Best practices for data veracity: Avoid surprises

Quality control and assurance of data can ensure that you and others can trust the data you generate during your research. This paradigm is important for both open data and data reused even within your team. Developing a QAQC plan before data collection ensures that you are well prepared for your data, capture the salient elements of the data, and provide the appropriate structures for data preservation. Avoid unpleasant surprises at the analysis stages by planning ahead. The most bang for your buck is ensured by QAQC planning, and practice planning data collection a priori leads to better data.