Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2021.10.12.464125

Applying And Promoting Open Science In Ecology - Surveyed Drivers And Challenges

Stromme, C. B.·Lane, A. K.·Halbritter, A. H.·Law, E.·Nater, C. R.·Nilsen, E. B.·Boutouli, G. D.·Egelkraut, D. D.·Telford, R. J.·Vandvik, V.·Cotner, S. H.
View original source ↗Export metadata ↓

Abstract

Open Science (OS) comprises a variety of practices and principles that are broadly intended to improve the quality and transparency of research, and the concept is gaining traction. Since OS has multiple facets and still lacks a unifying definition, it may be interpreted quite differently among practitioners. Moreover, successfully implementing OS broadly throughout science requires a better understanding of the conditions that facilitate or hinder OS engagement, and in particular, how practitioners learn OS in the first place. We addressed these issues by surveying OS practitioners that attended a workshop hosted by the Living Norway Ecological Data Network in 2020. The survey contained scaled-response and open-ended questions, allowing for a mixed-methods approach. Out of 128 registered participants we obtained survey responses from 60 individuals. Responses indicated usage and sharing of data and code, as well as open access publications, as the OS aspects most frequently engaged with. Men and those affiliated with academic institutions reported more frequent engagement with OS than women and those with other affiliations. When it came to learning OS practices, only a minority of respondents reported having encountered OS in their own formal education. Consistent with this, a majority of respondents viewed OS as less important in their teaching than in their research and supervision. Even so, many of the respondents suggestions for what would help or hinder individual OS engagement included more knowledge, guidelines, resource availability and social and structural support; indicating that formal instruction can facilitate individual OS engagement. We suggest that the time is ripe to incorporate OS in teaching and learning, as this can yield substantial benefits to OS practitioners, student learning, and ultimately, the objectives advanced by the OS movement.

Explore related subjects

scientific communication and educationPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Stromme, C. B., Lane, A. K., Halbritter, A. H., Law, E., Nater, C. R., Nilsen, E. B., Boutouli, G. D., Egelkraut, D. D., Telford, R. J., Vandvik, V., Cotner, S. H.. 2021-10-12. Applying And Promoting Open Science In Ecology - Surveyed Drivers And Challenges. https://doi.org/10.1101/2021.10.12.464125

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↗
bioRxiv2022

Welfare concerns for mounted load carrying by working donkeys in Pakistan

Working donkeys (Equus asinus) are vital to peoples livelihoods. They are essential for carrying goods, however globally, overloading is one of the primary welfare concerns of working donkeys. We studied mounted load carrying by donkeys and associated factors in Pakistan. A cross-sectional study of donkey owners (n = 332) was conducted, and interviews were undertaken based on a questionnaire. Owners estimated that the median weight of their donkeys was 110kg (interquartile range (IQR) 100-120kg), and that they carried a median mounted load of 81.5kg (IQR 63-99kg). We found that 87.4% of donkeys carried a load above 50% of their bodyweight ratio (BWR), the median BWR carried was 77.1% (IQR 54.5-90.7%), and 25.3% of donkeys carried above 90% BWR. Donkeys that were loaded at more than 50% BWR were more likely to sit, compared to donkeys loaded with less weight (p=0.01). Donkeys working in peri-urban and urban areas were more likely to carry a greater BWR than donkeys working in rural areas (P<0.001), as were those carrying construction materials or bricks, compared to agricultural materials (p=0.004). Age (p=0.03) and breed (p=0.01) were also associated with carrying a higher weight. Overloading based on current recommendations (50% BWR) was common, with the majority (87.4%) of donkeys reported to carry more than the recommended 50% limit. This survey provides evidence of on-the-ground working practices and factors associated with mounted load carrying, which is critical for developing evidence-based recommendations for loading, in order to improve the welfare of working donkeys.

scientific communication and education↗