AllenNeuralDynamics / AllenNeuralDynamics/aind-dynamic-foraging-bfm-dispatcher
Study 09 data: add two human transfer cohorts
- 主要語言
- Python
- 星號
- 0
- 分支
- 0
- 平均合併
- 2 小時 9 分鐘
- 30 天內合併 PR
- 31
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