THULab/warp_research-gotthatdata

Warp Research Dataset

This repository contains a TsFile conversion of the original Hugging Face dataset GotThatData/warp_Research.

Dataset Description

Dataset Summary

This dataset contains experimental results from warp field research, focusing on the relationship between warp factors, energy efficiency, and field characteristics.

Supported Tasks

• Tabular Regression: predict energy efficiency based on warp field parameters.
• Time Series Forecasting: analyze temporal patterns in warp field behavior.
• Optimization: identify optimal warp factor configurations for maximum energy efficiency.

Dataset Structure

• Number of source records: approximately 19,700.
• Time period: January 2025.
• Features: 15 variables including derived metrics.
• Original splits: train 80%, validation 10%, test 10%.

The converted TsFile files follow the original split hierarchy:

data/
  train.tsfile
  validation.tsfile
  test.tsfile

The original dataset's uppercase Data/ directory is also mirrored in this repository unchanged for reference. It contains the source research CSV, JSON, and TXT artifacts from the original repository. The TsFile conversion itself is based on the original dataset's Parquet split files under lowercase data/.

Features

Feature	Type	Range / Values	Description
timestamp	datetime	Jan 17, 2025	Time of experiment; converted to TsFile Time in nanoseconds
warp_factor	float64	1.0 - 10.0	Applied warp field intensity
expansion_rate	float64	0.1 - 5.0	Rate of spatial expansion
resolution	int64	20 - 40	Measurement resolution
stability_score	float64	20.2 - 45.5	Field stability metric
max_field_strength	float64	-0.08 - 97.1	Maximum achieved field strength
avg_field_strength	float64	-0.92 - 7.35	Average field strength
field_symmetry	float64	0	Symmetry measure of the field
energy_efficiency	float64	-1,055 - 13.9	Primary efficiency metric
source_file	string	31 unique	Original data source file; converted as a TsFile TAG
data_source	string	local	Data collection source; converted as a TsFile TAG
experiment_type	string	null / correlation	Experiment classification
hypothesis_id	string	3 values	Research hypothesis identifier
efficiency_ratio	float64	-1,055 - 9.43	Derived: energy_efficiency / warp_factor
field_strength_ratio	float64	-0.83 - 47.8	Derived: average/max field strength

Conversion Notes

• Source dataset: GotThatData/warp_Research.
• Source format converted to TsFile: Parquet split files from lowercase data/.
• Converted format: TsFile table model.
• Table name: warp_research.
• TAG columns: source_file, data_source.
• timestamp[ns] is converted to TsFile Time with nanosecond precision.
• Time-indexed rows are converted without dropping source measurement columns.
• The source Parquet carries a leftover pandas row index as a column named __index_level_0__; it is an artifact, not source data, so it is dropped during conversion.
• Rows without a timestamp cannot be represented on a TsFile time axis. These rows are excluded from the main TsFile outputs and included under invalid_timestamp_rows/ for traceability.
• Uppercase Data/ is included as an unchanged mirror of the original dataset's supplementary raw research artifacts.

Converted row counts:

Split	Source rows	Converted rows	Rows without timestamp
train	15,720	15,674	46
validation	1,965	1,954	11
test	1,966	1,962	4

Research Applications

• Propulsion System Optimization: identify optimal warp field configurations.
• Energy Efficiency Modeling: predict energy requirements for different warp factors.
• Temporal Pattern Analysis: study how warp fields behave over time.
• Stability Prediction: model field stability under various conditions.

Data Collection

Data was collected from experimental warp field simulations conducted in January 2025. Each record represents a single timestep measurement from various experimental runs.

Licensing Information

MIT License.

Citation Information

Please cite the original dataset as:

@dataset{warp_research_2025,
  author = {GotThatData},
  title = {Warp Research Dataset},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/GotThatData/warp_Research}
}

Usage

Install the Apache TsFile Python SDK (pip install tsfile) and read a converted file:

from pathlib import Path
from tsfile import TsFileReader

path = Path("test/test.tsfile")
with TsFileReader(str(path)) as reader:
    schemas = reader.get_all_table_schemas()
    print("tables:", list(schemas))
    table_name = next(iter(schemas))
    table = schemas[table_name]
    columns = [column.get_column_name() for column in table.get_columns()]
    print("columns:", columns)
    field_names = [
        column.get_column_name()
        for column in table.get_columns()
        if column.get_column_name() not in {"Time", "time"}
    ]
    if field_names:
        with reader.query_table(table_name, field_names[:3], batch_size=1024) as result:
            batch = result.read_arrow_batch()
            if batch is not None:
                print(batch.to_pandas().head())