Networks

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)

Data science and women’s cycling

Amelia Barber has written a post that combines her loves of women’s cycling and data science (link).

Her post focuses on on the demographics of the elite women’s road teams (46 teams registered with the UCI). For the post, Amelia scraped raw rider data (August 2019) from the Union Cycliste Internationale (UCI) website (link).

The code Amelia used for the post is shared by her in a GitHub repository (link). The analysis and plots were done in R and the interactive plots were made using Plotly.

I see Amelia’s work as a great example of open sharing and a desire to make much more public women’s performances.

Some yeas ago, I was involved in a project to film the final stages of women’s road races. At the time, there was very little, if any, multi-camera coverage of women’s races and the aim of the project was to see if we could make finishes much more authentic in training and competition. This was in pre-drone days and we manged with appropriate permissions to fly remote model aircraft, with video cameras, to track the final stages of races and training.

The footage obtained started to transform performance and it led to many conversations across the sport about positioning, techniques and tactics.

I am looking forward to Amelia opening up these conversations too. I am keen also to see where her work in R, ggplot2 and Plotly will take her.

Photo Credit

Peleton (BBC Sport, Twitter)

Grazing on the periphery

It has been a great week for grazing … much of it enabled by Mara Averick’s open sharing.

It started with news of Alison Hill’s speakerdeck presentation.

Alison discusses courage, enchantment, permission, persistence and trust as elements of creative learning. She concludes with this slide:

What fascinated me about Alison’s presentation was her synthesis of profound ideas about sharing and learning with each other in an aesthetic that grabbed and held my attention for 94 slides.

She is part of a remarkable R community that shares openly.

Three other members of this community enabled even more grazing this week. Each offered me possibilities to extend my knowledge of visualisation using R.

Matt Dancho has shared the Anomalize package that enables a “tidy” workflow for detecting anomalies in time series data. There is a vignette for the package to share the process of identifying these events. I think this will be very helpful in my performance research as I investigate seasonal and trend behaviours.

Ulrike Groemping shared the prepplot package in which “a figure region is prepared, creating a plot region with suitable background color, grid lines or shadings, and providing axes and labeling if not suppressed. Subsequently, information carrying graphics elements can be added”.  There is a detailed vignette to support the package.

Guangchuang Yu shared the ggplotify package that converts “plot function call (using expression or formula) to ‘grob’ or ‘ggplot’ object that compatible to the ‘grid’ and ‘ggplot2’ ecosystem”.  Guangchang shares a detailed vignette that illustrates the potential of the package.

Mara, Alison, Matt, Ulrike and Guangchuand epitomise for me the delights in open sharing. A post in The Scholarly Kitchen, written by Alice Meadows, added to my grazing on the margins of openly sharing.

In the post Alice shares a wide range of resources. She makes a particular mention of the Metadata 2020 project that is “a collaboration that advocates richer, connected, and reusable, open metadata for all research outputs, which will advance scholarly pursuits for the benefit of society.”

The opportunities for such collaboration are increasing as we find new ways to share synchronously and asynchronously. These become easier as we make a bold decision to think out loud and share our thoughts with others.

Alison’s presentation includes this slide as a stimulus for that sharing:

This sharing permits grazing for me in the sense of the word used in Leonard Cohen’s Preface to the Chinese translation of his collection of Beautiful Losers poems includes this passage:

When I was young, my friends and I read and admired the old Chinese poets. Our ideas of love and friendship, of wine and distance, of poetry itself, were much affected by those ancient songs. … So you can understand, Dear Reader, how privileged I feel to be able to graze, even for a moment, and with such meager credentials, on the outskirts of your tradition.

Photo Credits

Slide grabs from Alison Hill’s speakerdeck.

Pictures from Twitter and Beuth Hochschule.

Collaboration image from Alice Meadow’s post.