Highlighting Rigor, Reproducibility, and Transparency of Research in an Academic CV

ABSTRACT

In academia, the curriculum vitae (CV) is the central document used for hiring, promotion, or tenure applications. The content and format of these CVs and accompanying documents varies among institutions and is a direct reflection of the institution's values. Few institutions consider rigor, reproducibility, or transparency practices for hiring, promotion, or tenure decisions. We developed a new CV template for Stanford's School of Medicine for researchers to highlight their use of those practices, making it possible to evaluate faculty not just on research reports, but how they do the research itself. The new additions include listing (and linking) availability of protocols, analysis plans, analytic code, data, public accessibility of results for research outputs; registration number and result reporting for clinical trials; details of peer review activities; and candidate's persistent identifier (e.g., ORCID iD). We also include a variety of practices supporting research rigor and reproducibility that can be described in a candidate's narrative statement. For those who dedicate time and resources to maximize the rigor, reproducibility, and transparency of their work, our new CV template makes these efforts visible, a precondition for valuing them. This can serve as a model for other institutions, while future advances in scholarly metadata and systems could facilitate easier collection and reporting.

1. Introduction

The academic curriculum vitae (CV) is a requirement for decisions about hiring, promotion, or tenure. The CV contents are typically dictated by templates and their required elements. Those elements greatly differ between countries and institutions [1, 2, 3, 4, 5, 6, 7], as do criteria for advancement. Since any element not in the academic CV cannot be considered for professional advancement, the CV therefore embodies institutional values, powerfully impacting how academic faculty conduct their research and their careers. This behavior is also shaped by formal decisional criteria. While some institutions use quantitative criteria, for example, minimum four publications co‐authored with a student, principal investigator on at least one government grant, etc. [8], others provide more qualitative phrasing (e.g., “demonstrated excellence in the field”), which can be harder to assess [9].

A numerical publication requirement incentivizes faculty to maximize those numbers, potentially at the expense of quality or more impactful work (e.g., longer term projects that generate fewer publications) [10]. Practices recommended to increase biomedical research rigor, reproducibility, and transparency can increase the time needed to plan, conduct, or report research, so if institutions want their faculty to adopt such practices, they must be rewarded for their use, which is done at few institutions. Analysis of hiring and promotion criteria in 92 biomedical institutions (randomly selected from 2017 Leiden ranking of world universities) showed that only one mentioned data sharing, and none mentioned use of reporting guidelines, study preregistration, or open access [1]. Similarly, none of 15 top Canadian universities mentioned these criteria in the latest version of their guidelines, from 2004 to 2018 [2]. In 2019, out of 129 randomly selected North American and Canadian institutions, only 5% mentioned open accessibility of publications [3]. In 2020, none of 25 German medical university tenure application forms mentioned preregistration, protocol, data, code sharing, open access, or trial results reporting [5]. Finally, an analysis of 107 Universities documents from 2019 to 2021 across seven countries (Austria, Brasil, Germany, India, Portugal, UK, USA) showed no mention of open access or data sharing [6].

Recognizing this gap, we developed a new template for the Stanford School of Medicine's academic CV and candidate's statement that makes researchers' rigor, reproducibility, and transparency practices visible in the academic record.

1.1. Stanford School of Medicine CV Template and Candidate Statement

The Stanford University Faculty Handbook [9] and criteria [11] as well as Research Policy Handbook [12] are the main policy documents describing hiring, promotion, and tenure processes at Stanford University. The Stanford School of Medicine has a guide “How to Organize Your CV/Candidate's Statement” [13] that includes sample CV templates, of which the one for Professoriate consists of 15 required sections: (1) Personal Information; (2) Educational Background; (3) Professional Appointments; (4) Honors and Awards; (5) Scholarly Publications; (6) Grants; (7) Clinical Trials; (8) Patents; (9) Editorial Service; (10) Service as Grant Reviewer; (11) University Administrative Services; (12) Service to Professional Organizations; (13) Presentations; (14) Community Service; and (15) Trainees. The latest version of the template, updated in July 2021, served as our starting point for the new template development.

In addition to the academic CV, all Stanford appointment and promotion packages include a three page narrative “candidate's statement,” that provides a “faculty member the opportunity to explain their contributions, achievements and future plans in the research, clinical, teaching and administrative arenas.” The following key elements are recommended for scholarship:

• Major contributions and achievements.

• Major publications and scientific discoveries and how they have impacted knowledge/further research in the field and/or patient care.

