DataONE: A Data Federation with Provenance Support

Cao, Yang; Jones, Christopher S.; Cuevas-Vicenttín, Víctor; Jones, Matthew B.; Ludäscher, Bertram; McPhillips, Timothy M.; Missier, Paolo; Schwalm, Christopher R.; Slaughter, Peter; Vieglais, Dave; Lauren, Walker; Wei, Yaxing

doi:10.1007/978-3-319-40593-3_28

Cited by 16 publications

(18 citation statements)

References 2 publications

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“…Use of wfprov [102] to capture some workflow provenance aspects is also encouraged. Alternative extensions such as ProvOne [103] can also be utilized if the WMS or workflow executor is using these extensions already.…”

Section: Cwlprov 060 and Utilized Standardsmentioning

confidence: 99%

Sharing interoperable workflow provenance: A review of best practices and their practical application in CWLProv

et al. 2019

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BackgroundThe automation of data analysis in the form of scientific workflows has become a widely adopted practice in many fields of research. Computationally driven data-intensive experiments using workflows enable automation, scaling, adaptation, and provenance support. However, there are still several challenges associated with the effective sharing, publication, and reproducibility of such workflows due to the incomplete capture of provenance and lack of interoperability between different technical (software) platforms.ResultsBased on best-practice recommendations identified from the literature on workflow design, sharing, and publishing, we define a hierarchical provenance framework to achieve uniformity in provenance and support comprehensive and fully re-executable workflows equipped with domain-specific information. To realize this framework, we present CWLProv, a standard-based format to represent any workflow-based computational analysis to produce workflow output artefacts that satisfy the various levels of provenance. We use open source community-driven standards, interoperable workflow definitions in Common Workflow Language (CWL), structured provenance representation using the W3C PROV model, and resource aggregation and sharing as workflow-centric research objects generated along with the final outputs of a given workflow enactment. We demonstrate the utility of this approach through a practical implementation of CWLProv and evaluation using real-life genomic workflows developed by independent groups.ConclusionsThe underlying principles of the standards utilized by CWLProv enable semantically rich and executable research objects that capture computational workflows with retrospective provenance such that any platform supporting CWL will be able to understand the analysis, reuse the methods for partial reruns, or reproduce the analysis to validate the published findings.

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Section: Cwlprov 060 and Utilized Standardsmentioning

confidence: 99%

Sharing interoperable workflow provenance: A review of best practices and their practical application in CWLProv

et al. 2019

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“…Two provenance capture packages exist in R, the recordr package associated with the DataOne repository (Cao et al. ) and RDataTracker (Lerner and Boose ). In addition, although they do not collect formal data provenance, there are methods developed for “literate computing” that help to collect code along with details about the code and the intention of the analyses (e.g., the Jupyter notebook project: Shen and Barabasi ).…”

Section: Reproducibility: Open‐data Open‐source and Provenancementioning

confidence: 99%

“…Although there are many challenges in the development and application of data-provenance principles, multiple software packages do exist for collecting data provenance in the context of scientific investigations. Two provenance capture packages exist in R, the recordr package associated with the DataOne repository (Cao et al 2016) and RData-Tracker (Lerner and Boose 2014). In addition, although they do not collect formal data provenance, there are methods developed for "literate computing" that help to collect code along with details about the code and the intention of the analyses (e.g., the Jupyter notebook project: Shen and Barabasi 2014).…”

Section: Reproducibility: Open-data Open-source and Provenancementioning

confidence: 99%

Ecological network metrics: opportunities for synthesis

et al. 2017

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Abstract. Network ecology provides a systems basis for approaching ecological questions, such as factors that influence biological diversity, the role of particular species or particular traits in structuring ecosystems, and long-term ecological dynamics (e.g., stability). Whereas the introduction of network theory has enabled ecologists to quantify not only the degree, but also the architecture of ecological complexity, these advances have come at the cost of introducing new challenges, including new theoretical concepts and metrics, and increased data complexity and computational intensity. Synthesizing recent developments in the network ecology literature, we point to several potential solutions to these issues: integrating network metrics and their terminology across sub-disciplines; benchmarking new network algorithms and models to increase mechanistic understanding; and improving tools for sharing ecological network research, in particular "model" data provenance, to increase the reproducibility of network models and analyses. We propose that applying these solutions will aid in synthesizing ecological sub-disciplines and allied fields by improving the accessibility of network methods and models.

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“…Although there are many challenges in the development and application of data-provenance principles, multiple software packages do exist for collecting data provenance in the context of scientific investigations. Two provenance capture packages exist in R, the package associated with the DataOne repository (Cao et al 2016) and (Lerner and Boose 2014). In addition, although they do not collect formal data provenance, there are methods developed for “literate computing” that help to collect code along with details about the code and the intention of the analyses (e.g., the Jupyter notebook project: (Shen and Barabasi 2014)).…”

Section: Reproducibility: Open-data Open-source and Provenancementioning

confidence: 99%

Ecological Network Metrics: Opportunities for Synthesis

Lau

Borrett

Baiser

et al. 2017

Preprint

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13Network ecology provides a systems basis for approaching ecological ques-14 tions, such as factors that influence biological diversity, the role of particular 15 species or particular traits in structuring ecosystems, and long-term ecolog- producibility of network models and analyses. We propose that applying these 27 solutions will aid in synthesizing ecological subdisciplines and allied fields by 28 improving the accessibility of network methods and models.

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DataONE: A Data Federation with Provenance Support

Cited by 16 publications

References 2 publications

Sharing interoperable workflow provenance: A review of best practices and their practical application in CWLProv

Sharing interoperable workflow provenance: A review of best practices and their practical application in CWLProv

Ecological network metrics: opportunities for synthesis

Ecological Network Metrics: Opportunities for Synthesis

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