Before the Arctic Data Explorer (ADE), researchers needed to know which datasets were in which repositories. Since datasets are not discoverable through tools like Google, scientists and graduate students had to do considerable legwork to find and share reusable datasets. In fact, scientists often spent months planning data collection expeditions just to get their hands on data that already existed, but couldn’t be found.
Leadership Team – Mark Parsons, Lynn Yarmey
Development Team – Brendan Billingsley, Jonathan Kovarik, Stuart Reed, Michael Brandt, Chris Chalstrom, Kate Heightley, Matt Savoie, and Danielle Harper
I played multiple roles on this product. Originally my role was as UX Researcher, but eventually I added the role of tactical Product Owner.
Testing began with the ACADIS Advisory Committee in 2012. We used card sorting to determine the most important searchable aspects of a dataset then built requirements around them.
2013 was filled with pre-launch research of semi-structured interviews, contextual inquiry, surveys, and an A/B test. Each round resulted in developer stories that improved aesthetics, map interaction, and basic search functionality.
The post-launch research in 2014 included a heuristic evaluation, online semi-structured interviews with our international users, and keyword search analytics through Libre and Google Analytics. The results were improved error handling, better documentation (especially the internal API Swagger implementation), greater scan-ability of search results, and a redesign of search facet options.
Ideally, we wanted the feed from the repository to be automatically parsed. Our intent was to use open source software called GI-CAT, but the implementation ultimately slowed down the site and make the search tool prone to sluggishness and bugs. In 2014, we decided to remove GI-CAT and use internal data translators (and sometimes manual labor from devs) to transform data feeds into formats we needed. Instead, we documented our APIs and microservices with Swagger and waited for technology to catch up to where we wanted it to be.
The image below is an overview poster presented at the Research Data Alliance plenary in 2014. Other successes the Arctic Data Explorer can boast about are that the National Science Foundation used the software exclusively for an international data visualization workshop, the Jet Propulsion Laboratory and CalTech uses it for software development classes, it is an early adopter of data and software Digital Object Identifiers (DOI), and it is fully open source software including a Ruby gem.