Personal Science Wiki

Editing Autoethnography using one button tracker and Jupyter notebook

Warning: You are not logged in. Your IP address will be publicly visible if you make any edits. If you log in or create an account, your edits will be attributed to your username, along with other benefits.

The edit can be undone. Please check the comparison below to verify that this is what you want to do, and then save the changes below to finish undoing the edit.

Latest revision Your text
Line 1: Line 1:
−
{{Project Infobox|Self researchers=User:Esenabre, User:Gedankenstuecke|Related tools=One Button Tracker|Related topics=Autoethnography|Related projects=Smartphone Disengagement}}
+
'''Detailed protocol steps for autoethnography using Puck.js one button and Jupyter notebook'''
  
−
This page provides a step-by-step guide on how one can use a [[One Button Tracker|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|Smartphone disengagement]] that was done by [[User:Esenabre|Enric Senabre]]. As a derived outcome from this intervention, there's a preprint publication: [https://osf.io/a2rjc/ ''One button in my pocket instead of the smartphone – A methodological assemblage connecting self-research and autoethnography in a digital disengagement study.'']
+
# Get the open source one button tracker from [https://www.puck-js.com/ here] (around 35€).
−
 
 
−
 
−
 
 
−
== Initial setup of the one-button tracker ==
 
−
# Get the open source one button tracker from [https://www.puck-js.com/ here] (around 35€, may incur customs duties when shipping outside the UK).
 
 
# Connect it to the [https://tzovar.as/one-button-tracker/# online tool] for downloading data to your computer.
 
# Connect it to the [https://tzovar.as/one-button-tracker/# online tool] for downloading data to your computer.
 
# Calibrate and get to know the tool with some testing (pressing the button and checking data afterwards).
 
# Calibrate and get to know the tool with some testing (pressing the button and checking data afterwards).
−
 
+
# Define the initial categories for data collection (at least the main one).
−
== Preparing your project ==
 
−
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 ==
 
 
# Keep the one button in a safe pocket (away from accidental pressings).
 
# Keep the one button in a safe pocket (away from accidental pressings).
 
# Start gathering data in parallel with a notebook for fieldnotes (adding timestamp, category and estimation of intensity).
 
# 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.
+
# Download and merge data from the button and the notebook regularly (don’t leave it for the last moment!).
−
 
 
−
== Processing your data ==
 
−
# 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!).
 
 
# Clean all non-relevant data from the button (temperature, light, etc).
 
# Clean all non-relevant data from the button (temperature, light, etc).
 
# 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).
 
# 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).
Line 25: Line 13:
 
#* 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.
 
#* 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.
 
# 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.
 
# 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.
−
 
+
# 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:
−
== Visualizing your data set ==
+
## 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
−
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
+
## 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:
−
# 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
+
##* Open the notebook in Binder by [https://github.com/PeerProducedResearch/autoethnography-puck clicking the launch binder button here]. It can sometimes take a bit, but after a while you will see the notebook environment
−
# 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:
+
##* In the top menu select Cell and then Run all, this will prepare the whole notebook to create your visualization
−
#* Open the notebook in Binder by [https://github.com/PeerProducedResearch/autoethnography-puck clicking the launch binder button here]. It can sometimes take a bit, but after a while you will see the notebook environment
+
##* 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)
−
#* In the top menu select Cell and then Run all, this will prepare the whole notebook to create your visualization
+
##* 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).  
−
#* 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)
+
{{Project Infobox|Self researchers=|Related tools=|Related topics=|Related projects=}}
−
#* 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).
+
[[Category:Projects]]
−
The notebook is [https://github.com/PeerProducedResearch/autoethnography-puck 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!
 
−
[[Category:Projects]][[Category:Habits projects]][[Category:How to's]]
 

Please note that all contributions to Personal Science Wiki are considered to be released under the Creative Commons Attribution-ShareAlike 3.0 Unported (see PersonalScienceWiki:Copyrights for details). If you do not want your writing to be edited mercilessly and redistributed at will, then do not submit it here.
You are also promising us that you wrote this yourself, or copied it from a public domain or similar free resource. Do not submit copyrighted work without permission!

Cancel Editing help (opens in new window)

Template used on this page:

Retrieved from "https://wiki.openhumans.org/wiki/Autoethnography_using_one_button_tracker_and_Jupyter_notebook"