Telling the Whole Tale via Reproducible Data Reuse

Whole Tale provides a platform for reproducible research in which researchers can access data from archival repositories, mix that with locally uploaded data, and build and run reproducible analyses and models in common computing platforms like RStudio and Jupyter, among others.   Data can be accessed by reference from DataONE and Dataverse, and then the results can be published to DataONE repositories with a DOI in a re-executable research object format that preserves data, code, and results for later inspection and re-execution.  These archived 'tales' can be run on the Whole Tale platform by other researchers, or can be downloaded and run locally through Docker-based execution environments saved in the tale.  Whole Tale provides a mature platform for reproducible research that preserves code, data, and compute environments in a manner that allows other researchers to interpret and re-use all products of research.

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Identity

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PID https://www.doi.org/10.5281/zenodo.3471934
PID https://www.doi.org/10.5281/zenodo.3471933
URL http://dx.doi.org/10.5281/zenodo.3471934
URL https://zenodo.org/record/3471934
URL http://dx.doi.org/10.5281/zenodo.3471933
URL https://figshare.com/articles/Telling_the_Whole_Tale_via_Reproducible_Data_Reuse/11501535
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Access Right Open Access
Attribution

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Author Jones, Matthew B., 0000-0003-0077-4738
Publishing

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Collected From Datacite; figshare; FigShare
Hosted By Zenodo; figshare; FigShare
Publication Date 2019-07-16
Publisher Zenodo
Additional Info
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Language UNKNOWN
Resource Type Other literature type; Conference object
system:type publication
Management Info
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Source https://science-innovation-policy.openaire.eu/search/publication?articleId=dedup_wf_001::6ab368a584990ac2be6c27b12183f98e
Author jsonws_user
Last Updated 27 December 2020, 02:59 (CET)
Created 27 December 2020, 02:59 (CET)