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.
Dokumententyp: | Zeitschriftenartikel |
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Fakultät: | Psychologie und Pädagogik > Department Psychologie |
Themengebiete: | 100 Philosophie und Psychologie > 150 Psychologie |
URN: | urn:nbn:de:bvb:19-epub-122427-5 |
ISSN: | 1759-2879 |
Sprache: | Englisch |
Dokumenten ID: | 122427 |
Datum der Veröffentlichung auf Open Access LMU: | 19. Nov. 2024 10:39 |
Letzte Änderungen: | 19. Nov. 2024 10:39 |
DFG: | Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 441890184 |