Taverna: lessons in creating a workflow environment for the life sciences

Abstract

SUMMARYLife sciences research is based on individuals, often with diverse skills, assembled into research groups. These groups use their specialist expertise to address scientific problems. The in silico experiments undertaken by these research groups can be represented as workflows involving the co-ordinated use of analysis programs and information repositories that may be globally distributed. With regards to Grid computing, the requirements relate to the sharing of analysis and information resources rather than sharing computational power. The my Grid project has developed the Taverna workbench for the composition and execution of workflows for the life sciences community. This experience paper describes lessons learnt during the development of Taverna. A common theme is the importance of understanding how workflows fit into the scientists' experimental context. The lessons reflect an evolving understanding of life scientists' requirements on a workflow environment, which is relevant to other areas of data intensive and exploratory science.

Provenance Collection Support in the Kepler Scientific Workflow System

“…Although an increasing amount of middleware to accomplish these tasks has emerged in the last couple of years, using different middleware technologies and orchestrating them with minimal overhead still remains difficult for scientists. Scientific workflow systems [1,2,3], aim to improve this situation by creating interfaces to a variety of technologies and providing tools with domain-independent customizable graphical user interfaces that combine different Cyberinfrastructure [4] technologies along with efficient methods for using them. Workflows enormously improve data analysis, especially when data is obtained from multiple sources and generated by computations on distributed resources and/or various analysis tools.…”

Looking Inside the Black-Box: Capturing Data Provenance Using Dynamic Instrumentation

“…The provenance is generated by the middleware after inspection of the inputs and outputs of the wrapped components. This approach is popular with the scientific workflow community and includes systems such as Taverna [29], VisTrails [11], Kepler [2] and Wings [17]. Other middlewarebased systems like Karma [31] are not tied to a workflow system, but instead tap into the communication stack to capture provenance.…”

Towards victim-oriented crime modelling in a social science e-infrastructure

“…To this end, table 1 summarizes the current components that make up the NeISS infrastructure, which can be used to enhance the burglary simulation. All components are implemented as Web services, which allows them to be executed individually or chained together using a scientific workflow management system such as TAVERNA [7]. Figure 1 provides an example workflow, which can be used to generate a synthetic population of individuals that can be used as an input into the burglary simulation.…”

“…Scientific workflow management systems, such as TAVERNA [7], provide a mechanism to automatically orchestrate the execution of services such as those produced for NeISS. Rather than executing services manually, the user can chain them together in a workflow, which simplifies the process of running an experiment, enhances repeatability and also encourages sharing through the use of workflow publishing services such as MYEXPERIMENT [8].…”