bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2020.12.02.408989

Historical and cross-disciplinary trends in the biological and social sciences reveal an accelerating adoption of advanced analytics

Bolt, T. S.·Nomi, J. S.·Bzdok, D.·Uddin, L. Q.
View original source ↗Export metadata ↓

Abstract

Methods for data analysis in the biomedical, life and social sciences are developing at a rapid pace. At the same time, there is increasing concern that education in quantitative methods is failing to adequately prepare students for contemporary research. These trends have led to calls for educational reform to undergraduate and graduate quantitative research method curricula. We argue that such reform should be based on data-driven insights into within- and cross-disciplinary use of research methods. Our survey of peer-reviewed literature screened [~]3.5 million openly available research articles to monitor the cross-disciplinary usage of research methods in the past decade. We applied data-driven text-mining analyses to the methods and materials section of a large subset of this corpus to identify method trends shared across disciplines, as well as those unique to each discipline. As a whole, usage of T-test, analysis of variance, and other classical regression-based methods has declined in the published literature over the past 10 years. Machine-learning approaches, such as artificial neural networks, have seen a significant increase in the total share of scientific publications. We find unique groupings of research methods associated with each biomedical, life and social science discipline, such as the use of structural equation modeling in psychology, survival models in oncology, and manifold learning in ecology. We discuss the implications of these findings for education in statistics and research methods, as well as within- and cross-disciplinary collaboration.

Explore related subjects

scientific communication and educationPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Bolt, T. S., Nomi, J. S., Bzdok, D., Uddin, L. Q.. 2020-12-03. Historical and cross-disciplinary trends in the biological and social sciences reveal an accelerating adoption of advanced analytics. https://doi.org/10.1101/2020.12.02.408989

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↗
bioRxiv2021

Inferring livestock movement networks from archived data to support infectious disease control in developing countries

The use of network analysis to support livestock disease control in low middle-income countries (LMICs) has historically been hampered by the cost of generating empirical data in the absence of animal movement recording schemes. To fill this gap, methods which exploit freely available demographic and archived molecular data can be used to generate livestock networks based on gravity and phylogeographic modelling techniques, respectively. However, questions remain on the performance of these methods in capturing the topology of empirical networks. Here, we compare output from these network methodologies to a network constructed from either empirical data or randomly generated data. To facilitate this comparison, the spread of infectious diseases was simulated, it is this evaluation that demonstrates their potential utility to inform robust livestock disease control strategies. The molecular network was the closest approximation to the empirical network, both in relation to topological and epidemic characteristics, whereas size of epidemics in the gravity network tended to be larger, better agreement across all three networks was observed when; a) total nodes infected, b) percentage infection take off were compared. These methods consistently identified the same important animal movement and trade hotspots as the empirical networks. We therefore consider this proof-of-concept that demographic data such as censuses and archived molecular data could be repurposed to inform livestock disease management in LMICs. Author summaryLive animal movements in Africa represent a significant risk of transmission and spread of infectious diseases in livestock populations, and therefore, have direct implications on the food security of the continent. Here we explore the potential utility of available data to support control strategies, by comparing movement networks inferred from such data i.e. census and pathogen molecular data using gravity modelling and phylogeography respectively. Their utility is evaluated by comparing their topology and disease spread characteristics to empirical live animal movement. Based on our results, we posit that archived data can be repurposed to support infectious disease control on the African continent.

scientific communication and education↗
bioRxiv2021

scite: a smart citation index that displays the context of citations and classifies their intent using deep learning

Citation indices are tools used by the academic community for research and research evaluation which aggregate scientific literature output and measure scientific impact by collating citation counts. Citation indices help measure the interconnections between scientific papers but fall short because they only display paper titles, authors, and the date of publications, and fail to communicate contextual information about why a citation was made. The usage of citations in research evaluation without due consideration to context can be problematic, if only because a citation that disputes a paper is treated the same as a citation that supports it. To solve this problem, we have used machine learning and other techniques to develop a "smart citation index" called scite, which categorizes citations based on context. Scite shows how a citation was used by displaying the surrounding textual context from the citing paper, and a classification from our deep learning model that indicates whether the statement provides supporting or disputing evidence for a referenced work, or simply mentions it. Scite has been developed by analyzing over 23 million full-text scientific articles and currently has a database of more than 800 million classified citation statements. Here we describe how scite works and how it can be used to further research and research evaluation.

scientific communication and education↗