Add dataset card

README.md

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+---
+license: cc-by-4.0
+task_categories:
+  - audio-classification
+pretty_name: Beehive States (official HEAR folds)
+size_categories:
+  - 1K<n<10K
+configs:
+  - config_name: fold0
+    data_files:
+      - split: train
+        path: fold0/train-*
+      - split: validation
+        path: fold0/validation-*
+      - split: test
+        path: fold0/test-*
+  - config_name: fold1
+    data_files:
+      - split: train
+        path: fold1/train-*
+      - split: validation
+        path: fold1/validation-*
+      - split: test
+        path: fold1/test-*
+---
+
+# Beehive States for MTEB
+
+This artifact contains the two official full-mode Beehive States folds from
+HEAR release 2021.3. Each fold contains the same 576 ten-minute, mono, 48 kHz
+PCM16 NU-Hive recordings, but preserves HEAR's released preprocessing output
+and cross-hive split independently. Fold 0 trains and validates on Hive 1 and
+tests on Hive 3; fold 1 reverses the hives. Labels are `NOQUEEN` and `QUEEN`.
+
+The 576 IDs and labels agree across folds, but none of the corresponding WAV
+payloads are byte-identical. Consumers must therefore use the audio from the
+selected configuration instead of treating the two folds as split metadata
+over one shared audio table.
+
+The source recordings are from
+[NU-Hive Zenodo record 2667806](https://zenodo.org/records/2667806). The exact
+preprocessed archives are from
+[HEAR Zenodo record 6332517](https://zenodo.org/records/6332517):
+
+| Fold | Archive | Size | MD5 |
+| --- | --- | ---: | --- |
+| fold0 | `hear2021-beehive_states_fold0-v2-full-48000.tar.gz` | 20,806,900,496 | `f9e045f9b2ddf5643edc1143304c80aa` |
+| fold1 | `hear2021-beehive_states_fold1-v2-full-48000.tar.gz` | 20,806,854,397 | `8bced7ce8336bf23fd61c61f04aee109` |
+
+`beehive_artifact_manifest.json` records the immutable source record, HEAR
+preprocessing revision, split, label, and SHA-256 for every embedded WAV.
+
+## License and citation
+
+The source and HEAR release declare CC BY 4.0. Users must preserve attribution
+to the NU-Hive and HEAR authors. Cite:
+
+```bibtex
+@inproceedings{nolasco2019beehive,
+  author = {Ines Nolasco and Alessandro Terenzi and Stefania Cecchi and Simone Orcioni and Helen L. Bear and Emmanouil Benetos},
+  booktitle = {2019 IEEE International Conference on Acoustics, Speech and Signal Processing},
+  doi = {10.1109/ICASSP.2019.8682981},
+  pages = {8256--8260},
+  title = {Audio-based Identification of Beehive States},
+  year = {2019}
+}
+```