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February 10, 2022
Data Management in Research - Why It’s Important in 2022
Data management in research is crucial for maintaining productivity, efficiency, and compliance in the lab.

What is Research Data Management

Research data management (or RDM) involves gathering, organizing, and maintaining information throughout each step of the research process. RDM also includes appropriate archiving of data once the project is complete. Thus, accessible data management needs to be an ongoing activity throughout the research lifecycle.

Having a data management plan in place is also essential for data sharing, in all formats, throughout your lab, and other labs or institutions. Data management plans can enable reuse and exact reproductions of your experiments. Also, data curation throughout its lifecycle can help to ensure its own long-term availability.

The Research Data Lifecycle

The research data lifecycle model illustrates each stage of data management and highlights how information flows throughout a research project from beginning to end. It includes six main stages:


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Step 1: Data Management Plan

A data management plan (DMP) is a document that outlines your strategy for managing data within the lab. It facilitates consistent data management throughout the information lifecycle. There are several important steps in the plan. These include:

  • Determination of the research funder requirements: Typically, each lab will develop its own protocol for the management and dissemination of data. You can save significant time at the outset by understanding the requirements set out by your research sponsor. Always bear in mind that your main objective is to create a plan that will optimize your project. This means that you can include more data than is originally required by your funding agencies so long as it is relevant for your research team.
  • Identify data to be gathered: Each step of your data management plan is dependent upon this one. Keeping an eye on data volume is important because of the personnel and infrastructure costs involved in managing data. Implementing a digital data management system can help with this.
  • Define how the data will be organized and managed: This is an area of data management that should evolve as the project evolves. Using a data management software solution will not only facilitate this, but it will also mean you don’t have to use multiple programs to generate and populate your data tables.
  • Explain data documentation: No matter how many data tables you present, they have little meaning without the relevant documentation explaining when, where, and how they were collected and interpreted. A LIMs can help you provide this documentation and make it easy to access and share from one main dashboard.
  • Explain quality assurance: Quality assurance and control are the two measures to ensure that you are compliant with lab standards and that you can improve the quality of your products or processes. These measures will stretch beyond research methods to incorporate training, verification tests, and instrument calibration. This data is invaluable for detecting errors and troubleshooting.
  • Create a safe storage strategy: One common mistake made by inexperienced and experienced lab managers alike is to assume that their personal computer or tablet will last indefinitely. This overconfidence causes them to forget to duplicate their data. Having a cloud-based data storage solution in place means that you don’t need to worry about lost data because the storage is safe and the data is backed up and accessible to all team members.
  • Describe data dissemination: You need to have a strategy in place for how and when a project’s data will be disseminated to others, particularly those who are outside of your research group. You have the option to share data in the following ways:
  • Passively: For example, posting project updates on your website or mailing them out on request.
  • Actively: For example, submitting data for the appendix of a published article.
  • Assign responsibilities: A comprehensive data management plan explains the roles and responsibilities of every member of the lab team who is working on the project. Roles may include data gathering, data entry, quality assessment and control, backup, data submission to storage, and systems administration.
  • Prepare a budget: Collating, managing, publishing, and sharing research data are all equally important parts of the process. Part of developing a data management program is to factor in the budget. Data administration takes time, and it’s important to remember that using a digital data management option can significantly cut your budget.
Step 2: Dataset Creation and Collection

Now you have your data management plan in place, it’s time to look at the different aspects of the process. Dataset creation and collection is an integral part of every original research project, but performing it manually lays you open to human error and can be very time-consuming. Having a digital curation center for your lab eliminates these issues. Other advantages include:

  • Increased productivity
  • Improved efficiency
  • Easy access and sharing
  • Safer storage
Step 3: Research Process

Each lab research experiment is conducted under highly rigorous conditions, ensuring accurate standards and measurements. For example, where and when the research takes place, which members of the team are participating, and which protocols are used. Managing your research data adequately allows for:

  • Manipulation of conditions
  • Control of variables
  • A safe environment
  • Accurate research output
Step 4: Dataset Analysis

Data management software allows you to analyze and compare different types of data with one overarching resource. This means there’s no need to go searching for data tables to compare or assess because they are already available in one common dashboard. Other advantages include:

  • Easy and effective validation
  • Optimum security compliance
  • Automated quality control workflows
  • Connection to external data sources
Step 5: Metadata Archiving

Digital lab information management systems facilitate the storage and tracking of metadata archives. This frees up time for the members of your lab team, allowing them to focus on new research projects. Other benefits include:

  • Easily search for identifiers
  • Simple formats and data archive
  • Elimination of lost data
  • Quick location of DOI (digital object identifier)
Step 6: Data Sharing

If you really want to enhance your data sharing capabilities, a lab management system is the way to go. Whether you’re sharing with a team member or a university library, or you want to implement open access or decide who gets to see the results of your research, a digital solution can provide both. It can also:

  • Improve communication with partners
  • Allow easy data sharing with sponsors
  • Ensure standards and protocols are being met
  • Streamline operations and reduce costs
Step 7: Data Reuse

These days, the best lab management systems go way beyond inventory management. They also have templates for data input, implementation, and sharing. This means that you can enjoy the benefits of reusable and consistent calculations and data sets. These include:

  • Consistent methodology
  • Easy reproducibility
  • Facilitates reuse
  • Extending the data curation lifecycle

The Benefits of RDM

The benefits of RDM are easy to see. A LIMS facilitates and enhances every step of your data lifecycle from plan and design to access and reuse. Some of the other many benefits include:

  • Helps you fulfill funder requirements
  • Keeps you in line with publisher protocols
  • Saves time, resources, and money
  • Connects you to other data services
  • Creates safe data repositories
  • Protects your intellectual property
  • Facilitates data citation

Enhance Your Data Management in Research with Genemod

Good data management practices are vital for your lab. Gathering, maintaining, and storing all information manually can be costly and increase your risk of loss and error.

With a Genemod digital data management solution, your lab data processes will be optimized. Contact us today to find out more about how we can help you manage data efficiently.

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