Document the standardized training track

README.md

@@ -24,6 +24,8 @@ configs:
           - data/alpha_*/**
           - data/AR_*/**
           - data/NASA_*/**
+          - data/train/**
+          - data/validation/**
         split: train
   - config_name: cc_by_nc_sa_4_0_cases
     data_files:
@@ -38,7 +40,7 @@ viewer: false
 
 Companion dataset to [`anon-closure-challenge-v2/closure-challenge-v2`](https://huggingface.co/datasets/anon-closure-challenge-v2/closure-challenge-v2).
 
-This repository hosts the **full OpenFOAM cases** (mesh, fields, run scripts), VTK volume snapshots, and DNS-native partitioned VTUs for the 14 test cases of the Closure Challenge v2 benchmark.
+This repository hosts the **full OpenFOAM cases** (mesh, fields, run scripts), VTK volume snapshots, and DNS-native partitioned VTUs for the 14 test cases of the Closure Challenge v2 benchmark, together with the **standardized training and validation sets** under `data/train/` and `data/validation/`.
 
 The lightweight integral-profile portion needed for reproducing the scoring protocol is in the [companion dataset](https://huggingface.co/datasets/anon-closure-challenge-v2/closure-challenge-v2). **Most reviewers and submitters do not need this heavy dataset** — it is only required for re-running the CFD simulations from scratch or for extended analyses.
 
@@ -48,7 +50,7 @@ NeurIPS 2026 Evaluations & Datasets track submission. Authors anonymised for dou
 
 ## Contents
 
-Approximately 17 GB across 14 cases. Every case carries a converged solution and the mesh it was computed on.
+Approximately 18 GB. 14 test cases, plus 27 training and 5 validation cases. Every case carries a converged solution and the mesh it was computed on.
 
 ```
 data/
@@ -81,6 +83,31 @@ dat_to_csv.py                   NASA Tecplot zone-format .dat to flat CSV
 
 Per-case `compute_forces.py`, `extract_surface_Cp.py`, and `run_hpc_slurm.sh` sit inside the relevant case directories.
 
+## Standardized training track
+
+The benchmark fixes a train / validation / test split so that methodologies can
+be compared independently of the training data. All three parts are published
+here.
+
+```
+data/<case>/          14 held-out TEST cases
+data/train/           27 training cases
+data/validation/       5 validation cases
+```
+
+| Split | Contents |
+|---|---|
+| test | 4 periodic hills, 3 ducts, NASA wall-mounted hump, 6 v2 engineering cases |
+| training | 21 parametric periodic hills, 4 ducts (AR 1/3/5/10 at Re_tau=180), CBFS, PH_Breuer |
+| validation | 4 parametric periodic hills, 1 duct (AR 7 at Re_tau=180) |
+
+Training and validation cases carry the same OpenFOAM structure as the test
+cases: `constant/polyMesh`, `system/`, initial and converged time directories,
+and the high-fidelity reference fields (`U_LES`, `k_LES`, `tauij_LES`).
+
+**Using any test case during training or validation is not permitted.** The
+split above is the definition of the standardized track.
+
 ## Case notes
 
 ### Reading the periodic-hill case names
@@ -99,7 +126,7 @@ All 14 baselines are `simpleFoam` (or `rhoSimpleFoam` / `rhoPimpleFoam` for the
 
 This dataset aggregates material under two different licenses. See the `LICENSE` file at the repository root for the authoritative statement.
 
-- **CC-BY-4.0**: the 12 non-ERCOFTAC cases (`alpha_*`, `AR_*`, `NASA_*`), derived from public-domain or CC-BY upstream sources (NASA Turbulence Modeling Resource, Vinuesa duct database, Xiao parametric periodic-hill database).
+- **CC-BY-4.0**: the 12 non-ERCOFTAC test cases (`alpha_*`, `AR_*`, `NASA_*`) and everything under `data/train/` and `data/validation/`, derived from public-domain or CC-BY upstream sources (NASA Turbulence Modeling Resource, Vinuesa duct database, Xiao parametric periodic-hill database).
 - **CC-BY-NC-SA-4.0**: `ERCOFTAC_AhmedBody25` and `ERCOFTAC_WingBodyJunction`, curated extracts of ERCOFTAC kbwiki material (Classic Collection case082; DNS 1-6), inheriting the upstream non-commercial and share-alike conditions.
 - **Code**: extraction, plotting, and run scripts (`*.py`, `*.sh`) are MIT. See `LICENSE-CODE` in the [code repository](https://anonymous.4open.science/r/closure-challenge-v2).