Scholarlink

<p>Over the past decade, China has significantly improved the retention and utilization rates of scientific instruments. However, systemic inefficiencies persist due to fragmented governance structures, human capital mismatches, and uneven regional development. This study evaluates the sustainability of 15 regional organizations managing large-scale scientific instruments from 2013 to 2022 by applying ecological network analysis (ENA) to model use-time as flow-based networks. Based on the concept of ascendency, this work introduces the degree of order (α) as a dynamic indicator of ecological efficiency. Empirical results reveal that while total instrument use-time (TST) has more than doubled, efficiency gains have been primarily driven by scale expansion rather than improved coordination. Technical staffing in typical regions such as Lanzhou increased by only 112% over ten years, lagging behind a &gt; 200% growth in total use-time, leading to a decline in average mutual information (AMI) and suboptimal α performance. Furthermore, a statistically significant inverse relationship is found between average sharing time and ecological efficiency, indicating that quota-based sharing policies may inadvertently reduce systemic resilience. This paradox reflects a critical trade-off between quantity-driven incentives and the structural integrity of sharing networks. Regression analysis identifies GDP per capita and policy timeliness as significant predictors of organizational efficiency, yet accounts for only 65% of its variance, underscoring the need to incorporate operational and structural variables. To address these challenges, this study proposes an “efficiency window” framework that defines optimal α ranges, enabling more adaptive evaluation across development stages. The findings offer a transferable model for assessing sustainability in resource-sharing infrastructures, while emphasizing the contextual limits of applying centralized governance approaches to decentralized systems. For policymakers, this study underscores the need to align economic incentives, staffing structures, and network flexibility to foster resilient, inclusive, and efficient scientific innovation ecosystems.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Measuring China’s Open Sharing for scientific instruments: an ascendency analysis with benchmark use-time data

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月26日

查看全文
  • Xiaobo Wang,
  • Shanshan Qu

摘要

Over the past decade, China has significantly improved the retention and utilization rates of scientific instruments. However, systemic inefficiencies persist due to fragmented governance structures, human capital mismatches, and uneven regional development. This study evaluates the sustainability of 15 regional organizations managing large-scale scientific instruments from 2013 to 2022 by applying ecological network analysis (ENA) to model use-time as flow-based networks. Based on the concept of ascendency, this work introduces the degree of order (α) as a dynamic indicator of ecological efficiency. Empirical results reveal that while total instrument use-time (TST) has more than doubled, efficiency gains have been primarily driven by scale expansion rather than improved coordination. Technical staffing in typical regions such as Lanzhou increased by only 112% over ten years, lagging behind a > 200% growth in total use-time, leading to a decline in average mutual information (AMI) and suboptimal α performance. Furthermore, a statistically significant inverse relationship is found between average sharing time and ecological efficiency, indicating that quota-based sharing policies may inadvertently reduce systemic resilience. This paradox reflects a critical trade-off between quantity-driven incentives and the structural integrity of sharing networks. Regression analysis identifies GDP per capita and policy timeliness as significant predictors of organizational efficiency, yet accounts for only 65% of its variance, underscoring the need to incorporate operational and structural variables. To address these challenges, this study proposes an “efficiency window” framework that defines optimal α ranges, enabling more adaptive evaluation across development stages. The findings offer a transferable model for assessing sustainability in resource-sharing infrastructures, while emphasizing the contextual limits of applying centralized governance approaches to decentralized systems. For policymakers, this study underscores the need to align economic incentives, staffing structures, and network flexibility to foster resilient, inclusive, and efficient scientific innovation ecosystems.