I follow developments in High Performance Sport New Zealand with great interest, particularly now that Jacquie Tran is a Senior Insights Researcher there.
Today, Jacquie shared the announcement of the availability of two insights researcher positions in the Knowledge Edge team. The job descriptions are on the HPSNZ web site (link).
The advert has this section:
Some familiarity with (or readiness to learn) the following would be advantageous:
Thematic analysis and natural language processing
Relational databases (e.g., SQL)
Programming languages for working with data (e.g., R, Python, Stata)
Visualisation tools (e.g., Tableau, Power BI).
The job purpose notes that the insights researcher “will assist with capturing, analysing, exploring and reporting on qualitative and quantitative data”, “utilise a relational database” and “visualise data patterns and support the investigation of insights … to inform performance decision-making”.
I was delighted to read that the first line in the person specification highlights “curiosity and passion”. Candidates can have completed a tertiary degree with a research component or have experience in a research based field.
I hope they get lots of applications for these opportunities. Their availability signals are growing trend in sport and raises some very important pedagogical and experiential issues.
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))