bioRxiv Science⌕ Search

bioRxiv · 10.1101/104661

Creating a Community of Data Champions

Abstract

Research Data Management (RDM) presents an unusual challenge for service providers in Higher Education. There is increased awareness of the need for training in this area but the nature of discipline-specific practices involved make it difficult to provide training across a multi-disciplinary organisation. Whilst most UK universities now have a research data team of some description, they are often small and rarely have the resources necessary to provide targeted training to the different disciplines and research career stages that they are increasingly expected to support.\n\nThis practice paper describes the approach taken at the University of Cambridge to address this problem by creating a community of Data Champions. This collaborative initiative, working with researchers to provide training and advocacy for good RDM practice, allows for more discipline-specific training to be given, researchers to be credited for their expertise and an opportunity for those interested in RDM to exchange knowledge with others. The community of practice model has been used in many sectors, including Higher Education, to facilitate collaboration across organisational units and this initiative will adopt some of the same principles to improve communication across a decentralised institution. The Data Champions initiative at Cambridge was launched in September 2016 and this paper reports on the early months, plans for building the community in the future and the possible risks associated with this approach to providing RDM services.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Higman, R., Teperek, M., Kingsley, D.. 2017-02-20. Creating a Community of Data Champions. https://doi.org/10.1101/104661

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

KEEP EXPLORING

Related preprints

From Problems to Solutions: How Self-Questions Drive Strong Scientific Reasoning

Metacognition, or awareness and control of thinking, is essential for problem solving because it helps solvers select strategies for learning and decide when to enact them (planning), check their understanding in the moment (monitoring), and assess effectiveness of solutions and learning strategies (evaluating). Most studies that examine metacognition do so retrospectively, leaving a critical gap in how metacognition is used in-the-moment for problem solving. In this study, we utilized a think-aloud interview approach to capture and characterize the metacognitive skills that senior undergraduate life science students at three institution types in the United States used spontaneously while solving challenging biology problems. We found that the students (n = 39) displayed a wide variety and quantity of monitoring, some evaluating, and nearly no planning skills while creating a solution backed with high-quality reasoning. Students frequently used self-questions to monitor and evaluate their thinking. We found that self-questioning is a critical skill for generating solutions, but it only moves problem solving forward if the student asks and correctly answers the self-question out loud. This study illustrates how self-questioning and other metacognitive regulation skills provide links to bridge the gap between a student's prior knowledge and specific features of a challenging problem. Based on our student data, we conceptualized a model of metacognitive phases during problem solving with high-quality reasoning. Finally, we present a self-question scaffold to facilitate the proposed phases (planning, monitoring, reasoning, and evaluating) so educators can equip students with metacognitive and scientific reasoning tools for effective problem solving.

scientific communication and education↗

Preprint solicitation as a natural experiment in scholarly publishing

Preprinting is a growing and increasingly recognized mechanism for scientific communication and productivity and a way to facilitate global access to science. The interplay of preprinting practices with formal peer-reviewed publication is an open question that is now tractable to targeted analysis. Here, we take advantage of nearly a decade of preprint solicitation at Proceedings B to address this question, which offers a quasi-experimental setting for comparing outcomes for solicited preprints and traditionally submitted manuscripts. We used submission records from 15,368 manuscripts from 2018-2024 to assess whether preprint solicitation affects acceptance rates, journal scope, author geographic diversity, and post-publication uptake. Solicited manuscripts were accepted at nearly twice the rate of traditional submissions and received more downloads and citations after publication. Submission topics were broadly similar between the two routes, though solicited manuscripts showed marginally greater subject diversity. Geographic disparities favoring the Global North persisted, but acceptance rates for solicited manuscripts converged across regions, narrowing the gap seen in traditional submissions. These findings extend earlier work on this initiative and indicate that editor-driven solicitation can identify high-quality preprints, broaden geographic representation, and increase research visibility, offering a replicable model for journals seeking to diversify authorship while maintaining editorial standards.

scientific communication and education↗

Biology Undergraduates Use of AI-Enabled Feedback to Revise Scientific Writing: Recipience and Barriers

Background: Instructors and education researchers are increasingly developing feedback with the assistance of generative AI, which we refer to as AI-enabled feedback, yet we do not know whether established understandings of feedback uptake and effectiveness apply similarly in AI-mediated contexts. This makes the recipience of AI-enabled feedback a central concern to understanding how to best support students in developing their scientific writing skills. This study examined how two teams of undergraduate biology students used AI-enabled feedback to revise their scientific writing, and the extent to which their revisions addressed that feedback. The teams revised their research proposal drafts after receiving AI-enabled feedback developed by their instructor with the aid of a custom AI tool. Results: Our findings show that AI-enabled feedback prompted actionable revision across both teams. Each team addressed approximately half of the feedback comments, indicating comparable levels of uptake despite their differing approaches to revision. We observed that both teams were inconsistent in providing the additional information and reasoning requested in the feedback, and they struggled to address comments requiring self-evaluation. Taken together, we see that students often addressed the more easily actionable parts of AI-enabled feedback while omitting more demanding revisions. Conclusions: These findings provide behavioral evidence of how students used AI-enabled feedback to revise their scientific writing. The observed patterns suggest that the content of AI-enabled feedback supported meaningful revision, while limitations in students agency, strategies, and volition likely shaped their implementation of comments. Although the degree of uptake observed in this study is encouraging, it is unlikely that a single application of generative AI within the feedback process will resolve all the challenges associated with feedback uptake.

scientific communication and education↗