• If you have performed a significant amount of collaborative work, which Stanford also values, comment on that work and your role in it.

The emphasis in the Stanford researcher assessment package is on research outputs, not how the research is conducted. But in an effort to focus on the quality of the researcher's best work, and not on the volume of work, it requests that bibliometric indicators not be part of the evaluation, stating: “The School of Medicine does not use citation metrics or impact factors (e.g., H‐index, Google Scholar score, etc.), so please do not include these.”

1.2. New CV and Candidate Statement Template Development

We know of no systematic analyses of academic CV templates from universities. In recent years, funders and other institutions in Europe and the US have piloted narrative CV templates, often building upon research assessment reform recommendations [14]. An overview of those reforms is presented in our Table 1 (updated and adapted from 2018 Moher et al. [15]). A 2025 scoping review of 22 funders' narrative CV templates indicated that only 1 (5%) recommended mentioning open source, open research, and open access, while 4 (18%) mentioned preprints, and 7 (32%) data sharing [16]. Our recommendations were developed from those, including from biosketches and templates from the National Institutes of Health (NIH), the National Science Foundation (NSF), and the Institute of Education Sciences (IES) [17]; and various public dashboards, as well as transparency indicators and badges (e.g., SCIscore [18]), Transparency audit and leaderboard [19], ResearchSquare methods and data sharing badges [20], Dimensions AI Trust Markers [21], ScholarGPS [22], and PLOS Open Science Indicators [23].

TABLE 1.

Proposed solutions for assessing scientists, adapted and updated from Moher et al. [15].

