Update README.md

README.md

@@ -6,13 +6,13 @@ tags:
 - chemistry
 - mass-spectrometry
 - isotopic-patterns
-- chlorine
 - tabular
+- chlorine
 pretty_name: Cl-Containing Compound (MS1 Features)
 size_categories:
 - 100K<n<1M
 source_datasets:
-- PubChem (molecular formulas)
+- pubchem
 task_categories:
 - tabular-classification
 configs:
@@ -22,6 +22,12 @@ configs:
     path: 80%_618272_train_binary.rds
   - split: test
     path: 20%_154568_test_binary.rds
+- config_name: csv
+  data_files:
+  - split: train
+    path: train.csv
+  - split: test
+    path: test.csv
 dataset_info:
   features:
     - name: mz0
@@ -42,4 +48,47 @@ dataset_info:
 ---
 
 # Dataset Summary
-Binary classification of chlorine presence from simulated MS1 isotopic features (mz0, int2_o_int0, int1_o_int0, RI2_RI1, mz_2_0, mz_1_0). Splits: 80% train (618,272), 20% test
+Binary classification of chlorine presence using simulated MS1 isotopic patterns. Inputs are six engineered features; label has_cl indicates Cl-containing (1) vs non-Cl (0).
+
+## Data Sources and Generation
+- Simulated MS1 peaks (M, M+1, M+2, M+3, M+4) for PubChem molecular formulas.
+- Counts: 968,442 non-Cl; 386,420 Cl (1Cl: 185,303; 2Cl: 117,566; 3–5Cl: 83,551).
+- Features: mz0, int2_o_int0, int1_o_int0, RI2_RI1, mz_2_0, mz_1_0.
+- Class balancing: downsampled non-Cl to 386,420 (total 772,840).
+- Splits: 80% train (618,272), 20% test (154,568).
+- Files: 968442_non_cl_filter_S10.rds, 386420_cl_data.rds, 80%_618272_train_binary.rds, 20%_154568_test_binary.rds.
+
+## Features
+- mz0: m/z of M
+- int2_o_int0: intensity ratio M+2/M
+- int1_o_int0: intensity ratio M+1/M
+- RI2_RI1: (M+2/M) − (M+1/M)
+- mz_2_0: m/z(M+2) − m/z(M)
+- mz_1_0: m/z(M+1) − m/z(M)
+- has_cl: target label (0/1)
+
+## Usage
+```python
+from datasets import load_dataset
+
+# If you convert to CSV (recommended for Hub viewers)
+ds = load_dataset(
+  "chen1028/Cl-Containing-Compound",
+  name="csv",
+  data_files={"train":"train.csv","test":"test.csv"}
+)
+```
+
+## Citation
+```bibtex
+@article{doi:10.1021/acs.analchem.3c05124,
+  author = {Zhao, Tingting and Wawryk, Nicholas J. P. and Xing, Shipei and Low, Brian and Li, Gigi and Yu, Huaxu and Wang, Yukai and Shen, Qiming and Li, Xing-Fang and Huan, Tao},
+  title = {ChloroDBPFinder: Machine Learning-Guided Recognition of Chlorinated Disinfection Byproducts from Nontargeted LC-HRMS Analysis},
+  journal = {Analytical Chemistry},
+  volume = {96},
+  number = {6},
+  pages = {2590-2598},
+  year = {2024},
+  doi = {10.1021/acs.analchem.3c05124},
+  note = {PMID: 38294426},
+  url = {https://doi.org/10.1021/acs.analchem.3c05124},