My son, Sam, has just written a post about systems and networks (link). I found the post really interesting in a paternal sense and an epistemological sense.

The paternal part of me is delighted to read a blog post by Sam and to learn about his observations and reflections as a member of the #INF537 (link) Masters of Education (Knowledge Networks and Digital Innovation) online at Charles Sturt University.

The epistemological delight is in my commitment to self organising networks hinted at in Sam’s post. I have written a lot about networks (link) and have been thinking about these issues a great deal since the distributed, open course CCK08 (link), and becoming an accidental connectivist (link).

I am keen to persuade Sam privately and publicly to explore self organising networks (link) and to read more about Stephen Downes’ (link) and Alan Levine’s (link) work. I appreciate Sam’s particular working environment constraints (systemic) but am determined to explore the action possibilities he can address as a community driver and facilitate network flourishing within those constraints (link).

I sense that with energy anything is possible even in constrained contexts.

Photo Credit

The Maze (Keith Lyons, CC BY 4.0)

Writing a report

Earlier this week, Avinash Kaushik wrote about Responses to Negative Data (link). Shortly after his post was published, I found a link to a Turing Institute blog post, written by Franz Kiraly, What is a data scientific report? (link).

Both posts have helped me to think about the why, what and how of sharing observations, analyses and insights.

Franz, the author of the Turing blog post suggest that a stylised data report is characterised by:

  1. Topic. Addresses a domain question or domain challenge in an application domain specific to a data set.
  2. Aim. Data-driven answers to some domain question.
  3. Audience. Decision-makers or domain experts interested in ‘evidence’ to inform decision-making.

Franz suggest five principles that inform good reporting:

  1. Correctness and veracity
  2. Clarity in writing
  3. Reproducibility and transparency
  4. Method and process
  5. Application and context

Whilst there are some issues I take with Avinash’s and Franz’s posts, I do think they both raise some fundamental issues for us as we contemplate sharing our data-informed stories. I am particularly interested in how the curiosity and openness Avinash describes meets Franz’s five principles.

As I was concluding this post, up popped a link to Samuel Flender’s post How to be less wrong (link). This will be an excellent companion to the two posts discussed here. It also gives me an opportunity to extend my interest in Bayesian perspectives.

Photo Credit

Photo by Sandis Helvigs on Unsplash