How do you combine research management with data management?

1 Define your research question and data needs

The first step to combine research management with data management is to define your research question and the data you need to answer it. This will help you plan your data collection, analysis, and dissemination strategies, as well as identify any ethical, legal, or technical issues that may arise. You should also consider the type, format, size, and quality of your data, and how they will affect your research design and methods.

2 Develop a data management plan

A data management plan (DMP) is a document that outlines how you will handle your data throughout the research lifecycle, from creation to preservation. A DMP can help you organize, document, store, protect, share, and reuse your data, as well as comply with any funder, publisher, or institutional requirements. You should create a DMP at the beginning of your research project and update it as needed.

3 Use appropriate tools and platforms

To combine research management with data management, you need to use appropriate tools and platforms that can facilitate your data workflows and collaboration. For example, you can use online surveys, databases, or repositories to collect, store, and access your data. You can also use software, applications, or cloud services to analyze, visualize, or publish your data. You should choose tools and platforms that are compatible, secure, and user-friendly.

4 Implement data quality and security measures

Data quality and security are crucial for ensuring the validity, reliability, and integrity of your research results. To combine research management with data management, you need to implement data quality and security measures throughout your research process. For example, you can use data validation, cleaning, or standardization techniques to improve the accuracy and consistency of your data. You can also use encryption, backup, or access control methods to protect your data from loss, theft, or unauthorized use.

5 Document and cite your data

Documentation and citation are essential for making your data understandable, reproducible, and discoverable. To combine research management with data management, you need to document and cite your data in a clear and comprehensive way. For example, you can use metadata, codebooks, or readme files to describe the content, context, and structure of your data. You can also use persistent identifiers, such as DOIs or ORCIDs, to link your data to your publications and profiles.

6 Share and reuse your data

Sharing and reusing data can enhance the visibility, impact, and value of your research. To combine research management with data management, you need to share and reuse your data in a responsible and ethical way. For example, you can use open access platforms, such as Zenodo or Figshare, to make your data available and citable. You can also use FAIR principles, such as findability, accessibility, interoperability, and reusability, to make your data more useful and usable for yourself and others.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?