About Version 1.0 of this talk, titled "Towards a workflow for open and reproducible fMRI studies", was prepared for the "Discussion Series: Human Research Data in Practice" hosted by the Max Planck Digital Library (see details here) on 22nd June 2021. Abstract Achieving computational reproducibility and accessible data sharing can be challenging, in particular for neuroimaging research that involves large amounts of heterogeneous data and code. Here, we showcase a workflow that combines several software tools to allow reproducibility and transparent sharing of code and data of a human fMRI study. We recently published an open-access paper (Wittkuhn & Schuck, 2021, Nature Communications) together with the code, data and computational environments needed to reproduce the reported results. We shared > 10 datasets via GIN (G-Node Infrastructure) as modular version-controlled units, including fMRI data organized in BIDS format and derived data, such as pre-processed fMRI data and data quality metrics. Research data was version-controlled using DataLad. Following the DataLad YODA principles, we nested datasets as modular units, allowing to better establish data provenance, i.e., a clear overview which code used which input data to produce which output data. Code that reproduced the analyses was integrated with additional documentation using RMarkdown notebooks. The notebooks were automatically executed using continuous integration on GitLab. In this process, data was retrieved from GIN using DataLad, the notebooks were rendered and deployed to a website (https://wittkuhn.mpib.berlin/highspeed/). Code execution was performed using software containers (Docker and Singularity) and virtual environments, allowing to reproduce the computational environment.