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README.md

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-license: cc-by-4.0
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+---
+license: cc-by-4.0
+---
+
+# Nanobody Thermal Stability Dataset
+
+## Dataset Overview
+
+This dataset helps predict how stable nanobody sequences are at different temperatures. Thermal stability is important for nanobody engineering and applications, affecting how well they work in different environments.
+
+The dataset includes two types of stability measurements:
+- Melting temperature (Tm): The temperature at which nanobodies start to unfold
+- Sequence stability: Stability scores based on sequence properties
+
+## Data Collection
+
+This dataset comes from experimental measurements of various nanobody sequences. The data is collected from published scientific literature and laboratory measurements, then stratified split based on tm value.
+
+## Dataset Structure
+
+The dataset is split into training, validation, and test sets:
+- `train_tm.csv`, `val_tm.csv`, `test_tm.csv`: Melting temperature data
+- `train_seq.csv`, `val_seq.csv`, `test_seq.csv`: Sequence stability data
+
+### File Format
+
+Each CSV file contains these columns:
+- `seq`: Nanobody amino acid sequence
+- `label`: Thermal stability value (melting temperature or stability score)
+
+## Uses and Limitations
+
+### Uses
+- Develop machine learning models to predict nanobody thermal stability
+- Help design more stable nanobodies
+- Provide reference data for nanobody research
+
+### Limitations
+- Limited dataset size may not represent all nanobody families
+- Experimental conditions may affect measurements
+- Models should account for data distribution characteristics
+
+## Evaluation Metrics
+
+Model performance is evaluated using:
+- Spearman correlation
+- R² (coefficient of determination)
+- Root Mean Squared Error (MSE)
+- Mean Absolute Error (MAE)