Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/515643

Tracking the popularity and outcomes of all bioRxiv preprints

Abdill, R. J.·Blekhman, R.
View original source ↗Export metadata ↓

Abstract

Researchers in the life sciences are posting their work to preprint servers at an unprecedented and increasing rate, sharing papers online before (or instead of) publication in peer-reviewed journals. Though the popularity and practical benefits of preprints are driving policy changes at journals and funding organizations, there is little bibliometric data available to measure trends in their usage. Here, we collected and analyzed data on all 37,648 preprints that were uploaded to bioRxiv.org, the largest biology-focused preprint server, in its first five years. We find that preprints on bioRxiv are being read more than ever before (1.1 million downloads in October 2018 alone) and that the rate of preprints being posted has increased to a recent high of more than 2,100 per month. We also find that two-thirds of bioRxiv preprints posted in 2016 or earlier were later published in peer-reviewed journals, and that the majority of published preprints appeared in a journal less than six months after being posted. We evaluate which journals have published the most preprints, and find that preprints with more downloads are likely to be published in journals with a higher impact factor. Lastly, we developed Rxivist.org, a website for downloading and interacting programmatically with indexed metadata on bioRxiv preprints.

Source connections

Explore related subjects

scientific communication and educationPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Abdill, R. J., Blekhman, R.. 2019-01-13. Tracking the popularity and outcomes of all bioRxiv preprints. https://doi.org/10.1101/515643

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↗
bioRxiv2019

Tackling the research capacity challenge in Africa: An overview of African-led approaches to strengthen research capacity

BackgroundImproved capacity for research is a valuable and sustainable means of advancing health and development in Africa. Local leadership in research capacity strengthening is important for developing contextually appropriate programs that increase locally-driven research, and improve Africas ability to adapt and use scientific knowledge. This study provides an overview of African organisations that aim to strengthen research capacity in Africa, and the major initiatives or approaches being used for this purpose.\n\nMethodsA desk review of grey and published literature on research capacity strengthening in Africa was conducted, in addition to panel discussions on the determinants of research capacity in Africa. Data was analysed through thematic analysis and a framework developed by the Collaboration for Research Excellence in Africa (CORE Africa).\n\nResults11 organisations were identified, spread across South, Central, East and West Africa. The main approaches to improving research capacity were: providing opportunities for academic research and research training. Initiatives to provide research equipment, funding and facilitate research use for policy-making were limited; while strategies to increase research awareness, promote collaboration, and provide guidance and incentives for research were lacking. Most organisations had programs for researchers and academics, with none targeting funders or the general public.\n\nConclusionLocal leadership is essential for improving research capacity in Africa. In addition to providing adequate support to academics and researchers, initiatives that help revitalize the education system in Africa, promote collaboration and engage funders and the general public will be helpful for strengthening research capacity in Africa.

scientific communication and education↗
bioRxiv2019

Do Synthesis Centers Synthesize? A Semantic Analysis of Diversity and Performance

Synthesis centers are a recently-developed form of scientific organization that catalyzes and supports a form of interdisciplinary research that integrates diverse theories, methods and data across spatial or temporal scales, scientific phenomena, and forms of expertise to increase the generality, parsimony, applicability, or empirical soundness of scientific explanations. Research has shown the synthesis working group to be a distinctive form of scientific collaboration that reliably produces consequential, high-impact publications, but no one has asked: do synthesis working groups produce publications that are substantially more diverse than those produced outside of synthesis centers, and if so, how and with what effects? We have investigated these questions through a novel textual analysis. We found that if diversity is measured solely by mean difference in the Rao-Stirling (aggregate) measure of diversity, then the answer is no. But synthesis center papers have significantly greater variety and balance, but significantly lower disparity, than papers in the reference corpus. Synthesis center influence is mediated by the greater size of synthesis center collaborations (numbers of authors, distinct institutions, and references) but even when taking size into account, there is a persistent direct effect: synthesis center papers have significantly greater variety and balance, but less disparity, than papers in the reference corpus. We conclude by inviting further exploration of what this novel textual analysis approach might reveal about interdisciplinary research and by offering some practical implications of our results.

scientific communication and education↗