Update README.md

README.md

@@ -1,5 +1,9 @@
 ---
+language: en
 license: mit
+size_categories:
+- 100k<n<1M
+pretty_name: 'Enzyme Catalysis Database'
 tags:
 - enzymes
 - kinetics
@@ -38,7 +42,7 @@ configs:
 - **Paper:** [CatPred: A comprehensive framework for deep learning in vitro enzyme kinetic parameters](https://www.nature.com/articles/s41467-025-57215-9)
 - **GitHub:** https://github.com/maranasgroup/CatPred-DB
 
-### Dataset Description
+## Dataset Description
 
 CatPred-DB contains the benchmark datasets introduced alongside the CatPred deep learning framework for predicting in vitro enzyme kinetic parameters. The datasets cover three key kinetic parameters:
 
@@ -50,6 +54,8 @@ CatPred-DB contains the benchmark datasets introduced alongside the CatPred deep
 
 These datasets were curated to address the lack of standardized, high-quality benchmarks for enzyme kinetics prediction, with particular attention to coverage of out-of-distribution enzyme sequences.
 
+---
+
 ## Uses
 
 ### Direct Use
@@ -61,11 +67,15 @@ The dataset can be used to train or benchmark other machine learning models for
 ### Out-of-Scope Use
 This dataset reflects in vitro measurements and may not generalize to in vivo conditions. It should not be used as a sole basis for clinical or industrial enzyme selection without additional experimental validation.
 
+---
+
 ## Dataset Structure
 The repository contains:
 - datasets/ – CSV files for *k*cat, *K*m, and *K*i with train/test splits
 - scripts/ – Preprocessing and utility scripts
 
+---
+
 ## Data Fields
 
 Each entry typically includes:
@@ -89,10 +99,14 @@ Each entry typically includes:
 | `sequence_80cluster` | Sequence cluster ID at 80% identity threshold |
 | `sequence_99cluster` | Sequence cluster ID at 99% identity threshold |
 
+---
+
 ## Source Data
 
 Data was compiled and curated from public biochemical databases, including BRENDA and SABIO-RK, as described in the CatPred publication. Splits were designed to evaluate generalization to enzyme sequences dissimilar to those seen during training.
 
+---
+
 ## Dataset Splits
 Each kinetic parameter (kcat, km, ki) has two split strategies, described below.
 
@@ -139,9 +153,34 @@ This three-stage approach is standard practice in ML: you only touch the test se
 and the combined files make it easy to retrain on progressively more data as you move
 from experimentation to deployment.
 
-## Quick Start Guide
+---
+
+## Quickstart Usage 
+
+### Install HuggingFace Datasets package
 
+Each subset can be loaded into pytho using the HuggingFace [datasets](https://huggingface.co/docs/datasets/index) library. First, from the command line, install the `datasets` library
 
+```
+$ pip install datasets
+```
+
+then, from within python load the datasets library
+
+```
+>>> import datasets
+```
+
+### Load model datasets
+
+To load one of the `CatPred-DB` datasets, use `datasets.load_dataset(...):
+
+```
+>>> dataset_tag = ""
+>>> dataset_models =
+```
+
+---
 
 ## Citation