Science base usage analysis - AGU2016 - in21d08

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Information about Science base usage analysis - AGU2016 - in21d08

Published on December 13, 2016

Author: SkyBristol


1. Measuring the impact of an API- first mentality with ScienceBase after 4.5 years Sky Bristol1 Steve Tekell2 U.S. Department of the Interior U.S. Geological Survey 1. USGS Core Science Analytics, Synthesis and Libraries 2. USGS Fort Collins Science Center

2. AGU Fall Meeting 2014 Talking Points • ScienceBase – brief history • What does usage tell us about how the system is doing? – Live apps – Usage logs • Public search observations • Lessons and Implications

3. AGU Fall Meeting 2014 2006 2007 2009 2011- 2016 myUSGS Data Explorer/Data Uploader Scientific Data Catalog/Compr ehensive Science Catalog ScienceBase 1.0 and then 2.0 API-driven design Collaborative tools and simple file upload Metadata Cataloging & Research Item Concept Digital Repository & Research Item Faceting API use exceeds portal traffic with 70+ API-driven apps “In the research process, we need more than just a big catalog of data. We need all of the other important information connected to our work – published papers, manuscripts, software, and information about people, labs, projects, and others in our field.” Inspiration and History

4. AGU Fall Meeting 2014 API First

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11. AGU Fall Meeting 2014 Access to ScienceBase via code libraries is beginning to outpace access via the web portal and other clients

12. AGU Fall Meeting 2014 API access includes HTTP REST access to the ScienceBase Catalog along with OGC catalog requests and OGC data services (WMS, WFS, WCS, KML) for hosted data assets

13. AGU Fall Meeting 2014 Search engine optimization with metadata resulting in sometimes better results than our own search, easy custom search apps, and discovery “in the wild”

14. AGU Fall Meeting 2014 Full title search  top of the search list most times Adding “sciencebase” will get there every time

15. AGU Fall Meeting 2014 Simple searches without trigger words is still pretty good Note here the more appropriate search result coming from the ScienceBase- driven web app

16. AGU Fall Meeting 2014 Lessons & Implications • When a data system becomes successful and used, it becomes really difficult to pay down technical debt and invest in new capabilities. • While it is possible to detect a tremendous number of signals from RESTful request logs, it takes significant engineering work to bake in useful reporting and analysis tools. • Still work to do on semantics, linked data, and knowledge graph influence. • API keys are hard to implement once the cat is out of the bag. • It’s hard to convince managers that “stealth apps” are the greatest indicator of success.

17. AGU Fall Meeting 2014 Contacts Myriad other apps that may or may not indicate they are powered by ScienceBase 

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