Seven Key Steps for Effective Research Data Management

Discovery is essential in research, but sound data management practices are just as vital. Without them, even the most valuable datasets can become unusable, unshareable, or unpreservable. It doesn’t matter if you are working on an individual project or as part of a global research team. How you manage your data truly matters.

Proper research data management keeps your data organized, secure, and reusable. This is important not just during a project but long after it has concluded. Good data management helps you meet ethical obligations, funder requirements, and publication standards. Thankfully, tools like myLaminin make it easier to implement these practices.

1. Plan for Data Management Early

One common mistake researchers make is delaying data management considerations. Ideally, planning should be integrated into your project design — not relegated to an afterthought.

Begin by creating a Data Management Plan (DMP). This document outlines how you will collect, store, document, and share data throughout the lifecycle of your research. Many funders now require a DMP as part of the grant application.

Platforms like myLaminin help teams develop their DMP with built-in templates and actionable steps. This ensures that early planning translates into consistent daily practices.

2. Organize Data with Clear Structures and Naming Conventions

Unclear file names and folder structures can lead to confusion, especially in large, geographically dispersed teams or long-term projects.

To prevent this, establish a clear folder hierarchy from the beginning. Ensure that all team members adhere to it. Shared workspace platforms like myLaminin allow for logical project structuring and versioned file maintenance. This prevents accidental overwriting or mislabeling. Additionally, a recycle bin feature can help restore files that may have been deleted unintentionally or maliciously.

3. Use Standardized Metadata

Metadata is crucial for describing your data. It makes your data discoverable, understandable, and reusable.

Provide essential metadata such as title, creator, date, and file type. Where possible, adhere to established metadata standards like the DDI standard for social science data. The embedded metadata features in myLaminin enable researchers to annotate and manage DataCite, DDI, or Dublin Core metadata without needing technical assistance.

Using better metadata facilitates the eventual deposit of your data in a repository. This is increasingly expected by funders and journals in line with FAIR and Open Science principles.

4. Control Access and Permissions Carefully

Data access requirements can differ within project teams. Some members may need full editing capabilities, while others may only need read-only access. Managing permissions manually through email or cloud drives can lead to accidental data loss or security risks.

myLaminin lets you implement role-based access control, allowing you to assign permissions based on each member's role in the project. This maintains data security and ensures sensitive or restricted data remains protected according to your DMP protocols.

Why Data Permissions Matter

Understanding the nuances of data access can be a game-changer. Protecting sensitive data contributes to overall project integrity. The right access controls ensure that only qualified individuals can edit or view critical information.

5. Track Changes and Version History

Research data often undergoes changes; files may be cleaned, re-coded, merged, or edited. Without a reliable version control system, it's easy to lose track of modifications or overwrite crucial work.

Using a system that automatically saves previous versions, like myLaminin’s built-in version control, can be a lifesaver. This feature saves time and prevents mistakes, supporting reproducibility by allowing others to trace how files were modified throughout the project lifecycle.

6. Back Up Data Regularly and Securely

Accidental deletions, hardware failures, and cyber threats can jeopardize years of work. Therefore, regular and automated backups become essential.

Adhere to 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. myLaminin securely stores data in encrypted, versioned repositories, ensuring your backups are both accessible and protected.

Creating a Backup Plan

A solid backup plan should include schedules and checklists for reviews. Regularly test backup restorations to ensure data recovery processes work correctly in case of an emergency.

7. Prepare for Sharing and Archiving

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

Preparation involves:

• Cleaning and documenting datasets

• Packaging supporting files, such as codebooks and consent forms

• Selecting an appropriate repository

  
  

Many institutions and journals now require data sharing upon publication. Thus, it’s crucial to be ready in advance. myLaminin aids groups in packaging data into shareable formats that comply with ethical and legal requirements. This allows for the publication of datasets after meeting metadata standards for DOI minting to institutional Dataverse or Borealis instances.

Final Thoughts

Effective data management is not just about organization; it is also about strengthening your research. It fosters collaboration, improves transparency, and keeps your results relevant long after your project has concluded.

As demands for open science, data sharing, and reproducibility increase, managing research data carefully is more important than ever. The good news is that technologies like myLaminin eliminate much of the uncertainty associated with research data management. Good RDM services provide researchers with tools to manage data repositories, apply role-based access controls, maintain metadata, track file versions, and secure archiving.

Short answer: Good data management preserves your work, enhances its value, and fosters a more open, collaborative research culture for all.

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