An open source approach to enable the reproducibility of scientific workflows in the OCEAN SCIENCES

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Presented at the AGU Fall Meeting 2013


Every scientist should be able to rerun data analyses conducted by his or her team and regenerate the figures in a paper. However, all too often the correct version of a script goes missing, or the original raw data is filtered by hand and the filtering process is undocumented, or there is lack of collaboration and communication among scientists working in a team.

Here we present 3 different use cases in ocean sciences in which end-to-end workflows are tracked. The main tool that is deployed to address these use cases is based on a web application (IPython Notebook) that provides the ability to work on very diverse and heterogeneous data and information sources, providing an effective way to share the and track changes to source code used to generate data products and associated metadata, as well as to track the overall workflow provenance to allow versioned reproducibility of a data product. Use cases selected for this work are:

1) A partial reproduction of the Ecosystem Status Report (ESR) for the Northeast U.S. Continental Shelf Large Marine Ecosystem. Our goal with this use case is to enable not just the traceability but also the reproducibility of this biannual report, keeping track of all the processes behind the generation and validation of time-series and spatial data and information products. An end-to-end workflow with code versioning is developed so that indicators in the report may be traced back to the source datasets.

2) Realtime generation of web pages to be able to visualize one of the environmental indicators from the Ecosystem Advisory for the Northeast Shelf Large Marine Ecosystem web site.

3) Data and visualization integration for ocean climate forecasting. In this use case, we focus on a workflow to describe how to provide access to online data sources in the NetCDF format and other model data, and make use of multicore processing to generate video animation from time series of gridded data.

For each use case we show how complete workflows - from data source through results publication - are constructed and transparently published via the IPython Notebook. Our current work in development includes the incorporation of the W3C PROV provenance standard into the metadata of the JavaScript Object Notation (JSON) file of each Notebook. We are sharing our design principles for the granularity of these linked data provenance records with others in NOAA and NASA data communities. We conclude by reporting on end-user experience and satisfaction with these new capabilities.



DateCreated ByLink
February 23, 2014
Massimo Di Stefano Download
December 10, 2013
Patrick WestDownload

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