bioRxiv Science⌕ Search

bioRxiv · 10.1101/2024.12.09.627554

Investing in Open Science: Key Considerations for Funders

Abstract

The open science movement aims to transform the research landscape by promoting research transparency in order to enable reproducibility and replicability, lower the barriers for collaboration, and reduce unnecessary duplication. Recently, in recognition of the value of open science, funding agencies have begun to mandate open science policies as a condition in grantee awards. However, operationalization and implementation of an open science policy can have unanticipated costs and logistical barriers, which can impact both the funder, as well as the grantee. These factors should be considered when implementing an open science policy. The Aligning Science Across Parkinsons (ASAP) initiative utilizes a comprehensive open science policy, which, in addition to requiring immediate free online access to all publications, also requires all newly-generated datasets, protocols, code, and key lab materials be shared by the time of publication. Moreover, preprints must be posted to a preprint repository by the time of manuscript submission to a journal for review. Here, we outline the potential costs associated with implementing and enforcing this open science policy. We recommend that funders take these considerations into account when investing in open science policies within the biomedical research ecosystem.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Cobb-Lewis, D. E., Snyder, D., Dumanis, S., Thibault, R., Marebwa, B., Clark, E., St.Clair, L., Kirsch, L., Durborow, M., Riley, E. A. U.. 2024-12-10. Investing in Open Science: Key Considerations for Funders. https://doi.org/10.1101/2024.12.09.627554

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Evaluating Large Language Models as Tools to Navigate Researchers in Rapidly Evolving Research Landscapes: A Case Study in Cancer Drug Response Prediction

Large Language Models (LLMs) have emerged as promising tools for assisting researchers in automating and accelerating the synthesis of literature reviews. However, their reliability is a significant concern due to issues like factual inaccuracies and hallucinations. The key question is whether LLMs can reliably provide comprehensive, up-to-date overviews and analyses. This study evaluates the performance of three leading LLMs (OpenAI's ChatGPT, Google's Gemini, and DeepSeek) on the complex task of generating a comprehensive survey paper on deep learning for cancer Drug Response Prediction (DRP). By testing both standard and Deep Research (DR) / Deep Think (DT) modes of LLMs with prompts of varying detail, this paper assesses key academic dimensions, including reference management, content quality, and analytical depth. Key findings reveal that while DR modes of LLMs significantly improve reliability by eliminating hallucinations, performance variations exist across models and prompts. A trade-off between reference quantity and integration quality was observed, and even the best-performing models lacked the analytical depth of human experts, often requiring extensive human supervision. The study concludes that LLMs currently serve as powerful assistive tools but still cannot replace the critical validation and synthesis provided by human researchers. Choosing the best LLM to use depends on the task in hand, while several strategies can be implemented to improve the produced output.

scientific communication and education↗

The currency of research access: How undergraduates leverage social capital to gain research experience

BackgroundScience students who engage in undergraduate research experiences (UREs) benefit in numerous ways, including persisting in science at a higher rate compared to students who do not participate in UREs. However, UREs are a limited commodity and competition for access to these opportunities necessitates further investigation into why certain students succeed in accessing UREs, while others do not. Social capital, or the resources that students extract from their relationships with others, may play a key role in determining who engages in UREs. To begin to address this knowledge gap, we conducted semi-structured interviews with students who had recently started UREs (n=21). Informed by Lins conceptual definition of social capital, we qualitatively analyzed these interviews to characterize the social capital that science undergraduates found useful for accessing research. ResultsStudents described leveraging ten unique forms of social capital when accessing UREs that aligned with Lins conceptualization. Specifically, students described utilizing social capital to garner information about available UREs and how to best navigate the path to accessing them, to reinforce their confidence in pursuing UREs, to influence the views of opportunity holders, and to serve as social credentials lending credibility to their aptitude for research. Although students detailed how faculty served as sources of all of these forms of capital, they also described advisors, employers, peers, and family as influential sources. Furthermore, their institutions served as a source of capital, and students themselves engaged in a variety of proactive behaviors to access UREs. ConclusionsHere we describe the forms and sources of social capital that science undergraduates use to access research, which can be used as an operational definition of the construct. Such a definition is necessary for future research aimed at measuring social capital for undergraduate research and identifying its antecedents, correlates, and consequences. We also describe how institutions can serve as sources of capital, and how students proactive behaviors play a role in their pursuit of UREs. This work provides an important starting point for determining the influence of social capital in accessing UREs and further broadening access to research in the sciences.

scientific communication and education↗

Chromatin profiling for everyone: FFPE-CUTAC for the theory and practice of modern molecular biology

In 2025, together with the Fred Hutch Summer Undergraduate Research and Summer High School Internship programs, we developed and implemented a laboratory genomics research experience to introduce students to modern molecular biology techniques and bioinformatics. The course centered around using a new method we had developed in 2023 that uses readily available fixed tissue sections on glass slides. Students performed a series of steps to tagment genomic locations of RNA Polymerase II and then used PCR to enrich libraries for next-generation sequencing in a core facility. Students then visualized their data in genomic browser tracks and assessed the results. At the end of the summer, students prepared and presented their work and experiences in seminar format to their cohorts. Overall, the technical simplicity of on-slide chromatin profiling introduced the students to laboratory practice and current techniques in genomics, bioinformatics, and medical sciences.

scientific communication and education↗