Exploring the current state and trends in interdisciplinarity research with Dimensions (2014–2025) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Exploring the current state and trends in interdisciplinarity research with Dimensions (2014–2025) Serhiy Shtovba, Mykola Petrychko, Olena Shtovba This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9471375/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study explores the current state and trends in interdisciplinary research for the last dozen years. Typical ways of measuring interdisciplinarity, such as citation analysis, often suffer from time delays and data quality issues. While text-based methods offer a more detailed view, they require heavy computing power to process. To avoid these limitations, we use the Dimensions database, which relies on machine learning algorithms to map individual publications into established categories according to the Australian and New Zealand Standard Research Classification. It consists of 22 divisions and 171 research groups. Interdisciplinary is assesses as a level of interaction between research groups and between research divisions using the Jaccard index. The analysis evaluated 14,535 pairs of research groups for general interaction and 13,789 pairs for inter-divisional interaction, as well as 231 pairs of research divisions. It is also identified the research groups and divisions that act as focal points for substantive collaboration across various disciplinary partners, and triads for research groups with highest level of inner collaboration. The analysis breaks the timeline into three-year periods to carefully track changes over time. It has been determined that the interdisciplinary network is marked by structural stability in terms of the dynamics of power law inequality and dynamic semantic shifts. The proposed technique provides a very fast, scalable way to monitor how research fields merge and evolve without relying on heavy text processing or delayed citation data. interdisciplinarity research interaction Jaccard index Dimensions ANZSRC stickiness index Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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