Autoethnography using one button tracker and Jupyter notebook

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Project Infobox Question-icon.png
Self researcher(s) User:Esenabre, User:Gedankenstuecke
Related tools One Button Tracker
Related topics Autoethnography
Builds on project(s) Smartphone Disengagement
Has inspired Projects (0)

This page provides a step-by-step guide on how one can use a one-button tracker such as the Puck.js to do an autoethnography that combines qualitative and quantitative data. It also provides a description of how to visualize the resulting data set using a Jupyter notebook. An example of such a project is Smartphone disengagement that was done by Enric Senabre. As a derived outcome from this intervention, there's a preprint publication: One button in my pocket instead of the smartphone – A methodological assemblage connecting self-research and autoethnography in a digital disengagement study.

Initial setup of the one-button tracker[edit | edit source]

  1. Get the open source one button tracker from here (around 35€, may incur customs duties when shipping outside the UK).
  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).

Preparing your project[edit | edit source]

The next step is to define the initial categories for data collection (at least the main one), that is: Which phenomena do you want to record on the press of a button and how do you want to manually annotate them if it is a "strong" impression you have are having. The getting started with personal science page can offer some advice for which phenomena etc. might work well.

Collecting data[edit | edit source]

  1. Keep the one button in a safe pocket (away from accidental pressings).
  2. Start gathering data in parallel with a notebook for fieldnotes (adding timestamp, category and estimation of intensity).

For the gathering of field notes you can use a traditional paper notebook, a tablet or an e-ink notebook such as the reMarkable. Depending on your preferences, doing voice recordings might also work.

Processing your data[edit | edit source]

  1. Download and merge data from the button and the notebook regularly (don’t leave it for the last moment, in case the device runs out of memory or battery or even breaks!).
  2. Clean all non-relevant data from the button (temperature, light, etc).
  3. 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.
  4. 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.

Visualizing your data set[edit | edit source]

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 installed
  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).

The notebook is released under an open-source license on GitHub, if you make any improvements or fix any bugs, please consider making a pull request to help improve it!