Open and reproducible practices in developmental psychology research: The workflow of the WomCogDev lab as an example

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Abstract

The current paper presents an overview of the workflow of the Working Memory, Cognition and Development (WomCogDev) lab at the University of Geneva, Switzerland, as an example of how Open Science principles can be applied in a developmental psychology lab. We describe the importance and challenges of applying Open Science practices in developmental research and detail each step of our workflow from research design to dissemination. We provide examples and give emphasis to steps that typically receive little attention but that may hold promise for application in other labs (namely, project design, data collection and data analysis). Finally, we discuss the advantages and disadvantages of our workflow and summarize the main takeaways for other labs. By ‘opening up’ our lab's workflow, we want to encourage other labs to incorporate the aspects that they like into their own workflows and to share their own processes for the continued benefit of others.

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The FAIR Guiding Principles for scientific data management and stewardship

Mark D Wilkinson, Michel Dumontier, IJsbrand Jan Aalbersberg … (2017)

There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.

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Bayes Factors

Robert E. Kass, Adrian E. Raftery (1995)

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PsychoPy2: Experiments in behavior made easy

Jonathan Westley Peirce, Jeremy Gray, Sol Simpson … (2019)

PsychoPy is an application for the creation of experiments in behavioral science (psychology, neuroscience, linguistics, etc.) with precise spatial control and timing of stimuli. It now provides a choice of interface; users can write scripts in Python if they choose, while those who prefer to construct experiments graphically can use the new Builder interface. Here we describe the features that have been added over the last 10 years of its development. The most notable addition has been that Builder interface, allowing users to create studies with minimal or no programming, while also allowing the insertion of Python code for maximal flexibility. We also present some of the other new features, including further stimulus options, asynchronous time-stamped hardware polling, and better support for open science and reproducibility. Tens of thousands of users now launch PsychoPy every month, and more than 90 people have contributed to the code. We discuss the current state of the project, as well as plans for the future.