Initiative	Proposed solutions for assessing scientists
DORA (2012)	DORA made one general recommendation—do not use journal‐based metrics, such as JIFs, as surrogate measures of the quality of individual research articles to assess an individual scientist's contributions in hiring, promotion, or funding decisions and 17 specific recommendations A) for researchers: (1) focus on content; (2) cite primary literature; (3) use a range of metrics to show the impact of your work; (4) change the culture; B) for funders: (5) state that scientific content of a paper, not the JIF of the journal in which it was published, is what matters; (6) consider value from all outputs and outcomes generated by research; research institutions: (7) when hiring and promoting, state that scientific content of a paper, not the JIF of the journal in which it was published, is what matters; (8) consider value from all outputs and outcomes generated by research; C) for publishers: (9) cease to promote journals by impact factor; (10) provide an array of metrics; (11) focus on article‐level metrics; (12) identify different author contributions, open the bibliometric citation data; (13) encourage primary literature citations; and D) for organizations that supply metrics: (14) be transparent; (15) provide access to data; (16) discourage data manipulation; and (17) provide different metrics for primary literature and reviews.
ACUMEN (2014)	The consortium developed criteria and guidelines for Good Evaluation Practices (GEP) and designed a prototype for a web‐based ACUMEN performance portfolio. The GEP focuses on three indicators for academic assessment: (1) expertise, (2) outputs, and (3) impacts. The performance portfolio is divided into four parts: (1) narrative and academic age calculation, (2) expertise, (3) output, and (4) influence.
REWARD (2014)	The REWARD series made 17 recommendations covering a broad spectrum of stakeholders. We mention here only a few: (7) reward with funding and academic or other recognition reproducibility practices and reproducible research, and enable an efficient culture for replication of research; (12) institutions and funders should adopt performance metrics that recognize full dissemination of research and reuse of original datasets by external researchers.
Ioannidis (2014)	The author proposed 12 best practices to achieve truth and credibility in science. These include: (1) large‐scale collaborative research; (2) adoption of a replication culture; (3) registration; (4) sharing; (5) reproducibility practices; (6) better statistical methods; (7) standardization of definitions and analyses; (8) more appropriate (usually more stringent) statistical thresholds; (9) improvement in study design standards; (10) stronger thresholds for claims of discovery; (11) improvements in peer review, reporting, and dissemination of research; and (12) better training of the scientific workforce. These best practices could be used as research currencies for promotion and tenure. The author provides examples of how these best practices can be used in different ways as part of the reward system for evaluating scientists.
Ioannidis PQRST (2014)	To reduce reliance on traditional quantitative metrics for assessing and rewarding research, the authors proposed a best practice index—PQRST—revolving around productivity, quality, reproducibility, sharing, and translation of research. The authors also propose examples on how each item could be operationalized, for example, for productivity examples include number of publications in the top tier percentage of citations for the scientific field and year, proportion of funded proposals that have resulted in 2:1 published reports of the main results, and proportion of registered protocols that have been published 2 years after the completion of the studies. Similarly, one could count the proportion of publications that fulfill 2:1 quality standards; proportion of publications that are reproducible; proportion of publications that share their data, materials, and/or protocols (whichever items are relevant); and proportion of publications that have resulted in successful accomplishment of a distal translational milestone, for example, getting promising results in human trials for interventions tested in animals or cell cultures, or licensing of intervention for clinical trials.
The Leiden Manifesto (2015)	The Leiden Manifesto proposed 10 best practices: (1) quantitative evaluation should support qualitative, expert assessment; (2) measure performance against the research missions of the institution, group, or researcher; (3) protect excellence in locally relevant research; (4) keep data collection and analytical processes open, transparent, and simple; (5) allow those evaluated to verify data and analysis; (6) account for variation by field in publication and citation practices; (7) base assessment of individual researchers on a qualitative judgment of their portfolio; (8) avoid misplaced concreteness and false precision; (9) recognize the systemic effects of assessment and indicators; and (10) scrutinize indicators regularly and update them. Abiding by these 10 principles, research evaluation can play an important part in the development of science and its interactions with society.
Wilsdon (The Metric Tide) (2015)	The report proposed five attributes to improve the assessment of researchers: (1) robustness; (2) humility; (3) transparency; (4) diversity; and (5) reflexivity.
NAS (2015)	The authors “believe that incentives should be changed so that scholars are rewarded for publishing well rather than often. In tenure cases at universities, as in grant submissions, the candidate should be evaluated on the importance of a select set of work, instead of using the number of publications or impact rating of a journal as a surrogate for quality.” Incentives should also change to reward better correction (or self‐correction) of science. It includes improving the peer‐review process and reducing the stigma associated with retractions.
Mazumdar (2015)	To assess the research component of a biostatistician as part of a team collaboration, the authors proposed the following criteria: design activities, implementation activities, analysis activities, and manuscript reporting activities.
Benedictus (UMC Utrecht, 2016)	To move away from using bibliometrics to evaluate scientists, the authors proposed five alternative behaviors to assess: (1) managerial responsibilities and academic obligations; (2) mentoring students, teaching, and additional new responsibilities; (3) if applicable, a description of clinical work; (4) participation in organizing clinical trials and research into new treatments and diagnostics; and (5) entrepreneurship and community outreach.
Nature (2016)	The journal proposed two solutions: first, “applicants for any job, promotion or funding should be asked to include a short summary of what they consider their achievements to be, rather than just to list their publications.”, second, “journals need to be more diverse in how they display their performance”.
