开放源码软件，高效和透明的审查
Open Source Software for Efficient and Transparent Reviews
摘要：
为了帮助研究人员尽可能有效和透明地进行系统综述或荟萃分析，我们设计了一个工具（ASReview）来加速筛选标题和摘要的步骤。对于许多任务，包括但不限于系统综述和荟萃分析，需要系统地检查科学文献。目前，学者和从业者手工筛选数千项研究，以确定哪些研究应纳入其综述或荟萃分析。这是容易出错和效率低下，因为极不平衡的数据：只有一小部分筛选的研究是相关的。系统复习的未来将是与机器学习算法的互动，以处理可用文本的巨大增长。因此，我们开发了一个应用主动学习的开源机器学习辅助管道：ASReview。我们通过模拟研究表明，ASReview可以产生更有效的审查比手动审查，同时提供高质量。此外，我们描述了自由和开源的研究软件的选项，并提出了从用户体验测试的结果。我们邀请社区为开源项目做出贡献，比如我们自己的项目，这些项目提供了对当前实践的可衡量和可复制的改进。
英文摘要：
To help researchers conduct a systematic review or meta-analysis as efficiently and transparently as possible, we designed a tool (ASReview) to accelerate the step of screening titles and abstracts. For many tasks - including but not limited to systematic reviews and meta-analyses - the scientific literature needs to be checked systematically. Currently, scholars and practitioners screen thousands of studies by hand to determine which studies to include in their review or meta-analysis. This is error prone and inefficient because of extremely imbalanced data: only a fraction of the screened studies is relevant. The future of systematic reviewing will be an interaction with machine learning algorithms to deal with the enormous increase of available text. We therefore developed an open source machine learning-aided pipeline applying active learning: ASReview. We demonstrate by means of simulation studies that ASReview can yield far more efficient reviewing than manual reviewing, while providing high quality. Furthermore, we describe the options of the free and open source research software and present the results from user experience tests. We invite the community to contribute to open source projects such as our own that provide measurable and reproducible improvements over current practice.
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。
