New grant applications to the National Institutes of Health must include a plan for data-sharing and management. Researchers hope the broad requirement will boost development and adoption of tools that are more effective and easier to use.
This month marks a new era in how federally funded scientists think about their data. Beginning on January 25, researchers applying for grants from the National Institutes of Health must create a specific plan for how they will manage and store their data. The far-reaching new policy aims to boost the progress of science by improving reproducibility and encouraging more extensive use of data that is often expensive to collect. But it has also sparked concern over the time and effort required to meaningfully share data. “I think the pandemic and research on COVID illustrated how much faster we can communicate and move science forward if we make data available,” says Lyn Jakeman, director of the division of neuroscience at the National Institute of Neurological Disorders and Stroke. Jakeman notes that planning for the new policy, first announced in 2020, began long before the pandemic. The new regulations, set in motion a decade ago by a federal dictate to make publicly funded research more broadly available, arrive amid a growing push for data-sharing across scientific fields. Many scientific journals mandate or encourage data-sharing, as do non-federal grant agencies. The NIH already requires sharing of clinical and genomic data, as well as data generated from large-scale projects. But the new policy aims to tame a very different beast — the data produced from a wide range of basic science fields. Basic science involves experimental trial and error and methodological variability, both of which make data-sharing much more difficult.
Sharing and standardizing data has been a notorious challenge for neuroscience, particularly systems neuroscience, because of the diversity of data involved. Unlike, say, genomics or brain imaging, which typically uses standardized instruments, neurophysiology often employs custom-built tools and bespoke data processing pipelines, which produce data in a range of formats that can be difficult to share across labs. Moreover, the information that accompanies neurophysiology experiments, known as metadata, such as the animal’s behavior, genetics and other factors, can be difficult to keep straight. “Correlated data can be really hard to manage because you have to make sure connections across files and data types are maintained,” says Maryann Martone, a neuroscientist at the University of California, San Diego, who has been heavily involved in open science efforts. (For more on the challenges of data-sharing and standardization in neuroscience, see our 2019 series “The Data-Sharing Problem in Neuroscience.”) A number of new tools for standardizing, storing and processing neuroscience data have been developed over the last five years, but their adoption is far from comprehensive. While most researchers recognize the broad benefits of making data more easily and broadly available, the best way to do so remains a point of debate. Some want to settle on standardized tools, while others want to continue to experiment, and both ends of the spectrum require more buy-in from the community to be effective. Funders and others hope that the combination of new tools and the federal mandate will help catalyze more widespread use. “I’m a fan of the requirement even though we’re kind of not ready for it,” says Stephen Van Hooser, a neuroscientist at Brandeis University. “I think the typical lab will initially not do very well, but the requirements will stimulate development of new and better approaches to making data-sharing easier.
