Reduce, reuse, recycle: Introducing MetaPipeX, a framework for analyses of multi-lab data

Area of Science:

• Quantitative Sciences
• Collaborative Research
• Data Harmonization

Background:

• Multi-lab projects are large-scale scientific collaborations collecting empirical data, often analyzed via meta-analyses.
• These projects yield valuable datasets and resources for third-party researchers, enabling data reanalysis and research synthesis.
• However, inconsistencies in data storage, code structure, and file formats across multi-lab projects create complexity for data integration.

Purpose of the Study:

• To introduce MetaPipeX, a standardized framework designed to address the complexity of multi-lab data.
• To provide tools for harmonizing, documenting, and analyzing data from multiple collaborative research projects.
• To facilitate the re-use and synthesis of data from multi-lab initiatives.

Main Methods:

• Development of MetaPipeX, a standardized framework featuring a pipeline conceptualization for analysis and documentation.
• Implementation of the framework using an R-package.
• Creation of a Shiny App for user exploration and visualization of multi-lab datasets.

Main Results:

• MetaPipeX provides a structured approach to manage and analyze multi-lab data.
• The R-package and Shiny App offer practical tools for researchers working with collaborative datasets.
• The framework demonstrates successful application in harmonizing and analyzing multi-lab data through a practical example.

Conclusions:

• MetaPipeX reduces the effort required to create, re-use, harmonize, and learn about multi-lab replication projects.
• Standardizing multi-lab data management enhances the value and accessibility of collaborative research outputs.
• Integrating such frameworks is crucial for advancing quantitative sciences and collaborative research practices.