Analysis
In VRmaze, the run data is recorded in each trial of the experiments. This raw data is saved as a set of time trackers. These time trackers, of various types, allow all VRmaze data to be stored over time.
The interest of VRmaze lies in the possibility of exploitation of these saved data sets. In the form of data extraction, VRmaze offers a simple tool to calculate results according to the user's needs.
Editing a result file?
In VRmaze, a result file cannot be modified, at least, during normal use. Only the DataRecords can be manipulated and allow the addition of new nodes in the tree of the pass, which modifies its structure (The original protocol remains intact in a conventional use).
Methodology
The methodology for extracting results is simple: each data set (DataRecord) can be assigned an extraction filter. The result of the extraction produces a new DataRecord which can, in turn, be filtered and so on.
Note that a filtered dataset can just as easily create a new dataset on its own, or a dataset with the result of a calculation. For example, in VRmaze there is a total time calculation filter that takes a dataset as a parameter and extracts the time difference between the first tracker and the last. This total time filter outputs this total time value on the one hand, but also a new dataset, resulting from the original dataset. In the case of this filter, the resulting data set is identical to the input data set. Therefore, it is possible to use this new dataset to extract a new value or a new dataset.
Add filter
Actually, a DataRecord can be filtered by as many filters as needed. It actually has a filter list that accommodates all the desired filters as a direct child.
Saving filtered data
In VRmaze, only the root DataRecord contains data (Trackers). The child filters do not store the extraction results in the result file, they are recalculated when the file is opened. This ensures a contained result file weight.
Extraction structure
The difficulty lies in translating your needs into an extraction structure, which requires knowing where to find the basic dataset and knowing a minimum of the extraction filters available in the software.
Converting a dataset
Adding an extraction filter can not only reduce a dataset, but also and above all create a new dataset with a new type of tracker. This procedure is automatic and cannot be manipulated by the user. For example, it is possible to compute a discrete velocity from a position dataset. This new velocity dataset allows the creation of a dataset storing the discrete acceleration from these velocities. It is impossible to calculate an acceleration dataset from a position dataset in VRmaze. This calculation becomes possible with the velocity extraction filter.
Data conversion
Some filters are hidden because they are not compatible with the selected dataset. Each added filter can possibly convert a dataset into another type, making all filters applicable to this type of tracker accessible at the same time.
Example of a structure
Let's imagine a Morris Pool in which the subject has moved. We are asked this question: how long did the subject spend at a distance of 4 meters around the target?