Search bioRxiv⌕ Search

bioRxiv · 10.1101/2022.03.24.485684

Animal-reliance bias in publishing is a potential barrier to scientific progress

Abstract

Publication of scientific findings is fundamental for research, pushing innovation and generating interventions that benefit society, but it is not without biases. Publication bias is generally recognized as journals preference for publishing studies based on the direction and magnitude of results. However, early evidence of a newly recognized type of publication bias has emerged in which journal policy, peer reviewers, or editors request that animal data be provided to validate studies produced using nonanimal-based approaches. We describe herein "animal methods bias" in publishing: a preference for animal-based methods where they may not be necessary or where nonanimal-based methods may be suitable, which affects the likelihood of a manuscript being accepted for publication. To gather evidence of animal methods bias, we set out to collect the experiences and perceptions of scientists and reviewers related to animal- and nonanimal-based experiments during peer review. We created a survey with 33 questions that was completed by 90 respondents working in various biological fields. Twenty-one survey respondents indicated that they have carried out animal-based experiments for the sole purpose of anticipating reviewer requests. Thirty-one survey respondents indicated that they have been asked by peer reviewers to add animal experimental data to their nonanimal study; 14 of these felt the request was sometimes justified, and 11 did not think it was justified. The data presented provide preliminary evidence of animal methods bias and indicate that status quo and conservatism biases may explain such attitudes by peer reviewers and editors.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Krebs, C. E., Lam, A., McCarthy, J., Constantino, H., Sullivan, K.. 2022-03-27. Animal-reliance bias in publishing is a potential barrier to scientific progress. https://doi.org/10.1101/2022.03.24.485684

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↗

A Wildlife Health Outbreak Response Table-top Exercise for Pandemic Preparedness Planning

Zoonotic diseases have received significantly more attention over the last few decades, emerging with increasing frequency and causing the majority of notable disease outbreaks in this century, including the COVID-19 pandemic. As human activities and shifting climate patterns induce changes in the environment that alter habitat and range of reservoir species, the potential for human and animal interactions will increase and enhance the opportunity for spillover. Thus, any emergency response preparedness planning must take into account the function and coordination of agencies across the sectors of human, animal and environmental health. Within the Commonwealth of Pennsylvania a table-top exercise was performed to evaluate a multi-agency response during a hypothetical zoonotic disease investigation. The exercise was evaluated by the participants to gain feedback on the overall process and lessons learned. Here, we describe the tabletop exercise scenario and the insights gained. We found that differences in operational structure create challenges for interdepartmental communication and in the ability to resource a coordinated response, highlighting opportunities to develop infrastructure that will facilitate future actions. A set of recommendations are outlined that may enhance cross-agency activities and promote more effective and efficient emergency response.

scientific communication and education↗

Estimating the replicability of Brazilian biomedical science

Concerns over the replicability and reproducibility of published research have grown in many research fields, but empirical data to inform policies are still scarce. Biomedical research in Brazil expanded rapidly over the last three decades, with no systematic assessment of the replicability of its findings. With this in mind, we set up the Brazilian Reproducibility Initiative, a multicenter replication of published experiments from Brazilian science using three common experimental methods: the MTT assay, the reverse transcription polymerase chain reaction (RT-PCR) and the elevated plus maze (EPM). A total of 56 laboratories performed 143 replications of 56 experiments; of these, 90 replications of 45 experiments were considered valid by an independent committee. Replication rates for these experiments varied between 20 and 44% according to five predefined criteria. In median terms, ratios between group means were 58% lower in replications than in original experiments, while coefficients of variation were 82% higher. Effect size decrease was smaller for MTT experiments, original results with less variability and those considered more challenging to replicate, while t values for replications were positively correlated with researcher predictions about replicability, and negatively correlated with the rate of publications by the original articles last author. Deviations from preregistered protocols were very common in replications, most frequently due to reasons inherent to the experimental model or related to infrastructure and logistics. Our results highlight factors that limit the replicability of results published by researchers in Brazil and suggest ways by which this scenario can be improved.

scientific communication and education↗