AllenNeuralDynamics / AllenNeuralDynamics/aind-dynamic-foraging-bfm-dispatcher

Study 09 data: add two human transfer cohorts

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#137 0 則留言 0 個 reaction 已指派 1 人 已被 @hanhou 認領 在 GitHub 檢視
evaluation extension priority:P1
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Python
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2 小時 9 分鐘
30 天內合併 PR
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描述

Child of #134. Draft implementation: #139.

## Stage-A outcome

- [x] Eckstein: complete 306-person public release admitted as v2; the paper reports 291 and no machine-readable exclusion list was found, so all public files are retained.
- [x] Findling: complete 22-person, 132-session analytic cohort admitted as v1.
- [x] Release identities, terms, checksums, exact inclusion audits, and source metadata are committed.
- [x] Canonical adapters preserve available subject/session/trial and task metadata.
- [x] Eckstein first-half/second-half v2 and Findling odd/even-session v1 manifests pass regression tests.
- [x] Common-Q and author-model feasibility are documented; no new author model is implemented in Stage A.
- [x] Both D=614 seed-0 GPU smokes passed.
- [x] Full GRU matrices and common-Q fits completed and were frozen with exact ordered held-out trial-key parity.
- [x] Results and representative sessions are included in the Stage-A decision report.

The data-ingestion and Stage-A execution scope is complete and in review.

貢獻指南

這個儲存庫沒有索引到貢獻指南

研究方向

Start with parent issue #134 and the draft implementation in #139, then review the committed release identities, source metadata, canonical adapters, manifests, and Stage-A decision report. Run the regression tests and inspect the exact inclusion audits and held-out trial-key parity checks; done means both cohorts are reproducibly ingested and the documented validation checks pass.

由索引模型根據 Issue 內容生成。

評估

技術堆疊
python
領域
data-engineering, machine-learning
Issue 類型
功能
難度
4/5
預估耗時
3-5 天
活躍度
活躍
描述清晰度
基本清楚
新手友好度
35/100

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