Moher et al. (2018)	The authors proposed six principles: (1) research that addresses societal needs; (2) assessing faculty should be based on responsible indicators that reflect fully the contribution to the scientific enterprise; (3) publishing all research completely and transparently, regardless of the results, should be rewarded; (4) the culture of Open Research needs to be rewarded; (5) it is important to fund research that can provide an evidence base to inform optimal ways to assess science and faculty; (6) funding out of the box ideas needs to be valued in promotion and tenure decisions.
NASEM (2020)	Workshop atendees proposed: “The importance of focusing future efforts on identifying core challenges with the current system, such as transparency, consistency, and clarity.”; “instead of valuing what we measure, let's measure what we value.”; “scholarship evaluation should acknowledge the time scale of different research approaches (e.g., convergent science) and consider the impact of the source of the research funds on faculty accountability”.
The Hong Kong Principles for assessing researchers (2020)	Five principles were proposed: (1) responsible research practices; (2) transparent reporting; (3) open science (open research); (4) valuing a diversity of types of research; and (5) recognizing all contributions to research and scholarly activity.
NASEM (2022)	Workshop atednees proposed: “Non‐tenure‐track faculty account for 91% of the growth in faculty positions between 1975 and 2011. Today, only one in three faculty members are either tenured or on the tenure track.” “Spitzmueller cited research that shows that having diverse faculty increases the diversity of the graduate student body.” “The onboarding experience should provide new faculty members with an opportunity to learn about the institution and its culture, identity, and values.”
Paris Call (2022)	The Committee proposed researchers should be evaluated on the basis of their intrinsic merits and impact, rather than on the number of publications and where they are published, promoting qualitative judgment provided by peers, supported by a responsible use of quantitative indicators. Calls for a research assessment system that: values the diversity of research activities and outputs such as publications and preprints, data, methods, software, code and patents, as well as their societal impacts and activities related to training, innovation and public engagement; rewards not only research outputs, but also the appropriate conduct of research, and values good practices, in particular open practices for sharing research results and methodologies whenever possible; values collaborative work, as well as cross‐disciplinarity and citizen science, when appropriate; supports a diversity of researcher profiles and career paths.
LERU Framework for the Assessment of Researchers (2022)	The League of European Research Universities (LERU)—23 universities—proposed that universities should use a multidimensional perspective expected from academics today (research, training, impact, service). For research assessment: (1) contribution to the subject area through expertise, research output, scientific impact and influence; (2) recognition in the scientific community; (3) track record in funding; (4) collaborations and interdisciplinarity; (5) research strategy; (6) advancement and enablement of junior researchers (for some universities fall under education); (7) open Science (proposed, but not included in the paper's table of the 6 solutions). For education assessment: (1) engagement in high‐quality teaching, including individual supervision and feedback/assessment of student performance; (2) development of learning tools and methods; (3) reflection on teaching practices and curriculum development (includes teaching disadvantaged and minority groups); (4) educational engagement outside the university. For public engagement and outreach assessment—no agreed upon list of categories, includes: patient care in the university hospital, industry collaboration and technology transfer activities, contribution to north–south development programs, activities of government advice and public policy, science communication and public impact through the media, and engagement in professional practice and professional bodies, citizen science. For service to the institution—university positions and activities. They also consider the duties and responsibilities their researchers assume in their institution or in larger collaborations, although this dimension is rarely formally elaborated or structured. Two additional dimension are proposed: (2) developmental perspective, focusing on transversal dimensions such as leadership, innovation, and collaboration. To develop these dimensions over their careers, researchers need to develop themselves and their interpersonal skills. (3) contextual perspective, taking into account the particular context of the researcher who is under assessment. The term context can refer both to the professional circumstances in which the researcher works, as to his or her personal situation. The document also contains examples from 14 universities and their assessment procedures.
Coalition on Advancing Research Assessment (CoARA 2022)	The Coalition proposed that diverse outputs are rewarded, practices and activities that maximize the quality and impact of research, practices that contribute to robustness, openness, transparency, and the inclusiveness of research and the research process including: peer review, teamwork and collaboration. Originality of ideas should be rewarded, the professional research conduct, and results beyond the state‐of‐the‐art. Addtionally, tasks like peer review, training, mentoring and supervision of PhD candidates, leadership roles, and, as appropriate, science communication and interaction with society, entrepreneurship, knowledge valorization, and industry‐academia cooperation, team science and collaboration.
Project Tara (2023 planned dashboard)	The project propsed a dahsboard to highlight universities that reward: (1) conducting research involving, and having impact on, local communities; (2) service, teaching and mentorship; (3) openness and rigor; (4) efforts to improve equity, diversity, and inclusion.
Cobey et al. (2023)	The core set of 19 open science practices identified by participants will form the foundation for institutional dashboards that display compliance with open science practices, among which are: registering clinical trials before starting recruitment, sharing study data openly at time of publication (with limited exceptions), sharing study code openly at time of publication (with limited exceptions), use of reporting guideline checklists reporting author conflicts of interest, reporting author contributions, use of ORCID identifiers, reporting results of clinical trial in a registry within 2 years.
HELIOS Joint Statement on Reforming Hiring, Reappointment, Tenure, and Promotion (2023)	*Not endorsed by all HELIOS members*. Highlights the commitment to engage in dialog on assessment reform, have regular meetings, share progress reports, and produce framework, guidance, resources and trainings.
DORA Responsible use of METRICS (2024)	Because no indicator can capture the complexities of research quality in one number, this document describes five principles that can help prevent indicators from being misused in assessment: (1) be clear; (2) be transparent; (3) be specific; (4) be contextual; (5) be fair.

