Jason Griffey is the Director of Strategic Initiatives at NISO, where he works to identify new areas of the information ecosystem where standards expertise is useful and needed. Prior to joining NISO in 2019, Jason ran his own technology consulting company for libraries, has been both an Affiliate at metaLAB and a Fellow and Affiliate at the Berkman Klein Center for Internet & Society at Harvard University, and was an academic librarian in roles ranging from reference and instruction to Head of IT at the University of TN at Chattanooga.
Jason has written extensively on technology and libraries, including multiple books and a series of full-periodical issues on technology topics, most recently AI & Machine Learning in Libraries and Library Spaces and Smart Buildings: Technology, Metrics, and Iterative Design from 2018. His newest book, co-authored with Jeffery Pomerantz, will be published by MIT Press in 2024.
He has spoken internationally on topics such as artificial intelligence & machine learning, the future of technology and libraries, decentralization and the Blockchain, privacy, copyright, and intellectual property. A full list of his publications and presentations can be found on his CV.
He is one of eight winners of the Knight Foundation News Challenge for Libraries for the Measure the Future project (http://measurethefuture.net), an open hardware project designed to provide actionable use metrics for library spaces. He is also the creator and director of The LibraryBox Project (http://librarybox.us), an open source portable digital file distribution system.
Jason can be stalked obsessively online, and spends his free time with his daughter Eliza, reading, obsessing over gadgets, and preparing for the inevitable zombie uprising.
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2 replies on “BiblioMashups – Reading Radar”
great example.
the nyt best sellers is definitely worth looking into. a few gotchas that I found when trying it out awhile back:
1. there is a week delay for new lists so it isn’t the current one. depending on your use this may or may not matter.
2. In my attempts it was difficult to get the full history of popular items. For example a harry potter item that was popular when it came out, then again when the movie came out. Since it is on the list for so long it can be hard to get what it was without going through every weekly list. the history was not paginated well though they may have fixed this
Another use of this data is to tag or machine tag items. You can then search or limit items based on the tags. For example you could find all the books that have ever #1 on the list with a search for nyt:rank=1 , etc
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