论文标题
迈向机器可读文献：基于上传粉末衍射图案查找相关论文
Towards a machine-readable literature: finding relevant papers based on an uploaded powder diffraction pattern
论文作者
论文摘要
我们研究了用于机器可读文献的原型应用程序。该程序称为“ pydatarecognition”，是数据驱动文献搜索的示例，其中文献搜索查询是用户提供的实验数据集。用户将粉末图案与辐射波长一起上传。该程序将用户数据与与已发表论文相关的现有粉末模式的数据库进行比较，并根据其相似性得分产生排序。该程序将返回数字对象标识符（DOI），以及顶级论文的完整引用，以及用户数据的堆栈图以及前五名数据库条目。本文描述了该方法并探讨了成功和挑战。
We investigate a prototype application for machine-readable literature. The program is called "pyDataRecognition" and serves as an example of a data-driven literature search, where the literature search query is an experimental data-set provided by the user. The user uploads a powder pattern together with the radiation wavelength. The program compares the user data to a database of existing powder patterns associated with published papers and produces a rank ordered according to their similarity score. The program returns the digital object identifier (doi) and full reference of top ranked papers together with a stack plot of the user data alongside the top five database entries. The paper describes the approach and explores successes and challenges.
