How do you maintain research data integrity?

1 Plan your data management

Before you start collecting any data, you should have a clear and comprehensive data management plan (DMP) that outlines how you will handle the data throughout the research lifecycle. A DMP should cover aspects such as data collection methods, formats, standards, quality control, storage, backup, security, access, sharing, documentation, archiving, and disposal. A DMP can help you avoid data loss, corruption, duplication, or misuse, and facilitate data reuse and verification. You should also review and update your DMP regularly to reflect any changes or challenges in your data management practices.

2 Document your data

Documentation is essential for keeping track of the data sources, methods, processes, and results in your research project. Documentation can include metadata, codebooks, lab notebooks, protocols, scripts, logs, reports, and publications. Documentation can help you and others understand, interpret, and reproduce the data and the research outcomes. You should document your data as soon as possible, using consistent and standardized formats, terms, and conventions. You should also document any changes, corrections, or modifications made to the data, and provide clear and accurate references and citations for the data.

3 Protect your data

Data protection involves safeguarding the data from unauthorized access, use, disclosure, modification, or destruction. Data protection can include physical, technical, and legal measures, such as locking cabinets, encrypting files, using passwords, applying access controls, complying with ethical and legal regulations, and obtaining informed consent. Data protection can help you prevent data breaches, leaks, theft, or tampering, and respect the privacy and confidentiality of the data subjects. You should also be aware of the potential risks and threats to your data security, and report any incidents or issues promptly.

4 Share your data

Data sharing is the practice of making the data available and accessible to other researchers, stakeholders, or the public. Data sharing can enhance the transparency, accountability, and credibility of your research, as well as foster collaboration, innovation, and impact. Data sharing can also reduce duplication, waste, and bias in research. You should share your data in a timely and responsible manner, using appropriate platforms, formats, licenses, and policies. You should also follow the FAIR principles of making your data findable, accessible, interoperable, and reusable.

5 Train your team

Training your team is a key component of maintaining research data integrity. Training can help your team members acquire the necessary skills, knowledge, and attitudes to handle the data properly and ethically. Training can also promote a culture of data quality, responsibility, and integrity within your team. You should provide regular and relevant training to your team members on topics such as data management planning, documentation, protection, sharing, and ethics. You should also monitor and evaluate your team's performance and compliance with the data management standards and policies.

6 Learn from others

Learning from others is a valuable way of improving your research data integrity. Learning from others can involve seeking feedback, advice, or guidance from your peers, mentors, supervisors, or experts on your data management practices and challenges. Learning from others can also involve participating in communities, networks, or events that focus on research data integrity, such as workshops, webinars, forums, or conferences. Learning from others can help you discover new ideas, insights, or solutions, as well as benchmark your data management practices against the best practices in your field.

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?