Datasets:

SparseWake
/

sparsewake

@@ -14,8 +14,7 @@ tags:
 # SparseWake
-SparseWake is an anonymous benchmark artifact for sparse temporal hydrodynamic sensing. It contains processed HDF5 datasets, metadata, summary CSV files, figures, checksums, and lightweight Python scripts for inspecting the data and reproducing benchmark-level evaluation outputs.
 ## Dataset Summary
 SparseWake evaluates whether sparse body-fixed temporal flow measurements can recover the relative state of a neighboring wake-producing fish in a controlled single-leader setting. The benchmark data are synthetic and were generated from a DNS-parameterized fish-schooling agent-based model. The artifact starts from processed HDF5 files and does not require the upstream simulator or high-fidelity solver.
@@ -27,9 +26,8 @@ The released data support held-out-pose evaluation, temporal-history sweeps, sen
 - Full processed HDF5 benchmark datasets in `data/processed/`.
 - A small sample HDF5 file in `data/sample/` for inspection and smoke tests.
 - Dataset metadata, schema documentation, split protocol, provenance notes, and limitations in `docs/`.
-- Summary CSV files for reported tables and figures in `data/results/`.
 - Reproduced table CSVs in `tables/`.
-- Manuscript-compatible figure PDFs and script-generated figure outputs in `figures/`.
 - Lightweight loading, verification, plotting, and smoke-test training scripts in `scripts/`.
 - Minimal reusable Python utilities in `src/sparsewake/`.
 - Checksums in `data/checksums.sha256` and a full file manifest in `MANIFEST.json`.
@@ -92,7 +90,7 @@ python scripts/reproduce_tables.py --results data/results --out tables
 On Unix-like systems, use `.venv/bin/pip` and `.venv/bin/python`.
-## Reproduce Stored Tables and Figures
 ```bash
 python scripts/reproduce_tables.py --results data/results --out tables
@@ -100,8 +98,7 @@ python scripts/make_main_figures.py --results data/results --out figures
 python scripts/make_supp_figures.py --results data/results --out figures
 ```
-These commands use stored summary CSV files and are intended for fast verification. Full training reruns are supported by `scripts/train_temporal_mlp.py` and `scripts/run_main_evaluation.py` when the full processed HDF5 files are present.
 ## License
 The SparseWake processed datasets, metadata, result summaries, documentation, and release utilities are made available under the Creative Commons Attribution 4.0 International license (CC BY 4.0). Upstream simulator source code and solver assets are not redistributed and are not covered by this dataset release.

 # SparseWake
+SparseWake is an anonymous benchmark artifact for sparse temporal hydrodynamic sensing. It contains processed HDF5 datasets, metadata, summary CSV files, checksums, and lightweight Python scripts for inspecting the data and reproducing benchmark-level evaluation outputs.
 ## Dataset Summary
 SparseWake evaluates whether sparse body-fixed temporal flow measurements can recover the relative state of a neighboring wake-producing fish in a controlled single-leader setting. The benchmark data are synthetic and were generated from a DNS-parameterized fish-schooling agent-based model. The artifact starts from processed HDF5 files and does not require the upstream simulator or high-fidelity solver.
 - Full processed HDF5 benchmark datasets in `data/processed/`.
 - A small sample HDF5 file in `data/sample/` for inspection and smoke tests.
 - Dataset metadata, schema documentation, split protocol, provenance notes, and limitations in `docs/`.
+- Summary CSV files for reported tables and figure-level results in `data/results/`.
 - Reproduced table CSVs in `tables/`.
 - Lightweight loading, verification, plotting, and smoke-test training scripts in `scripts/`.
 - Minimal reusable Python utilities in `src/sparsewake/`.
 - Checksums in `data/checksums.sha256` and a full file manifest in `MANIFEST.json`.
 On Unix-like systems, use `.venv/bin/pip` and `.venv/bin/python`.
+## Reproduce Stored Tables and Diagnostic Plots
 ```bash
 python scripts/reproduce_tables.py --results data/results --out tables
 python scripts/make_supp_figures.py --results data/results --out figures
 ```
+These commands use stored summary CSV files and are intended for fast numerical verification and diagnostic plotting. Final manuscript figures may include manual layout adjustments and are not part of the dataset artifact.
 ## License
 The SparseWake processed datasets, metadata, result summaries, documentation, and release utilities are made available under the Creative Commons Attribution 4.0 International license (CC BY 4.0). Upstream simulator source code and solver assets are not redistributed and are not covered by this dataset release.