The what, why, and how of born-open data

Area of Science:

• Data Science
• Scientific Computing
• Research Methodology

Background:

• Scientific data sharing is crucial for transparency and reproducibility.
• Technical challenges in data preparation and archiving hinder widespread data availability.
• Current practices often lack efficient and automated solutions for raw data management.

Purpose of the Study:

• To introduce and advocate for the

Main Methods:

• Automated data archiving system developed in the laboratory.
• Data are versioned, logged, time-stamped, and uploaded nightly without human intervention.
• GitHub is utilized as the central repository for all archived data.

Main Results:

• Successful implementation of an automated system for archiving raw scientific data.
• All data, including aborted runs and pilot subject data, are captured and stored.
• Born-open data is now readily available for scrutiny and analysis.

Conclusions:

• Automated data archiving ('born-open') overcomes technical hurdles in data sharing.
• This approach promotes transparency, reproducibility, and accelerates scientific discovery.
• Addressing concerns about pre-publication data sharing is essential for wider adoption.