Sharing neuroimaging data upon a direct personal request can be challenging both for researchers who request the data and for those who agree to share their data. Unlike sharing through repositories under standardized protocols and data use/sharing agreements, each party often needs to negotiate the terms of sharing and use of data case by case. This negotiation unfolds against a complex backdrop of ethical and regulatory requirements along with technical hurdles related to data transfer and management. These challenges can significantly delay the data-sharing process, and if not properly addressed, lead to potential tensions and disputes between sharing parties. This study aims to help researchers navigate these challenges by examining what to consider during the process of data sharing and by offering recommendations and practical tips. We first divided the process of sharing data upon a direct personal request into six stages: requesting data, reviewing the applicability of and requirements under relevant laws and regulations, negotiating terms for sharing and use of data, preparing and transferring data, managing and analyzing data, and sharing the outcome of secondary analysis of data. For each stage, we identified factors to consider through a review of ethical principles for human subject research; individual institutions’ and funding agencies’ policies; and applicable regulations in the U.S. and E.U. We then provide practical insights from a large-scale ongoing neuroimaging data-sharing project led by one of the authors as a case study. In this case study, PET/MRI data from a total of 782 subjects were collected through direct personal requests across seven sites in the USA, Canada, the UK, Denmark, Germany, and Austria. The case study also revealed that researchers should typically expect to spend an average of 8 months on data sharing efforts, with the timeline extending up to 24 months in some cases due to additional data requests or necessary corrections. The current state of data sharing via direct requests is far from ideal and presents significant challenges, particularly for early career scientists, who often have a limited time frame—typically 2 to 3 years—to work on a project. The best practices and practical tips offered in this study will help researchers streamline the process of sharing neuroimaging data while minimizing friction and frustrations.

Empirical studies reporting low test–retest reliability of individual blood oxygen-level dependent (BOLD) signal estimates in functional magnetic resonance imaging (fMRI) data have resurrected interest among cognitive neuroscientists in methods that may improve reliability in fMRI. Over the last decade, several individual studies have reported that modeling decisions, such as smoothing, motion correction, and contrast selection, may improve estimates of test–retest reliability of BOLD signal estimates. However, it remains an empirical question whether certain analytic decisions consistently improve individual- and group-level reliability estimates in an fMRI task across multiple large, independent samples. This study used three independent samples ( N s: 60, 81, 119) that collected the same task (Monetary Incentive Delay task) across two runs and two sessions to evaluate the effects of analytic decisions on the individual (intraclass correlation coefficient [ICC(3,1)]) and group (Jaccard/Spearman rho ) reliability estimates of BOLD activity of task fMRI data. The analytic decisions in this study vary across four categories: smoothing kernel (five options), motion correction (four options), task parameterizing (three options), and task contrasts (four options), totaling 240 different pipeline permutations. Across all 240 pipelines, the median ICC estimates are consistently low, with a maximum median ICC estimate of .43 – .55 across the 3 samples. The analytic decisions with the greatest impact on the median ICC and group similarity estimates are the Implicit Baseline contrast, Cue Model parameterization, and a larger smoothing kernel. Using an Implicit Baseline in a contrast condition meaningfully increased group similarity and ICC estimates as compared with using the Neutral cue. This effect was largest for the Cue Model parameterization; however, improvements in reliability came at the cost of interpretability. This study illustrates that estimates of reliability in the MID task are consistently low and variable at small samples, and a higher test–retest reliability may not always improve interpretability of the estimated BOLD signal.

Sharing research data has been widely promoted in the field of neuroimaging and has enhanced the rigor and reproducibility of neuroimaging studies. Yet the emergence of novel software tools and algorithms, such as face recognition, has raised concerns due to their potential to reidentify defaced neuroimaging data that are thought to have been deidentified. Despite the surge of privacy concerns, however, the risk of reidentification via these tools and algorithms has not yet been examined outside the limited settings for demonstration purposes. There is also a pressing need to carefully analyze regulatory implications of this new reidentification attack because concerns about the anonymity of data are the main reason that researchers think they are legally constrained from sharing their data. This study aims to tackle these gaps through rigorous technical and regulatory analyses. Using a simulation analysis, we first tested the generalizability of the matching accuracies in defaced neuroimaging data reported in a recent face recognition study ( Schwarz et al., 2021 ). The results showed that the real-world likelihood of reidentification in defaced neuroimaging data via face recognition would be substantially lower than that reported in the previous studies. Next, by taking a US jurisdiction as a case study, we analyzed whether the novel reidentification threat posed by face recognition would place defaced neuroimaging data out of compliance under the current regulatory regime. Our analysis suggests that defaced neuroimaging data using existing tools would still meet the regulatory requirements for data deidentification. A brief comparison with the EU’s General Data Protection Regulation (GDPR) was also provided. Then, we examined the implication of NIH’s new Data Management and Sharing Policy on the current practice of neuroimaging data sharing based on the results of our simulation and regulatory analyses. Finally, we discussed future directions of open data sharing in neuroimaging.

The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, the mechanisms by which it has been extended, and some of the challenges being addressed as it evolves. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS.