Jupyter Notebook Activity Dataset (rsds-20241113)

<h2>List of data</h2> <ul> <li>rsds-20241113.zip: Collection of SQLite database files</li> <li>image.tar.gz: Docker image provided in our data collection experiment</li> <li>redspot-341ffa5.zip: Redspot source code (<a href="https://github.com/tomokinakamaru/redspot/tree/341ffa56cb941b6f1ad74bd50d23fcf0ee96b270" target="_blank" rel="noopener">redspot@341ffa5</a>)</li> </ul> <div> <h2>Extended version of Section 2D of our paper</h2> Redspot is a Jupyter extension (i.e., Python package) that records activity signals. However, it also offers interfaces to read recorded signals. The following shows the most basic usage of its command-line interface:<br> <div>&nbsp;</div> <div><code>redspot replay &lt;path-to-db&gt;</code></div> <br> <div>This command generates snapshots (.ipynb files) restored from the signal records. Note that this command does not produce a snapshot for every signal. Since the change represented by a single signal is typically minimal (e.g., one keystroke), generating a snapshot for each signal results in a meaninglessly large number of snapshots. <em><strong>However, we want to obtain signal-level snapshots for some analyses. In such cases, one can analyze them using the application programming interfaces:</strong></em></div> <br> <div><code>from redspot import database</code></div> <div><code>from redspot.notebook import Notebook</code></div> <div><code>nbk = Notebook()</code></div> <div><code>for signal in database.get("path-to-db"):</code></div> <div><code>&nbsp; &nbsp; time, panel, kind, args = signal</code></div> <div><code>&nbsp; &nbsp; nbk.apply(kind, args) # apply change</code></div> <div><code>&nbsp; &nbsp; print(nbk) # print notebook</code></div> <br> <div>To record activities, one needs to run the Redspot command in the recording mode as follows:</div> <br> <div><code>redspot record</code></div> <br> <div>This command launches Jupyter Notebook with Redspot enabled. Activities made in the launched environment are stored in an SQLite file named ``redspot.db'' under the current path.</div> <br> <div>To launch the environment we provided to the participants, one first needs to download and import the image (image.tar.gz). One can then run the image with the following command:</div> <br> <div><code>docker run --rm -it -p8888:8888 &lt;image-name&gt;</code></div> <br> <div>Note that the SQLite file is generated in the running container. The file can be downloaded into the host machine via the file viewer of Jupyter Notebook.</div> </div>