STEP 5: Data collection

Before starting with data collection, it is recommended to create a standardized manual (SOP; see Step 3 and Table 1; Manghani, 2011) and document the experimental procedure in a lab-book (Schnell, 2015) that lists unforeseen events and information for each participant/session. The latter ensures that important details, such as equipment malfunctioning, reasons for participant dropout, noticeable participant behavior, and any crucial decisions or modifications made on the fly, are not lost or forgotten. Note that writing up the method section before Step 5 promises to save time prospectively and enhances the precision and reproducibility of the research project. Here, data management strategies such as intuitive data saving structures can help to avoid misunderstandings as well as waste of time due to data rearrangement or rewriting scripts (Michener, 2015). Making sure you have all data backed up is essential to prevent valuable data from being accidentally lost. These practices later facilitate data, code, and material sharing as part of the publication (Step 9; Contaxis et al., 2022). Even though the scientific community still lacks consensus on data arrangement structures and is constantly finding new approaches, there are already well-established structures such as the ➜ Brain Imaging Data Structure (BIDS; Gorgolewski et al., 2017) for complex neuroimaging data, which are listed in ARIADNE. Furthermore, data anonymization or pseudonymization are critical techniques to protect participants’ rights and privacy (Meyer, 2018 for ethical data sharing; Hallinan et al., 2023 for European Union regulations on data privacy). ARIADNE also provides examples of existing data that can be used for some research questions.