The general purpose of a scientific publication is the exchange and spread of knowledge. A publication usually reports a scientific result and tries to convince the reader that it is valid. With an ever-growing number of papers relying on computational methods that make use of large quantities of data and sophisticated statistical modeling techniques, a textual description of the result is often not enough for a publication to be transparent and reproducible. While there are efforts to encourage sharing of code and data, we currently lack conventions for linking data sources to a computational result that is stated in the main publication text or used to generate a figure or table. Thus, here I propose a data citation format that allows for an automatic reproduction of all computations. A data citation consists of a descriptor that refers to the functional program code and the input that generated the result. The input itself may be a set of other data citations, such that all data transformations, from the original data sources to the final result, are transparently expressed by a directed graph. Functions can be implemented in a variety of programming languages since data sources are expected to be stored in open and standardized text-based file formats. A publication is then an online file repository consisting of a Hypertext Markup Language (HTML) document and additional data and code source files, together with a summarization of all data sources, similar to a list of references in a bibliography.
Graduate School of Education, Stanford University（斯坦福大学教育研究生院）；Public Knowledge Project, Simon Fraser University（公共知识项目，西蒙弗雷泽大学）；DataCite, Hannover, Germany（DataCite，德国汉诺威）；California Digital Library, University of California Office of the President（加州数字图书馆，加州大学校长办公室）
Overcoming Barriers to Computational Reproducibility
Ludwig-Maximilians-Universität (LMU)（路德维希-马克西米利安大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Weierstrass Institute for Applied Analysis and Stochastics（魏尔斯特拉斯应用分析与随机过程研究所）；Max Planck Institute for Demographic Research（马克斯·普朗克人口研究研究所）；Technical University of Munich（慕尼黑工业大学）；Medical University of Vienna（维也纳医科大学）；Lancaster University（兰卡斯特大学）；Clemson University（克莱姆森大学）；University of California, Berkeley（加州大学伯克利分校）；INRAE（法国国家农业、食品与环境研究院）；AgroParisTech（巴黎农业学院）；Université Paris-Saclay（巴黎萨克雷大学）
University of California, San Diego（加州大学圣地亚哥分校）；University of Toronto（多伦多大学）；Dunlap Institute for Astronomy & Astrophysics, University of Toronto（多伦多大学邓普尔天文与天体物理研究所）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；FAIR Data Innovations Hub, California Medical Innovations Institute（公平数据创新中心，加州医疗创新研究所）；National Center for Supercomputing Applications, University of Illinois Urbana-Champaign（美国国家超级计算应用中心，伊利诺伊大学厄巴纳-香槟分校）；SciOS；University of California Berkeley（加州大学伯克利分校）；Center for Astrophysics | Harvard & Smithsonian（哈佛&史密森尼天体物理中心）；Astrophysics Source Code Library | University of Maryland College Park（天体物理学源代码库，马里兰大学帕克分校）；Universität Heidelberg, Zentrum für Astronomie (ZAH), Institut für Theoretische Astrophysik（海德堡大学，天文学中心（ZAH），理论天体物理研究所）；Institute for Cyber-Enabled Research, Michigan State University（密歇根州立大学网络使能研究所）；The US Research Software Engineer Association（美国研究软件工程师协会）；San Diego Supercomputer Center, University of California, San Diego（加州大学圣地亚哥分校圣地亚哥超级计算中心）；Center for Computational Astrophysics, Flatiron Institute（平顿研究所计算天体物理中心）
The Reproducible Research Platform establishes a unified open science environment bridging data and software lifecycles across disciplines, from proposal to publication
