Researchers are tasked with both properly collecting and managing research data through the life of a project. Taken from the National Institutes of Health (NIH), “the data lifecycle represents all of the stages of data throughout its life from its creation for a study to its distribution and reuse. The data lifecycle begins with a researcher(s) developing a concept for a study; once a study concept is developed, data is then collected for that study. After data is collected, it is processed for distribution so that it can be archived and used by other researchers at a later date. Once data reaches the distribution stage of the lifecycle, it is stored in a location (i.e. repository, registry) where it can then be discovered by other researchers. Data discovery leads to the repurposing of data, which creates a continual loop back to the data processing stage where the repurposed data is archived and distributed for discovery."
To aid in the management of data, researchers use, or are often required to use, a Data Management Plan (DMP). A DMP is a formal document which outlines guidelines and requirements to help you organize and manage your data during and after the research project. There are many benefits for developing a robust DMP, such as:
- Saves time: Planning for your data management needs ahead of time will save you time and resources in the long run.
- Increases your research efficiency: Documenting your data throughout its lifecycle saves time because it ensures that in the future you and others will be able to understand and use your data.
- Meets grant requirements: Many funding agencies now require that researchers deposit in an archive data which they collect as part of a research project.
- Ensures compliance: To meet the requirements mandated of the institution or funding agency
- Improves accessibility: Ensuring that the quality and integrity of the data is maintained during and beyond the life cycle of the project
- Safeguards research data: By establishing appropriate storage, back-up, and management
Data management is best addressed in the early stages of a research project, and there are many resources to help you develop a data management plan.
Online Tool for Creating a DMP
The University of Arizona Libraries can provide you access to an online Data Management Planning (DMP) Tool. This DMP Tool includes a wealth of information and assistance to guide you through the process of creating a ready-to-use DMP for your specific research project and funding agency. Please visit the UA Libraries Data Management webpage to request access to this tool. For additional support, consultation and training, contact UA Libraries Data Management Services at Data Management Support.
If you prefer not to use the DMP Tool, you can use the Data Management Checklist below and any specific funding agency data management requirements to write your own DMP.
Data Management Checklist:
Ensure the DMP you create is consistent with data collection, use, and/or disclosure procedures outlined in your IRB approved Application.
Project Description
- What is the purpose of the research?
- What is the data?
- Are you using data that someone else produced? Is so, where is it from?
Documentation, Organization, and Storage
- What type of data will be produced?
- How and in what format will the data be collected? Is it numerical data, image data, text sequences or modeling data?
- Will it be reproducible? What would happen if it got lost or became unusable later?
- How much data will your project produce, and at what growth rate? How often will it change?
- Are there tools or software needed to create/process/visualize the data?
- How will you document data collection methods?
- Are you using metadata that is standard to your field? How will the metadata be managed and stored?
- What file formats will be used? Do these formats conform an open standard and/or are they proprietary?
- What directory and file naming convention will be used?
- What project and data identifiers will be assigned?
- Is there a community standard for data sharing/integration?
- What is your local storage and back up procedures? Will this data require secure storage?
Access, Sharing and Reuse
- What steps will be taken to protect privacy, security, confidentiality, intellectual property, or other rights?
- Who controls this data (e.g., PI, student, lab, University, funder)?
- Are there any special privacy or security requirements (e.g., personal data, high-security data)?
- Are there any embargo periods to uphold?
- Who holds intellectual property rights for the data and other information created by the project?
- Will any copyrighted or licensed material be used? Do you have permission to use/disseminate this material?
- If you allow others to reuse your data, how will the data be accessed and shared?
- Are there any sharing requirements (e.g., HIPAA regulations, funder data sharing policy)?
- Who is the audience? Who will use it now? Who will use it later?
- When will you publish it and where?
- Are there tools/software needed to work with the data?
Archiving
- How will the data be archived for preservation and long-term access?
- How long should it be retained (e.g., 6 years, 10-20 years, permanently)? Note, University policy is that data must be retained for a minimum of 6 years. When children are enrolled, informed consent documents must be maintained for 6 years after the youngest child in the research reaches the age of majority (18 years in Arizona).
- What file formats? Are they long-lived?
- Are there data archives that the data is appropriate for? Are these data archives subject or institutionally based?
- Who will maintain the data for the long-term?
Ethics and Privacy
- How will informed consent documents be handled? Note, University policy is that informed consent documents be maintained for 6 years after conclusion of the study. When children are enrolled, informed consent documents must be maintained for 6 years after the youngest child in the research reaches the age of majority (18 years in Arizona).
- How will privacy be protected, including any exceptional arrangements that might be needed to protect participant confidentiality, and other ethical issues that may arise?
Budget
- What is the cost for preparing data and documentation for archiving and how will it be paid?
Quality Assurance
- What procedures will be in place to ensure data quality and integrity during the project?
Requirements and Resources
Funding Agency Requirements
Many funding agencies require a DMP with every funding request. Each agency or directorate creates its own set of policies for data management. The Scholarly Publishing and Academic Resources Coalition (SPARC) has compiled an excellent resource of data management information and data sharing requirements from all the federal funding agencies: SPARC's Data Sharing Requirements by Federal Agency.
Resources
The UA Libraries offers many resources related to data management:
- A list of the top federal funding agencies of research being done at the University of Arizona and their associated data management requirements
- Examples of Data Management Plans from various federal agencies and universities
- Data Management Best Practices
- Workshops and Events on data management
- Services readily available to researchers
For more information, please visit to UA Libraries Research Data Management Services.
For more information about research data security and retention, please review our Data Security and Records Retention guidance.