Run Computation

execution
scenario
job
simulation
optimization
stepper
wizard
Run Computation icon

Intent

Enables users to run computation(s) through a multi-step flow to generate results.

Computations could be optimizations, calculations, or simulations for a model, scenario, or experiment with attributes to observe and compare results. Attributes may include input data and settings. Computations may be long running and require the ability for a user to leave the flow and return later.

Overview

Guidelines for adapting the Task Flow

  • Break the task flow into multiple workable steps and use a progress indicator / stepper to help users see the progress and remaining steps in the process to complete.

  • Organize information into sections that are easy to digest. This helps improve the readability and searchability.

  • Offer guidance, tips, and links to detailed documentation for complex inputs & interactions.

  • Pre-fill the forms with sensible default values wherever possible, especially if data inputs require long forms.

  • Consider allowing users to upload input data as external files or spreadsheets, especially for computations that require large amounts of input data.

  • Make attributes searchable and filterable to make it easy to find attributes of interests.

  • Give real time error warnings for mis-configurations, especially for long complex forms. For example, specify bounds for values and provide warnings if values are outside of bounds.

  • Help users recover from failed computations by providing meaningful and actionable error messages and allow them to go back and change configurations and inputs.

  • Don’t break the workflow into too many small steps. Users may perceive this as a long and overwhelming process to complete.

  • Don’t assume users will understand each input field and the values to configure it. Provide contextual information when able.

Related Task Flows

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STRUDEL is an effort of the Lawrence Berkeley National Lab Scientific Data (SciData) Division UX team.
The project is generously funded by the Alfred P. Sloan Foundation, Liz Vu & Josh Greenberg Program Officers, grants #10074 and #10572