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Fünderich, Jens H. ORCID logoORCID: https://orcid.org/0000-0002-7185-9248; Beinhauer, Lukas J. ORCID logoORCID: https://orcid.org/0000-0001-9841-3089 und Renkewitz, Frank ORCID logoORCID: https://orcid.org/0000-0001-8072-6802 (2024): Reduce, reuse, recycle: Introducing MetaPipeX, a framework for analyses of multi‐lab data. In: Research Synthesis Methods, Bd. 15, Nr. 6: S. 1183-1199 [PDF, 2MB]

Abstract

Multi-lab projects are large scale collaborations between participating data collection sites that gather empirical evidence and (usually) analyze that evidence using meta-analyses. They are a valuable form of scientific collaboration, produce outstanding data sets and are a great resource for third-party researchers. Their data may be reanalyzed and used in research synthesis. Their repositories and code could provide guidance to future projects of this kind. But, while multi-labs are similar in their structure and aggregate their data using meta-analyses, they deploy a variety of different solutions regarding the storage structure in the repositories, the way the (analysis) code is structured and the file-formats they provide. Continuing this trend implies that anyone who wants to work with data from multiple of these projects, or combine their datasets, is faced with an ever-increasing complexity. Some of that complexity could be avoided. Here, we introduce MetaPipeX, a standardized framework to harmonize, document and analyze multi-lab data. It features a pipeline conceptualization of the analysis and documentation process, an R-package that implements both and a Shiny App (https://www.apps.meta-rep.lmu.de/metapipex/) that allows users to explore and visualize these data sets. We introduce the framework by describing its components and applying it to a practical example. Engaging with this form of collaboration and integrating it further into research practice will certainly be beneficial to quantitative sciences and we hope the framework provides a structure and tools to reduce effort for anyone who creates, re-uses, harmonizes or learns about multi-lab replication projects.

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