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

Jens H Fünderich1,2, Lukas J Beinhauer1, Frank Renkewitz1

  • 1Department of Psychology, University of Erfurt, Erfurt, Germany.

Research Synthesis Methods
|June 29, 2024
PubMed
Summary
This summary is machine-generated.

Multi-lab projects generate valuable data but lack standardization. MetaPipeX offers a framework to harmonize, document, and analyze multi-lab data, simplifying research synthesis and promoting collaboration in quantitative sciences.

Keywords:
codedata documentationdata visualizationexperimentmeta‐analysismulti‐lab

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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.