The importance of software citation

Hong, Neil Chue; Clark, Tim W.; Muench, August; Stall, Shelley; Bouquin, Daina; Cannon, Matthew; Edmunds, Scott; Faez, Telli; Surname, Bob; Fenner, Martin; Friedman, Michael; Grenier, Gerry; Harrison, Melissa; Heber, Joerg; Leary, Adam; MacCallum, Catriona; Murray, Hollydawn; Pastrana, Érika; Perry, Katherine; Schuster, Douglas; Stockhause, Martina; Yeston, Jake

doi:10.12688/f1000research.26932.1

Cited by 10 publications

(3 citation statements)

References 5 publications

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“…There is now beginning to be an additional, and necessary, focus on software citation, and provenance. Principles and methods for achieving these goals have been defined and are in various stages of transition to practice in the scientific communications ecosystem [1][2][3][4][5][6][7][8][9][10][11].…”

Section: Motivationmentioning

confidence: 99%

Evidence Graphs: Supporting Transparent and FAIR Computation, with Defeasible Reasoning on Data, Methods, and Results

Manir

Niestroy

Levinson

et al. 2021

Preprint

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Introduction: Transparency of computation is a requirement for assessing the validity of computed results and research claims based upon them; and it is essential for access to, assessment, and reuse of computational components. These components may be subject to methodological or other challenges over time. While reference to archived software and/or data is increasingly common in publications, a single machine-interpretable, integrative representation of how results were derived, that supports defeasible reasoning, has been absent. Methods: We developed the Evidence Graph Ontology, EVI, in OWL 2, with a set of inference rules, to provide deep representations of supporting and challenging evidence for computations, services, software, data, and results, across arbitrarily deep networks of computations, in connected or fully distinct processes. EVI integrates FAIR practices on data and software, with important concepts from provenance models, and argumentation theory. It extends PROV for additional expressiveness, with support for defeasible reasoning. EVI treats any com- putational result or component of evidence as a defeasible assertion, supported by a DAG of the computations, software, data, and agents that produced it. Results: We have successfully deployed EVI for very-large-scale predictive analytics on clinical time-series data. Every result may reference its own evidence graph as metadata, which can be extended when subsequent computations are executed. Discussion: Evidence graphs support transparency and defeasible reasoning on results. They are first-class computational objects, and reference the datasets and software from which they are derived. They support fully transparent computation, with challenge and support propagation. The EVI approach may be extended to include instruments, animal models, and critical experimental reagents.

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Section: Motivationmentioning

confidence: 99%

Evidence Graphs: Supporting Transparent and FAIR Computation, with Defeasible Reasoning on Data, Methods, and Results

Manir

Niestroy

Levinson

et al. 2021

Preprint

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“…Many venues now mandate that data, and increasingly analysis software, be archived and made available alongside a paper [13], in a welcome step towards improving the reproducibility of research. This works well when the software is a straightforward analysis script, but the process of archiving quickly becomes complex with anything beyond this.…”

Section: Citing Softwarementioning

confidence: 99%

Software must be recognised as an important output of scholarly research

Jay,

Haines,

Katz

2020

Preprint

Self Cite

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Software now lies at the heart of scholarly research. Here we argue that as well as being important from a methodological perspective, software should, in many instances, be recognised as an output of research, equivalent to an academic paper. The article discusses the different roles that software may play in research and highlights the relationship between software and research sustainability and reproducibility. It describes the challenges associated with the processes of citing and reviewing software, which differ from those used for papers. We conclude that whilst software outputs do not necessarily fit comfortably within the current publication model, there is a great deal of positive work underway that is likely to make an impact in addressing this.1 We use "scholarly research" as a general term for research in science, engineering, humanities, etc.

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“…Recommended software citation practices have more recently been clarified by the FORCE11 Software Citation Implementation Working Group in "Recognizing the value of software: a software citation guide" (Katz et al, 2021). With the current intensive use of software including specialized tools and models for scientific research problems, software has evolved to become a key research resource requiring transparency and disclosure to the readers of any scientific article.…”

Section: Introductionmentioning

confidence: 99%

Journal Production Guidance for Software and Data Citations

Stall¹,

Bilder

Cannon³

et al. 2022

Preprint

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Software and data citation are emerging best practices in scholarly communication. This article provides structured guidance to the academic publishing community on how to implement software and data citation in publishing workflows. These best practices support the verifiability and reproducibility of scientific results; sharing and reuse of valuable data and software tools, and attribution to the creators of the software and data. While data citation is increasingly well-established, software citation is rapidly maturing. With the current intensive use of software, including specialized tools and models for scientific research problems, the research community has begun to recognize that software, as a key research result and resource, requires the same level of transparency, accessibility, and disclosure as data. Software and data that support scientific results should be preserved and shared in scientific repositories for discovery, transparency, and use by other researchers. These goals can be supported by citing these products in the Reference Section of papers and effectively associating them to the software and data preserved in scientific repositories. Publishers need to mark up these references in a specific way to enable downstream processes, specifically those that enable automated attribution. Academic publishers wishing to stay current with best practices in the field are encouraged to follow the guidance provided here.

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The importance of software citation

Cited by 10 publications

References 5 publications

Evidence Graphs: Supporting Transparent and FAIR Computation, with Defeasible Reasoning on Data, Methods, and Results

Evidence Graphs: Supporting Transparent and FAIR Computation, with Defeasible Reasoning on Data, Methods, and Results

Software must be recognised as an important output of scholarly research

Journal Production Guidance for Software and Data Citations

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