7 Best Practices Every Research Team Should Consider When Managing Data

Discovery lies at the heart of research data, but without sound, ongoing data management practices, even the most valuable datasets can be rendered unusable, unshareable, or unpreservable. It doesn't matter whether you're conducting an individual project or working as part of a collaborative global research group, what you do with your data matters.

Proper research data management makes your data organized, secure, and reusable, not just during a project, but long after it has finished. It also meets ethical obligations, funder requirements, and publication requirements. And with the support of tools like myLaminin, it's even easier to apply good practices.

Here are seven key practices every research team should consider when managing data.

1. Plan for Data Management Early

One of the most common mistakes researchers make is waiting too long to think about how they’ll handle their data. Ideally, data management should be part of your project design — not an afterthought.

Start by developing a Data Management Plan (DMP). This document outlines how you’ll collect, store, document, and share data throughout the research lifecycle. Many funders now require a DMP as part of the grant application process.

Platforms like myLaminin can help teams map their DMP with built-in templates into actionable steps, ensuring early planning translates into consistent daily practices.

2. Organize Data with Clear Structures and Naming Conventions

Unclear file names and folder structures quickly lead to confusion — especially on large, geographically distributed teams or long-term projects.

Establish a folder hierarchy early and make sure all team members follow it. Shared workspace platforms like myLaminin allow you to structure projects logically and maintain versioned files, which helps avoid accidental overwriting or mislabeling. A recycle bin capability also allows you to restore file that may have been inadvertently or maliciously deleted.

3. Use Standardized Metadata

Metadata is a description of your data and is needed to make it discoverable, understandable, and reusable.

Supply minimal metadata like title, creator, date, and file type, and where possible, adhere to existing metadata standards like the DDI standard for social science data. Embedded metadata features in myLaminin allow researchers to annotate, manage, and organize DataCite, DDI, or Dublin Core metadata without the need for technical assistance.

Better metadata also facilitates the eventual deposit of your data in a repository, which is a growing expectation by funders and journals to meet FAIR and Open Science principles.

4. Control Access and Permissions Carefully

Data access needs vary across project teams. Some members might need full editing capabilities, while others may only require read-only access. Managing these permissions manually through email or cloud drives can lead to accidental data loss, security risks, or data breaches.

myLaminin allows role-based access control allowing you to assign permissions based on a team member’s role on the project. This helps maintain data security and keeps sensitive or restricted data appropriately protected in adherence to your DMP protocols.

5. Track Changes and Version History

Research data often evolves — files are cleaned, re-coded, merged, and edited. Without clear version control, it's easy to lose track of changes or overwrite important work.

Using a system that automatically saves previous versions — like myLaminin's built-in version control — can save time and prevent mistakes. This feature also supports reproducibility by allowing others to trace how files were modified throughout the project lifecycle.

6. Back Up Data Regularly and Securely

Accidental deletion, hardware failures, or cyber threats can compromise years of work. That’s why regular, automated backups are essential.

A best practice is the 3-2-1 rule: keep three copies of your data, on two different types of storage, with one copy stored offsite or in the cloud. Platforms like myLaminin store data securely in encrypted, versioned repositories, ensuring that your backups are both accessible and protected.

7. Prepare for Sharing and Archiving

At the end of a project, your data should be ready for others to use — whether that’s for replication, follow-up research, or institutional archiving.

That means:

• Cleaning and documenting datasets

• Packaging supporting files (codebooks, consent forms, README files)

• Choosing an appropriate repository

Many institutions and journals now require data sharing upon publication, so it is wise to be ready in advance. myLaminin helps groups package data into shareable formats and comply with ethical and legal requirements and allows for publication of those datasets, after compliance with metadata standards, to institutional Dataverse or Borealis instances for DOI minting.

Final Thoughts

Effective data management isn't just a means of being organized, it's also a means of strengthening your research. It allows for collaboration, improves transparency, and keeps your results relevant years after your project has been finished.

As demands for open science, data sharing, and reproducibility increase, it's more important than ever to manage research data carefully. The good news is that technologies such as myLaminin eliminate the uncertainty of RDM. Good RDM services provide researchers with tools to easily manage data repositories, apply role-based access controls, maintain metadata, manage file versioning, and secure archiving.

Short answer: good data management preserves your work, makes it more valuable, and helps to create a more open, collaborative research culture for everyone.

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Keagan James (article author) is a myLaminin intern studying Arts and Business at the University of Waterloo.