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跨学科研究通过融合不同领域的知识，激发创新思维，促进资源共享，以解决复杂的科学问题和挑战，从而为社会经济发展创造新机遇。本研究从跨学科团队合作视角，基于1950—2022年间的8260万篇科学论文，通过合作强度（JBS）、研究影响力（MCC），以及合作规模（MAC）三个关键指标来揭示跨学科团队合作研究的整体发展趋势和演变过程，并进一步识别了四种跨学科团队合作模式，主要包括兴趣导向型、层级结构型、亲缘关系型以及资源密集型。研究发现2010年后资源密集型的跨学科团队合作模式开始涌现，成为主流范式之一；部分兴趣导向型跨学科合作研究正逐渐向层级结构型转变，意味着某些跨学科合作研究呈现专业化、领域化趋势。最后，本研究从研究议题方面深度分析了模式转变的诱因，揭示了跨学科合作过程中面临的挑战，提出了相应的决策询证与建议。
Abstract：Interdisciplinary research plays a pivotal role in driving innovation and development. It integrates knowledge from various fields, stimulates innovative thinking, and promotes resource sharing to address complex scientific problems and challenges, thereby creating new opportunities for socioeconomic development. This study, from the perspective of interdisciplinary team collaboration, is based on 82.6 million scientific papers published between 1950 and 2022. It uses three key indicators: Jaccard-based Strength (JBS), Medium Citation Counts (MCC), and Median Authors Counts (MAC) to reveal the overall development trends and evolutionary processes of collaborative research in interdisciplinary teams. Furthermore, based on these three indicators, the study identifies four patterns of interdisciplinary team collaboration: Emerging and Hot Topics, Structured Hierarchy, Relative Close Discipline models, and Large Project models. The research finds that after 2010, the Large Project model of interdisciplinary team collaboration began to emerge, becoming one of the dominant paradigms. Additionally, some Emerging and Hot Topics interdisciplinary collaborative research is gradually transforming into a Structured Hierarchy model, indicating a trend towards specialization and domain-specification in certain interdisciplinary collaborations. Finally, this study conducts an in-depth analysis of the driving factors behind the shifts in collaboration models from the perspective of research topics. It identifies five key risks and challenges encountered in the process of interdisciplinary collaboration and further proposes evidence-based policy recommendations to support interdisciplinary system development and promote high-quality scientific and technological innovation.
[1] Wang Q, Schneider J W. Consistency and validity of interdisciplinarity measures. Quantitative Science Studies, 2020, 1（1）: 239–263.
[2] Sinatra R, Wang D, Deville P, et al. Quantifying the evolution of individual scientific impact. Science, 2016, 354（6312）: aaf5239.
[3] Ramadi K B. Inter-disciplinary collaboration for innovation in gastroenterology. Nature Reviews Gastroenterology & Hepatology, 2023, 20（12）: 757–757.
[4] Azoulay P, Graff-Zivin J, Uzzi B, et al. Toward a more scientific science. Science, 2018, 361（6408）: 1194–1197.
[5] Okamura K. Interdisciplinarity revisited: evidence for research impact and dynamism. Palgrave Communications, 2019, 5（1）.
[6] Klein J T. Interdisciplinarity: History, Theory, and Practice. Detroit: Wayne State University Press, 1990: 1−331.
[7] 杨良斌. 跨学科学的理论基础探讨. 图书情报工作, 2011, 55（16）: 29–32+41.
[8] Aram J D. Concepts of interdisciplinarity: Configurations of knowledge and action. Human Relations, 2004, 57（4）: 379–412.
[9] Szostak R. The Interdisciplinary Research Process. Case Studies in Interdisciplinary Research, 2012: 3−20.
[10] 李江. “跨学科性”的概念框架与测度. 图书情报知识, 2014, （3）: 87–93.
[11] Beers P J, Bots P W. Eliciting conceptual models to support interdisciplinary research. Journal of Information Science, 2009, 35(3): 259–278
[12] Porter A, Rafols I. Is science becoming more interdisciplinary? Measuring and mapping six research fields over time. Scientometrics, 2009, 81（3）: 719–745.
