Providing access to research data collected as part of scientific publications and publicly funded research projects is now regarded as a central aspect of an open and transparent scientific practice and is increasingly being called for by funding institutions and scientific journals. To this end, researchers should strive to comply with the so-called FAIR principles (of scientific data management), that is, research data should be findable, accessible, interoperable, and reusable. Systematic data management supports these goals and, at the same time, makes it possible to achieve them efficiently. With these revised recommendations on data management and data sharing, which also draw on feedback from a 2018 survey of its members, the German Psychological Society (Deutsche Gesellschaft für Psychologie; DGPs) specifies important basic principles of data management in psychology. Initially, based on discipline-specific definitions of raw data, primary data, secondary data, and metadata, we provide recommendations on the degree of data processing necessary when publishing data. We then discuss data protection as well as aspects of copyright and data usage before defining the qualitative requirements for trustworthy research data repositories. This is followed by a detailed discussion of pragmatic aspects of data sharing, such as the differences between Type 1 and Type 2 data publications, restrictions on use (embargo period), the definition of "scientific use" by secondary users of shared data, and recommendations on how to resolve potential disputes. Particularly noteworthy is the new recommendation of distinct "access categories" for data, each with different requirements in terms of data protection or research ethics. These range from completely open data without usage restrictions ("access category 0") to data shared under a set of standardized conditions (e.g., reuse restricted to scientific purposes; "access category 1"), individualized usage agreements ("access category 2"), and secure data access under strictly controlled conditions (e.g., in a research data center; “access category 3"). The practical implementation of this important innovation, however, will require data repositories to provide the necessary technical functionalities. In summary, the revised recommendations aim to present pragmatic guidelines for researchers to handle psychological research data in an open and transparent manner, while addressing structural challenges to data sharing solutions that are beneficial for all involved parties.

In a sequential hypothesis test, the analyst checks at multiple steps during data collectionwhether sufficient evidence has accrued to make a decision about the tested hypotheses.As soon as sufficient information has been obtained, data collection is terminated. Here,we compare two sequential hypothesis testing procedures that have recently been proposedfor use in psychological research: the Sequential Probability Ratio Test (SPRT; Schnuerch& Erdfelder, 2020) and the Sequential Bayes Factor Test (SBFT; Schönbrodt et al., 2017).We show that although the two methods have been presented as distinct methodologies inthe past, they share many similarities and can even be regarded as two instances of thesame overarching hypothesis testing framework. We demonstrate that the two methods usethe same mechanisms for evidence monitoring and error control, and that differences inefficiency between the methods depend on the exact specification of the statistical modelsinvolved. Given the close relationship between the SPRT and SBFT, we argue that thechoice of the sequential testing method should be regarded as a continuous choice withina unified framework rather than a dichotomous choice between two methods. We presentseveral considerations researchers can make to navigate the design decisions in the SPRTand SBFT.