We produce a mind-boggling 2.5 quintillion bytes of data each day. In fact, 90% of the data in the world today was generated over the past two years alone.1 This is a statistic that researchers and scientists can well identify with, given the fact that they, too, create and collect vast amounts of data during their work. In an age of data-driven research, the proper management of research data is essential for ensuring its integrity, accessibility, and usability. Effectively managing, collating, and archiving data is a crucial skill that early career researchers and even experienced academics need to develop.
Research data management is the process of organizing, documenting, storing, and sharing research data. But what does research data management mean? Why is it important? Below, we will answer these questions and provide expert tips on how to develop effective research data management plans for each research project.
The importance of research data management
While conducting research, a large amount of data is generated, which must be labeled, organized, and stored correctly to allow easy access for future use. This is where research data management comes in. It entails the organizing of different forms of data, from quantitative and qualitative, digital and physical, to raw and processed. In practice, research data includes data derived from surveys, interviews, experiments, observations, simulations, models, images, videos, audio files, software code, and working models. Research data management helps us in:
- Ensuring the quality and integrity of our data
- Avoiding data loss or corruption in case of unforeseen circumstances
- Facilitating data analysis and interpretation
- Complying with ethical and legal requirements
- Meeting the expectations of funders, publishers, and reviewers
- Enhancing the visibility and impact of research
- Enabling data reuse and collaboration
In other words, efficient research data management makes research more accessible by organizing it in a structured manner that allows easy access when required.
Developing an effective research data management plan
A research data management plan outlines how to manage research data during research work. It is essential to ask the following questions when creating an effective research data management plan –
- What data will we collect or generate, and how? It is important to clearly describe the types of data collected, including their formats, structures, and relationships.
- How will we document and describe our data? Outline the organization scheme and file naming conventions to be used. Define data variables, units of measurement, and any associated metadata.
- How will we protect our data from unauthorized access or misuse? A robust research data management plan includes measures to protect data integrity, confidentiality, and security. Define the level and type of security measures that will be required to protect research data. This safeguards sensitive data from unauthorized access or loss.
- How will we share our data with others? Identify the intended audience for data sharing and determine access restrictions if applicable. Specify the platforms or repositories where data will be deposited, along with any embargo periods or licensing requirements.
- How will we preserve our data for the long term? Determine the duration for which data should be retained after the completion of the project. Identify strategies for long-term data preservation, including considerations for format migration and future accessibility.
A good research data management plan is clear, concise, and consistent. It is also flexible to accommodate changes or challenges that arise with any groundbreaking research. Here are some expert tips to help you create an effective research data management plan:
- Start early: Ideally, one should create a research data management plan before collecting or generating data for research. This helps in planning and avoids any potential issues later on.
- Check established guidelines: Different investors, institutions, journals, and disciplines generally have different requirements or standards for their research data management, as per their requirements. It is essential to go through the relevant guidelines and policies before creating a research data management plan and ensure that it is in line with institutional policies.
- Use a template: Several templates and tools are available online that help in creating an efficient research data management plan. For example, DMPonline is a web-based tool that provides customized templates based on the funder or institution for which you are making the research data management plan. You can also refer to the work of other researchers in your field to help create a customized template for your needs.
- Seek feedback: It is always a good idea to seek input from supervisors and peers on a research data management plan that may have worked well for them. You can use ideas from their experience to create a research data management plan that works well for you.
- Review and update: Remember that a research data management plan is not a static document. It should be reviewed and updated regularly throughout the research project to reflect changes or developments in your data or methods during research.
Developing and implementing a sound research data management plan can help researchers and PhD students better manage their data, making it more accessible and ensuring the quality and integrity of the research data. By taking the time to plan and organize your research data management, you can streamline your research process, avoid potential issues, and improve the overall quality of your research.
Reference:
- Marr, B. How Much Data Do We Create Every Day? The Mind-Blowing Stats Everyone Should Read. Forbes, May 2018. Available online at https://www.forbes.com/sites/bernardmarr/2018/05/21/how-much-data-do-we-create-every-day-the-mind-blowing-stats-everyone-should-read/?sh=aef8d3860ba9
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