Kristi L. Holmes

State-supported research funding agencies are critical to the scientific enterprise. However, it remains unclear how funding agencies cooperate with academic communities to realize common scientific goals. Here, we present a fully digital archive assembled by the National Human Genome Research Institute (NHGRI), focusing on the nascent stages of “genomics” as a scientific field and the everyday workings of the Human Genome Project and subsequent major genomics projects. We identify early events behind the conception of genome-wide association studies, clarify hitherto obscured factors around funding decisions, and how NHGRI and academics outside NHGRI ensured continuity in technical expertise across projects. The computational models we developed correctly recapitulate how academic experts and NHGRI increased adoption of genomics by jointly deciding which organisms’ genomes to sequence. Taken together, these findings reveal how a funding agency contributed to scientific innovation in a nascent field of science by repeatedly cooperating with the broader scientific community.

As generative artificial intelligence (GenAI) revolutionizes how research is conducted, it also challenges traditional methods of scholarly evaluation. Productivity metrics such as publication and citation counts are widely understood to be poor proxies for gauging meaningful impact. These metrics are becoming even less reliable as GenAI accelerates text-based and computational work while leaving other forms of research labor (e.g. community engagement, in-person mentorship and team development) largely unaffected. This uneven effect risks exacerbating existing evaluative biases. We argue that evaluation reforms should be organized around two categories of ‘distinctly human contributions’ that are indispensable to research, but which are inadequately captured by metrics: (1) the epistemic-ethical category, encompassing situated judgment under accountability (e.g. deciding what to trust, justifying that decision, and standing behind it); and (2) the socio-relational category, encompassing sustained forms of valuable human engagement (e.g. mentoring, teaching, community partnership and trust-building). We suggest practical mechanisms for supporting evaluation reform including modified CRediT (Contributor Role Taxonomy) statements, recognition of a broader array of outputs, and strengthened narrative CVs and third-person testimonies. However, we acknowledge that these suggestions, particularly those relying on narrative self-presentation, are themselves vulnerable to GenAI manipulation and are insufficient on their own. If distinctly human contributions to research require judgment and relationships that resist automation, then evaluation cannot be reduced to instruments designed to minimize human evaluative effort. GenAI, therefore, does not require entirely new systems of evaluation. Rather, it increases the cost of avoiding what good and ethically sound performance evaluation has always required.

The Contributor Roles Taxonomy (CRediT) uses 14 standard roles to specify scholarly contributions to research publications. CRediT has been adopted by hundreds of journals and integrated into journal submission workflows. In 2022, CRediT was formalized as a standard by the American National Standards Institute (ANSI) and the National Information Standards Organization (NISO), providing a sustained support system and custodianship. Subsequently, forming the CRediT Standing Committee aimed to ensure inclusive and transparent evolution and enhance understanding and adoption of CRediT in the research community. This paper outlines the CRediT Standing Committee's ongoing strategies to support uptake and reduce ambiguity and confusion when using roles. These strategies include developing user instructions to provide guidance on how to use CRediT, creating persona profiles to improve understanding of existing roles and designing badges for each role to make them more memorable. These resources will address challenges reported by the scholarly community or identified by the CRediT Standing Committee and will be shared openly upon completion.

Researchers have been using generative artificial intelligence (GenAI) to support writing manuscripts for several years now. However, as GenAI evolves and scientists are using it more frequently, the case for mandatory disclosure of GenAI for writing assistance continues to diverge from the initial justifications for disclosure, namely (1) preventing researchers from taking credit for work done by machines; (2) enabling other researchers to critically evaluate a manuscript and its specific claims; and (3) helping editors determine if a submission satisfies their editorial policies. Our initial position (communicated through previous publications) regarding GenAI use for writing assistance was in favor of mandatory disclosure. Nevertheless, as we show in this paper, we have changed our position and now support instituting a voluntary disclosure policy because currently (1) the credit due to machines for assisting researchers is moving below the threshold of requiring recognition; (2) it is impractical (if not impossible) to accurately specify what parts of the text are human-/GenAI-generated; and (3) disclosures could increase biases against non-native speakers of the English language and compromise the integrity of the peer review system. Consequently, we argue, it should be up to the authors of manuscripts to disclose their use of GenAI for writing assistance. For example, in disciplines where writing is the hallmark of originality, or when authors believe disclosure is beneficial, a voluntary checkbox in manuscript submission systems, visible only after publication (rather than a free-text note in the manuscripts) would be preferable.

