🕒 Simulate file timestamps in Linux/Unix applications by hijacking access functions, enabling seamless time manipulation without altering the filesystem.

Foundational tools for BCG X's data science packages.

simstudy: Illuminating research methods through data generation

Симулятор рыночного мониторинга с автоматическим сбором данных и SQL-аналитикой (MAX, MIN, AVG). 📈🗄

[ECCV 2024] Brain-ID: Learning Contrast-agnostic Anatomical Representations for Brain Imaging

An application for randomly generating telecommunication payment data.

A CDISC-compliant synthetic clinical trial simulation (Python/R) for Oncology Data Science and Regulatory submission practice.

R package designed to generate, distribute, and evaluate enzyme kinetics data for teaching and assessment in biochemistry and related laboratory courses.

Code for the paper "Temporal Causal-based Simulation for Realistic Time-series Generation".

Temporal Causal-based Simulation (TCS)

Quantitative MRI Made Easy with qMRLab: MRI software for data Simulation, analysis and visualization

🧠 Model-driven synthetic test data for CI/CD and analytics - deterministic, privacy-preserving, and domain-aware. Includes Python APIs, XML pipelines, and MCP/IDE integration to orchestrate realistic datasets for finance, healthcare, and other regulated environments.

Data Forge — a modern data stack playground to practice flows and best practices, not just tools. Spark, Trino, Kafka, Iceberg, ClickHouse, Airflow, MinIO, Superset — all wired together locally with Docker Compose.

This project simulates server performance data (CPU, Memory, Network Traffic) for multiple servers across time intervals and applies K-Nearest Neighbors (KNN) for similarity analysis and prediction.

R Package With Shiny App to Perform and Visualize Clustering of Count Data via Mixtures of Multivariate Poisson-log Normal Model

High-performance, multi-stream data ingestion simulator Built for testing real-time pipelines, PB-scale throughput, and stream processing systems like Kafka, Flink, FastAPI, and Iceberg.

A Python-based tool for generating customizable synthetic datasets tailored for Generative AI applications. Built with simplicity and flexibility in mind, this project helps researchers and developers simulate realistic data for training, testing, and experimentation.

Agent4Edu: Generating Learner Response Data by LLM-based Agents for Intelligent Education Systems (AAAI 2025)

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