Title: Module 4: Assignment answers
Course: Teaching Open Science and Research Data Management for Undergraduates > Section: Module 4: Introduction to Research Data Management

1. Explain in your own words the difference between Open Data and data that complies with the FAIR principles. Is Open Data always FAIR? Is FAIR data always open?

Example answer: Open Data is freely available to everyone without restrictions, meant for use, reuse, and redistribution by anyone. FAIR data, on the other hand, follows principles that make data Findable, Accessible, Interoperable, and Reusable, with an emphasis on machine-readability and research quality.

Is Open Data always FAIR? – No, Open Data is not always FAIR. While it’s accessible to everyone, it may not meet the specific criteria for being easily findable, interoperable, or reusable by machines.

Is FAIR data always open? – No, FAIR data is not always open. FAIR data can have access restrictions, but its discovery metadata must be openly available.

2. Have a look at the research data lifecycle. Why is it important to consider the final step (reuse) already when starting with the planning?

Example answer: Considering the final step of reuse during the planning phase is crucial because it ensures that the data is managed in a way that supports long-term reusability, accessibility, sustainability, and sharing. By planning for reuse from the beginning, researchers can choose appropriate formats, document data thoroughly, and select the right licenses and repositories. This forward-thinking approach enhances the likelihood that the data will be valuable and usable for future research, education, or societal applications, making the entire research process more impactful and efficient.

3. What are the key differences between anonymized and pseudonymized data, and how does each type relate to GDPR compliance?

Answer: Anonymized data cannot be re-identified with reasonable effort and is not subject to GDPR compliance. Pseudonymized data, while protected, still contains codes or pseudonyms that can be re-identified with additional information, so it remains subject to GDPR regulations.

4. Are the following examples personal data, sensitive data, or special categories of personal data?

a. Car license number (answer: Personal data. It can identify an individual but does not fall under the sensitive data categories defined by GDPR.)

b. Child’s chickenpox diagnosis (answer:  Special category of personal data. It pertains to health information, which is classified as sensitive under GDPR.)

c. Cousin’s home address (answer: Personal data. It can identify an individual but is not classified as sensitive data under GDPR.)

d. Your fingerprint (answer: Special category of personal data. It is biometric data used for uniquely identifying a person, which is classified as sensitive under GDPR.)

e. Endangered bird nesting location (answer: Potentially sensitive data. Ecological information about endangered species can be considered sensitive data because its disclosure could lead to harm to the species, such as poaching or habitat disruption.)