Search bioRxivSearch

bioRxiv · 10.1101/734798

The state of discovery-driven neuroscience research and experimental organism usage in the United States

Abstract

Purely discovery-driven biological research \"...performed without thought of practical ends...\" establishes fundamental conceptual frameworks for technological and medical breakthroughs that often occur many years later. Despite the critical importance of discovery-driven research for scientific progress, there is increasing concern that it is increasingly less favored by funding agencies than research with explicit goals of application and innovation, resulting in a decline in discovery-driven research output. This in turn appears to promote the use of genetically modified organisms (those with advanced molecular toolkits for gene manipulation and visualization) for which genetic models of human disease can be studied at molecular and cellular resolution using state of the art methodology, and to discourage use of other experimental organisms that provide necessary evolutionary context. This field of neuroscience encompasses both applied and discovery-driven research, providing an opportunity to empirically determine whether funding and publication rates for the latter have indeed declined. Additionally, the diversity of experimental organisms traditionally employed in neuroscience research provides a means to quantify changes in use of study organisms that lack genetic tools over time. In particular, the basic research field of neuroethology is characterized by its distinct approach to selection of study organisms based on their adaptive behaviors, evolutionary history, and suitability for answering the question of interest, providing a stronger basis for the assumption that findings reflect fundamental concepts of nervous system function and behavior. A 30-year analysis of National Science Foundation (NSF) funding of neuroethology research finds that the agency has funded progressively fewer researchers with smaller average award amounts, with a decline in awards for research on non-genetically modified organisms. Neuroscience funding by the National Institutes of Health (NIH) shows the same trend but also increasing support for genetically modified organisms. The same pattern is observed in the neuroscience literature but occurs prior to changes in funding, suggesting that the shift to genetically modified organisms was likely initiated by researchers but may potentially have been later reinforced by funding agency and journal publisher preferences.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Farris, S. M.. 2019-08-20. The state of discovery-driven neuroscience research and experimental organism usage in the United States. https://doi.org/10.1101/734798

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

PlotTwist - a web app for plotting and annotating time-series data

The results from time-dependent experiments are often used to generate plots that visualize how the data evolves over time. To simplify state-of-the-art data visualization and annotation of data from such experiments, an open source tool was created with R/shiny that does not require coding skills to operate. The freely available web app accepts wide (spreadsheet) and tidy data and offers a range of options to normalize the data. The data from individual objects can be shown in three different ways: (i) lines with unique colors, (ii) small multiples and (iii) heatmap-style display. Next to this, the mean can be displayed with a 95% confidence interval for the visual comparison of different conditions. Several color blind friendly palettes are available to label the data and/or statistics. The plots can be annotated with graphical features and/or text to indicate any perturbations that were applied during the time-lapse experiments. All user-defined settings can be stored for reproducibility of the data visualization. The app is dubbed PlotTwist and is available online: https://huygens.science.uva.nl/PlotTwist\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=166 SRC=\"FIGDIR/small/745612v2_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (34K):\norg.highwire.dtl.DTLVardef@1bad5e6org.highwire.dtl.DTLVardef@1312023org.highwire.dtl.DTLVardef@350cb5org.highwire.dtl.DTLVardef@d556c6_HPS_FORMAT_FIGEXP M_FIG C_FIG

scientific communication and education

The Five-Primer Challenge: An inquiry-based laboratory module for synthetic biology

New technologies in DNA synthesis and assembly give genetic engineers complete freedom in genetic design, where virtually any plasmid DNA sequence can be created efficiently and economically. Learning how to design, construct, and test new DNA sequences is a critical skill for researchers in molecular biology and biotechnology. Here we present a student-centered, inquiry-based module in which students learn how to control bacterial gene expression by appplying various DNA assembly techniques. The central activity in this learning module is termed the Five-Primer Challenge. Each student is allowed to order up to five 60-mer oligonucleotide primers to then modify a GFP expression plasmid with the goal of increasing GFP expression as much as possible. This module was developed and implemented at the 2016 Cold Spring Harbor Laboratory Synthetic Biology Course, and was effective at engaging students in critical thinking and in promoting student learning.

scientific communication and education