Thanks for the suggestion! I updated the widget so it uses the metrics that are discovered dynamically at runtime from tvb.analyzers in tvb-library, and bctpy library .
For now I’m using mock time-series data (np.random.randn) to test the pipeline. The metrics are computed from the neuron/region time-series values and visualized with the projection plot and a histogram to show the distribution.
GitHub Prototype
I also explored the available BCT analyzers/adapters in tvb_framework/tvb/adapters/analyzers. I’m currently working on integrating the adapters as well so the widget follows the same analyzer pipeline used in TVB.
Github Prototype
Would this be sufficient as a prototype for now?
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Hi @greg_incf and @liadomide, I’m Rafi, a 2nd-year AIML student. I am very interested in Brain-Score as it aligns with my focus on ML benchmarking. I currently use GitHub Codespaces to build automated data parsers for ArduPilot logs, and I’d like to apply this Python-based workflow to your benchmarking pipelines. Do you have a recommended ‘starter’ task or a specific dataset I should explore to test my environment?
Hi Rafi.
Are you aware you are writing to a GSOC 2024 proposal?
Could it be that you are mixing the proposals ?
Best,
Lia
yes. these should suffice
Hi Lia (@liadomide), thank you for catching that! My apologies—I definitely navigated to the wrong year’s thread.
I am a 2026 applicant, and I’m very interested in applying my Python diagnostic workflow to current Brain-Score or structural connectivity tasks. Could you point me toward the active 2026 project ideas or the best channel to discuss this year’s requirements? I’d love to make sure I’m contributing to the right roadmap!
Hey! You can check the updated INCF 2026 project ideas here.
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