Ten simple rules for recognizing data and software contributions in hiring, promotion, and tenure
Bibliographic record
Abstract
Changes in science practices are often perceived to be slow. It took about 10 years from the Collins and Tabak editorial on scientific reproducibility in 2014 [<a class="ref-tip" href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012296#pcbi.1012296.ref001">1</a>] to see data management mandates implemented by US funding agencies [<a class="ref-tip" href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012296#pcbi.1012296.ref002">2</a>]. However, open science practices have seen a sharp increase in adoption over the last few years, supported by policy (for example, those by the European Commission or the 2022 White House Office of Science and Technology Policy (OSTP) memo) [<a class="ref-tip" href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012296#pcbi.1012296.ref003">3</a>,<a class="ref-tip" href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012296#pcbi.1012296.ref004">4</a>] as well as new generations of digital tools and scientists who are embedding open values in their research practices. In this faster-paced open science environment, universities are key to fostering adoption among researchers. Universities drive implementation by advancing best practices and accounting for the needs and norms of diverse departments and disciplines. Universities are positioned to catalyze adoption of open practices through their academic evaluation processes, particularly, recruitment, tenure, and promotion. The capacity of researchers and instructors to engage with data and software scholarship will shape the next generation of students and scientists, and universities will play a crucial role in nurturing those skills by rewarding such contributions and expertise among their faculty. <a id="article1.body1.sec1.p2" class="link-target" name="article1.body1.sec1.p2"></a> 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 [<a class="ref-tip" href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012296#pcbi.1012296.ref005">5</a>] 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 [<a class="ref-tip" href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012296#pcbi.1012296.ref006">6</a>]. 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. <a id="article1.body1.sec1.p3" class="link-target" name="article1.body1.sec1.p3"></a> 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.157 | 0.318 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.015 | 0.021 |
| Scholarly communication | 0.029 | 0.023 |
| Open science | 0.009 | 0.011 |
| Research integrity | 0.017 | 0.019 |
| Insufficient payload (model declined to judge) | 0.016 | 0.020 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".