Ideas, thinking and projects learning about creating n=1 network experiments etc.
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I am working on an open source project called the DIY HealthScience toolkit. This is where I am experimenting with the concept of the networked-self and NXP a networked n=1 self experiment. A couple of blog posts to introduce the thinking https://medium.com/@DIYHSLAB/nxp-shells-modules-entry-dce8aaa93cf6?sk=3fffa617ac3b6f6f4ebb05387c036ba2 https://medium.com/@DIYHSLAB/healthscience-toolkit-2020-networked-b2293779e6e6
I like the post your made before https://medium.com/@DIYHSLAB/holistic-network-self-7d67ae41c914 and explained your point on networked-self.
Which type of visualisations for such networked-self do you think are lacking?
I am asking as network-addict person 
I think the question of “modules” in self research is really interesting, because they can help with re-use and collaboration – notably, if they have properties like… (a) they can be worked on independently (b) they are re-usable in new contexts, in new configurations.
It sounds like you’re describing the following as modules,
- question being asked
- data used
- computation/analysis
- sampling/blinding controls
- scientific review
- type/features of experiment
- type of visualization
The last two (and maybe others) look like reusable tags/labels/categories for communication purposes… which is helpful for others to find/sort things, but I haven’t thought of as modules. (I think because they’re not necessary for conducting the experiment itself, e.g. the way a modular analysis might be.)
I have also thought an education module, material that explains concepts within the experiment. Could look at it as pre learning. I am sure with time, peers will think of many more. The question is can we find a way to connect them all together like lego?
Sure, visualisation we are tapping into the existing range of tools. I think we see simulation overtaking charts and tables by the close of this decade.
I think simulations will be the future of visualisation. How that will emerge to mainstream in still uncertain e.g. VR AR but I think the computer games industry shows the path forward.
Ok, let me know how I could help here and which data types do you have in mind.
Library In todays community call the topic of naming data types was raised again. How about a Open Humans Library for Datatypes that can be collected into data packaging formats? Or does this exist? In quantified flu data columns need to be described for the symptoms. In my hay fever logging I too have symptoms, same for the headache apps etc. If the Open Humans Library of data types published the standards (governance from community participation) then I could use these data types in my column logs. Now if I do not find a data type in the Library then I propose a new data types and this goes through community governance for acceptance or proposals of better or alternatives to think about.
In my own project, I have working on the idea of a CNRL (computational network reference layer) Library or a network library. Works like above but uses cryptography to provide some guarantees on data interoperability and back / forward compatibility over time.
Regarding “data types” in Open Humans: this isn’t attempting to be a “standard”, but instead a user-defined set of categories to classify data files, to enable flexibility and use over time. This is created so data can be requested/shared in the system by the “type” rather than “source” (since one source can have multiple types, and multiple sources may have the same type). It’s not fully implemented (and so I think we’re behind sometimes on using it), but there are categories are here: https://www.openhumans.org/data-management/datatypes/
The set of symptoms for Quantified Flu are currently static, but the underlying code hasn’t “hard coded” them, so it’s possible to enable user-defined items at some point in the future. 
I didn’t imagine individual symptoms as each being their own “data type” though; more that the set would be something like “symptom tracking data”.
Here is the network library using datatypes to describe the packaging of data https://youtu.be/_HVw-t8qBZM and here I using them in the data science toolkit I ve been working on https://youtu.be/MgDwUnqpiro and https://youtu.be/o036QCCsSQA