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README.md

@@ -8,6 +8,8 @@ tags:
 - DOM
 - formula-assignment
 - knn
+- dissolved-organic-matter
+- molecular-formula
 pretty_name: DOM Formula Assignment Dataset
 size_categories:
 - n<1K
@@ -15,11 +17,23 @@ size_categories:
 
 # DOM Formula Assignment Dataset
 
-This dataset contains training and testing data for Dissolved Organic Matter (DOM) formula assignment using K-Nearest Neighbors (KNN) machine learning models.
+[![GitHub](https://img.shields.io/badge/GitHub-pcdslab/dom--formula--assignment--using--ml-blue?logo=github)](https://github.com/pcdslab/dom-formula-assignment-using-ml)
+
+**Training and Testing Data for Machine Learning-Based Molecular Formula Assignment in Fulvic Acid DOM Mass Spectra**
+
+> **Paper**: Under review
+
+---
+
+## Abstract
+
+Dissolved organic matter (DOM) is a critical component of aquatic ecosystems, with the fulvic acid fraction (FA-DOM) exhibiting high mobility and ready bioavailability to microbial communities. While understanding the molecular composition is a vital area of study, the heterogeneity of the material, with a vast number of diverse compounds, makes this task challenging. Existing methods often struggle with incomplete formula assignment or reduced coverage highlighting the need for a better approach. In this study, we developed a machine learning approach using the k-nearest neighbors (KNN) algorithm to predict molecular formulas from ultra-high-resolution mass spectrometry data. The model was trained on chemical formulas assigned to multiple DOM samples using 7 Tesla(7T) and a 21 Tesla(21T) Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) system, and tested on an independent 9.4 T FT-ICR MS Fulvic Acid dataset. A synthetic dataset of plausible elemental combinations (C, H, O, N, S) was also generated to enhance generalization. Our approach achieved a 99.9% assignment rate on the labeled test set and assigned a total of 13,605 formulas for unlabeled peaks compared to the existing approach, which assigned 5914 formulas, achieving up to a 2.3X improvement in formula assignment coverage compared to existing methods.
+
+---
 
 ## Dataset Description
 
-The dataset includes mass spectrometry data from different sources and instruments used to train and evaluate KNN models for automated molecular formula assignment in DOM samples.
+This dataset contains training and testing data for Dissolved Organic Matter (DOM) formula assignment using K-Nearest Neighbors (KNN) machine learning models. The dataset includes mass spectrometry data from different sources and instruments (7T, 21T FT-ICR MS) used to train and evaluate KNN models for automated molecular formula assignment in DOM samples.
 
 ### Dataset Structure
 
@@ -50,10 +64,34 @@ The dataset includes mass spectrometry data from different sources and instrumen
 │   ├── PPFA/                       # Pahokee Peat Fulvic Acid
 │   ├── SRFA2/                      # Suwannee River Fulvic Acid 2
 │   └── SRFA3/                      # Suwannee River Fulvic Acid 3
-│
-└── knn_comparison_summary.csv     # Model performance comparison results
 ```
 
+---
+
+## Data Preview
+
+### Synthetic Data (formula_combinations_100-110.csv)
+```csv
+Formula,Mass_Daltons
+CH9OS2,100.0023
+CHO2N4,100.0027
+C3H3O3N,100.0041
+C2H3O2N3,100.0153
+...
+```
+
+### Testing Set Peaklists (SRFA3_neg_8M_0.5s_5ppm_aFTk_PeakList_Rec.csv)
+```csv
+m/z Exp.,Intensity
+101.0410446,0.002990723
+101.045957,0.006607056
+101.0480263,0.003494263
+101.0494431,0.006072998
+...
+```
+
+---
+
 ## Data Sources
 
 ### Training Data
@@ -90,9 +128,10 @@ Training and testing files contain:
 
 ### CSV Files
 - **Peaklists**: m/z, intensity, and spectral information
-- **Synthetic data**: Formula combinations with element counts
+- **Synthetic data**: Formula combinations with element counts and mass values
 - **Families tables**: Chemical family classifications
-- **Comparison summary**: Model performance metrics
+
+---
 
 ## Usage
 
@@ -105,18 +144,35 @@ This dataset is designed to be used with the DOM KNN models available at:
 from huggingface_hub import hf_hub_download
 import pandas as pd
 
-# Download a specific file
+# Download a specific training file
 file_path = hf_hub_download(
     repo_id="bilalsm/dom-formula-assignment-data",
-    filename="knn_comparison_summary.csv",
+    filename="DOM_training_set_ver2/Table_Harney_River_1.xlsx",
     repo_type="dataset"
 )
 
 # Load with pandas
-df = pd.read_csv(file_path)
+df = pd.read_excel(file_path)
 print(df.head())
 ```
 
+### Download Synthetic Data
+
+```python
+from huggingface_hub import hf_hub_download
+import pandas as pd
+
+# Download synthetic formula combinations
+file_path = hf_hub_download(
+    repo_id="bilalsm/dom-formula-assignment-data",
+    filename="synthetic_data/formula_combinations_200-210.csv",
+    repo_type="dataset"
+)
+
+df = pd.read_csv(file_path)
+print(f"Loaded {len(df)} synthetic formulas")
+```
+
 ### Download Entire Dataset
 
 ```python
@@ -130,6 +186,8 @@ data_path = snapshot_download(
 print(f"Dataset downloaded to: {data_path}")
 ```
 
+---
+
 ## Statistics
 
 - **Total files**: ~82 files (17 Excel, 65 CSV)
@@ -141,30 +199,25 @@ print(f"Dataset downloaded to: {data_path}")
 - **Testing samples**: 3 independent DOM samples
 - **Peaklists**: 9 detailed acquisition files
 
-## Model Performance
-
-The models trained on this dataset achieve:
-
-| Model Type | Assignment Rate | Accuracy |
-|-----------|----------------|----------|
-| Synthetic (synthetic_data) | 99.975% | 99.65% |
-| 7T-21T Ensemble (ver2 + ver3) | 95.355% | 99.84% |
-| 7T Only (ver2) | 81.815% | 99.84% |
-| 21T Only (ver3) | 81.815% | 99.84% |
-
-See `knn_comparison_summary.csv` for detailed results across all model variants.
+---
 
 ## Citation
 
 This dataset supports research on automated formula assignment in DOM analysis using machine learning. A manuscript describing the methodology is currently under review.
 
+---
+
 ## License
 
-This dataset is released under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).
+This model and associated code are released under the CC-BY-NC-ND 4.0 license and may only be used for non-commercial, academic research purposes with proper attribution. Any commercial use, sale, or other monetization of this model and its derivatives, which include models trained on outputs from the model or datasets created from the model, is prohibited and requires prior approval. Downloading the model requires prior registration on Hugging Face and agreeing to the terms of use. By downloading this model, you agree not to distribute, publish or reproduce a copy of the model. If another user within your organization wishes to use the model, they must register as an individual user and agree to comply with the terms of use. Users may not attempt to re-identify the deidentified data used to develop the underlying model. If you are a commercial entity, please contact the corresponding author.
+
+---
 
 ## Contact
 
-For questions or issues with the dataset, please open an issue on the model repository or contact the authors.
+For any additional questions or comments, contact Fahad Saeed (fsaeed@fiu.edu).
+
+---
 
 ## Related Resources