Scientific rigor is typically defined as the strict application of the scientific method to ensure robust and unbiased experimental design, methodology, analysis, interpretation, and reporting of results [24]. A study has good reproducibility if its design, data gathering, analysis, and inferences are reported with sufficient transparency and completeness so that it can be re‐run and corroborated. Reproducibility requires transparency, that is, the availability and accuracy of study protocols, registrations, statistical analysis plans, and methods. While computational reproducibility implies obtaining the same (statistical) results by re‐running the published analysis using the researchers' detailed methods and shared code or data [25, 26].

Based on all of the above, we compiled our recommendations and then shared them with the Stanford Program on Research Rigor and Reproducibility (SPORR) advisory board, the Vice‐Dean of the School of Medicine, and the Stanford Office of Academic Affairs, who approved them for official posting as an option for CV formats. We present them here with explanatory notes. The full CV template with Rigor and Reproducibility items and the accompanying guideline document can be found at the Stanford's Office of Academic Affairs website [13]. A summary of the new CV additions is presented in Figure 1, with detailed explanations of items explained below. The new candidate's statement suggestions are presented in Box 1.

FIGURE 1.

New recommendations to enhance the Stanford CV with annotated bibliography, peer review experience, clinical trial registration and reporting transparency, and presentation availability.

BOX 1. Rigor and reproducibility elements to include in the candidate's statement.

• Use and sharing of study protocols with analysis plans, code, data and meta‐data.

• Documented re‐use of shared research products.

• Use of data management practices and tools in the research group to assure version control and trackable data curation from point of acquisition to published research outputs (e.g., images, figures and tables).

• Use of research methods or designs to maximize internal validity, for example, detailed protocols, blinding, randomization, sample size calculations, independent replications and validation.

• Use of validation for biological materials and reagents.

• Use of independent code/data/figure/table reproducibility checks.

• Registration and results reporting practices of all clinical research.

• Use of a lab manual and orientation practices to teach R&R practices, tools, philosophy and expectations of all lab members.

• How R&R practices are implemented in multi‐team collaborations.

• Contribution to or participation in replication efforts.

• Development and/or dissemination of R&R tools and practices to others.

• Recognitions for R&R practices received.

1.2.1. New Recommendations Explained

1. Researcher's Persistent Identifier

   1. Report persistent digital identifier (e.g., ORCID iD).
2. Annotated Bibliography

   1. Annotate bibliography (after the standard reference format) with information and links to the protocol, analysis plans, code sharing, data sharing, and public availability (e.g., open access status). Additionally, for clinical trials, we recommend adding the trial registration number. The annotation can be done using a combination of symbols (e.g., —present, 🚫 = Absent, N/A = Not applicable) or hyperlinks for ease of readability and accessibility. Example:
      Author. Title. Journal. Date. DOI. Pre‐registration 🚫 Protocol 🚫 Analysis plan 🚫 Code—N/A Data Public Access
   2. Summarize their practices at the start of the bibliography by listing the number of their research outputs and number or percentage of the above listed indicators. Example:
      Peer‐reviewed original research (14 total, 1 in press, 1 submitted).
      Rigor and Reproducibility Summary (Protocols shared: 6, Analysis plans shared: 5, Code shared: 3, Data shared: 7, Publicly accessible articles: 12).
3. Clinical Trial Registration and Results reporting

   1. Links to trial registries and date when the results were reported in the registry.
4. Peer Review Records

   1. Public reviewer profile identifier (e.g., ORCID, or Web of Science/Publons iD).
   2. List the number and (when available) links to review reports, including preprint reviewers, post‐publication reviews, and conference reviews. Example:
      Review activity: 2023 BMC Medicine—( 1 open peer review ) 2024 PubPeer—( 2 post publication reviews )
5. Annotated Presentations

   1. Describe and link presentations (e.g., slides) and video recordings. If the materials are available, but have not been published previously, we recommend depositing them at Stanford Digital Repository [27] or other public repositories [28]. Like for the annotated bibliography above, we recommend using both symbols and links. Example:
      Title. Conference. City. Country. Date. Presentation materials Video

2. Discussion

The academic CV is a statement of institutional values, reflecting activities and achievements that the institution deems meritorious of academic rewards (e.g., appointment, promotion, and other forms of recognition). The first among these is how the institution assesses the importance and quality of research, which in turn profoundly affects how faculty members conduct their work. A sine‐qua‐non of high quality research is that the findings or claims are true, but that can only be ascertained over times typically longer than needed for institutional decisions. More easily measured short‐term bibliometric proxies are therefore often used, such as the number of publications and journal prestige. These imperfect measures often affect research quality adversely, as has been described in many reports calling for research assessment reform [29, 30, 31]. Several investigations have also demonstrated that the quality and completeness of reporting in published papers is typically insufficient to reproduce the research or assess the quality of findings [32]. Stanford's new CV template allows research quality to be assessed not just from the published record, but from the reliability of the methods used to produce it and the openness of the research to scrutiny by others. It complements the Stanford efforts to reduce the role of bibliometrics in such assessments.

