Fix paper link and example usage in README

README.md

@@ -8,14 +8,51 @@ task_categories:
 
 # LibriBrain (Sherlock Holmes 1–7)
 
-[Paper](https://huggingface.co/papers/2602.02494) | [Code](https://github.com/neural-processing-lab/MEG-XL)
+[Paper](https://huggingface.co/papers/2506.02098) | [Code](https://github.com/neural-processing-lab/pnpl)
 
 This repository contains the LibriBrain data organised by book: MEG recordings (`.h5`), event annotations (`.tsv`), and the audiobook stimulus audio (`.wav`). 
 
-LibriBrain is used as a fine-tuning dataset in the paper ["MEG-XL: Data-Efficient Brain-to-Text via Long-Context Pre-Training"](https://huggingface.co/papers/2602.02494) to evaluate word decoding from brain data.
+LibriBrain was first open-sourced as part of the [2025 PNPL Competition](https://libribrain.com/).
+
+In addition, LibriBrain is used as a fine-tuning dataset in the paper ["MEG-XL: Data-Efficient Brain-to-Text via Long-Context Pre-Training"](https://huggingface.co/papers/2602.02494) to evaluate word decoding from brain data.
 
 ## Sample Usage
+The easiest way to get started with the dataset is using the [pnpl Python library](https://github.com/neural-processing-lab/pnpl). There, the following two datasets are available:
+
+### LibriBrainSpeech
+This wraps the LibriBrain dataset for use in speech detection problems.
+```python
+from pnpl.datasets import LibriBrainSpeech
+
+speech_example_data = LibriBrainSpeech(
+    data_path="./data/",
+    partition="train"
+)
+
+sample_data, label = speech_example_data[0]
+
+# Print out some basic info about the sample
+print("Sample data shape:", sample_data.shape)
+print("Label shape:", label.shape)
+```
 
+### LibriBrainPhoneme
+This wraps the LibriBrain dataset for use in phoneme classification problems.
+```python
+from pnpl.datasets import LibriBrainPhoneme
+
+phoneme_example_data = LibriBrainPhoneme(
+    data_path="./data/",
+    partition="train"
+)
+sample_data, label = phoneme_example_data[0]
+
+# Print out some basic info about the sample
+print("Sample data shape:", sample_data.shape)
+print("Label shape:", label.shape)
+```
+
+### Usage in MEG-XL
 To fine-tune the MEG-XL model on the LibriBrain dataset for word decoding, you can use the following command from the [official repository](https://github.com/neural-processing-lab/MEG-XL):
 
 ```bash
@@ -24,7 +61,8 @@ python -m brainstorm.evaluate_criss_cross_word_classification \
   model.criss_cross_checkpoint=/path/to/your/checkpoint.ckpt
 ```
 
-Note: You will need to adjust the dataset paths in the configuration files to point to your local download of the data.
+Note: For the MEG-XL repo, you will need to adjust the dataset paths in the configuration files to point to your local download of the data. The pnpl library includes automatic downloads from HuggingFace.
+
 
 ## Repository structure