[13] Porter A, Cohen A, David Roessner J, Perreault M. Measuring researcher interdisciplinarity. Scientometrics, 2007, 72（1）: 117–147.
[14] Xu J, Bu Y, Ding Y, et al. Understanding the formation of interdisciplinary research from the perspective of keyword evolution: A case study on joint attention. Scientometrics, 2018, 117: 973–995.
[15] 谢彩霞. 对科学合作促进科学跨学科发展的计量分析. 科技进步与对策, 2007, （8）: 171–174.
[16] Morillo F, Bordons M, Gómez I. Interdisciplinarity in science: A tentative typology of disciplines and research areas. Journal of the American Society for Information Science and technology, 2003, 54（13）: 1237–1249.
[17] Barthel R, Seidl R. Interdisciplinary collaboration between natural and social sciences–status and trends exemplified in groundwater research. PloS one, 2017, 12（1）: e0170754.
[18] Renwick C. Biology, social science and history: interdisciplinarity in three directions. Palgrave Communications, 2016, 2（1）: 1–5.
[19] 王旻霞, 赵丙军. 跨学科知识交流网络结构特征研究. 情报科学, 2016, 34（5）: 46–50+104.
[20] 杜瑾, 熊回香, 向瀛泓. 基于学术网络的跨学科论文推荐研究. 情报学报, 2024, 43（5）: 516–527.
[21] Iacopini I, Milojević S, Latora V. Network dynamics of innovation processes. Physical review letters, 2018, 120（4）: 048301.
[22] 刘佳豪, 宋凯, 李玉莹, 等. 数据集引用视角下的知识领域跨学科性和主题演变研究. 情报理论与实践, 2025, 48(2): 156–167+177
[23] Berkes E, Marion M, Milojević S, et al. Slow convergence: Career impediments to interdisciplinary biomedical research. Proceedings of the National Academy of Sciences, 2024, 121（32）: e2402646121.
[24] 杨良斌. 跨学科指标测度的数据集构建及数据预处理研究. 图书情报工作, 2013, 57（11）: 90–95.
[25] Xie Z, Li M, Li J, et al. Feature analysis of multidisciplinary scientific collaboration patterns based on PNAS. EPJ Data Science, 2018, 7: 1–7.
[26] 韩普, 王东波. 跨学科性的理论与实践研究综述. 情报学报, 2014, 33（11）: 1222–1232.
[27] 孔晶, 杨媛, 廖倩, 等. 第一个基础教育领域跨学科学习实践研究——基于系统性文献综述法. 现代教育技术, 2024, （6）: 63–70.
[28] 张宝生, 张庆普. 基于耗散结构理论的跨学科科研团队知识整合机理研究. 科技进步与对策, 2014, 31（21）: 132–136.
[29] 唐磊, 刘霓, 高媛, 等. 跨学科研究的理论与实践: 基于研究文献的考察. 北京: 中国社会科学出版社, 2016.
[30] Wu X, Zou H, Xing Y, et al. Intelligent innovation dataset on scientific research outcomes and patents. Journal of Social Computing, 2025, 6（1）: 63–73.
[31] 冯志刚, 李长玲, 刘小慧, 等. 基于引用与被引用文献信息的图书情报学跨学科性分析. 情报科学, 2018, 36（3）: 105–111.
[32] Liben-Nowell D, Kleinberg J. The link prediction problem for social networks. Proceedings of the twelfth international conference on Information and knowledge management, 2003, 556-559.
[33] Fletcher S, Islam M Z. Comparing sets of patterns with the Jaccard index. Australasian Journal of Information Systems, 2018, 22.
[34] Tan P N, Steinbach M, Kumar V. Introduction to Data Mining. New Delhi: Pearson Education India, 2016: 1−839.
[35] Leydesdorff L. On the normalization and visualization of author co‐citation data: Salton's Cosine versus the Jaccard index. Journal of the American Society for Information Science and Technology, 2008, 59（1）: 77–85.
[36] Bornmann L, Daniel H D. What do citation counts measure? A review of studies on citing behavior. Journal of documentation, 2008, 64（1）: 45–80.