The value of research transparency is not just for external scrutiny; it might be most important for the research team itself. The same practices making it possible for others to assess the quality and integrity of the work allow team members to do the same. We believe a major contributor to non‐reproducible research is data pipelines that only specific individuals within a research group understand, or that involve multiple hand‐offs among team members. The principal investigator attesting to the trustworthiness of the data pipeline may have never observed or even fully understood it, particularly with large datasets. That attestation, recommended by ICMJE and required by some journals [33, 34], is often merely a statement that the PI trusts the people involved. Data pipelines supported by systems allowing interrogation by the entire team, or at a minimum by the research guarantor, are much more likely to withstand external scrutiny. Our new CV template allows these processes to be assessed as part of a researcher's evaluation, and can thereby be an incentive to use them.

The new template, however, cannot be fully effective in isolation. The values it embodies need to be supported by other aspects of the institutional enterprise, from education to monitoring and technical support, and even as a routine part of research presentations. Similarly, these values must be ultimately embraced by other institutions and within disciplines. Letters from external referees play a large role in faculty assessment, and if those referees come from places or disciplines that do not value the practices included in the new template, that will be reflected in their letters.

Research has also shown that students and early career researchers feel more is expected of them than of the previous generations, that is, that they require more publications for promotion and that those publications are expected to adhere to open, rigorous, and transparent practices. However, they also perceive that these practices have a positive effect on the credibility and visibility of their work, as well as future collaborations, and they welcome better alignment between academic norms and reward systems [35]. Recent increase of code and data sharing mandates or replication packages by scholarly journals [36], especially in the fields of psychology, sociology, and economics, point to cultural shifts happening across disciplines. This has also been followed by the increase in preprint uptake across disciplines, with more than a quarter of recent mathematics outputs being preprinted [37].

This Stanford new template is currently optional, to not penalize candidates who do not use the included practices. It is hoped that as its usage increases and as rigor and reproducibility practices become more widely used that many of its elements will be incorporated into the standard CV.

2.1. Limitations

We do not yet know how often faculty will use this CV format and how it will affect the adoption of the practices it reports. We will be monitoring both of those elements going forward. Also, the new CV elements need to be added manually. While more services are integrating ORCID iDs with publishing and submission systems, journals and publishers still rarely implement and share machine readable metadata for these items. Furthermore, no current reference management software allows easy addition of those items, even when they are available as relationship metadata (e.g., in Crossref, or OpenAlex). We hope that future developments will allow easy formatting of references to match the recommended or similar formats, and even expand it to include additional information (e.g., conflict of interest and ethics statement availability, funding information, CREDIT contribution, etc.). Examples of recent developments in this area include publication fact labels announced for Open Journal Systems [38], and Collaborative Metadata Enrichment Taskforce (COMET) which is working on, among other things, increasing linkage of preprints, data, and code, with peer‐reviewed publications [39].

3. Conclusions

The R&R CV allows faculty efforts to maximize the rigor and reproducibility of their research to be recognized and rewarded. It aligns with Stanford School of Medicine's commitment to encouraging these practices and to reducing dependence on bibliometrics in researcher assessments. The more universities that adopt models like these, the more these practices can become normative.

Funding

Work of MM and SNG on the development of the template was supported by the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Numbers UL1TR003142 and UM1TR004921. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The work of MM and SNG is also supported by the Stanford School of Medicine Research Office.

Disclosure

Protocol and analysis plan availability: This project was not preregistered as no statistical analysis was planned or needed.

Reporting: No reporting guideline was used.

Ethics Statement

The authors have nothing to report.

Conflicts of Interest

The authors declare no conflicts of interest.

Declarations

Additional ICMJE criteria: All authors approved this version to be published, agreed to be accountable for their contributions, and agreed to address any questions related to the accuracy or integrity of any part of the work, including of parts they were not personally involved in.

Presentations at meetings/conferences: The draft of the template was presented at the Incentivizing Collaborative and Open Research (ICOR) Public Meeting on July 20, 2023, and at the Transforming Research Cultures event, held on March 15, 2024.

Data Availability Statement

CV template and guidelines and the candidate’s statement guidelines are available at Stanford Digital Repository at: https://doi.org/10.25740/pv042xk5014. No code was produced for this study.