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a happy approach to online behavioral research

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A happy approach to online behavioral research.

The Smile project is a new way to develop rich and interactive online experiments. Smile prioritizes modularity and reusability. Unlike tools that cater to non-programmers, Smile is designed to help reasonably competent programmers accomplish more in less time.

Online docs: [https://smile.gureckislab.org](https://smile.gureckislab.org)

### Highlighted features:

- 🌈 Fast and fun front-end interface development with [Vue.js](https://vuejs.org),
  [Tailwind CSS](https://tailwindcss.com/), and
  [Shadcn/vue](https://www.shadcn-vue.com/). Create complex games, animations, and
  surveys with ease.
- 👩‍💻 Custom [developer mode tools](https://smile.gureckislab.org/coding/developing.html) provide a novel interface for specifying and debugging interactive
  experiments. Quickly jump between phases and trials in your experiments,
  [autofill forms and generate mock data for testing](https://smile.gureckislab.org/coding/autofill.html),
  [hot-reload](https://smile.gureckislab.org/coding/developing.html#hot-module-replacement) your code without restarting the entire experiment, and more!
- 🧩 Built-in support for
  [common experiment elements](https://smile.gureckislab.org/coding/views.html#built-in-views) like consent forms,
  instructions, and surveys. Just add your custom experiment logic and start
  collecting data.
- 🤖 Code writing is greatly accelerated using AI tools, as LLMs are
  trained on extensive codebases covering Vue, Tailwind, and other popular web
  standards used by the project.
- 👫 Supports multiple [recruitment services](https://smile.gureckislab.org/recruit/recruitment.html)
  including Prolific, MTurk, CloudResearch, and more.
- 📝 [Data provenance features](https://smile.gureckislab.org/analysis.html#data-provenance) include an audit trail
  of which version of the code was used to create each data file.
- 🐍 Easy-to-use [Python library](https://smile.gureckislab.org/analysis.html#python-analysis-library-smiledata) for data analysis with Polars DataFrames, built-in plotting, and support for Jupyter and Marimo notebooks.
- 😎 Great-looking and detailed docs, if we do say so ourselves!

## License

_Initial development was supported by National Science Foundation Grant [BCS-2121102](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2121102&HistoricalAwards=false) to T. M. Gureckis._

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