“All is data,” a famous sociologist once asserted. In today’s hyper-networked information economy, data has been likened to some of the world’s most precious resources.
The value of research data is equally important, as it fuels our scientific breakthroughs. In the research context, to make the most of our research outputs for both current and future studies, data needs to be highly structured, systematized, and detailed, as well as meticulously managed and archived.
Fueling data sharing and reuse
Compliance with data standards makes it easier to manage your projects and empowers your resulting data to be shared with other researchers, extending the impact and value of the original datasets. It also helps funding bodies determine if a project can be, or continue to be, funded.
Back in 2017, Wellcome, a health-focused global funding group, made it their policy to require shareable and preservable datasets in order to maximize “long-term value” for future research projects and their overall returns on investment. Data that is structured, maintained, and easily accessible can allow future projects to leapfrog the early steps of data collection. It has also been suggested that open datasets linked to a journal article can boost overall visibility and citation impacts.
Funders like Wellcome want to help jumpstart future studies, and reduce overall spend on future research grants, by the reuse of existing data. The US government is another large funder that now mandates data generated within federally funded research is publicly available by 2025. To prime the research community for this change, 2023 was declared the Year of Open Science, establishing resources to support a variety of research opportunities funded by various government agencies.
FAIR data mandates
An increasing number of research funders expect resulting data to comply with the FAIR principles, which aims to ensure data is Findable, Accessible, Interoperable, and Reusable.
When your data is FAIR, it can be shared with other experts that may want to build upon your work and serves as a signal to the wider community that your data is trusted and reliable.
So how do you go about keeping your data FAIR?
- First, establish your own Research Data Management (RDM) workflows for organizing and structuring your data through its lifecycle, from creation and collection to storage and sharing. From file naming protocols to version control, a solid RDM is the foundation for FAIR compliance. Power up your RDM workflows with free or low-cost tools, like DMPTool, Google’s Looker or QuestionPro’s solution.
- Next, you’ll need to incorporate new routines that will ensure your data will be interoperable and reusable by other researchers. Depending on your field of study, there may already be formatting standards for commonly used software or equipment. Your funder will likely itemize data requirements, such as assigning a DOI (Digital Object Identifier), a unique ID for individual datasets. Companies like Dataseer.ai are making the formatting, discovery, and reuse of data easier than ever. If you need extra help for regulatory compliance or auditing purposes, look to tools from providers like InfoEd or AvePoint.
- Then, you’ll want to prepare your data to be available via research archives and data repositories. Funding agencies that mandate FAIR principles will sometimes host their own repository or provide a list of approved platforms for sharing your data. For example, see these NIH guidelines for some important factors when considering data repositories.
For more guidance on making your data FAIR, try this checklist from the Open Science Foundation, which can help you set up your data management process and kick off your new FAIR workflows. Whatever your research purposes, making your data open and shareable within the appropriate repositories is a great step toward opening the doors to new funding opportunities.
In today’s open science ecosystem, we must be FAIR to get funded!