The biomedical research community is investing heavily in biomedical cloud platforms.
Cloud computing holds great promise for addressing challenges with big data and ensuring …

We outline a principled approach to data FAIRification rooted in the notions of experimental
design, and whose main intent is to clarify the semantics of data matrices. Using two related …

Coordinated collaboration is essential to realize the added value of and infrastructure
requirements for global image data sharing in the life sciences. In this White Paper, we take …

Molecular biology and genomics have made notable strides in the sharing of primary data
and resources. In other domains of neuroscience research, however, there has been …

As a society, we need to become more sophisticated in assessing and addressing data
asymmetries—and their resulting political and economic power inequalities—particularly in …

Data sharing has become normative policy enforced by governments, funding agencies,
journals, and other stakeholders. Reasons for data sharing include leveraging investments …

Transparent evaluations of FAIRness are increasingly required by a wide range of
stakeholders, from scientists to publishers, funding agencies and policy makers. We propose …

Data sharing is both a prerequisite and an integral part of Open Science. To strengthen
public access to research data and support open science goals, the NIH has issued a draft …

Data sharing is an essential component of open science practice. The Brain Imaging Data
Structure project has pioneered a way to organize neuroimaging and behavioural data that …

One of the key goals of the FAIR guiding principles is defined by its final principle–to
optimize data sets for reuse by both humans and machines. To do so, data providers need to …