  High-quality computational and data-intensive (CDI) applications are critical
for advancing research frontiers in almost all disciplines. Despite their
importance, there is a significant gap due to the lack of comprehensive best
practices for developing such applications. CDI projects, characterized by
specialized computational needs, high data volumes, and the necessity for
cross-disciplinary collaboration, often involve intricate scientific software
engineering processes. The interdisciplinary nature necessitates collaboration
between domain scientists and CDI professionals (Xperts), who may come from
diverse backgrounds.
  This paper aims to close the above gap by describing practices specifically
applicable to CDI applications. They include general software engineering
practices to the extent that they exhibit substantial differences from those
already described in the literature as well as practices that have been called
pivotal by Xperts in the field.
  The practices were evaluated using three main metrics: (1) participants'
experience with each practice, (2) their perceived impact, and (3) their ease
of application during development. The evaluations involved participants with
varying levels of experience in adopting these practices. Despite differing
experience levels, the evaluation results consistently showed high impact and
usability for all practices.
  By establishing a best-practices guide for CDI research, the ultimate aim of
this paper is to enhance CDI software quality, improve approaches to
computational and data-intensive challenges, foster interdisciplinary
collaboration, and thus accelerate scientific innovation and discovery.
