Datasets:

mostlyai
/

mostlyaiprize

license: apache-2.0
tags:
  - tabular
  - synthetic-data
viewer: true
configs:
  - config_name: flat
    data_files:
      - split: train
        path: data/flat/train/*.csv
  - config_name: sequential
    data_files:
      - split: train
        path: data/sequential/train/*.csv

MOSTLY AI Prize Dataset

This repository contains the dataset used in the MOSTLY AI Prize competition.

About the Competition

Generate the BEST tabular synthetic data and win 100,000 USD in cash. Competition runs for 50 days: May 14 - July 3, 2025.

This competition features two independent synthetic data challenges that you can join separately:

The FLAT DATA Challenge
The SEQUENTIAL DATA Challenge

For each challenge, generate a dataset with the same size and structure as the original, capturing its statistical patterns — but without being significantly closer to the (released) original samples than to the (unreleased) holdout samples.

Train a generative model that generalizes well, using any open-source tools (Synthetic Data SDK, synthcity, reprosyn, etc.) or your own solution. Submissions must be fully open-source, reproducible, and runnable within 6 hours on a standard machine.

Timeline

Submissions open: May 14, 2025, 15:30 UTC
Submission credits: 3 per calendar week (+bonus)
Submissions close: July 3, 2025, 23:59 UTC
Evaluation of Leaders: July 3 - July 9
Winners announced: on July 9 🏆

Dataset Description

This dataset consists of two CSV files used in the MOSTLY AI Prize competition:

Flat Data

File: data/flat/train/flat-training.csv (26MB, MD5 d5642dd9b13da0dc1fbac6f92f8e4b20)
100,000 records
80 data columns: 60 numeric, 20 categorical

Sequential Data

File: data/sequential/train/sequential-training.csv (6.6MB, MD5 dd024fe8130cb36ad9374e23ccbffc4a)
20,000 groups
Each group contains 5-10 records
11 data columns: 7 numeric, 3 categorical + 1 group ID

Data Format

You can load them directly using pandas:

import pandas as pd

flat_df = pd.read_csv('data/flat/train/flat-training.csv')
sequential_df = pd.read_csv('data/sequential/train/sequential-training.csv')

Or using Hugging Face's datasets:

from datasets import load_dataset

flat_dataset = load_dataset("mostlyai/mostlyaiprize", "flat", split="train")
sequential_dataset = load_dataset("mostlyai/mostlyaiprize", "sequential", split="train")

Dataset Schema

The schema of each dataset can be retrieved as follows:

# pandas
print(flat_df.dtypes)
print(sequential_df.dtypes)

# HF datasets
print(flat_dataset.features)
print(sequential_dataset.features)

Column Description

Note: Detailed column descriptions are intentionally not provided as part of the competition challenge. The task is to generate synthetic data that preserves the statistical properties of the original data without needing to understand the semantic meaning of each column.

Notes on Holdout Data

The competition evaluates submissions against a hidden holdout set that:

Has the same size as the training data
Does not overlap with the training data
Comes from the same source
Has the same structure and statistical properties

Your synthetic data generation approach should generalize well to this unseen data.

Evaluation

CSV submissions are parsed using pandas.read_csv() and checked for expected structure & size
Evaluated using the Synthetic Data Quality Assurance toolkit
Compared against the released training set and a hidden holdout set (same size, non-overlapping, from the same source)

Citation

If you use this dataset in your research, please cite:

@dataset{mostlyaiprize,
  author = {MOSTLY AI},
  title = {MOSTLY AI Prize Dataset},
  year = {2025},
  url = {https://www.mostlyaiprize.com/},
}

License

This dataset is provided under the Apache License 2.0. See the LICENSE file for full licensing information.