Data citation

Data citation is the provision of accurate, consistent and standardised referencing for datasets just as bibliographic citations are provided for other published sources like research articles or monographs. Typically the well established Digital Object Identifier (DOI) approach is used with DOIs taking users to a website that contains the metadata on the dataset and the dataset itself.[1][2]

A 2011 paper reported an inability to determine how often data citation happened in social sciences.[3]

2012-13 papers reported that data citation was becoming more common but the practice for it was not standard.[4][5]

In 2014 the Force11 Scholarly Communication Institute published a statement recommending data citation.[6]

In October 2018 CrossRef expressed its support for cataloging datasets and recommending their citation.[7]

A popular data-oriented journal reported in April 2019 that it would now use data citations.[8]

A June 2019 paper suggested that increased data citation will make the practice more valuable for everyone by encouraging data sharing and also by increasing the prestige of people who share.[9]

Data citation is an emerging topic in computer science and it has been defined as a computational problem.[10] Indeed, citing data poses significant challenges to computer scientists and the main problems to address are related to:[11]

• the use of heterogeneous data models and formats – e.g., relational databases, Comma-Separated Values (CSV), eXtensible Markup Language (XML),[12][13] Resource Description Framework (RDF);[14]
• the transience of data;
• the necessity to cite data at different levels of coarseness – i.e., deep citations;[15]
• the necessity to automatically generate citations to data with variable granularity.