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README.md

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+# Dataset Card for "Promoter Validation Data"
+
+## Table of Contents
+
+- [Overview](#overview)
+- [Dataset Description](#dataset-description)
+  - [Data Collection and Processing](#data-collection-and-processing)
+  - [Dataset Structure](#dataset-structure)
+- [Data Partitioning and Utilization](#data-partitioning-and-utilization)
+- [Dataset Splits](#dataset-splits)
+- [Dataset Fields](#dataset-fields)
+- [Use Cases & Limitations](#use-cases--limitations)
+- [How to Use](#how-to-use)
+- [Citation](#citation)
+- [Contact](#contact)
+
+## Overview
+
+This dataset contains promoter sequences used for validation purposes in genetic research, focusing on prokaryotic promoters. It serves to facilitate the study of gene expression regulation, providing a comprehensive set of promoter sequences from various organisms.
+
+## Dataset Description
+
+### Data Collection and Processing
+
+- **Data Source**: The positive samples, known promoters, are primarily drawn from the Prokaryotic Promoter Database (PPD), containing experimentally validated promoter sequences from 75 organisms. Non-promoter sequences are obtained from the NCBI RefSeq database, sampled specifically from CDS regions.
+- **Preprocessing**: The dataset includes non-promoter sequences constructed via higher and zero-order Markov chains, which mirror compositional characteristics of known promoters. An independent test set based on _E.coli_ sigma70 promoters is also included.
+
+### Dataset Structure
+
+- **Dataset Splits**: The dataset is systematically divided into training, validation, and test subsets.
+- **Data Fields**:
+  - `segment_id`: Unique identifier for each segment.
+  - `ppd_original_SpeciesName`: Original species name from the PPD.
+  - `Strand`: The strand of the DNA sequence.
+  - `segment`: The DNA sequence of the promoter region.
+  - `label`: The label indicating whether the sequence is a promoter or non-promoter.
+  - `L`: Length of the DNA sequence.
+  - `prom_class`: The class of the promoter.
+  - `y`: Binary label indicating the presence of a promoter.
+
+## Data Partitioning and Utilization
+
+To ensure comprehensive evaluation, the dataset was split randomly into three parts:
+1. **Training set**: 80% of the total data for model development and training.
+2. **Validation set**: 10% of the data, aiding in fine-tuning model parameters.
+3. **Test set**: Remaining 10% of the data, crucial for unbiased model performance evaluation.
+
+## Dataset Splits
+
+- **Training Set**: Primary dataset used for model training.
+- **Test Set (Sigma70)**: Independent test set focusing on _E.coli_ sigma70 promoters.
+- **Multispecies Set**: Additional test set including various species, ensuring generalization across different organisms.
+
+## Use Cases & Limitations
+
+This dataset is intended for use in training models for promoter prediction and understanding gene expression regulation. Limitations include the specific focus on prokaryotic promoters and the method of sequence generation using Markov chains.
+
+## How to Use
+
+You can load the dataset using the Hugging Face `datasets` library:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("your_dataset_name")