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{ "anonymized_video_present": true, "capture_metadata_present": true, "imu_metadata_present": true, "qc_report_present": true }
{ "reference": "clip_relative_timeline", "imu_trimmed_to_nominal_10s_window": true }
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public_sample_ready

Origin Data Lab --- Real-World Multimodal Field Sample

A compact technical sample demonstrating Origin Data Lab's real-world field data collection and structured data-operations workflow.

Capture → Metadata / IMU → Validation → QC → Privacy Processing → Structured Delivery

This public repository is intentionally limited in scope. It is designed to provide verifiable evidence of our workflow without publishing sensitive raw footage, contributor-identifying information, precise location data, or proprietary pipeline details.

What's Included

data/session_001/

  • video_anonymized_10s.mp4 --- privacy-processed field video sample
  • capture_metadata.json --- structured capture metadata
  • imu_metadata.json --- IMU sensor metadata/sample data
  • validation_summary.json --- automated validation summary
  • qc_report.json --- quality-control output

docs/

  • DATA_SCHEMA.md --- description of the public sample schema
  • WORKFLOW.md --- overview of the capture-to-delivery workflow

Root

  • LICENSE --- public evaluation sample license
  • README.md --- this dataset card

What This Sample Demonstrates

This repository demonstrates a practical multimodal data workflow built around real-world field collection:

  1. Field video capture
  2. Structured capture metadata
  3. IMU data packaging
  4. Validation checks
  5. Quality-control outputs
  6. Privacy processing
  7. Structured delivery

The sample is intended to demonstrate the data operations workflow and delivery structure, not to claim that this specific footage matches every buyer's target device, environment, geography, or collection protocol.

Project-Specific Collection

Origin Data Lab can configure collection workflows around project-specific requirements such as:

  • Target geography and environment
  • Device and mounting configuration
  • Participant or contributor profile
  • Video and sensor requirements
  • Metadata schema
  • QC and acceptance criteria
  • Privacy-processing requirements
  • Delivery structure

Project-specific feasibility, capacity, recruitment, hardware, and collection procedures are determined after reviewing the buyer's requirements.

Privacy & Public Release Scope

The public sample is deliberately privacy-restricted.

It does not publish:

  • Unredacted raw facial imagery
  • Readable vehicle license plates
  • Precise GPS coordinates
  • Contributor-identifying information
  • Internal server paths or infrastructure information
  • API credentials or access keys
  • Proprietary pipeline source code
  • Buyer-specific information

The included video is an anonymized public demonstration.

Limitations

This is a small technical demonstration, not a complete production dataset.

It should not be interpreted as a claim of:

  • A specific production volume
  • A guaranteed collection capacity
  • A particular device configuration
  • A universal synchronization specification
  • A guaranteed model-performance or accuracy metric

Final collection specifications are defined per project.

Documentation

For additional technical context, see:

Work With Origin Data Lab

Need project-specific real-world or Physical AI training data?

Origin Data Lab supports custom field data collection, multimodal sensor capture, structured metadata, validation/QC, privacy processing, and structured delivery.

Share your Project Brief:

Email: founder@origindatalab.io
Website: https://origindatalab.io

We can review the target geography, device setup, participant profile, collection volume, metadata requirements, and QC criteria and respond with a scoped feasibility and pilot proposal.


Origin Data Lab
Real-World Field Data Collection & Data Operations

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