Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2023.02.01.526587

EntomonVR: a New Virtual Reality Game for Learning Insect Morphology

Pasandideh Saqalaksari, M.·Talebi, A. A.·van de Kamp, T.·Reyhani Haghighi, S.·Zimmermann, D.·Richter, A.
View original source ↗Export metadata ↓

Abstract

In recent years, the study of insect morphology has benefited greatly from the emergence of new digital imaging and analysis technologies such as X-ray micro-computed tomography (-CT), digital 3D reconstruction, and animation. Through interactive gaming and virtual reality, the external morphology of insects can be studied by a broad audience of both entomologists and non-specialists. EntomonVR is a serious game designed to investigate the external morphology of insects with adequate quality for the virtual reality platform. We discuss the advantages of virtual reality, introduce the EntomonVR new educational game, and conclude about future perspectives, validations, and cost-effectiveness. For game assessment, we have tested this game on 25 participants with an entomological background and improved the game based on their feedback. This study demonstrates the efficacy of virtual reality technology for an experimental learning environment in teaching the morphology of insects and the crucial needs for advancing an efficient and interactive educational program.

Source connections

Explore related subjects

scientific communication and educationPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Pasandideh Saqalaksari, M., Talebi, A. A., van de Kamp, T., Reyhani Haghighi, S., Zimmermann, D., Richter, A.. 2023-02-03. EntomonVR: a New Virtual Reality Game for Learning Insect Morphology. https://doi.org/10.1101/2023.02.01.526587

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

KEEP EXPLORING

Related preprints

bioRxiv2026

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↗
bioRxiv2024

SIMHYB 2: a software tool to explore and illustrate evolutionary forces in Population Genetics teaching and research. Application to Conservation Genetics

Practical approaches have become a standard in many scientific disciplines, including population genetics. By analyzing properly selected datasets, the students can calculate parameters and draw conclusions about genetic diversity, differentiation and evolution of populations with higher efficiency than if based exclusively on theoretical lessons. However, preparing the appropriate datasets is a hard task and a wrong selection can spoil a well-aimed practice. Here we present SO_SCPLOWIMC_SCPLOWHO_SCPLOWYBC_SCPLOW 2, a software tool specifically intended to ease the full understanding of evolutionary forces by the students and to help the teacher to prepare adequate datasets and examples for the practices. It simulates the course of a mixed population under user-defined reproductive and evolutionary conditions. Outputs can be easily adapted for downstream analysis with other popular tools as GO_SCPLOWENC_SCPLOWAO_SCPLOWLC_SCPLOWEO_SCPLOWXC_SCPLOW or SO_SCPLOWTRUCTUREC_SCPLOW. Thus, SO_SCPLOWIMC_SCPLOWHO_SCPLOWYBC_SCPLOW 2 is very suitable for project-based-learning approaches: students can produce their own datasets in different scenarios of genetic drift, migration, selective advantage, reproductive success... Additionally, SO_SCPLOWIMC_SCPLOWHO_SCPLOWYBC_SCPLOW 2 is the only simulation software available to date providing traceable pedigrees of individuals, being therefore very convenient for preparing datasets for parentage analysis, spatial genetic structure or conservation genetics study cases. Satisfactory results from its ongoing utilization in higher education and research are reported.

scientific communication and education↗
bioRxiv2024

Postdoctoral Scholar Recruitment and Hiring Practices in STEM: A Pilot Study

Despite the importance of the postdoctoral position in the training of scientists for independent research careers, few studies have addressed recruiting and hiring of postdocs. We conducted a pilot study on postdoctoral hiring in the Division of Chemistry and Chemical Engineering at the California Institute of Technology to serve as a starting point to better understand postdoctoral recruiting and hiring processes. From this survey of both postdocs and faculty, together with the available literature, the picture emerges that the postdoc hiring process is more decentralized than either faculty hiring or graduate admissions. Postdoc positions are often filled through a passive process where the initial expression of interest from a prospective postdoc is through a "cold-call" contact to a prospective advisor. Individual faculty members are often responsible for developing and implementing their own outreach and recruitment plans and deciding who to hire into a postdoc position. The overall opacity of the processes and practices by which postdocs are identified, recruited, and hired make it difficult to pinpoint where interventions could be effective to ensure equitable hiring practices. Implementation of such practices is critical to training a diverse postdoc population and subsequently of the future STEM faculty recruited from this group.

scientific communication and education↗