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Four simple recommendations to encourage best practices in research software.

ABSTRACT: Scientific research relies on computer software, yet software is not always developed following practices that ensure its quality and sustainability. This manuscript does not aim to propose new software development best practices, but rather to provide simple recommendations that encourage the adoption of existing best practices. Software development best practices promote better quality software, and better quality software improves the reproducibility and reusability of research. These recommendations are designed around Open Source values, and provide practical suggestions that contribute to making research software and its source code more discoverable, reusable and transparent. This manuscript is aimed at developers, but also at organisations, projects, journals and funders that can increase the quality and sustainability of research software by encouraging the adoption of these recommendations.

SUBMITTER: Jimenez RC

PROVIDER: S-EPMC5490478 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Four simple recommendations to encourage best practices in research software.

Jiménez Rafael C RC Kuzak Mateusz M Alhamdoosh Monther M Barker Michelle M Batut Bérénice B Borg Mikael M Capella-Gutierrez Salvador S Chue Hong Neil N Cook Martin M Corpas Manuel M Flannery Madison M Garcia Leyla L Gelpí Josep Ll JL Gladman Simon S Goble Carole C González Ferreiro Montserrat M Gonzalez-Beltran Alejandra A Griffin Philippa C PC Grüning Björn B Hagberg Jonas J Holub Petr P Hooft Rob R Ison Jon J Katz Daniel S DS Leskošek Brane B López Gómez Federico F Oliveira Luis J LJ Mellor David D Mosbergen Rowland R Mulder Nicola N Perez-Riverol Yasset Y Pergl Robert R Pichler Horst H Pope Bernard B Sanz Ferran F Schneider Maria V MV Stodden Victoria V Suchecki Radosław R Svobodová Vařeková Radka R Talvik Harry-Anton HA Todorov Ilian I Treloar Andrew A Tyagi Sonika S van Gompel Maarten M Vaughan Daniel D Via Allegra A Wang Xiaochuan X Watson-Haigh Nathan S NS Crouch Steve S

F1000Research 20170613

Scientific research relies on computer software, yet software is not always developed following practices that ensure its quality and sustainability. This manuscript does not aim to propose new software development best practices, but rather to provide simple recommendations that encourage the adoption of existing best practices. Software development best practices promote better quality software, and better quality software improves the reproducibility and reusability of research. These recomme ...[more]

PMID: 28751965

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Project description:Non-detectable (ND) and outlying concentration values (OV) are a common challenge of biomarker investigations. However, best practices on how to aptly deal with the affected cases are still missing. The high methodological heterogeneity in biomarker-oriented research, as for example, in the field of psychoneuroendocrinology, and the statistical bias in some of the applied methods may compromise the robustness, comparability, and generalizability of research findings. In this paper, we describe the occurrence of ND and OV in terms of a model that considers them as censored data, for instance due to measurement error cutoffs. We then present common univariate approaches in handling ND and OV by highlighting their respective strengths and drawbacks. In a simulation study with lognormal distributed data, we compare the performance of six selected methods, ranging from simple and commonly used to more sophisticated imputation procedures, in four scenarios with varying patterns of censored values as well as for a broad range of cutoffs. Especially deletion, but also fixed-value imputations bear a high risk of biased and pseudo-precise parameter estimates. We also introduce censored regressions as a more sophisticated option for a direct modeling of the censored data. Our analyses demonstrate the impact of ND and OV handling methods on the results of biomarker-oriented research, supporting the need for transparent reporting and the implementation of best practices. In our simulations, the use of imputed data from the censored intervals of a fitted lognormal distribution shows preferable properties regarding our established criteria. We provide the algorithm for this favored routine for a direct application in R on the Open Science Framework (https://osf.io/spgtv). Further research is needed to evaluate the performance of the algorithm in various contexts, for example when the underlying assumptions do not hold. We conclude with recommendations and potential further improvements for the field.

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Four simple recommendations to encourage best practices in research software.

Publications

Four simple recommendations to encourage best practices in research software.

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