Update README.md

Add arXiv link and Quick Start section

README.md

@@ -140,12 +140,13 @@ The pipeline ensures perfect note-by-note score-performance synchronization for
 
 ## Related Resources
 
-*	**Publication:** https://transactions.ismir.net/articles/10.5334/tismir.333
+*	**TISMIR:** https://doi.org/10.5334/tismir.333
+*	**arXiv:** https://arxiv.org/abs/2605.06627
 *   **GitHub:** https://github.com/ilya16/PianoCoRe
-*	**Zenodo:** https://zenodo.org/records/19186016
+*	**Zenodo:** https://doi.org/10.5281/zenodo.19186016
 
 **Note**: This Hugging Face version stores data in compressed Parquet files with raw bytes. 
-If you prefer plain MIDI files in a directory structure, please use the [Zenodo Mirror](https://doi.org/10.5281/zenodo.19186016).
+If you prefer the original MIDI files in a directory structure, please use the [Zenodo Mirror](https://doi.org/10.5281/zenodo.19186016).
 
 ## Dataset Tiers
 
@@ -171,6 +172,68 @@ To support different research applications, the dataset is organized into tiered
 
   *Applications*: expressive piano performance rendering, performance-to-score transcription.
 
+## Quick Start
+
+Use the following example code to access the metadata:
+```python
+from datasets import load_dataset
+
+# Load the train split of the PianoCoRe dataset (streaming mode)
+dataset = load_dataset("SyMuPe/PianoCoRe", split="train", streaming=True)
+
+# Optionally drop heavy columns with bytes (e.g., MusicXML/MXL data)
+# dataset = dataset.remove_columns(["score_xml_bytes"])
+
+# Filter for high-confidence samples (PianoCoRe-A*)
+dataset_a_star = dataset.filter(lambda x: x["tier_a_star"])
+
+# Take one sample
+sample = next(iter(dataset_a_star))
+print(f"ID: {sample['id']}")  
+print(f"Work: {sample['composer']} - {sample['composition']}" + (f" - {sample['movement']}" if sample["movement"] else ""))
+print(f"Score: {sample['score_midi_path']}")
+print(f"Performance: {sample['performance_midi_path']}\n")
+```
+
+The **raw** MIDI data and alignments can be accessed using:
+```python
+from symusic import Score
+from symupe import Alignment
+
+# Load raw score and performance MIDI data
+score_midi = Score.from_midi(sample["score_midi_bytes"]) if sample["score_midi_bytes"] is not None else None
+performance_midi = Score.from_midi(sample["performance_midi_bytes"])
+print(f"Score MIDI: {score_midi}")
+print(f"Performance MIDI: {performance_midi}")
+
+# Load raw alignment
+if sample['raw_alignment_bytes'] is not None:
+    raw_alignment = Alignment.from_bytes(sample["raw_alignment_bytes"])
+    print(f"Raw alignment: {len(raw_alignment)} total and {raw_alignment.num_full_pairs} matched pairs")
+
+    # Save in a human-readable format
+    # raw_alignment.save("alignment.txt")
+```
+
+The **refined** MIDI data and alignments can be accessed using:
+```python
+import io
+import numpy as np
+from symusic import Score
+
+if sample["refined_performance_midi_bytes"] is not None:
+    # Load refined score and performance MIDI data
+    score_midi = Score.from_midi(sample["refined_score_midi_bytes"])
+    performance_midi = Score.from_midi(sample["refined_performance_midi_bytes"])
+    print(f"Refined Score MIDI: {score_midi}")
+    print(f"Refined Performance MIDI: {performance_midi}")
+
+    # Load refined alignment
+    refined_alignment = np.load(io.BytesIO(sample["refined_alignment_bytes"]))
+    print(f"Refined Alignment:")
+    print(f"  performance indices: {refined_alignment['perf_idx']}")
+    print(f"  interpolation mask: {refined_alignment['interpolated']}")
+```
 ---
 
 ## Dataset Metadata