黑龙江省2016-2020年科技查新工作分析——以黑龙江省科学技术情报研究院为例

Analysis of Sci-Tech Novelty Retrieval Work in Heilongjiang Province from 2016 to 2020——Through Taking Heilongjiang Sci-tech Information Institute as an Example

以黑龙江省科技查新工作为研究对象,结合黑龙江省科学技术情报研究院数据,根据查新目的、行业分布、委托单位和月份分布情况,分析了2016-2020年黑龙江省科技查新动态。过去5年中,查新项目总数量最高的为2019年的2570项,占5年总查新量的22.98%;其余年份为,2016年16.63%、2017年19.37%、2018年22.01%、2020年19.02%。查新目的多为立项查新和成果鉴定,年均占比为82.07%。查新行业分类上,工学比重始终最高,平均为48.69%,占比最小的农学只有5.35%。委托单位上主要分为4类,企业平均查新量最多,为38.33%,其余依次是医疗机构33.22%、大学14.78%、科研单位10.77%、其他委托单位2.90%。查新最多的月份为3月和4月,其次是11月。应加强查新人员综合素质的培养,完善评估系统,建设网络平台,通过构建多维度的综合评价模型对查新质量评价指标进行评估,并完成高水平的查新报告。

This paper focused on the sci-tech novelty retrieval in Heilongjiang Province,and analyzed the trends of sci-tech novelty retrieval in Heilongjiang Province from 2016 to 2020 according to the purpose of novelty search,industry distribution,entrusted units and monthly distribution with the data of Heilongjiang Sci-tech Information Institute.In the past five years,the largest number of novelty retrieval projects was 2570 in 2019,accounting for 22.98%,while the rest were 16.63%in 2016,19.37%in 2017,22.01%in 2018 and 19.02%in 2020.The main purpose of novelty retrieval was project novelty retrieval and achievement appraisal,accounting for 82.07%annually.In the classification of novelty retrieval industry,the proportion of engineering is always the largest,with an average of 48.69%,while the proportion of agriculture was only 5.35%.There were four types of entrusted units,enterprises had the most average novelty retrieval,accounting for 38.33%,followed by 33.22%of medical institutions,14.78%of universities,10.77%of scientific research institutions and 2.90%of other entrusted units.The most frequent months for novelty retrieval were March and April,followed by November.It is suggested to promote the training of the comprehensive quality of novelty search staff,perfect evaluation system,establish internet platform,access novelty quality evaluation index through the establishment of multiple dimensional comprehensive evaluation model.