Represent a research workflow as dependencies

Order follows dependencies

Python graphlib provides topological sorting for directed graphs and detects cycles that prevent a valid dependency order. This is a useful foundation for a computational workflow in which a task consumes outputs from earlier tasks. A diagram becomes operational only when its edges describe actual input relationships and its nodes have defined execution behavior. [1]

Define a node contract

For each step, identify required inputs, produced outputs, configuration, and failure behavior. Distinguish a dependency needed to execute a task from a purely visual connection in the interface. Validate the graph before starting computation. A preparation task should not be marked complete until the output required by its consumers is available and usable.

Our suggested execution record gives each task a stable identifier and records the inputs actually consumed. Preserve status changes, output paths, and configuration with the run. Decide what happens when a task fails, is cancelled, or is retried. If results can be reused, define how the system determines that a saved result matches the current inputs.

Keep the graph and run together

A saved graph describes an intended workflow; a run record describes what happened. Store both. Review whether a resumed calculation can distinguish completed results from partial files and whether errors reach the user clearly. This approach can support docking, trajectory analysis, data preparation, or model training. The graph is valuable when it makes the computation traceable rather than merely adding a visual editor around a collection of commands.