Ten simple rules for recognizing data and software contributions in hiring, promotion, and tenure

Abstract

The ways in which promotion and tenure committees operate vary significantly across universities and departments. While committees often have the capability to evaluate the rigor and quality of articles and monographs in their scientific field, assessment with respect to practices concerning research data and software is a recent development and one that can be harder to implement, as there are few guidelines to facilitate the process. More specifically, the guidelines given to tenure and promotion committees often reference data and software in general terms, with some notable exceptions such as guidelines in [5] and are almost systematically trumped by other factors such as the number and perceived impact of journal publications. The core issue is that many colleges establish a scholarship versus service dichotomy: Peer-reviewed articles or monographs published by university presses are considered scholarship, while community service, teaching, and other categories are given less weight in the evaluation process. This dichotomy unfairly disadvantages digital scholarship and community-based scholarship, including data and software contributions [6]. In addition, there is a lack of resources for faculties to facilitate the inclusion of responsible data and software metrics into evaluation processes or to assess faculty’s expertise and competencies to create, manage, and use data and software as research objects. As a result, the outcome of the assessment by the tenure and promotion committee is as dependent on the guidelines provided as on the committee members’ background and proficiency in the data and software domains.

The presented guidelines aim to help alleviate these issues and align the academic evaluation processes to the principles of open science. We focus here on hiring, tenure, and promotion processes, but the same principles apply to other areas of academic evaluation at institutions. While these guidelines are by no means sufficient for handling the complexity of a multidimensional process that involves balancing a large set of nuanced and diverse information, we hope that they will support an increasing adoption of processes that recognize data and software as key research contributions.