Autoethnography using one button tracker and Jupyter notebook

Detailed protocol steps for autoethnography using Puck.js one button and Jupyter notebook

1. Get the open source one button tracker from here (around 35€).
2. Connect it to the online tool for downloading data to your computer.
3. Calibrate and get to know the tool with some testing (pressing the button and checking data afterwards).
4. Define the initial categories for data collection (at least the main one).
5. Keep the one button in a safe pocket (away from accidental pressings).
6. Start gathering data in parallel with a notebook for fieldnotes (adding timestamp, category and estimation of intensity).
7. Download and merge data from the button and the notebook regularly (don’t leave it for the last moment!).
8. Clean all non-relevant data from the button (temperature, light, etc).
9. Prepare the final dataset adding additional retrospective categories, rechecking and, if needed, recalculating duration data for specific events (according to comparison of relevance a posteriori).
   • A basic version of this qualitative dataset would contain the fieldnotes properly classified according to the defined codebook.
   • A more reflexive and theory-grounded version would also contain additional field(s) for retrospective annotations, observations, references or other interpretation based data, matching the previously defined and classified fields.
10. Export the final data as a CSV (comma-separated value) file. Most spreadsheet software, such as Google Docs, Microsoft Excel or Open/Libre Office offer this option in the “exporting/save as” menu.
11. Now you can visualize your final data set. While there are multiple ways to do so, we provide a Jupyter Notebook that can create the interactive visualizations that were shown above in the field device description. You have two options for creating these visualizations:
    1. Creating them locally: To use this notebook on your own computer, you need to have Python as well as the Jupyter notebooks and plotly libraries installe
    2. Visualizing in the cloud: The link above also provides the option to create interactive visualizations in the cloud by using MyBinder. This free cloud service creates a privately accessible virtual environment for you, to use it:
       • Open the notebook in Binder by clicking the launch binder button here. It can sometimes take a bit, but after a while you will see the notebook environment
       • In the top menu select Cell and then Run all, this will prepare the whole notebook to create your visualization
       • Scroll down to the bottom, and press the Upload button. Now select your final data file, which will then be uploaded into the temporary cloud server (your data will be automatically deleted 15 minutes after the last use of the notebook)
       • Depending on your data set it can take a few seconds, but two additional dropdown menus should now appear, one to select the variable you want to use to color your data points (color_var) and one to select which text you want to appear when hovering over a data point (text_var).

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