This paper addresses the problem of information burying in social sciences, where a large amount of experimental findings reported in multiple scientific articles may be missed by scholars due to the lack of an active accumulation, organization and synthesis of these findings into a centralized information system. To tackle the information burying problem, in this paper we present a new network-based data model and methodology for aggregating, organizing, linking and mining quantitative results published in multiple academic articles in particular sub-fields of social sciences. The goal of the proposed methodology is to provide researchers with a wider perspective when viewing scientific results in their own fields and utilize it for their research. To validate the proposed approach, we conducted a manual experiment with a corpus of 41 scientific articles in the field of personal information management. The experiment indicates that the constructed network-based information system can be effectively used to explore the relationships between the results of various articles, raising new research questions and hypotheses based on results from multiple articles that tested similar variables. The proposed system can serve as a catalyst for the advancement of research in various fields of social science.

中文翻译：

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使用基于数千个结果的变量网络，从更广阔的社会科学视角出发

本文解决了社会科学中的信息埋藏问题，在多篇科学文章中报告的大量实验结果，由于缺乏将这些发现积极积累、组织和综合到一个集中的信息系统中，可能会被学者遗漏。为了解决信息隐藏问题，在本文中，我们提出了一种新的基于网络的数据模型和方法，用于聚合、组织、链接和挖掘发表在多篇学术文章中特定社会科学子领域的定量结果。拟议方法的目标是为研究人员提供更广阔的视野，以便在查看他们自己领域的科学成果并将其用于研究时。为了验证所提出的方法，我们对个人信息管理领域的 41 篇科学文章进行了人工实验。实验表明，构建的基于网络的信息系统可以有效地用于探索各种文章结果之间的关系，基于多篇文章测试相似变量的结果提出新的研究问题和假设。拟议的系统可以作为促进社会科学各个领域研究进步的催化剂。根据测试类似变量的多篇文章的结果提出新的研究问题和假设。拟议的系统可以作为促进社会科学各个领域研究进步的催化剂。根据测试类似变量的多篇文章的结果提出新的研究问题和假设。拟议的系统可以作为促进社会科学各个领域研究进步的催化剂。