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| license: apache-2.0 |
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| # *Synthetic AI Developer Productivity Dataset — Behavioral + Cognitive Simulation* |
| *A synthetic data generation resource for modeling behavioral and cognitive dynamics in developers.* |
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| ## 📘 **About This Dataset** |
| This dataset simulates productivity data from **AI-assisted software developers**. It blends behavioral signals, physiological inputs, and productivity metrics to explore the nuanced relationships between **deep work, distractions, caffeine, AI usage, and cognitive strain**. |
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| Created to **push the boundaries of applied machine learning in behavioral productivity modeling**, this **synthetic data generation** dataset is perfect for: |
| ✔ **Binary Classification** |
| ✔ **Regression** |
| ✔ **Clustering** |
| ✔ **Time-Series Analysis** |
| ✔ **Exploratory Data Analysis (EDA)** |
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| **Keyword Focus:** generate synthetic dataset, developer productivity, cognitive modeling, behavioral simulation for LLM training. |
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| ## ✅ **What’s in This Repo?** |
| This repository contains: |
| - ✅ **Synthetic Developer Productivity Dataset (CSV)** → [Download Here](https://huggingface.co/datasets/syncora/developer-productivity-simulated-behavioral-data/blob/main/Developer_Productivity_Synthetic_Syncora.csv) |
| - ✅ **Example Jupyter Notebook for Analysis & Modeling** → [Open Notebook](https://huggingface.co/datasets/syncora/developer-productivity-simulated-behavioral-data/blob/main/developer-productivity%20(1)%20(2)_clean.ipynb) |
| - ✅ **Documentation & Use Cases** for building behavioral ML models. |
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| ## 🧠 **Use Case Context** |
| Modern developers balance AI tools, caffeine, sleep, and coding time while navigating digital distractions. This dataset allows you to simulate and model these real-world trade-offs: |
| - **How much does AI assistance impact productivity?** |
| - **What’s the relationship between caffeine, sleep, and bugs?** |
| - **Can we cluster different developer working styles over time?** |
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| This is a **prime example of synthetic data generation applied to real-world behavioral modeling**. |
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| ## 🔧 **Dataset Format & Details** |
| - **Records:** 500 synthetic daily logs |
| - **Structure:** Tabular CSV (one row per day) |
| - **Target column:** `task_success` (0 = goal not achieved, 1 = goal achieved) |
| - **Date simulation:** Optional (rolling analysis possible via indexing) |
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| ## 📁 **Feature Descriptions** |
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| | Column | Description | |
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| | **hours_coding** | Focused hours of raw coding per day (0–12) | |
| | **coffee_intake_mg**| Caffeine intake in milligrams (0–600) | |
| | **distractions** | Daily distractions count (Slack, meetings, etc.) (0–10) | |
| | **sleep_hours** | Hours of sleep the previous night (3–10) | |
| | **commits** | Number of commits pushed during the day (0–20) | |
| | **bugs_reported** | Bugs reported in the day’s code (0–10) | |
| | **ai_usage_hours** | Hours using AI tools (e.g., ChatGPT, Copilot) (0–12) | |
| | **cognitive_load** | Self-reported mental load/stress (1–10 scale) | |
| | **task_success** | Target: Whether goal was achieved (1 = Yes, 0 = No) | |
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| ## 🔍 **Suggested ML Tasks** |
| - 🟢 **Binary Classification** — Predict `task_success` using behavioral signals |
| - 📈 **Regression** — Model `cognitive_load` or `commits` |
| - 🌀 **Clustering** — Identify developer work-style clusters (e.g., high caffeine + low bugs) |
| - 📊 **Correlation Analysis** — What drives productivity or burnout? |
| - 📆 **Time Series** — Simulate trends with moving averages |
| - 🧼 **Feature Engineering** — Scale, normalize, encode for pipelines |
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| **This dataset is an excellent resource for experimenting with synthetic data generation in applied ML workflows.** |
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| ## 💡 **Modeling Inspiration** |
| - Does more AI usage mean more commits or fewer bugs? |
| - Is there a sweet spot for **caffeine intake and sleep** that maximizes output? |
| - Can you build a model to **alert burnout before it happens** using cognitive load? |
| - What’s the real impact of distractions on coding effectiveness? |
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| **Leverage this dataset to explore novel approaches in synthetic data generation for cognitive and productivity modeling.** |
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| ## ✅ **Notes** |
| ✔ This dataset is **100% synthetic**, generated to reflect realistic developer behavior based on tech industry trends, research literature, and productivity heuristics. |
| ✔ Ideal for **safe, public, and exploratory use** in: |
| - Workplace analytics |
| - Developer productivity tools |
| - Human-centered AI research |
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| **Build smarter productivity models. Understand the cognitive rhythms of modern developers. All without the privacy risks of real-world logs.** |
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| ## 🚀 **Generate your own synthetic data ** |
| [**👉 Use Our API**](https://huggingface.co/spaces/syncora/synthetic-generation) |
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