Work

Software and Data Citation

Year: 2020

Type: article

Abstract: This special issue is intended to inform the scientific computing community about recent advances and the current state of the art in software and data citation. Initial work has been done elsewhere to define standards and principles for software and data citation, and the basic required infrastructure is now in place. The challenge now is to adopt these practices. As their uptake increases, we believe that our professional culture will increasingly encourage production and sharing of software and data, leading to better and more reproducible and reusable results. The articles in this special issue discuss how software, data, and related digital objects are cited and otherwise mentioned in publications today, how to best cite them to make them accessible in the future, and what we can learn from the citations.

Source: Computing in Science & Engineering

DOI: 10.1109/mcse.2020.2969730

Authors: Daniel S. Katz, Neil P. Chue Hong, Tim Clark, Martin Fenner, Maryann E. Martone

Institutions: University of Illinois Urbana-Champaign, University of Edinburgh, University of Virginia, DataCite, University of California San Diego

Language: English

Topic: Research Data Management Practices

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,995

Funder: Engineering and Physical Sciences Research Council