2018-19 Academic Year Research Report
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This is an addendum to my 2018-19 Academic Year Research Report submitted for review via Digital Measures.
Online documentation for research accomplishments:
- We were able to successfully convert publicly accessible JSON-encoded statistical play-by-play datasets for all 14 games in the 2017 Alabama Crimson Tide football season into a linked data application using Wikibase. One can now navigate from play to play, drive to drive, and game to game within the 2017 football season using data drawn from existing statistical play-by-play datasets incorporated into the application by way of linked data methods. Highlights:
- Example of a JSON-encoded statistical play-by-play dataset from 2017 Alabama versus Florida State football game.
- Property list for the ontology developed for this application.
- Examples of three types of entities ("play" "drive" "game") in our linked data application. Each example provides links to Mediawiki site pages and to Wikibase item pages (each item page contains the metadata statements (each statement a "triple") about each entity using properties drawn from our ontology):
- Mediawiki site page for a typical play (here is this play's corresponding Wikibase item page)
- Mediawiki site page for a typical drive (here is this drive's corresponding Wikibase item page)
- Mediawiki site page for a typical game (here is this game's corresponding Wikibase item page)
- Infobox templates deployed to generate infoboxes on each Mediawiki page:
- Example SPARQL queries for retrieving entity instances (to run each query, click on the Blue Arrow icon in lower left portion of screen after clicking on links below):
- One important result of our research work during this academic year is the demonstration of the use of linked data to provide access to individual multimedia assets that document individual plays. As shown above, an individual play can be discovered by navigating the linked data application or by querying the triplestore using SPARQL. In these examples, you will find video clips for each play thus demonstrating how linked data navigation or querying methods can lead to multimedia assets of those plays:
- Important collaborators contributing to the research reported here:
- Dr. Greg Bott, Assistant Professor, UA Culverhouse School of Business. Dr. Bott is co-PI on the RGC grant providing data management expertise focusing on the optimizing the efficiency of data wrangling methods using Python scripting
- David McMillan, IT Team Leader, Enterprise Development & Application Support, UA Office of Information Technology (OIT). David has been a long time collaborator beginning in the late 1990s when he was Systems Admin in the School of Library and Information Studies, and we were co-authors on a UA patent. In the current research project, David has contributed crucial support in the installation, optimization, and ongoing management of the Mediawiki/Wikibase instance hosted by UA OIT.
- Huapu Liu, Graduate Research Assistant and MLIS student. Huapu served as my graduate research assistant for the entire 2018-19 academic year serving as an indispensable collaborator in the development of our understanding of Wikibase and linked data, which was essentially a long series of trial and error steps. Huapu helped me compose over 25 pages of data wrangling procedures needed to transform the JSON-encoded statistical play-by-play datasets into linked data in Wikibase. He also did more than his share of data wrangling! (to run each query, click on the Blue Arrow icon in lower left portion of screen after clicking on link)
- Christine Schultz-Richert, MLIS student