Add metadata (task categories, license, tags, language) and improve dataset card structure

README.md

@@ -1,12 +1,30 @@
+---
+language:
+- en
+task_categories:
+- audio-to-audio
+- automatic-speech-recognition
+- audio-classification
+license: cc-by-nc-4.0
+tags:
+- audio
+- rir
+- acoustics
+- dereverberation
+- robust-asr
+- simulation
+- room-acoustics
+---
+
 # RIR-Mega
 
+[Paper](https://huggingface.co/papers/2510.18917) | [Code](https://github.com/mandip42/rirmega) | [Project page (Zenodo)](https://doi.org/10.5281/zenodo.17387402)
+
 RIR-Mega provides thousands of simulated room impulse responses for research in dereverberation, robust speech recognition, and acoustic scene analysis.
 This Hugging Face release hosts a lightweight, representative subset — 1 000 linear-array and 3 000 circular-array RIRs — for quick exploration, tutorials, and reproducible baselines.
 
 The complete 50 000-RIR archive is permanently preserved on Zenodo and described in the accompanying paper:
 
-🗂 Full dataset (Zenodo DOI): (https://doi.org/10.5281/zenodo.17387402)
-💾 Code + tools: (https://github.com/mandip42/rirmega)
 🤗 Subset for streaming: (https://huggingface.co/datasets/mandipgoswami/rirmega)
 📦 Technical Paper: ([arxiv.org/abs/2510.18917](https://doi.org/10.48550/arXiv.2510.18917))
 
@@ -18,28 +36,28 @@ The complete 50 000-RIR archive is permanently preserved on Zenodo and described
 - *(optional)* `data-mini/` — tiny subset for quick demos and Spaces
 
 ## Contents
-| Folder                                    | Description                                                                    |
+| Folder | Description |
 | ----------------------------------------- | ------------------------------------------------------------------------------ |
-| `data/audio/linear`                       | 1 000 RIRs simulated for linear microphone arrays                              |
-| `data/audio/circular`                     | 3 000 RIRs simulated for circular arrays                                       |
+| `data/audio/linear` | 1 000 RIRs simulated for linear microphone arrays |
+| `data/audio/circular` | 3 000 RIRs simulated for circular arrays |
 | `data/metadata/metadata.csv` / `.parquet` | Compact schema linking each file to acoustic metrics and simulation parameters |
-| `rirmega/dataset.py`                      | Hugging Face Datasets loader (supports streaming)                              |
-| `benchmarks/rt60_regression/`             | Baseline RT60 regression example                                               |
-| `scripts/`                                | Validation + checksum utilities                                                |
-| `figs/`                                   | Overview and validation plots for reference                                    |
+| `rirmega/dataset.py` | Hugging Face Datasets loader (supports streaming) |
+| `benchmarks/rt60_regression/` | Baseline RT60 regression example |
+| `scripts/` | Validation + checksum utilities |
+| `figs/` | Overview and validation plots for reference |
 
 
 ## 📦 Schema (compact)
-| Column                                 | Meaning                                                                       |
+| Column | Meaning |
 | -------------------------------------- | ----------------------------------------------------------------------------- |
-| `id`                                   | unique identifier                                                             |
-| `family`                               | “linear” or “circular”                                                        |
-| `split`                                | train / valid / test                                                          |
-| `fs`                                   | sampling rate (Hz)                                                            |
-| `wav`                                  | relative path to audio file                                                   |
-| `room_size`, `absorption`, `max_order` | simulation parameters                                                         |
-| `metrics`                              | JSON string with `rt60`, `drr_db`, `c50_db`, `c80_db`, and band-limited RT60s |
-| `rng_seed`                             | random seed for reproducibility                                               |
+| `id` | unique identifier |
+| `family` | “linear” or “circular” |
+| `split` | train / valid / test |
+| `fs` | sampling rate (Hz) |
+| `wav` | relative path to audio file |
+| `room_size`, `absorption`, `max_order` | simulation parameters |
+| `metrics` | JSON string with `rt60`, `drr_db`, `c50_db`, `c80_db`, and band-limited RT60s |
+| `rng_seed` | random seed for reproducibility |
 
 ## 🚀 Getting started
 
@@ -71,9 +89,9 @@ python benchmarks/rt60_regression/train_rt60.py --target rt60
 ## Technical Validation 
 A random subset of 1 000 samples was analyzed for internal consistency.
 The RT60 values derived from Schroeder energy decay curves correlated strongly with the metadata values:
-| Metric                 | Correlation | MAE (s) | RMSE (s) |
+| Metric | Correlation | MAE (s) | RMSE (s) |
 | ---------------------- | ----------- | ------- | -------- |
-| RT60 (metadata vs EDC) | 0.96        | 0.013   | 0.022    |
+| RT60 (metadata vs EDC) | 0.96 | 0.013 | 0.022 |
 
 
 ### Reference numbers (example)
@@ -104,11 +122,4 @@ Please cite the dataset:
   doi={10.5281/zenodo.17387402},
   url={https://github.com/mandip42/rirmega}
 }
-```
-
-## 📜 Licenses
-- Metadata & docs: CC BY 4.0
-- Audio: specify here (e.g., CC BY-NC 4.0). If mixed, list per-subset.
-
-## 🏷️ Tags
-audio, rir, acoustics, dereverberation, robust-asr, simulation, room-acoustics
+```