Sharing data pipelines: Why sharing data may not be enough, and what to do about it.

Kurzfassung

New research challenges and low-cost technological solutions drive the motivation to record behavior in multivariate ways using high temporal resolution. Making such data accessible and usable is more complicated than it may seem. Methods like ECG, EEG, and eye tracking can produce very large amounts of data in a short time. Further, context data to explain the observed behavior must be recorded as well. E.g., in a field study using an instrumented research vehicle, the position of the vehicle and the distance to the vehicle in front could act as context data. To make this multitude of data analyzable, data must be cleaned and fused in data pipelines. Cleaning happens in multiple stages, and requires decisions which have direct effects on patterns in the data. Time series data are often up- or down sampled, potentially altering characteristics of signals of interest. Sharing the data pipeline alongside an uncleaned version of the data therefore should be the default when publishing research results. Data science has developed a number of solutions to store and document data and data pipelines, whose benefits and costs will be discussed in this talk. These approaches can be structured in three interdependent dimensions: data storage, data processing, and competencies required by developers and users of data pipelines. Data from empirical studies can be very challenging to store, process, and document. Solutions to these issues do exist, but they require a training which is yet to be implemented in the typical Psychology curriculum.