Making Sense of Sensors
In the making sense of sensors course I worked on setting up a research proposal, especially applying the FAIR principles and selected, converted and analyzed data, which was very educational to me in the area of planning and documentation. Moreover, it handled analyzing data in practice.
Gathering and analyzing data is important to validate products in design, this course improved my basic data analytics skills to be able to find correlations and conclude from this, using tests and graphs. Furthermore it taught to select and aggregate data to select meaningful data on its turn.
Furthermore, it dealt with selecting data from different diversely constructed datasets, representing different participants. You always have to check whether or not you have selected the right data, since you sometimes seem to have the right data, but time and dates might not be right when taking a closer look.
One of the most important things this course taught, is that you have to document what you initially want to do and what you actually did. In our executed research for example, not all participants generated data on the exact same dates. In theory, they might be able to wear the Mi Band every day, but in practice this was not the case. Instead of looking at it with hindsight, you should document the differences immediately to prevent making mistakes and forgetting things.
Additionally, it is important to make a research legal, ethical and FAIR. Holding on to tight regulations and formats give other researchers the possibility to use your research and prevents participants from getting harmed. Which is the most important condition for an executable research.