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README.md

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+# YCbench
+
+**A dataset for benchmarking models that predict Y Combinator (YC) startup performance.**
+
+This dataset collects structured data related to Y Combinator startups, primarily focused on **W26** (Winter 2026) batch and related signals. It supports research and development of forecasting models that aim to identify which early-stage startups will **outperform their batch peers** in the short term (e.g., next 90 days), as explored in the [YC Bench](https://ycbench.com/) live benchmark.
+
+## Dataset Overview
+
+YCbench aggregates public and derived signals for YC companies, including:
+- Google/web mentions (as a proxy for visibility/traction)
+- Pre-demo day scores and predictions
+- Startup profiles
+- Traction metrics
+
+The data is useful for:
+- Training/evaluating ML models for startup success prediction
+- Cohort-relative benchmarking (comparing startups within the same YC batch)
+- Analyzing early signals of velocity and outperformance
+- Research on founder signals, traction, and short-term startup dynamics
+
+## Files
+
+The dataset repository contains the following CSV files:
+
+- `yc_mentions.csv` — Google/web mention counts for YC-related domains
+- `yc_mentions_early.csv` — Early-stage mention data
+- `yc_w26_startups.csv` — List of W26 startups with basic information
+- `yc_w26_traction.csv` — Traction metrics for W26 companies
+- `yc_w26_pre_demo_scores.csv` — Pre-demo day scores, velocity scores, hybrid scores, etc.
+
+**Note:** These files have varying schemas (different columns). When loading with the `datasets` library, you may need to load them individually or as separate configurations rather than as a single default split.
+
+## Loading the Dataset
+
+You can load individual files using pandas or the Hugging Face `datasets` library with manual configuration:
+
+```python
+import pandas as pd
+from datasets import load_dataset
+
+# Load with pandas (recommended for now due to schema differences)
+df_mentions = pd.read_csv("hf://datasets/benstaf/ycbench/yc_mentions.csv")
+df_w26 = pd.read_csv("hf://datasets/benstaf/ycbench/yc_w26_startups.csv")
+
+# Or load specific files via datasets (if you define configs)
+# dataset = load_dataset("benstaf/ycbench", "w26_startups")  # example config
+```
+
+A preview of the data shows columns such as:
+- `domain` (string)
+- `google_mentions` (float)
+
+Other files include scores like `mentions_score`, `Velocity_Score`, `hybrid_score`, etc.
+
+## Motivation & Related Work
+
+This dataset is connected to **YC Bench** — a live benchmark that evaluates forecasting models on their ability to predict which YC startups will show the strongest execution velocity in the 90 days following the start of a batch.
+
+Key advantages of using YC batches for benchmarking:
+- Startups enter at roughly the same time and stage
+- Similar funding environment within a batch
+- Clean peer comparison (cohort-relative ranking)
+
+Instead of waiting years for exits or unicorn status, models are scored on measurable short-term outperformance.
+
+## Usage Ideas
+
+- Build baseline models using mention counts and traction signals
+- Experiment with hybrid scoring (mentions + velocity + other features)
+- Create leaderboards for LLM-based or traditional ML predictors
+- Analyze correlations between early signals and later performance
+
+## Limitations
+
+- Data is currently focused on W26 and general YC mentions
+- Column schemas are not fully unified across files
+- Some fields may contain null values
+- No official splits (train/validation/test) are provided yet
+
+## License
+
+No license specified. Please check with the uploader (`benstaf`) for intended use. For research and non-commercial purposes, it is generally safe to assume open use, but verify before commercial applications.
+
+## Author
+
+- **Ben Staf** ([benstaf on Hugging Face](https://huggingface.co/benstaf))
+
+## Contributing / Improvements
+
+Feel free to open a discussion or pull request if you want to:
+- Unify schemas into a single consistent dataset
+- Add more batches (S25, W27, etc.)
+- Include additional signals (LinkedIn activity, GitHub stars, funding news, etc.)
+- Add proper dataset cards, splits, or evaluation scripts
+
+## Related Links
+
+- [YC Bench website](https://ycbench.com/) — Live leaderboard and benchmark
+- [Ben Staf's other datasets](https://huggingface.co/benstaf/datasets)