Scientific scholarship generates an unimaginable amount of data – so how do researchers keep track of it? And how do they make sure that it’s accessible for use by both humans and machines? To improve and advance science, scientists need to be able to reproduce others’ data or combine data from multiple sources to learn something new. Accessible and usable data can help scientists reproduce prior results.
Research data management is an area of scholarship that focuses on data discovery and reuse. As a field, it encompasses research data services, resources, and cyberinfrastructure. For example, one type of infrastructure, the data repository, gives researchers a place to deposit their data for long-term storage so that others can find it. Research data management encompasses the data’s life cycle from cradle to grave to reincarnation in the next study.
Proper research data management also allows scientists to use the data already out there rather than recollecting data that already exists, which saves time and resources. Scientists and data managers can work together to redesign the systems scientists use to make data discovery and preservation easier. Integrating AI can make this data more accessible and reusable.
AI makes it highly desirable for any data to be machine-actionable, and usable by machines without human intervention. Now, scholars can consider machines as tools and potential autonomous data reusers and collaborators.
The key to machine-actionable data is metadata. Metadata are the descriptions scientists set for their data and may include elements such as creator, date, coverage and subject. It takes a cadre of research data managers and librarians to make machine-actionable data a reality. These information professionals work to facilitate communication between scientists and systems by ensuring the quality, completeness, and consistency of shared data.
The FAIR data principles, created by a group of researchers called FORCE11 in 2016 and used across the world, provide guidance on how to enable data reuse by machines and humans. FAIR data is findable, accessible, interoperable, and reusable – meaning it has robust and complete metadata.
FAIR data should be machine-actionable, meaning digital and complete with comprehensive metadata. Many librarians have worked to digitize historical data, which may be hard copy, and make it FAIR. Data management plans describe what, where, when, why, and who of managing research data. Scientists fill them out, and they outline the roles and activities for managing research data during and long after the research ends. They answer questions like, “Who is responsible for long-term preservation,” “Where will the data live,” “How do I keep my data secure,” and “Who pays for all of that?” Making all research data as FAIR and open as possible will improve the scientific process.
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