服务科技创新及效能评价研究数据集
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服务科技创新及效能评价研究数据集主要面向服务科技创新成果转化评估、金融科技赋能效应测度、智慧物流产业升级分析等需求建设。数据集基于国家自然科学基金官网、企业官网公开信息、专利数据库、上市企业财务报告等多源渠道整合产生，采用智能文本检索、机器学习算法、文本分类与实体识别等技术手段完成数据采集与处理工作。数据集包含三个核心组成部分，一是服务科技创新效能评价数据，收集2015-2020年国家自然科学基金信息学部项目3408项（剔除硬件基础研究领域），涵盖申请人、中英文摘要、资助经费等基础信息以及发表期刊论文、专著、科研奖励、会议论文数量等成果信息，并获取专利类型、专利引证、专利同族、技术稳定性等详细字段，构建了包含可靠性、关注度、创新性、影响力等10个二级指标在内的评价体系，聚合形成经济价值、学术价值和社会价值三个维度的总分；二是金融科技赋能区域发展评价数据，通过智能文本检索技术分析企业官网简介、业务范围等公开信息，结合高技术特征筛选出拥有专利的在营技术类和应用类金融科技企业共计14284家，构建了生产力、盈利性等二级指标体系，计算各省市的经济价值、创新价值和生态价值得分；三是智慧物流赋能产业发展评价数据，采用2023年上市企业样本并以产业为单位聚合，通过产业内企业均值反映产业整体水平，建立了普及性、创新性、协同性、引领性等二级指标体系，获得技术价值、经济价值和社会价值三个维度的总分。该数据集为服务科技创新效能评估、金融科技与智慧物流赋能效应研究提供了多层次、多维度的数据支撑，可服务于科技政策制定、产业发展规划和学术研究等场景。
Research Dataset for Service-oriented Scientific and Technological Innovation and Efficiency Evaluation is developed to meet the needs of scientific and technological innovation achievement transformation evaluation, fintech-enabled effect measurement, and smart logistics industry upgrading analysis. This dataset is integrated from multiple public sources including the official website of the National Natural Science Foundation of China (NSFC), official enterprise websites, patent databases, and financial reports of listed companies, and data collection and processing are completed through technical means such as intelligent text retrieval, machine learning algorithms, text classification and entity recognition. The dataset consists of three core components: The first core component is service-oriented scientific and technological innovation efficiency evaluation data: a total of 3,408 projects from the Department of Information Science of the NSFC from 2015 to 2020 are collected (excluding hardware-based basic research fields), covering basic information such as applicants, Chinese and English abstracts, and funded amounts, as well as achievement information including the number of published journal papers, monographs, scientific research awards and conference papers. Detailed fields such as patent types, patent citations, patent families and technical stability are also obtained. An evaluation system including 10 secondary indicators such as reliability, attention, innovation and influence is constructed, and the total scores under three dimensions of economic value, academic value and social value are aggregated. The second core component is fintech-enabled regional development evaluation data: through intelligent text retrieval technology, public information such as enterprise official website introductions and business scopes is analyzed, and a total of 14,284 operating technical and application-oriented fintech enterprises with patents are screened out based on high-tech characteristics. A secondary indicator system including productivity, profitability and other indicators is constructed, and the scores of economic value, innovation value and ecological value of each province and municipality are calculated. The third core component is smart logistics-enabled industrial development evaluation data: 2023 listed company samples are used and aggregated by industry, with the overall industrial level reflected by the average value of enterprises within the industry. A secondary indicator system including popularity, innovation, synergy, leadership and other indicators is established, and total scores under three dimensions of technical value, economic value and social value are obtained. This dataset provides multi-level and multi-dimensional data support for the evaluation of service-oriented scientific and technological innovation efficiency, research on fintech-enabled effects and smart logistics-enabled effects, and can be applied to scenarios such as science and technology policy formulation, industrial development planning and academic research.
