What Research Project Metadata Should be Captured to Support FAIR and Open Science Principles?

Alain Lai
May 28
4 min read

Updated: 4 days ago

In today’s data-driven research landscape, the FAIR principles (findable, accessible, interoperable, reusable), along with overarching Open Science practices, are frameworks that redefine how data is managed, shared, and leveraged. These frameworks are essential in promoting transparency, fostering collaboration, and ensuring that different pieces of research remain valuable over long periods.

At the core of this movement is research project metadata, which is structured and standardized information that outlines or locates datasets. Metadata acts as the backbone of effective Research Data Management (RDM), empowering different datasets to be easily classified, understood, and reused for numerous goals. Let's explore the key metadata elements that are crucial in supporting alignment with FAIR and Open Science Principles:

Descriptive Metadata

Information that defines your research, profiling it so that others can understand what it is and how to find it. Key examples of this include:

Project Title: Provides a concise headline summarizing the key thesis of your research and the promoted hypothesis. This allows potential users to quickly gain an understanding of what your project’s goal was and whether your findings relate to the topic they are researching.
Keywords: Important terms or concepts that relate to your research. When standardized classification vocabularies such as MeSH (Medical Subject Headings) are used, your data becomes easily discoverable through online databases, making it accessible to the right parties.
Principal Investigators: Names, affiliations, and backgrounds of the researchers involved in the study, providing credibility and a glimpse into the research’s approach.
Research Discipline: Classification of the work under a specific academic domain, such as the OECD Fields of Science, which allows your data to be surrounded by other pieces of similar work in the field.

Descriptive metadata is crucial for increasing work visibility, ensuring that others can locate and leverage your project even before fully digging into the raw data.

Administrative Metadata

The management and legal aspects of research data, including who owns the insights, who established the existing restrictions, and the circumstances under which the data can be accessed or repurposed. Key examples of this include:

Funding agency or Grant Numbers - This information is often a requirement by the provider of funds as it promotes accountability. With this, data repository groups can segment different projects by funding source, streamlining large-scale evaluations and audits.
Project Timeline - Start and end dates which provide crucial context for the dataset, particularly in industries where the timing of data collection can drastically affect the outcome, i.e. climate science.
Ethics Approval - In projects that relate to human or animal subjects, there must be a publication of the ethical board approvals and consent details. This informs future users that the data was collected ethically and any possible restrictions in the case of data repurposing.
Licensing and Access - Definition of what people can do with your data. For example, some research findings may be restricted and purely published for knowledge sharing due to privacy concerns. Meanwhile, putting datasets under a Creative Commons license would indicate that others can reuse them, given proper attribution.

Administrative metadata is essential for ensuring compliance with institutional and funding mandates and promoting legal and ethical data repurposing.

Technical Metadata

The “how” behind your dataset, including how the inputs and outputs were generated, stored, and structured. Key examples of this include:

File Formats & Software - Detailing the format of your files, e.g. .csv, .xlsx, .psd, and the software that will be needed to access them, e.g. R, ArcGIS, allows observers to decide if they can even use your data.
Instrumentation - Listing out the tools, methodologies, and frameworks used to collect and process the data, which is crucial, particularly in field-based research, where the precision and data collection process drastically affect outcomes.
Data Structure - Explanation of the data organization, such as whether the rows of names are the subjects or the researcher's names. For more complex pieces of data, this promotes observer comprehension and understanding.
Version control - Projects often evolve and expand beyond their base version, so tracking these developments will allow users to know what datasets they are currently observing and whether there have been developments since release.

Technical metadata empowers others to understand your research process, approach to making your conclusions, and possibly reproduce your process.

Conclusion

Overall, comprehensive metadata is a crucial piece of research that allows data and findings to retain their usefulness beyond just its original project. Without it, research becomes more tedious and difficult, which ultimately undermines the goals of both FAIR and Open Science ideologies.

Ultimately, metadata is not just a backend concern for data managers; rather, it is a crucial part of research. By making the collection of descriptive, administrative, and technical metadata a priority, researchers will contribute to fostering a more transparent and effective scientific ecosystem.

If you are interested in maintaining the effectiveness of your work in the long run, transforming a dataset into a lasting scholarly resource, you should look into platforms like myLaminin. As a comprehensive and secure Research Data Management solution, myLaminin supports researchers in the research lifecycle from data collection through to archiving. With important features like metadata capture, REB integration, and a complete audit trail, research teams can be confident knowing they meet FAIR and Open Science requirements. By leveraging innovative technology like myLaminin, researchers can reduce operational friction, safeguard confidential research, and ensure their work provides sustainable value for years to come.

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