Display of Results Running Taverna Workflows within IPython Notebook Alan Williams, Aleksandra Pawlik, Carole Goble, University of Manchester, UK Future Work Collation of provenance • The provenance of the workflow runs will be linked with the history generated by IPython Notebook Creation of Research Objects • Combine the IPython Notebook, the workflows and the linked provenance within a “Research Object” - http://www.researchobject.org/ Creation of documentation • Use IPython or dexy to generate reports about workflow runs Running the Taverna Player directly on the Taverna Server • The Taverna Player currently has to be within a Taverna Portal, or similar workflow registry. • In the future, Taverna Player will be extended to run directly against a Taverna Server • Will allow running of “arbitrary” workflows and allow them to be exposed in IPython Notebook Improved interaction • Allow the data entered in an interaction to come from values within IPython Notebook Teaching • Use the combination of IPython Notebook and Taverna to provide training materials Taverna Player • Allows Taverna workflows to be run as part of a website like embedding a video from Youtube • Uses Taverna Portal for user authorization • Reads workflows from a Taverna Portal • Runs workflows on a Taverna Server • Has a REST API Links IPython and IPython Notebook www.ipython.org The Taverna Player code on GitHub at www.github.com/myGrid/taverna-player The Taverna Player Client package at www.pypi.python.org/pypi/tavernaPlayerClient and the code on GitHub at www.github.com/myGrid/DataHackLeiden Taverna’s homepage at www.taverna.org.uk Acknowledgments This work was enabled by the BioVeL project, that has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no 283359, and also enabled by the Software Sustainability Institute supported by the UK Engineering and Physical Sciences Research Council (EPSRC) through grant EP/H043160/1 Much of the work on the Taverna Player Client was carried out during the “Data enrichment hackathon” organized by pro-IBiosphere. Thanks are due to Ross Mounce of the University of Bath, UK and to Youri Lammers of Naturalis, Netherlands for their work on the client during the hackathon. Thanks are also due to the developers of IPython Notebook for their help and advise during the hackathon. Architecture Use Case The example notebook shows the running of a series of workflows for Ecological Niche Modelling (ENM), using the Taverna Player Client and the pandas and random Python packages. A file of csv data representing occurrences of species is read. 100 rows are selected at random for use in the Ecological Niche Modelling. A model is created and projected for the present day and for 2050. The results of the ENM are re-organized and then used as inputs to two other workflows: one to produce statistics and one to calculate the differences (climatic effects) between the projections. Client Creation Taverna Player Client • Uses Taverna Player and its Server to run workflows within an IPython Notebook • Data passed from the Notebook to the executing Taverna Workflows • Workflow run’s requests for data answered within the Notebook using Taverna’s interaction service • Results retrieved from the run and fed back into the Notebook • Available from the PyPi registry • Tested with BioVeL workflows for data refinement and ecological niche modelling. Interaction with Run Organization of Data Taverna Server • Runs arbitrary Taverna 2 workflows • Secure, isolated, user-specific workflow execution • Manages data for separate runs • Publishes interactions and provenance Use Case Introduction IPython Notebook, originally developed by Fernando Perez of University of Berkeley (where the core development team is currently based) is a browser-based environment for interactive computing. Users can write, edit and re-run Python scripts. IPython Notebook has suppor