README.md

metadata

license: apache-2.0
task_categories:
  - text-generation
language:
  - code
tags:
  - code
  - scientific-computing
  - domain-specific
  - chemistry
  - biology
  - physics
size_categories:
  - 1M<n<10M

DATA1: Domain-Specific Code Dataset

Dataset Overview

DATA1 is a large-scale domain-specific code dataset focusing on code samples from interdisciplinary fields such as biology, chemistry, materials science, and related areas. The dataset is collected and organized from GitHub repositories, covering 178 different domain topics with over 1.1 billion lines of code.

Dataset Statistics

• Total Datasets: 178 CSV files
• Total Data Size: ~115 GB
• Total Lines of Code: Over 1.1 billion lines
• Data Format: CSV (Comma-Separated Values)
• Encoding: UTF-8

Dataset Structure

Each CSV file corresponds to a specific domain topic, with the naming format dataset_{Topic}.csv, where {Topic} is the domain keyword (e.g., Protein, Drug, Genomics).

Data Field Description

Each CSV file contains the following fields:

Field Name	Type	Description
keyword	String	Domain keyword used to identify the domain of the code sample
repo_name	String	GitHub repository name (format: owner/repo)
file_path	String	Relative path of the file in the repository
file_extension	String	File extension (e.g., .py, .java, .cpp)
file_size	Integer	File size in bytes
line_count	Integer	Number of lines of code in the file
content	String	Complete file content
language	String	Programming language (e.g., Python, Java, C++)

Domain Categories

The dataset covers the following major domain categories:

Biology-Related

• Molecular Biology: Protein, DNA, RNA, Gene, Enzyme, Receptor, Ligand
• Cell Biology: Cell_biology, Single_cell, Cell_atlas, Organoid
• Genomics: Genomics, Genotype, Phenotype, Epigenetics, Metagenomics
• Transcriptomics: Transcriptomics, Spatial_Transcriptomics, Transcription, Translation
• Proteomics: Proteomics, Protein_Protein_Interactions, Folding
• Metabolomics: Metabolomics, Metabolic, Lipidomics, Glycomics
• Systems Biology: System_biology, Signaling, Pathway, Networks

Chemistry-Related

• Computational Chemistry: Computational_Chemistry, Quantum_Chemistry, DFT, QM_MM
• Medicinal Chemistry: Drug, ADMET, QSAR, Docking, Lead_discovery, Lead_optimization
• Materials Chemistry: Material, Crystal, Conformation, Chemical_space
• Reaction Chemistry: Reaction, Kinetics, Mechanism, Redox

Medicine and Pharmacology

• Pharmacology: Pharmacology, Pharmacokinetics, Pharmacogenomics, Pharmacogenetics
• Medicine: Medicine, Disease, Diagnostics, Pathology, Vaccine
• Toxicology: Toxicology, Biomarker, Marker

Computational Methods

• Machine Learning: Transformer, GAN, VAE, Diffusion, Flow_matching, Reinforcement_learning
• Quantum Computing: Quantum_mechanics, Quantum_biology, Electronic_structure
• Modeling Methods: Modeling, Multi_scale_modeling, Agent_based_model, Stochastic_modeling
• Numerical Methods: Monte_Carlo, Finite_element_method, Phase_field_technique

Other Specialized Fields

• Bioinformatics: Bioinformatics, Cheminformatics, Next_generation_sequencing
• Bioengineering: Bioengineering, Biotechnology, Biosensors
• Immunology: Immunology, Antibody, Antigen, Antagonist
• Virology: Viral, Pandemic, Pathogens, AMR (Antimicrobial Resistance)

Data Source

The data is collected from open-source repositories on GitHub through the following process:

1. Keyword Search: Search for relevant repositories on GitHub using domain-specific keywords
2. Repository Filtering: Filter repositories based on relevance scores and code quality
3. File Extraction: Extract code files from filtered repositories
4. Categorization: Classify files into corresponding topic datasets based on keywords and domain characteristics

Dataset Characteristics

1. Wide Domain Coverage: Covers multiple interdisciplinary fields including biology, chemistry, materials science, and medicine
2. Diverse Code Types: Includes multiple programming languages such as Python, Java, C++, R, and MATLAB
3. Large Scale: Over 1.1 billion lines of code with a total data size of 115 GB
4. Structured Storage: Each domain topic is stored independently as a CSV file for convenient on-demand usage
5. Rich Metadata: Contains comprehensive metadata including repository information, file paths, and language types

Usage Guidelines

Data Loading

import pandas as pd

# Load dataset for a specific domain
df = pd.read_csv('dataset_Protein.csv')

# View basic dataset information
print(f"Dataset size: {len(df)} files")
print(f"Programming language distribution: {df['language'].value_counts()}")
print(f"File type distribution: {df['file_extension'].value_counts()}")

Data Filtering

# Filter by programming language
python_files = df[df['language'] == 'Python']

# Filter by file size (e.g., files smaller than 100KB)
small_files = df[df['file_size'] < 100000]

# Filter by line count
medium_files = df[(df['line_count'] > 50) & (df['line_count'] < 1000)]

Domain-Specific Analysis

# Analyze code characteristics for a specific domain
protein_df = pd.read_csv('dataset_Protein.csv')
print(f"Number of code files in Protein domain: {len(protein_df)}")
print(f"Average file size: {protein_df['file_size'].mean():.2f} bytes")
print(f"Average line count: {protein_df['line_count'].mean():.2f} lines")

Important Notes

1. File Size: Some dataset files are large (up to several GB), please be mindful of memory usage when loading
2. Encoding: All files use UTF-8 encoding; ensure proper handling of special characters if encountered
3. Data Quality: Data is sourced from public repositories and may vary in code quality; preprocessing is recommended before use
4. License Compliance: Please comply with the license requirements of the original repositories when using the data