1. Scientific Workflows Lecture 15
all images are screenshots from the respective applications
add slides on e-cell, computational physics (n-body)

2. The Hypothetico-Deductive View of Science
Recall the HD view of science with its particular emphasis on comparing predictions to observations.
deduction
predictions
hypotheses
comparison
evaluation
observations
In practice, making predictions and analyzing observations can be far more complex than this view suggests.

3. Detailed Examination of the HD View
On the side of data analysis, scientists may
integrate data from multiple sources,
process data for missing values and noise, and
calculate various summary statistics.
On the prediction side, a scientist’s workflow could include:
simulating models to produce the predictions,
integrating results from heterogeneous models, and
transforming the results for comparison to observations.
If you unpack the HD method for a particular application in a particular domain, it would include all these extra steps.

4. Scientific Workflows: Introduction
Automating or aiding the scientific process requires computational tools that integrate the detailed steps.
Scientific workflow systems handle this problem by organizing computational transformations of data sets.
Independently, the workflows formalize and record scientific activities to assist in the reproduction of an analysis.
The informatics tools that support scientific workflows help scientists build, execute, revise, and share them.

5. Scientific Workflows: Representation
Workflows consist of nodes that indicate computational procedures and links that indicate data flow.
This Kepler workflow gathers data and simulation results from the internet, integrates them, and plots the results.

6. Scientific Workflows: Benefits
In theory, informatics tools for scientific workflows should
hide execution-level details for the transformations,
track input/output and parameter constraints for the transformations and ensure that they are satisfied,
store provenance to support replication, and
enable rapid reconfiguration for unique tasks.
Workflows replace specialized programs connected through scripting languages with modules connected graphically.
Workflow systems let scientists focus more on their data and less on program implementation.

7. Taverna Workbench
component library
workflow window
component editor

8. Taverna: Workflow Example
This workflow
grabs remotely stored sequences of DNA from multiple species,
aligns the sequences, and
plots the alignment results.
One plot from the alignment results as displayed in Taverna.

9. Taverna: Local Processing
Some nodes refer to local operations, such as this one that has associated code and ports for input and output.

10. Taverna: External Processing
Other nodes grab data from external sources or call executable code from external locations.
Accesses data from the BioMart data repository.
Sends a computation request to the SoapLab web service.

11. Kepler Workbench
component library
workflow window

12. Kepler: Workflow Example
This workflow
grabs the results of a climate change model for 2020 CE,
grabs historical climate change data for 1961–1990 CE,
calculates the annual average values,
rescales the grid size for the simulations to merge the data,
plots the difference in average annual radiation.

13. Kepler: Node Internals
This node imports data from an outside source.
The parameters for this node are similar to those for importing data into Taverna.

14. Kepler: Node Internals
This node merges two sources of computational data with the specified merge operation.
To plot the results, the scientist must write executable code.

15. Scientific Workflows: A Commentary
Claim: Workflow systems let scientists formalize and record their activities.
Superficially this is true, although all workflow systems have adopted a functional view of scientific activity.
Nodes operate as functions that take data and parameters as input and produce transformed data as output.
Claim: Workflow systems let scientists focus more on their data and less on program implementation.
In reality, configuring nodes can be a complex task that simply replaces function calls with GUI frames.
Recall that Taverna and Kepler have nodes that include program source code.