensure the framework adapts to evolving scientific standards and computational methods.

11. Encourage Transparency and Accessibility

• Make the framework and benchmarks open-source and accessible to the community.
• Publish results and protocol captures on a public website for reference and validation.
• Ensure users can easily integrate the framework with their own workflows.

12. Allocate Resources

• Budget for computational resources, storage, and development time.
• Invest in training for maintainers and contributors to understand and utilize the framework effectively.

Checklist for community engagement / social factors {#checklist-for-community-engagement-/-social-factors}

1. Stakeholder Engagement: Identify key stakeholders, ensure they understand the benchmark’s purpose and impact, and involve them early for input on metrics and evaluation criteria.

2. Community Involvement: Engage the scientific community for feedback on benchmark design, establish transparent contribution processes, and recognize contributors for their work.

3. Collaboration Dynamics: Define clear roles and responsibilities within the team, establish conflict resolution protocols, and promote interdisciplinary collaboration when relevant.

4. Transparency and Trust: Publish results openly with detailed methodologies, disclose potential conflicts of interest, and use version-controlled repositories to ensure reproducibility.

5. Ethical Considerations: Ensure ethical data use and privacy compliance, avoid biases in data selection or evaluation criteria, and consider long-term sustainability and fair resource distribution.

6. Recognition and Incentives: Provide academic credit through citations and publications, offer recognition through events or awards, and align benchmarks with career development incentives.

7. Governance and Policy: Define clear roles and responsibilities for maintaining and evolving the benchmark. Start simple: assign a maintainer or small review team, and document how decisions (e.g., updates, versioning, deprecations) are made. An advisory board may be needed if the benchmark impacts multiple institutions, has high stakes (e.g., influences funding or publication), or if disagreements arise that require neutral oversight. If your benchmark becomes widely adopted or politically sensitive, formal governance becomes more important.