Literature DB >> 29599662

Automating data citation: the eagle-i experience.

Abdussalam Alawini1, Leshang Chen1, Susan B Davidson1, Natan Portilho Da Silva2, Gianmaria Silvello3.   

Abstract

Data citation is of growing concern for owners of curated databases, who wish to give credit to the contributors and curators responsible for portions of the dataset and enable the data retrieved by a query to be later examined. While several databases specify how data should be cited, they leave it to users to manually construct the citations and do not generate them automatically. We report our experiences in automating data citation for an RDF dataset called eagle-i, and discuss how to generalize this to a citation framework that can work across a variety of different types of databases (e.g. relational, XML, and RDF). We also describe how a database administrator would use this framework to automate citation for a particular dataset.

Entities:  

Year:  2017        PMID: 29599662      PMCID: PMC5868434          DOI: 10.1109/JCDL.2017.7991571

Source DB:  PubMed          Journal:  Proc ACM/IEEE Joint Conf Digit Libr        ISSN: 1552-5996


  1 in total

1.  Why Data Citation Is a Computational Problem.

Authors:  Peter Buneman; Susan Davidson; James Frew
Journal:  Commun ACM       Date:  2016-09       Impact factor: 4.654

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.