Software citation principles

Smith, Arfon M.; Katz, Daniel S.; Niemeyer, Kyle E.

doi:10.7717/peerj-cs.86

Cited by 193 publications

(135 citation statements)

References 23 publications

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“…For example, the FORCE11 Software Citation Working Group used three different peer review models and methods to iteratively improve their principles document, leading to a journal publication ( Smith et al , 2016). Initially, the document that was produced was made public and reviewed by GitHub issues ( github.com/force11/force11-scwg [see Section 3.4]).…”

Section: Potential Future Modelsmentioning

confidence: 99%

A multi-disciplinary perspective on emergent and future innovations in peer review

et al. 2017

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Peer review of research articles is a core part of our scholarly communication system. In spite of its importance, the status and purpose of peer review is often contested. What is its role in our modern digital research and communications infrastructure? Does it perform to the high standards with which it is generally regarded? Studies of peer review have shown that it is prone to bias and abuse in numerous dimensions, frequently unreliable, and can fail to detect even fraudulent research. With the advent of web technologies, we are now witnessing a phase of innovation and experimentation in our approaches to peer review. These developments prompted us to examine emerging models of peer review from a range of disciplines and venues, and to ask how they might address some of the issues with our current systems of peer review. We examine the functionality of a range of social Web platforms, and compare these with the traits underlying a viable peer review system: quality control, quantified performance metrics as engagement incentives, and certification and reputation. Ideally, any new systems will demonstrate that they out-perform and reduce the biases of existing models as much as possible. We conclude that there is considerable scope for new peer review initiatives to be developed, each with their own potential issues and advantages. We also propose a novel hybrid platform model that could, at least partially, resolve many of the socio-technical issues associated with peer review, and potentially disrupt the entire scholarly communication system. Success for any such development relies on reaching a critical threshold of research community engagement with both the process and the platform, and therefore cannot be achieved without a significant change of incentives in research environments.

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Section: Potential Future Modelsmentioning

confidence: 99%

A multi-disciplinary perspective on emergent and future innovations in peer review

et al. 2017

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“…This ensures that both the researchers and software developers working upstream of the current project are properly credited, encouraging future sharing and development. Remember, the people who did the research are not necessarily the same people as the developers and maintainers of the software, so it is important to reward both appropriately with citations: take note of the FORCE11 Software Citation Principles [51].…”

Section: Lineage (Or: "Standing On the Shoulders Of Giants")mentioning

confidence: 99%

Reproducibility in Research: Systems, Infrastructure, Culture

Crick¹,

Hall²,

Ishtiaq

2017

JORS

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The reproduction and replication of research results has become a major issue for a number of scientific disciplines. In computer science and related computational disciplines such as systems biology, the challenges closely revolve around the ability to implement (and exploit) novel algorithms and models. Taking a new approach from the literature and applying it to a new codebase frequently requires local knowledge missing from the published manuscripts and transient project websites. Alongside this issue, benchmarking, and the lack of open, transparent and fair benchmark sets present another barrier to the verification and validation of claimed results.In this paper, we outline several recommendations to address these issues, driven by specific examples from a range of scientific domains. Based on these recommendations, we propose a high-level prototype open automated platform for scientific software development which effectively abstracts specific dependencies from the individual researcher and their workstation, allowing easy sharing and reproduction of results. This new e-infrastructure for reproducible computational science offers the potential to incentivise a culture change and drive the adoption of new techniques to improve the quality and efficiency -and thus reproducibility -of scientific exploration.

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“…FORCE11 and its stakeholder communities have already undertaken very significant policy development work enabling common definition of data citation practices [1,2,4,5,[7][8][9] [13]; (f) a set of Software Citation Principles [14].…”

Section: Barriers (And Burdens or Costs) To Data Stewardship And Sharmentioning

confidence: 99%

Open Science strategies for NIH data management, sharing, and citation

Clark

Cousijn

Fenner

2017

Preprint

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This document summarizes a series of authoritative views on research data management, sharing and citation, developed in Expert Groups, Working Groups, and other activities organized through FORCE11 (http://force11.org), an international community of over 2,000 members dedicated to advancing research communications and e-scholarship.It was prepared in response to NIH Request for Information (RFI) NOT-OD-17-015 (URL: https://grants.nih.gov/grants/guide/notice-files/NOT-OD-17-015.html), and has been reviewed and approved by the FORCE11 Board of Directors on January 19, 2017.

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Software citation principles

Cited by 193 publications

References 23 publications

A multi-disciplinary perspective on emergent and future innovations in peer review

A multi-disciplinary perspective on emergent and future innovations in peer review

Reproducibility in Research: Systems, Infrastructure, Culture

Open Science strategies for NIH data management, sharing, and citation

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