five

trackr: A Framework for Enhancing Discoverability and Reproducibility of Data Visualizations and Other Artifacts in R

收藏
Taylor & Francis Group2019-06-03 更新2026-04-16 收录
下载链接:
https://tandf.figshare.com/articles/trackr_A_Framework_for_Enhancing_Discoverability_and_Reproducibility_of_Data_Visualizations_and_Other_Artifacts_in_R/7859684/1
下载链接
链接失效反馈
官方服务:
资源简介:
Research is an incremental, iterative process, with new results relying and building upon previous ones. Scientists need to find, retrieve, understand, and verify results to confidently extend them, even when the results are their own. We present the trackr framework for organizing, automatically annotating, discovering, and retrieving results. We identify sources of automatically extractable metadata for computational results, and we define an extensible system for organizing, annotating, and searching for results based on these and other metadata. We present an open-source implementation of these concepts for plots, computational artifacts, and woven dynamic reports generated in the R statistical computing language. Supplementary materials for this article are available online.
创建时间:
2019-03-18
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作