AllenNeuralDynamics / AllenNeuralDynamics/aind_data_outreach
compile training-relevant exaspim datasets
- 主要言語
- Python
- スター
- 0
- フォーク
- 0
- PR マージ指標
- 30日以内にマージされた PR はありません
説明
To better coordinate work on training models for reconstruction correction predictions, we want to create combined data assets and/or DocDB queries that point to all available samples with complete reconstructions from the v2 scope (and possibly also the v1 scope separately). This will likely involve some updates to the metadata, and will need support from Peter and maybe Anna.
not strictly for the NMCP but that's the closest milestone.
コントリビューションガイド
このリポジトリのコントリビューションガイドは索引されていません
調査の方向性
Start by reviewing the available DocDB queries and metadata for complete reconstructions in the v2 scope, then check whether v1 should be handled separately. Coordinate with Peter and possibly Anna on the required metadata and training assets; done means all in-scope samples are discoverable through combined assets and/or queries.
索引モデルが issue の本文から書いたものです。
評価
- 技術スタック
- python
- 領域
- data-engineering, databases
- issue の種類
- 機能追加
- 難易度
- 5/5
- 見積もり時間
- 1週間以上
- 活発さ
- 活発
- 明瞭さ
- 説明が足りない
- 初心者へのやさしさ
- 25/100