基于监督学习的科研数据元数据学科分类
Using Supervised Learning to Classify Metadata of Research Data by Discipline of Research
摘要：
研究数据的元数据的自动分类，其学科的研究，可用于科学计量研究，由存储库服务提供商，并在研究数据聚合服务的背景下。研究数据的DataCite索引的公开可用元数据被用于编译由609，524条记录组成的大型训练和评估集，该数据集与本文一起发布。这些数据允许可再现地评估分类方法，例如基于树的模型和神经网络。根据我们对20个基本类（多标签分类）的实验，多层感知器模型表现最好，f1宏得分为0.760，紧随其后的是长短期记忆模型（f1宏得分为0.755）。训练的分类模型的一个可能的应用是对数字学术产出的跨学科趋势进行定量分析，或者对研究数据的增长模式进行表征，并按研究学科进行分层。这两个应用程序执行规模与拟议的模型，可供重用。
英文摘要：
Automated classification of metadata of research data by their discipline(s) of research can be used in scientometric research, by repository service providers, and in the context of research data aggregation services. Openly available metadata of the DataCite index for research data were used to compile a large training and evaluation set comprised of 609,524 records, which is published alongside this paper. These data allow to reproducibly assess classification approaches, such as tree-based models and neural networks. According to our experiments with 20 base classes (multi-label classification), multi-layer perceptron models perform best with a f1-macro score of 0.760 closely followed by Long Short-Term Memory models (f1-macro score of 0.755). A possible application of the trained classification models is the quantitative analysis of trends towards interdisciplinarity of digital scholarly output or the characterization of growth patterns of research data, stratified by discipline of research. Both applications perform at scale with the proposed models which are available for re-use.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
