Dallinger is a tool to automate experiments that use combinations of automated bots and human subjects recruited on platforms like Mechanical Turk.
Dallinger allows crowd sourced experiments to be abstracted into single function calls that can be inserted into higher-order algorithms. It fully automates the process of recruiting participants, obtaining informed consent, arranging participants into a network, running the experiment, coordinating communication, recording and managing the data, and paying the participants.
The Dallinger technology stack consists of: Python, Redis, Web Sockets, Heroku, AWS, Mechanical Turk, boto, Flask, PostgreSQL, SQLAlchemy, Gunicorn, Pytest and gevent among others.
These documentation topics are intended to assist people who are attempting to launch experiments and analyse their data. They cover the basics of installing and setting up Dallinger, as well as use of the command line tools.
.. toctree:: :caption: User Documentation :maxdepth: 1 installing_dallinger_for_users dallinger_with_anaconda aws_etc_keys demoing_dallinger command_line_utility configuration email_setup python_module monitoring_a_live_experiment experiment_data postico_and_postgres running_bots registration_on_OSF troubleshooting
Several demos demonstrate Dallinger in action:
.. toctree:: :caption: Dallinger Demos :maxdepth: 1 demo_index
Experiment Author Documentation
These documentation topics build on the previous set to include help with designing new experiments for others to use.
Core Contribution Documentation
This section covers extra topics relevant to those wishing to contribute to the development of Dallinger itself. This is not needed in order to develop new experiments. Follow the Developer Installation process from the previous section to get started.
.. toctree:: :caption: Core Contribution Documentation :maxdepth: 1 running_the_tests contributing_to_dallinger
.. toctree:: :caption: General Information :maxdepth: 1 acknowledgments dallinger_the_scientist