Technology influences Open Science (OS) practices, because conducting science in transparent, accessible, and participatory ways requires tools and platforms for collaboration and sharing results. Due to this relationship, the characteristics of the employed technologies directly impact OS objectives. Generative Artificial Intelligence (GenAI) is increasingly used by researchers for tasks such as text refining, code generation/editing, reviewing literature, and data curation/analysis. Nevertheless, concerns about openness, transparency, and bias suggest that GenAI may benefit from greater engagement with OS. GenAI promises substantial efficiency gains but is currently fraught with limitations that could negatively impact core OS values, such as fairness, transparency, and integrity, and may harm various social actors. In this paper, we explore the possible positive and negative impacts of GenAI on OS. We use the taxonomy within the UNESCO Recommendation on Open Science to systematically explore the intersection of GenAI and OS. We conclude that using GenAI could advance key OS objectives by broadening meaningful access to knowledge, enabling efficient use of infrastructure, improving engagement of societal actors, and enhancing dialogue among knowledge systems. However, due to GenAI’s limitations, it could also compromise the integrity, equity, reproducibility, and reliability of research. Hence, sufficient checks, validation, and critical assessments are essential when incorporating GenAI into research workflows.

Medical films, once a staple of medical education in the twentieth century, are now scarce collection items. Many 16 mm films have been weeded from collections because of their outdated content and obsolete format, and those that remain can suffer rapid deterioration from improper storage conditions. Digitization is imperative to preserve these remnants of our cultural heritage. After digitization is complete and rights are cleared, libraries typically make their collections openly accessible online, but this practice is sometimes complicated due to the content of historical medical films. Without a mechanism to regulate patron access to films with potentially problematic content, libraries must tightly restrict these newly preserved resources. This paper presents a framework that defines problematic content based on four criteria and proposes a tiered approach to access.

Contributions to scholarly work are mostly recognized by means of assigning authorship credit, which can later be used in academic evaluations and for career progression. Sociologists of science describe authorship as a commodity that is bartered among scholarly contributors as well as with resources such as research samples. On the flip side, authorship also functions as a mechanism for holding contributors responsible for their work. Despite its significance as means of attributing credit and responsibilities, when beginning a collaboration, researchers might feel uneasy about discussing authorship—specifically, who will be an author and in what order—or about openly communicating expectations regarding commitments and extent of contributions that ultimately determine authorship. These challenges are particularly more pronounced for junior researchers who are inexperienced in authorship negotiations and are at an inherent disadvantage in terms of power disparity with more senior researchers. Consequently, tensions and disagreements may arise and provide the impetus for discussions about authorship, but this is often too late and is more likely to be emotionally charged.

Academic health sciences libraries (“libraries”) offer services that span the entire research lifecycle, positioning them as natural partners in advancing clinical and translational science. Many libraries enjoy active and productive collaborations with Clinical and Translational Science Award (CTSA) Program hubs and other translational initiatives like the IDeA Clinical & Translational Research Network. This article explores areas of potential partnership between libraries and Translational Science Hubs (TSH), highlighting areas where libraries can support the CTSA Program’s five functional areas outlined in the Notice of Funding Opportunity. It serves as a primer for TSH and libraries to explore potential collaborations, demonstrating how libraries can connect researchers to services and resources that support the information needs of TSH.

Technology influences Open Science (OS) practices, because conducting science in transparent, accessible, and participatory ways requires tools/platforms for collaborative research and sharing results. Due to this direct relationship, characteristics of employed technologies directly impact OS objectives. Generative Artificial Intelligence (GenAI) models are increasingly used by researchers for tasks such as text refining, code generation/editing, reviewing literature, data curation/analysis. GenAI promises substantial efficiency gains but is currently fraught with limitations that could negatively impact core OS values such as fairness, transparency and integrity, and harm various social actors.In this paper, we explore possible positive and negative impacts of GenAI on OS. We use the taxonomy within the UNESCO Recommendation on Open Science to systematically explore the intersection of GenAI and OS. We conclude that using GenAI could advance key OS objectives by further broadening meaningful access to knowledge, enabling efficient use of infrastructure, improving engagement of societal actors, and enhancing dialogue among knowledge systems. However, due to GenAI limitations, it could also compromise the integrity, equity, reproducibility, and reliability of research, while also having potential implications for the political economy of research and its infrastructure. Hence, sufficient checks, validation and critical assessments are essential when incorporating GenAI into research workflows.