Evaluation of a novel cloud-based software platform for structured experiment design and linked data analytics

Area of Science:

• Computational Biology
• Biotechnology
• Scientific Software Development

Background:

• Traditional scientific data sharing lacks precise experimental procedure definitions, hindering data contextualization and reproducibility.
• Current methods for sharing protocols and data are insufficient for the demands of open science.

Purpose of the Study:

• To implement and evaluate a novel cloud-based software system for defining experimental procedures in an academic research setting.
• To enhance data contextualization and promote reproducibility through machine-computable experiment descriptions.

Main Methods:

• Development and implementation of a cloud-based software system for systematic, visual definition of experimental procedures.
• Acquisition and analysis of primary data, linked to procedure definitions in a machine-computable format.
• Testing the software on quantitative microbial-physiology experiments.

Main Results:

• The software enabled more precise and unambiguous experimental definitions compared to conventional protocols.
• Defined procedures were reusable and composable into complex experimental workflows.
• Automatic coupling of procedure definitions to data facilitated correlation analysis between procedural details and experimental outcomes.

Conclusions:

• Software-based experiment descriptions can replace ambiguous "Materials and Methods" sections in scientific publications.
• This approach significantly promotes the reproducibility and reusability of scientific studies.
• The system offers a pathway to more robust and transparent scientific research.