I have spent some time discussing learning in recent weeks.
In one of my conversations, I spoke with my daughter, Beth, about her interest in approriate technology.
I was really impressed with her sense of appropriate technology as small-scale technology. My subsequent reading led me to understand that appropriate technology “is simple enough that people can manage it directly and on a local level. Appropriate technology makes use of skills and technology that are available in a local community to supply basic human needs” (link). I think directness and localness are keys for me in thinking about transformational change.
This does involve profound listening. It requires a sensitivity to relationships of power. It also requires us as Susanto Basu and David Neil (1996) pointed out that technology diffuses slowly (link) and discussed more recently by Deborah Healey (2018) (link).
I believe these conversations will lead me to more reflections about the relationships between appropriate technologies and the pursuit and recognition of microlearning (link).
Researchers have some important decisions to make about the ways they share their discoveries.
Back in 2017, I was struck by Biecek Przemysław and Marcin Kosiński’s discussion of the use of the R package archivist (link). They discussed the opportunities we have to enable auditable and replicable analysis. Two years earlier, Data Carpentry facilitated a Reproducible Research in R workshop (link).
This week ,two finds have sent me off thinking about the explicit sharing of research journeys and discoveries.
The first find was Stencila, an open source project, that aims to make reproducible research more accessible (link). I noted that “Stencila provides a set of open-source software components enabling researchers to enable reproducible research … using interactive source code”.
I found Stencila through a link to Giuliano Maciocci, Michael Aufreiter and Nokome Bentley’s (2019) paper Introducing eLife’s first computationally reproducible article (link). This exemplifies the potential of a Reproducible Document Stack approach to open sharing. Researchers can use their existing word processing and spreadsheet tools and can embed R and Python code blocks that can generate live interactive plots using the Plotly.js library. Stencila uses the Mini formula language (link).
A second find, thanks to a Stephen Downes’ alert, was Alice Meadows, Laurel Haak and Josh Brown’s (2019) discussion of persistent identifiers (link). They note persistent identifiers “for people (researchers), places (their organizations) and things (their research outputs and other contributions) are foundational elements in the overall research information infrastructure”.
Supporting research includes supporting the research information infrastructure: the tools and services that researchers use which enable them to spend more time doing research and less time managing it – as well as the virtual building blocks on which those tools and services depend, such as metadata, standards and, the topic of this article, persistent identifiers (PIDs).
Meadows, Alice, Laurel L. Haak, and Josh Brown. 2019. “Persistent Identifiers: The Building Blocks of the Research Information Infrastructure”. Insights 32 (1): 9. DOI: http://doi.org/10.1629/uksg.457
I have mentioned before that one of my founding ideas for the International Journal of Performance Analysis in sport was to enable a paper in any language (with an English abstract or summary) that shared openly video and data resources. At that time the platforms available did not permit open sharing.
This week has brought back those memories of a global community sharing research journeys. It must be profoundly exciting entering the research community now or transforming existing practices as we become much more transparent about these journeys.
Each day, my inbox delivers treasures that are growing in scale.
Today, thanks to Mara Averick (link), I discovered Danielle Navarro’s personal essay on Bayes factors (link).
Danielle’s post has given me a holiday reading list that will help me redefine my naive Bayes views and thinking.
As I was contemplating the references her post unlocked, I came across these images that I have taken to be the innocent climb of continuing learning and the joy of finding new inclines (aka steep learning curves):
(Source of this idea was from R-Ladies Sydney (link) via Real Python (link))