[37] Wang D, Song C, Barabási A L. Quantifying long-term scientific impact. Science, 2013, 342（6154）: 127–132.
[38] Radicchi F, Fortunato S, Castellano C. Universality of citation distributions: Toward an objective measure of scientific impact. Proceedings of the National Academy of Sciences, 2008, 105（45）: 17268–17272.
[39] Wuchty S, Jones B F, Uzzi B. The increasing dominance of teams in production of knowledge. Science, 2007, 316（5827）: 1036–1039.
[40] Lee S, Bozeman B. The impact of research collaboration on scientific productivity. Social studies of science, 2005, 35（5）: 673–702.
[41] Ikotun A M, Ezugwu A E, Abualigah L, et al. K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data. Information Sciences, 2023, 622: 178–210.
[42] Kaufman L, Rousseeuw P J. Finding Groups in Data: An Introduction to Cluster Analysis. Hoboken: John Wiley & Sons, 2009: 1−368.
[43] Alam M S, Rahman M M, Hossain M A, et al. Automatic human brain tumor detection in MRI image using template-based K means and improved fuzzy C means clustering algorithm. Big Data and Cognitive Computing, 2019, 3（2）: 27.
[44] Nanda S J, Gulati I, Chauhan R, et al. A K-means-galactic swarm optimization-based clustering algorithm with Otsu’s entropy for brain tumor detection. Applied Artificial Intelligence, 2019, 33（2）: 152–170.
[45] Nandapala E Y, Jayasena K P. The practical approach in Customers segmentation by using the K-Means Algorithm. In2020 IEEE 15th international conference on industrial and information systems （ICIIS）, 2020, 344-349.
[46] Jain A K. Data clustering: 50 years beyond K-means. Pattern recognition letters, 2010, 31（8）: 651–666.
[47] Tibshirani R, Walther G, Hastie T. Estimating the number of clusters in a data set via the gap statistic. Journal of the royal statistical society: series b （statistical methodology）, 2001, 63（2）: 411–423.
[48] Rousseeuw P J. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics, 1987, 20: 53–65.
[49] 李智恒, 桂颖溢, 杨志豪, 等. 基于生物医学文献的化学物质致病关系抽取, 计算机研究与发展, 2018, 55（1）: 198-206. DOI: 10.7544/issn1000-1239.2018.20160893.
[50] Javier Yanes. Más de 3.600 científicosconstruyen un manual de todas las célulashumanas: “Un Google Maps de la biología”. ELPAÍS, 2024. DOI: https://elpais.com/ciencia/2024-11-20/mas-de-3600-cientificos-construyen-un-manual-de-todas-las-celulas-humanas-un-google-maps-de-la-biologia.html.
[51] Thompson L R, Sanders J G, McDonald D, et al. A communal catalogue reveals Earth’s multiscale microbial diversity. Nature, 2017, 551（7681）: 457–463.
[52] Gilbert J A, Jansson J K, Knight R. Earth microbiome project and global systems biology. MSystems, 2018, 3（3）: 10–1128.
[53] Stephens N, Khan I, Errington R. Analysing the role of virtualisation and visualisation on interdisciplinary knowledge exchange in stem cell research processes. Palgrave Communications, 2018, 4（1）: 1–3.
[54] Freiband A, Dickin K L, Glass M, et al. Undisciplining the university through shared purpose, practice, and place. Humanities and Social Sciences Communications, 2022, 9（1）: 172.
基本信息:
DOI：10.19524/j.cnki.10-1009/g3.2026.03.074
引用信息:
[1]邹慧1,2, 徐琨鹏3, 陈李伟4, 刘博恩5, 瞿晶晶2, 傅晓明6,7.跨学科合作的历史规律、模式演进及展望——基于全球1950 — 2022年科学文献数据的实证研究[J],2026(03):74-100.DOI:10.19524/j.cnki.10-1009/g3.2026.03.074.
基金信息:
科技创新 2030 —“新一代人工智能”重大项目“人工智能治理与安全能力评估研究课题5：人工智能安全发展评估和预警体系研究”（2022ZD0116205）。