Research collaboration and networking can enhance your skills, expand your knowledge, and increase your impact. However, sharing and managing your research data with others also poses some challenges and risks. How can you safeguard your research data when collaborating? Here are some tips and best practices to help you protect your data integrity, security, and ethics.
Before you start working with other researchers, make sure you have a clear and mutual understanding of your roles, responsibilities, and expectations. Discuss how you will collect, store, access, analyze, and share your data, and what standards and protocols you will follow. Establish a data management plan (DMP) that outlines the goals, methods, and outcomes of your collaboration, and how you will handle any issues or disputes. Review and update your DMP regularly as your project progresses.
One thing I have found helpful was that the Roles and Responsibilities should be specific and every member of the collaborative community should use the limited resource within the corners of his/her role. This is how the intrigue, influence, interception and transgression can be controlled. Collaborative work often is riskier but these risks can be mitigated by adopting some security measure and a confidence in the team leader. Everyone wants appreciation, recognition and acknowledgement which is a human nature, but if everyone gets fair recognition and appreciation wouldn’t encroach and transgress.
When you collaborate with others, you need to use tools that allow you to communicate, store, and share your data effectively and securely. Choose platforms and services that have robust encryption, backup, and authentication features, and that comply with your institutional and ethical policies. Avoid using personal or unapproved devices or accounts to access or transfer your data. Use version control and metadata to track and document any changes or updates to your data.
One of the most important aspects of safeguarding your research data is respecting the rights and interests of the data owners and subjects. Depending on the nature and source of your data, you may need to obtain consent, permission, or approval from the relevant parties before you can use or share their data. You also need to adhere to the ethical and legal guidelines for protecting the confidentiality, anonymity, and sensitivity of your data. Use anonymization, encryption, or access control techniques to prevent unauthorized or inappropriate use of your data.
By collecting data from multiple diverse resources, you may find a repetition of data/opinion/concluding synopsis’. It’s important to reference each resources with equal validity, providing credit to each contributor, whether a smaller or larger data owner, as long as each source is proven valid. Diversity in contribution and ownership is fundamental in the formation of painting an accurate reflection to the question at hand.
Another way to safeguard your research data is to acknowledge and cite the sources of your data, whether they are your own or from others. Giving proper credit and attribution to the data creators and contributors not only shows respect and integrity, but also helps to avoid plagiarism, duplication, or misrepresentation of your data. Follow the citation standards and formats of your discipline, journal, or funder, and use persistent identifiers (such as DOIs) to link to your data sources.
Give proper acknowledgement to everyone who contributed to your project either in the list of authors or in the acknowledgement section.
Sharing your research data can benefit both you and the wider research community, but it also requires careful consideration and planning. Before you share your data, make sure you have the right and permission to do so, and that you have prepared your data for reuse and discovery. Use trusted and reputable data repositories or platforms that meet the FAIR principles (findable, accessible, interoperable, and reusable), and that provide adequate metadata, documentation, and licensing information. Choose a license that reflects your preferences and conditions for how others can use your data.
Finally, safeguarding your research data is an ongoing process that requires regular monitoring and review. You need to check the quality, accuracy, and validity of your data throughout your collaboration, and address any errors, inconsistencies, or gaps. You also need to evaluate the impact and outcomes of your data sharing, and collect feedback and suggestions from your collaborators and users. You can use metrics, indicators, or surveys to measure and improve your data performance and quality.
Been wondering, in a a funded collaborative study, where the funding agency only needs a report of the study, who does the data belong to? the Principle Investigator? Can the PI publish without including the others? What if the others are not interested?