Telling the Whole Tale via Reproducible Data Reuse
Description
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.
Files
2019-jones-whole-tale-v02.pdf
Files
(10.4 MB)
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Additional details
Funding
- DataONE (Data Observation Network for Earth) 1430508
- National Science Foundation