Dimensions: Building Context for Search and Evaluation

Frontiers in Research Metrics and Analytics

Daniel W. Hook²  Christian Herzog³  Simon J. Porter³ 

[1] Centre for Complexity Science, Imperial College London, London, United Kingdom;Department of Physics, Washington University in St. Louis, St. Louis, MO, United States;Digital Science, London, United Kingdom;

关键词: scholarly search;    contextual search;    open data;    artificial intelligence;    machine learning;    unique identifiers;   

DOI  :  10.3389/frma.2018.00023

来源: DOAJ

【 摘 要 】

Dimensions is a new scholarly search database that focuses on the broader set of use cases that academics now face. By including awarded grants, patents, and clinical trials alongside publication and Altmetric attention data, Dimensions goes beyond the standard publication-citation ecosystem to give the user a much greater sense of context of a piece of research. All entities in the graph may be linked to all other entities. Thus, a patent may be linked to a grant, if an appropriate reference is made. Books, book chapters, and conference proceedings are included in the publication index. All entities are treated as first-class objects and are mapped to a database of research institutions and a standard set of research classifications via machine-learning techniques. This article gives an overview of the methodology of construction of the Dimensions dataset and user interface.

【 授权许可】

Unknown