Fix model-card YAML metadata and document permissive public reuse

CITATION.cff

@@ -0,0 +1,11 @@
+cff-version: 1.2.0
+message: "If you use TrackFormer, cite this software and the upstream weather/track data described in the source notices."
+type: software
+title: "TrackFormer 1.2: Northwest Pacific pressure-field and typhoon forecasting"
+version: "1.2"
+authors:
+  - family-names: "Yu"
+    given-names: "Yao-Hsing"
+repository-code: "https://github.com/yu314-coder/typhoon-predict"
+url: "https://huggingface.co/euler314/typhoon-predict"
+license: MIT

CONTRIBUTING.md

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+# Contributing to TrackFormer
+
+Anyone may inspect, fork, modify and retrain the public release under its MIT license. No contributor agreement or account approval is required to download or use the code and weights. Submitting a GitHub pull request requires your own GitHub account; local use does not.
+
+## Reproduce first
+
+1. Follow `training/trackformer_1_2/README.md` and pin the asset revision from `PUBLICATION.md`.
+2. Verify dataset/checkpoint SHA-256 values before changing anything. Keep original weights and source artifacts unchanged and write experiments into new output directories.
+3. Use `--preflight` for the archive/architecture check and `test_training_cpu.py` for the portable CPU loss/backward check. These reconstruct a writable local mmap, so use your own data copy rather than an active trainer's directory.
+4. For pressure export changes, use the existing `release_tools/test_pressure_field_export.py` checks as applicable. There is no requirement to run the full multi-hour training job for a documentation correction.
+
+## Share a change
+
+Open an issue or pull request at https://github.com/yu314-coder/typhoon-predict . Explain the problem, changed behavior, exact model/data revision and relevant checks. Document new dependencies and retain third-party notices. Never commit credentials, private data or unrelated large caches. New project contributions should use the existing MIT license; third-party material must keep its own license and attribution.
+
+For scientific claims, preserve issue-time causal inputs and whole-storm partitions; keep future observations as labels only. Compare on identical cases, grids, times and masks. Report misses and false alarms as well as mean errors, and identify previously reused development data. Do not infer forecast skill from a smoother map or training loss. Label changed architectures and weights as new candidates rather than silently replacing released 1.2.
+
+The MIT license permits broad reuse; scientific review criteria apply to what this repository accepts or claims, not to your permission to fork or experiment independently.

OPENNESS.md

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+# Open access and reuse
+
+TrackFormer 1.2 is publicly accessible without account approval, a download gate, a paid API or Google Drive permissions. Project code and project model weights/checkpoints are distributed under the [MIT license](LICENSE). You may use, study, modify, retrain, redistribute, sublicense and sell copies, including commercial use, subject to retaining the required copyright and permission notice. No research-only or non-commercial restriction is added by this project. The research/safety cautions describe capability limits; they do not replace or narrow the MIT license.
+
+## What is public
+
+| Component | Public location |
+| --- | --- |
+| Architecture, inference and physical pressure exporter | `models/trackformer_1_2_field/` on both repositories |
+| Original training source, losses, sampler, curriculum and resume logic | `training/trackformer_1_2/source/v173/` |
+| Portable launcher, configuration, tests and exact saved training history | `training/trackformer_1_2/` |
+| Exact six derived dataset files, masks and original dataset manifest | Hugging Face: `training/trackformer_1_2/data/` |
+| Original warm start and selected full training checkpoint with optimizer/RNG state | Hugging Face: `training/trackformer_1_2/checkpoints/` |
+| Hashes and anonymous checksum-verifying asset downloader | `training/trackformer_1_2/assets.json` and `download_assets.py` |
+| Benchmark protocols, measured results and figure provenance | `evaluation/`, `docs/` and `release_tools/` |
+
+[GitHub source](https://github.com/yu314-coder/typhoon-predict) · [Hugging Face source and binary assets](https://huggingface.co/euler314/typhoon-predict) · [Training instructions](training/trackformer_1_2/README.md) · [Immutable publication links](training/trackformer_1_2/PUBLICATION.md) · [Contributing](CONTRIBUTING.md)
+
+Large arrays and optimizer checkpoints are hosted on Hugging Face and linked from GitHub, rather than duplicated in ordinary Git history. Downloads are standard public HTTPS; the asset downloader uses Python's standard library, verifies exact hashes and requires no token. Training/inference still require the documented open-source runtime dependencies. There is no hosted-service dependency in the published training launcher.
+
+## Upstream data terms
+
+NOAA/IBTrACS and Copernicus/ERA5 data retain their own provenance, attribution and applicable upstream terms. The project's MIT license cannot remove third-party conditions. See [the source notices](training/trackformer_1_2/THIRD_PARTY_NOTICES.md) and [data provenance](docs/trackformer_1_2_training_data.md). The root `license: mit` metadata describes project software and model artifacts, not a blanket relicensing of all weather data.
+
+## What is not claimed
+
+The frozen derived arrays and their exact hashes are public, but the original raw IBTrACS snapshot/download ID was not recorded. A complete original runtime lockfile, original process argv and earlier warm-start training history have not been recovered. The archived recipe and warm-start checkpoint make the released v173 path runnable; bitwise full retraining across devices has not been demonstrated. The report selects epoch 4, while the saved aggregate validation-loss minimum is epoch 2. Previously inspected tests and examples are development evidence, not an untouched holdout.
+
+Public availability is not a claim that every earlier 1.2 experiment or later rainfall experiment is this released model. No unavailable artifact, qualification result or score is invented to claim complete reproducibility. These remaining gaps stay visible so others can audit and improve the release.

README.md

@@ -1,3 +1,21 @@
+---
+license: mit
+language:
+  - en
+  - zh
+library_name: pytorch
+tags:
+  - pytorch
+  - weather-forecasting
+  - tropical-cyclones
+  - typhoon
+  - atmospheric-pressure
+  - geospatial
+  - time-series
+  - open-source
+  - reproducible-research
+---
+
 # TrackFormer 1.2 — pressure maps and five-day typhoon forecasts
 
 An AI weather-research model designed to be trained and run on an ordinary home computer.
@@ -128,6 +146,10 @@ Routes and central pressures are averaged separately. Physical member pressure f
 - **NVIDIA GeForce RTX 3070 / CUDA:** verified execution of the completed DeepMind reference benchmark. This is hardware, not a software package.
 - **Python / PyTorch**, NumPy, SciPy, xarray, netCDF4 and Matplotlib support modelling, data handling and figures. The Chinese report uses XeLaTeX / xeCJK.
 
+## Open access and reuse
+
+Code and project weights are **MIT licensed**, including modification, redistribution and commercial use. Source, training arrays and checkpoints are public without account approval or a download gate. [Openness inventory and remaining provenance gaps](OPENNESS.md) · [Contributing](CONTRIBUTING.md) · [Software citation](CITATION.cff). Upstream weather data retain their own terms and attribution.
+
 ## Train the released 1.2 architecture
 
 The [open training package](training/trackformer_1_2/README.md) contains the original trainer, architecture, losses, sampler, resume logic, tests, data-preparation utilities and explicit launch configuration. The **exact six derived training-array files**, original manifest, **v168 epoch-3 warm start** and selected **v173 epoch-4 full checkpoint with optimizer/RNG state** are public on [Hugging Face](https://huggingface.co/euler314/typhoon-predict/tree/main/training/trackformer_1_2). A checksum-verifying downloader works without the old Drive setup. Source and instructions are mirrored on both repositories; large binary assets are served by Hugging Face and linked from GitHub.