The creation of a corpus of compositions in symbolic formats is an essential step for any project in systematic research. There are, however, many potential pitfalls, especially in early music, where scores are edited in different ways: variables include clefs, note values, types of barline, and editorial accidentals. Different score editors and optical music recognition software have their own ways of storing and exporting musical data. Choice of software and file formats, and their various parameters, can thus unintentionally bias data, as can decisions on how to interpret potentially ambiguous markings in original sources. This becomes especially problematic when data from different corpora are combined for computational processing, since observed regularities and irregularities may in fact be linked with inconsistent corpus collection methodologies, internal and external, rather than the underlying music. This paper proposes guidelines, templates, and workflows for the creation of consistent early music corpora, and for detecting encoding biases in existing corpora. We have assembled a corpus of Renaissance duos as a sample implementation, and present machine learning experiments demonstrating how inconsistent or naïve encoding methodologies for corpus collection can distort results.