Your role: Principal Investigator (PI)

Introduction

As a Principal Investigator (PI), you may have recently acquired project funding. More and more funders require data management plans (DMP), stimulating the researcher to consider, from the beginning of a project, all relevant aspects of data management.

Funders often refer to the FAIR principles. Applying these principles to your research data would greatly ease reusing and repurposing of data, either by you or others, and enable automation of processes.

Data management responsibilities

Your data reflects objective research, generating independent, high quality and reproducible results. Managing, monitoring and ensuring data integrity in collaborative research projects is thus an essential aspect of research.

In your role of PI, you may need to:

• Define your project’s data management strategy, plan resources and budget, via a data management plan submitted to the funder.
• Define data responsibilities and roles, to create awareness and collaboration in your team.
• Anticipate the ethical and legal aspects of your project in an early stage, like protecting human data against unauthorised access.
• Consider a common work environment and lab notebook, to limit the risk of information loss and unauthorised access, and start creating metadata from the beginning of a project.
• Ensure maximum reproducibility, such as data organisation, data documentation and providing workflows and code.
• Share data as it allows others to build upon your work, enables meta-analysis, increases visibility as it is a requirement for grant funding.

Data management guidance

RDMkit pages

• At the heart of FAIR science lies good data management practice. The RDM life cycle pages guide you in complying with the FAIR requirements of funders.
• A DMP should address a broad range of data management aspects, so it is important to be aware of the current best practices in DMPs.
• To organise data management in collaborative projects, it will benefit from a formalised way of working via a Data Management Working Group (DMWG).
• The costs of data management page helps you budget for your project, including costs for data storage and preservation.
• The national resources pages provide country-specific guidance, to help you choose the best services, tools and pipelines to manage your data.
• The human data page gathers information that needs to be taken into consideration when working with human data. Make sure to protect the data in your project well and prevent unauthorised access.
• Consider your data storage needs in an early stage, including long-term storage at the project end.
• The data organisation page helps you with file naming, versioning and folder structures.
• Data documentation, like README files and metadata, help secondary users to understand and reuse your data.
• The data processing and data analysis pages provide useful tips, including ensuring maximum reproducibility
• The data publication page guides you in publishing your data via a public (domain-specific) repository.
• To make research more robust, consider reusing existing data yourself.

Other resources

• Your institution may have web pages about RDM. Check if there is an institutional RDM office and/or data steward(s), and contact them for support and training available.
• Data Stewardship Wizard (DSW) guides you through creating a data management plan.

Contributors