Quantified Self: DigiMe Project
Sometimes I write “I’ll be posting this or that somewhere in the future…” But actually my activities get distracted a lot by what I see and experience every day. Recently this again is the case. I’ve been quite interested in the ‘Quantified Self’ hype which is going on lately. I find it interesting to put ‘my’ data to work for me, I think (at least for now).
So at the moment, and since a few weeks, I’ve been thinking about this a lot. Collecting all sorts of data concerning my daily routines, my interests, etc. I now made a start with collecting data that might be interesting for me. Hopefully this data could affect my life in a positive way in the future? I’m not sure yet, but the thought alone is intriguing.
Since I started collecting my musical behavior, I loaded the first data yesterday. This data gets collected via Last.fm. After that I enrich this data with the spotify API to get more metadata about these tracks. I already spotted some data quality issues for the extract that I did, but nevertheless this data and insights are interesting enough for me to life with that for now.
This is a result I came up with for now regarding my ‘audio’ profile. I’m not sure if I would call this a dashboard or an infographic. What do you think?
I hope this makes sense or in the ideal situation ‘inspires’ somebody.
I just wanted to share this.
See you next post. 😉
I love this. Stumbled across a way to load mp3 attributes the other day, but that doesn’t give me usage data. This is really interesting. Wondering if we can pull this from Amazon Music Player or even Pandora. Good work.
Thanks Aaron! I used Spotify in combination with Last.fm to achieve this result. But maybe there are more interesting API’s out there that could do something similar. Are you aware of those?
Hallo,
how do you get the data from last.fm / spotify?
Can you please share your example.
Best wishes
Bastian
Bastian, what is your Last.fm username?
Hi Patrick,
Hi Patrick,
Great posts. About this one, how did you come up with the data? Was it through an automated (API)connection? If so, did you use QVsource or did you create a connection within the script? And final question: could you share the QVW or method used in case you created a connection yourself? Much appreciated!
Jasper, I’m still working on it. So there will be more content on this topic…
Hi Patrick,
Firstly really enjoying your website. Have been browsing through for a couple of hours and already taken away a few ideas to try.
I too am entering the world of “Life Logging”. A colleague and I were talking about what data is out there that is commonly overlooked, over the past week I have manage to pull a wealth of data including skype call history, emails, phone data usage, tv shows watched. I have also been using several apps to track location and travelling as well as perform self surveys.
My next “project” is to mash this all together in Qlikview and see what i can find.
Once the new Jawbone UP 3 is released I will be getting one of those. I think an interesting comparison would be to compare the surveys I complete about how I am feeling/doing vs what my body is actually saying.
Anyway will be keeping an eye on whatever you come up with for your own analysis.
Keep posting great content!
Thanks
Darren
Darren, nice hearing more and more people are gathering their own data now. I think this will be a very big deal in the somewhat distant future. If you have good examples to share or new ways of working with personal data, let me know. We might as well be sharing it on Qlikshow, if you’d like…
Hi Patrick,
Great looking app. Sine its the start of 2016 I’m going to be working on a similar project of my own – basically will be building a Media Diary of significant books, movies, articles I consume.. have already started collecting the data over 6 days of 2016 so far and was looking around the web for a good way to display this. Your app has given me some inspiration as to how to visualise it. Will you be making this available for download?
What is it you need for your project?