Practical with the Data Information System (DAISY)

Data Information System (DAISY) is a data bookkeeping application designed to help biomedical research institutions with their GDPR accountability. In this practical you'll use DAISY to record your research project and the human-subject data involved in your project.

Required material

Before starting the practical, make sure you have the following ready, if not, please notify your session instructor.

• The URL link for the DAISY training instance.
• User credentials to login to the training instance.
• The example scenario of our workshop.

Exercise 1: Record project

Record your research project as per example scenario. Pay attention to the following:

• Document people related to the project:
  • The responsible persons for the project are your lab's PI, yourself and the PI of the sequencing platform.
  • Another postdoc from your lab is also working on the project (developing the pipeline), but he is not considered responsible for the human data.
  • You collaborator PI at the hospital is an (external) contact person, and needs to be documented as the provider of data/samples.
• Document that the ethics approval for your project is covered by a parent research programme.
• You have a draft data management plan for your project, generated in the earlier session. You can upload your DMP as a project document.

Exercise 2: Link project to its parent

There already exists a project definition in DAISY describing a research programme that encompasses several activities including your project. In this exercise, you're asked to:

• Search for the (parent) project record in DAISY for this research programme (HINT: it is categorised as an "epidemiological study", has a "CNER approval" and runs until 2022).
  • Make a note of the parent project's name.
  • Make a note of the other sub-project under the parent (you will need this info in Exercise 4).
• Locate your own project, the one you created in Exercise 1.
• Update your own project so that there is a structural link between your project and the parent/umbrella project.

Exercise 3: Record dataset

Record the samples and the data generated from those samples by creating a dataset within your newly created project. Pay attention to the following:

• The people responsible for the project are also the responsible for the dataset.
• Record the details about data using "data declaration(s)".
  • Record the source of your data, it is received from a collaborator i.e. the hospital.
  • Record what is received from collaborator and also what data is generated.
  • Record the fact that data subjects are (healthy) controls.
  • Record a storage duration for the data, what would be a reasonable duration?
  • Record the user restrictions on data.
• Record the GDPR legal basis for your processing of data.
• Record where you're storing the master and working copies of data.

Exercise 4: Record data access in and across projects

Record the data access policy you follow in your project. To do so:

• Locate your dataset, the one you created in Exercise 3.
• Document access policy for your dataset. Which categories of users have access to which locations within your project?
• Assume you shared your data with other project(s) under the same research programme. Document this fact.