Recognizing the value of software: a software citation guide

Katz, Daniel S.; 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 S.

doi:10.12688/f1000research.26932.2

Cited by 38 publications

(34 citation statements)

References 9 publications

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“…229 The recognition of sharing reproducible works, the reproductions, and more extensive reviewing efforts as important scientific contributions are paramount for their adoption and reproducibility practices will not be established without tackling the shortcomings of researcher evaluation culture. 230 Software citation 231 and software publications 232 are concrete mechanisms to value tools for reproducible computational research, and these mechanisms need higher uptake beyond disciplines that are naturally close to software. Only then can related metadata reach a broad adoption via generally used research software.…”

Section: Discussionmentioning

confidence: 99%

The role of metadata in reproducible computational research

et al. 2021

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Summary Reproducible computational research (RCR) is the keystone of the scientific method for in silico analyses, packaging the transformation of raw data to published results. In addition to its role in research integrity, improving the reproducibility of scientific studies can accelerate evaluation and reuse. This potential and wide support for the FAIR principles have motivated interest in metadata standards supporting reproducibility. Metadata provide context and provenance to raw data and methods and are essential to both discovery and validation. Despite this shared connection with scientific data, few studies have explicitly described how metadata enable reproducible computational research. This review employs a functional content analysis to identify metadata standards that support reproducibility across an analytic stack consisting of input data, tools, notebooks, pipelines, and publications. Our review provides background context, explores gaps, and discovers component trends of embeddedness and methodology weight from which we derive recommendations for future work.

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Section: Discussionmentioning

confidence: 99%

The role of metadata in reproducible computational research

et al. 2021

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“…Research software should be made available through collaborative development platforms such as GitHub (github.com) or Bitbucket (bitbucket.org), versioned, and licensed to describe terms of reuse and access (Lamprecht et al, 2020;American Meteorological Society, 2021). Both the data and snapshots of software versions that were used to support research outcomes should be archived in trusted data (e.g., https://repositoryfinder.datacite.org) and software (e.g., https://zenodo.org, https://figshare.com) repositories for longterm preservation and sharing, and assigned digital object identifiers to facilitate discovery and credit (Data Citation Synthesis Group, 2014; Katz et al, 2021).…”

Section: Need For Curation Supportmentioning

confidence: 99%

Open Science Expectations for Simulation-Based Research

2021

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There is strong agreement across the sciences that replicable workflows are needed for computational modeling. Open and replicable workflows not only strengthen public confidence in the sciences, but also result in more efficient community science. However, the massive size and complexity of geoscience simulation outputs, as well as the large cost to produce and preserve these outputs, present problems related to data storage, preservation, duplication, and replication. The simulation workflows themselves present additional challenges related to usability, understandability, documentation, and citation. These challenges make it difficult for researchers to meet the bewildering variety of data management requirements and recommendations across research funders and scientific journals. This paper introduces initial outcomes and emerging themes from the EarthCube Research Coordination Network project titled “What About Model Data? - Best Practices for Preservation and Replicability,” which is working to develop tools to assist researchers in determining what elements of geoscience modeling research should be preserved and shared to meet evolving community open science expectations.Specifically, the paper offers approaches to address the following key questions:• How should preservation of model software and outputs differ for projects that are oriented toward knowledge production vs. projects oriented toward data production?• What components of dynamical geoscience modeling research should be preserved and shared?• What curation support is needed to enable sharing and preservation for geoscience simulation models and their output?• What cultural barriers impede geoscience modelers from making progress on these topics?

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“…The Computational Environment criterion and the Input Data criterion accounted for a significant number of 0 values, which clearly signals an impediment to reproduction. It also shows a rather low recognition of data and software as academic outputs, because both data and software should be properly cited to give credit to their creators [18,12].…”

Section: Reproducibility Of Giscience Conference Papersmentioning

confidence: 99%

Reproducible Research and GIScience: an evaluation using GIScience conference papers

Ostermann¹,

Nüst²,

Granell³

et al. 2020

Preprint

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GIScience conference authors and researchers face the same computational reproducibility challenges as authors and researchers from other disciplines who use computers to analyse data. Here, to assess the reproducibility of GIScience research, we apply a rubric for assessing the reproducibility of 75 conference papers published at the GIScience conference series in the years 2012-2018. Since the rubric and process were previously applied to the publications of the AGILE conference series, this paper itself is an attempt to replicate that analysis, however going beyond the previous work by evaluating and discussing proposed measures to improve reproducibility in the specific context of the GIScience conference series. The results of the GIScience paper assessment are in line with previous findings: although descriptions of workflows and the inclusion of the data and software suffice to explain the presented work, in most published papers they do not allow a third party to reproduce the results and findings with a reasonable effort. We summarise and adapt previous recommendations for improving this situation and propose the GIScience community to start a broad discussion on the reusability, quality, and openness of its research. Further, we critically reflect on the process of assessing paper reproducibility, and provide suggestions for improving future assessments. The code and data for this article are

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Recognizing the value of software: a software citation guide

Cited by 38 publications

References 9 publications

The role of metadata in reproducible computational research

The role of metadata in reproducible computational research

Open Science Expectations for Simulation-Based Research

Reproducible Research and GIScience: an evaluation using GIScience conference papers

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