De-anonymize and polish README: add ICML 2026 paper info, arXiv DOI, cross-links to vocsim/* repos

README.md

@@ -26,57 +26,82 @@ configs:
   data_files:
   - split: train
     path: data/train-*
+license: cc-by-4.0
+tags:
+- audio
+- animal-vocalization
+- ultrasonic-vocalization
+- mouse
+- bioacoustics
+- classification
+- benchmark
+- vocsim
+size_categories:
+- 10K<n<100K
+pretty_name: VocSim — Mouse Strain Classification
 ---
-# Dataset Card for VocSim - Mouse Strain Classification
 
-## Dataset Description
+# VocSim — Mouse Strain Classification
 
-This dataset is used in the **VocSim benchmark** paper for evaluating the ability of neural audio embeddings to distinguish between mouse strains based on their ultrasonic vocalizations (USVs). It contains recordings from C57BL/6J (C57) and DBA/2J (DBA) mouse strains.
+[![GitHub](https://img.shields.io/badge/GitHub-vocsim%2Fbenchmark-black?logo=github)](https://github.com/vocsim/benchmark)
+[![Core dataset](https://img.shields.io/badge/%F0%9F%A4%97%20Core-vocsim%2Fpublic-blue)](https://huggingface.co/datasets/vocsim/public)
+[![License: CC BY 4.0](https://img.shields.io/badge/License-CC%20BY%204.0-blue.svg)](https://creativecommons.org/licenses/by/4.0/)
 
-The primary task associated with this dataset is supervised classification: training a model to predict the correct strain (`label` field) given an audio input or its derived features.
+A companion dataset for the **VocSim** benchmark that tests whether audio embeddings preserve **strain identity** in mouse ultrasonic vocalizations (USVs). It contains pre-segmented USV syllables from **C57BL/6J (C57)** and **DBA/2J (DBA)** mice, sampled at the native 250 kHz so high-frequency structure is preserved.
 
-## Dataset Structure
+> Basha, M., Zai, A. T., Stoll, S., & Hahnloser, R. H. R. *VocSim: A Training-free Benchmark for Zero-shot Content Identity in Single-source Audio.* ICML 2026. [arXiv:2512.10120](https://doi.org/10.48550/arXiv.2512.10120)
 
-### Data Instances
+## Task
 
-A typical example in the dataset looks like this:
+Supervised binary classification: given an audio syllable (or features derived from it), predict the correct strain (`label` ∈ {`C57`, `DBA`}). In the paper we use this dataset to validate that VocSim-top embeddings transfer to a downstream bioacoustic task.
+
+## Schema
 
 ```python
 {
-  'audio': {'path': '/path/to/datasets/mouse_strain/C57/C57_file_001.wav', 'array': array([...], dtype=float32), 'sampling_rate': 250000},
-  'subset': 'mouse_strain',
-  'index': 101,
-  'speaker': 'C57_file_001', # Example speaker/file ID
-  'label': 'C57', # The crucial strain label
-  'original_name': 'C57/C57_file_001.wav' # Example original relative path
+  "audio":  {"array": np.ndarray, "sampling_rate": 250000},
+  "subset": "mouse_strain",
+  "index":  101,
+  "speaker": "C57_file_001",
+  "label":   "C57",                    # target: C57 or DBA
+  "original_name": "C57/C57_file_001.wav"
 }
 ```
-### Citation Information
 
+## Quick start
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("vocsim/mouse-strain-classification-benchmark", split="train")
+print(ds[0])
 ```
-@unpublished{vocsim2025,
-  title={VocSim: A Training-Free Benchmark for Content Identity in Single-Source Audio Embeddings},
-  author={Anonymous},
-  year={2025},
-  note={Submitted manuscript} 
-}
 
-@article{VanSegbroeck2017,
-    author = {Van Segbroeck, Maarten and Knoll, Aaron T. and Levitt, Patricia and Narayanan, Shrikanth},
-    title = "{MUPET}-Mouse Ultrasonic Profile ExTraction: A Signal Processing Tool for Rapid and Unsupervised Analysis of Ultrasonic Vocalizations",
-    journal = {Neuron},
-    volume = {94},
-    number = {3},
-    pages = {465--485.e5},
-    year = {2017},
-    doi = {10.1016/j.neuron.2017.04.018}
+For end-to-end evaluation, use [github.com/vocsim/benchmark](https://github.com/vocsim/benchmark) — see `reproducibility/scripts/mouse_strain.py`.
+
+## Source data
+
+USV recordings and segmentation rely on MUPET (Van Segbroeck et al., 2017). Please cite both that work and the VocSim paper if you use this dataset.
+
+## Citation
+
+```bibtex
+@inproceedings{basha2026vocsim,
+  title     = {VocSim: A Training-free Benchmark for Zero-shot Content Identity in Single-source Audio},
+  author    = {Basha, Maris and Zai, Anja T. and Stoll, Sabine and Hahnloser, Richard H. R.},
+  booktitle = {Proceedings of the 43rd International Conference on Machine Learning (ICML)},
+  year      = {2026},
+  doi       = {10.48550/arXiv.2512.10120}
 }
 
-@misc{mupetwiki2019,
-    author = {{Van Segbroeck}, Maarten and Knoll, Aaron T. and Levitt, Patricia and Narayanan, Shrikanth},
-    title = {MUPET Wiki},
-    year = {2019},
-    howpublished = {\url{https://github.com/mvansegbroeck/mupet/wiki/MUPET-wiki}},
-    note = {Accessed: 2025}
+@article{VanSegbroeck2017,
+  author  = {Van Segbroeck, Maarten and Knoll, Aaron T. and Levitt, Patricia and Narayanan, Shrikanth},
+  title   = {{MUPET}-Mouse Ultrasonic Profile ExTraction: A Signal Processing Tool for Rapid and Unsupervised Analysis of Ultrasonic Vocalizations},
+  journal = {Neuron},
+  volume  = {94},
+  number  = {3},
+  pages   = {465--485.e5},
+  year    = {2017},
+  doi     = {10.1016/j.neuron.2017.04.018}
 }
-```
+```