5 Elements Of A Comprehensive Data Management Plan

You’ll need a comprehensive data management plan if you have a project requiring scientific data collection. Furthermore, the National Science Foundation (NSF) makes it mandatory for researchers to attach a data management plan (DMP) for most projects that require gathering or producing data except those that are collected during public engagements.

The purpose of having a comprehensive data management plan is to explain how you intend to handle all the data collected during the project. The plan should also provide details on the preservation of the data for future re-use in similar or different studies.

It’s crucial for your data management plan to be detailed. It’s because this will be evaluated by peer reviewers along with the project proposal, thus contributing to whether your project will be approved. With that said, here are five elements of a comprehensive data management plan:

1. Access, Sharing, And Privacy Of Data

The access, sharing, and privacy of the data collected during research are crucial in the DMP because you have to describe which data would be shared and with whom. You’ll also have to describe the resources utilized, such as equipment, systems, and professionals hired.

For instance, if you’re hiring an IT support service, you’ll have to provide details about them and the system they use to manage the data. In addition, you can also outline the benefits of having IT support services for the project.

You can also indicate the process of accessing the data and when the data can be made available to the public. At this point, you’ll have to consider any specific data handling regulations and how to best adhere to them.

2. Intellectual Property Rights

Another element that has to be included in the DMP is the intellectual property rights (IP) clause. In this section, you’ll provide details on who owns the data and who can license its use in other projects. You can also state how the owner of the IP intends to protect their rights over the data. However, you should ensure that the IP claims on the data collected align with your country’s IP laws and regulations and can be enforced.

3. Roles And Responsibilities

Provide a detailed list of all the staff members involved in the project and the role they play in data management. The roles and responsibilities of the staff should be assigned depending on their expertise to ensure proper data handling. You can use HR analytics in your organization to determine the best people to work with. 

You can also consider the process of changing data management personnel if the person responsible leaves the organization or is unavailable. In addition, you can indicate how often evaluations would be made to ensure that all personnel adhere to the DMP and are executing their roles and responsibilities as required. This would ensure your group sticks to the plan and stay calm.

4. Ethics And Legal Requirements

When collecting data from individuals, you must consider ethics and legal requirements. Private data is critical; if mishandled, it can damage the individual user as a subject in the research. Thus, the first step should be acquiring consent from the participants in the research.

Another essential aspect is ensuring that the person whose data is being used understands the study. Thus, you have to explain the project to them and the data you’ll be collecting. The person can then agree to which data they’re comfortable sharing. Therefore, you must obtain their informed consent to protect you and the participants.

In the DMP, you should have details on the ethics that will be adhered to in data handling, including protecting the privacy of the sample subjects. You can also indicate how the project adheres to all data protection and legal privacy requirements. This is essential in protecting the confidentiality of the sample data collected.

5. Costs

Once you’ve established the cost of data management during the project, you can discuss with the sponsors where it should be indicated. For instance, the funding agency might want the costs of data management indicated in the DMP. In contrast, others might want the costs consolidated with the project budget included in the proposal.

Furthermore, the costs should include data management during and after the project because the data has to be preserved. If there are prospects of licensing the data to other users, you can also include the anticipated income. 

Conclusion

When drafting a data management plan, you must be detailed and provide all the necessary information. This is because the DMP plays a crucial role in the approval of your project by sponsors and peer reviewers. There are many elements of a DMP, but this article has outlined the essential ones. Furthermore, ascertain the type of data you’ll collect before creating a DMP to determine the important elements that must be present.