Shining Light into Black Boxes

Morin, Andrew; Urban, Jennifer M.; Adams, Paul D.; Foster, Ian; Šali, Andrej; Baker, David; Sliz, Piotr

doi:10.1126/science.1218263

Cited by 174 publications

(147 citation statements)

References 19 publications

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“…A Roundtable at Yale Law School in 2009 focused on the issue of reproducibility by bringing together computational scientists from many different disciplines and producing a declaration addressing the need for data and code sharing in computational science [7,8]. Over the past few years many editorials and commentaries have continued these efforts [9][10][11][12][13]. The theme is similar: Without the data and computer codes that underlie scientific discoveries, published findings are all but impossible to verify.…”

Section: Introductionmentioning

confidence: 99%

Best Practices for Computational Science: Software Infrastructure and Environments for Reproducible and Extensible Research

Stodden

Miguez

2013

SSRN Journal

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The goal of this article is to coalesce a discussion around best practices for scholarly research that utilizes computational methods, by providing a formalized set of best practice recommendations to guide computational scientists and other stakeholders wishing to disseminate reproducible research, facilitate innovation by enabling data and code re-use, and enable broader communication of the output of computational scientific research. Scholarly dissemination and communication standards are changing to reflect the increasingly computational nature of scholarly research, primarily to include the sharing of the data and code associated with published results. We also present these Best Practices as a living, evolving, and changing document at http://wiki.stodden.net/Best_Practices.

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Section: Introductionmentioning

confidence: 99%

Best Practices for Computational Science: Software Infrastructure and Environments for Reproducible and Extensible Research

Stodden

Miguez

2013

SSRN Journal

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“…These authors highlight various publications (e.g. Ince, Hatton, & Graham-Cumming, 2012;Morin et al, 2012;Rocchini & Neteler, 2012) arguing against "proprietary-'black box' -programs that hinder scientific advancement and testing" (p. 147). Specifically, Rocchini and Neteler (2012) urge ecologists to embrace Stallman's (1985) "four freedoms" paradigm of FOSS to freely execute, modify, and share programs, while also identifying the need for better mechanisms (i.e.…”

Section: The Evolution Of a Collaborative Spatial Data Science Workflowmentioning

confidence: 99%

A review of the emergent ecosystem of collaborative geospatial tools for addressing environmental challenges

Palomino

Muellerklein

Kelly

2017

Computers, Environment and Urban Systems

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a b s t r a c t a r t i c l e i n f oTo solve current environmental challenges such as biodiversity loss, climate change, and rapid conversion of natural areas due to urbanization and agricultural expansion, researchers are increasingly leveraging large, multiscale, multi-temporal, and multi-dimensional geospatial data. In response, a rapidly expanding array of collaborative geospatial tools is being developed to help collaborators share data, code, and results. Successful navigation of these tools requires users to understand their strengths, synergies, and weaknesses. In this paper, we identify the key components of a collaborative Spatial Data Science workflow to develop a framework for evaluating the various functional aspects of collaborative geospatial tools. Using this framework, we then score thirty-one existing collaborative geospatial tools and apply a cluster analysis to create a typology of these tools. We present this typology as a map of the emergent ecosystem and functional niches of collaborative geospatial tools. We identify three primary clusters of tools composed of eight secondary clusters across which divergence is driven by required infrastructure and user involvement. Overall, our results highlight how environmental collaborations have benefitted from the use of these tools and propose key areas of future tool development for continued support of collaborative geospatial efforts.

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“…Moving forward, we plan to release all code related to our publications in this format and we encourage other potential users to share their workflows too. By promoting the public release of code, CoreFlow can help eliminate the 'black box' issue [17][18][19] of unpublished code and reduce the use of unsuitable software [20] in publications. In this manner, we expect that the content of CoreFlow will become as valuable as the software itself.…”

mentioning

confidence: 99%

CoreFlow: A computational platform for integration, analysis and modeling of complex biological data

Pasculescu

Schoof

Creixell

et al. 2014

Journal of Proteomics

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Shining Light into Black Boxes

Abstract: Funders, publishers, and research institutions must act to ensure that research computer code is made widely available.

Cited by 174 publications

References 19 publications

Best Practices for Computational Science: Software Infrastructure and Environments for Reproducible and Extensible Research

Best Practices for Computational Science: Software Infrastructure and Environments for Reproducible and Extensible Research

A review of the emergent ecosystem of collaborative geospatial tools for addressing environmental challenges

CoreFlow: A computational platform for integration, analysis and modeling of complex biological data

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