Standards: NISO and UKSG Announce Five More Publishers Endorse KBART
From a NISO News Release:
BioOne, JSTOR, LOCKSS, the Royal Society of Chemistry and SpringerLink (hosted by Metapress) are the most recent organizations to publicly endorse the Phase I recommendations of the KBART (Knowledge Bases And Related Tools) Working Group, a joint NISO/UKSG initiative that is exploring data problems within the OpenURL supply chain. KBART’s Phase I Recommended Practice (NISO RP-9-2010), published in January 2010, contains practical recommendations for the timely exchange of accurate metadata between content providers and knowledge base developers.
All content providers, from major databases to small publishers, are encouraged to publicly endorse the KBART Recommended Practice by submitting a sample file to the KBART working group. Once the file’s format and content has been reviewed and approved, and the provider has made it publicly available (in line with the recommendations), the provider will be added to a public list of endorsing providers. Knowledge base developers can endorse the KBART Recommended Practice by confirming that their systems can process KBART formatted files. In addition, a contacts registry is available on the KBART Information Hub at www.uksg.org/kbart or www.niso.org/workrooms/kbart where content providers and knowledge base developers can register their organization’s information for downloading holdings metadata.
About Gary Price
Gary Price (firstname.lastname@example.org) is a librarian, writer, consultant, and frequent conference speaker based in the Washington D.C. metro area. He earned his MLIS degree from Wayne State University in Detroit. Price has won several awards including the SLA Innovations in Technology Award and Alumnus of the Year from the Wayne St. University Library and Information Science Program. From 2006-2009 he was Director of Online Information Services at Ask.com. Gary is also the co-founder of infoDJ an innovation research consultancy supporting corporate product and business model teams with just-in-time fact and insight finding.