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Competing Interests: The authors declare no competing interests.

Twelve evidence-based profiles of roles across the Clinical and Translational Science (CTS) workforce and two patient profiles were developed by CTS Personas collaborators in 2019 as part of the CTSA Program National Center for Data to Health (CD2H). Based on feedback received from the community, CTS Personas team members collaborated to produce five additional Personas to broaden representation of the CTS workforce and enhance the existing portfolio. This paper presents the rationale and methodology used in the latest CTS Personas initiative. This work also includes an implementation scenario incorporating multiple Personas. Using the new National Institutes of Health's (NIH) Data Management and Sharing Policy as an example, we demonstrate how administrators, researchers, support staff, and all CTS collaborators can use the Personas to respond to this new policy while considering the needs of service providers and users, CTS employees with short- and long-term needs, and interdisciplinary perspectives., Competing Interests: The authors report no competing interests., (© The Author(s) 2023.)

Twelve evidence-based profiles of roles across the translational workforce and two patients were made available through clinical and translational science (CTS) Personas, a project of the Clinical and Translational Science Awards (CTSA) Program National Center for Data to Health (CD2H). The persona profiles were designed and researched to demonstrate the key responsibilities, motivators, goals, software use, pain points, and professional development needs of those working across the spectrum of translation, from basic science to clinical research to public health. The project's goal was to provide reliable documents that could be used to inform CTSA software development projects, educational resources, and communication initiatives. This paper presents the initiative to create personas for the translational workforce, including the methodology, engagement strategy, and lessons learned. Challenges faced and successes achieved by the project may serve as a roadmap for others searching for best practices in the creation of Persona profiles., Competing Interests: The authors have no conflicts of interest to declare., (© The Association for Clinical and Translational Science 2020.)

Academic health centers, CTSA hubs, and hospital libraries experience similar funding challenges and charges to do more with less . In recent years academic health center and hospital librarians have risen to these challenges by examining their service models, and beyond that, examining their patron base and users' needs. To meet the needs of employees, patients, and those who assist patients, hospital librarians can employ the CTS Personas, a project of the Clinical and Translational Science Awards (CTSA) Program National Center for Data to Health. The Persona profiles, which outline the motivations, goals, pain points, wants, and needs of twelve employees and two patients in translational science, provide vital information and insights that can inform everything from designing software tools and educational services, to advertising these services, to designing impactful and collaborative library spaces., Competing Interests: Conflict of Interest Statement: The authors have no conflicts of interest to declare.

Data sharing is crucial to the advancement of science because it facilitates collaboration, transparency, reproducibility, criticism, and re-analysis. Publishers are well-positioned to promote sharing of research data by implementing data sharing policies. While there is an increasing trend toward requiring data sharing, not all journals mandate that data be shared at the time of publication. In this study, we extended previous work to analyze the data sharing policies of 447 journals across several scientific disciplines, including biology, clinical sciences, mathematics, physics, and social sciences. Our results showed that only a small percentage of journals require data sharing as a condition of publication, and that this varies across disciplines and Impact Factors. Both Impact Factors and discipline are associated with the presence of a data sharing policy. Our results suggest that journals with higher Impact Factors are more likely to have data sharing policies; use shared data in peer review; require deposit of specific data types into publicly available data banks; and refer to reproducibility as a rationale for sharing data. Biological science journals are more likely than social science and mathematics journals to require data sharing.

Data are the foundation of science, and there is an increasing focus on how data can be reused and enhanced to drive scientific discoveries. However, most seemingly "open data" do not provide legal permissions for reuse and redistribution. The inability to integrate and redistribute our collective data resources blocks innovation and stymies the creation of life-improving diagnostic and drug selection tools. To help the biomedical research and research support communities (e.g. libraries, funders, repositories, etc.) understand and navigate the data licensing landscape, the (Re)usable Data Project (RDP) (http://reusabledata.org) assesses the licensing characteristics of data resources and how licensing behaviors impact reuse. We have created a ruleset to determine the reusability of data resources and have applied it to 56 scientific data resources (e.g. databases) to date. The results show significant reuse and interoperability barriers. Inspired by game-changing projects like Creative Commons, the Wikipedia Foundation, and the Free Software movement, we hope to engage the scientific community in the discussion regarding the legal use and reuse of scientific data, including the balance of openness and how to create sustainable data resources in an increasingly competitive environment., Competing Interests: The authors have declared that no competing interests exist.

While research metrics may seem well established in the scholarly landscape, it can be challenging to understand how they should be used and how they are calculated. The Metrics Toolkit is an online evidence-based resource for researchers, librarians, evaluators, and administrators in their work to demonstrate or assess the impact of research.

Background: There is wide agreement in the biomedical research community that research data sharing is a primary ingredient for ensuring that science is more transparent and reproducible. Publishers could play an important role in facilitating and enforcing data sharing; however, many journals have not yet implemented data sharing policies and the requirements vary widely across journals. This study set out to analyze the pervasiveness and quality of data sharing policies in the biomedical literature., Methods: The online author's instructions and editorial policies for 318 biomedical journals were manually reviewed to analyze the journal's data sharing requirements and characteristics. The data sharing policies were ranked using a rubric to determine if data sharing was required, recommended, required only for omics data, or not addressed at all. The data sharing method and licensing recommendations were examined, as well any mention of reproducibility or similar concepts. The data was analyzed for patterns relating to publishing volume, Journal Impact Factor, and the publishing model (open access or subscription) of each journal., Results: A total of 11.9% of journals analyzed explicitly stated that data sharing was required as a condition of publication. A total of 9.1% of journals required data sharing, but did not state that it would affect publication decisions. 23.3% of journals had a statement encouraging authors to share their data but did not require it. A total of 9.1% of journals mentioned data sharing indirectly, and only 14.8% addressed protein, proteomic, and/or genomic data sharing. There was no mention of data sharing in 31.8% of journals. Impact factors were significantly higher for journals with the strongest data sharing policies compared to all other data sharing criteria. Open access journals were not more likely to require data sharing than subscription journals., Discussion: Our study confirmed earlier investigations which observed that only a minority of biomedical journals require data sharing, and a significant association between higher Impact Factors and journals with a data sharing requirement. Moreover, while 65.7% of the journals in our study that required data sharing addressed the concept of reproducibility, as with earlier investigations, we found that most data sharing policies did not provide specific guidance on the practices that ensure data is maximally available and reusable., Competing Interests: The authors declare there are no competing interests.