Letter of intent to submit (with abstract) deadline: 4 February 2019 (to email@example.com)
Full paper submission deadline: 7 May 2019
Publication date: January/February 2020
This is a call for papers for an upcoming special issue of Computing in Science & Engineering to be published in January/February 2020. The goal of this special issue is to inform the scientific computing community about recent advances and the current state of the art in software and data citation. Both software and data are increasingly essential to modern science and engineering across fields, particularly as science becomes more digital and more open. Initial work has been done 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. Ideally, researchers who produce software and data should be cited when their products are used; researchers who use software and data should understand how to cite these products when they use them and should do so; journal and conferences should tell their authors, reviewers, and editors/chairs about best practices and provide guidance for applying them; citation manager tools and editorial workflow systems should make it easy to enter and process metadata relating to software and data; indexing systems should track the use of software and data via citations; and hiring and promotion should take software and data into account. As these practices increase, 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.
This special issue of Computing in Science & Engineering will examine the state of software and data citation, inform the community of the excellent resources available to advance research and education goals, and inform future directions for research and implementation.We are soliciting papers on topics that include, but are not limited to,
- the state-of-the-art in advancement and adoption of software and data citation,
- how software and data citation can advance discovery,
- case studies of implementing software and data citation,
- lessons learned,
- efforts in community policies and development of guidance,
- efforts to track and measure the adoption of software and data citation, and
- effective methods for addressing stakeholder requirements.
Manuscripts of particular interest will describe efforts and impacts that improve the ability of researchers in science and engineering to benefit from software and data citation by bridging between different stakeholder groups, including those involved in research, scholarly communication and platforms, and their funding.
Only submissions that describe previously unpublished, original, state-of-the-art research and that are not currently under review by a conference or journal will be considered. Extended versions of conference papers must be at least 30 percent different from the original conference works.
Abstracts: Please contact the guest editors at firstname.lastname@example.org to ask whether your topic is suited for the special issue. Letters of intent with a one-page abstract are requested in advance of full papers. Please send them to email@example.com by 4 February 2019.
Manuscripts for peer review (Scholar One): Please submit electronically through ScholarOne Manuscripts by 7 May 2019, selecting this special-issue option. When preparing your manuscript, please see the CiSE-specific author guidelines and the general author guidelines. Manuscripts should not exceed 7,200 words—including all main body, abstract, keyword, bibliography, biography, and table text—and 20 references. Each table and figure counts for 300 words. Articles should be understandable by a broad audience of computer science and engineering professionals, avoiding a focus on theory, mathematics, jargon, and abstract concepts. Accepted papers will be lightly edited for grammar and formatting.
Contact the guest editors at firstname.lastname@example.org.
Daniel S. Katz (University of Illinois Urbana-Champaign)
Neil Chue Hong (University of Edinburgh)
Tim Clark (University of Virginia)
Martin Fenner (DataCite)
Maryann E. Martone (University of California, San Diego)