SUSHI stands for Standardized Usage Statistics Harvesting Initiative. It is a standard protocol (ANSI/NISO Z39.93-2003) that can be used by electronic resource management (ERM) systems (and other systems) to automate the delivery of COUNTER usage statistics.
What COUNTER reports can be delivered with SUSHI?
SUSHI 1.6 supports retrieval of any COUNTER report.
How do I set up automatic delivery of my ACS COUNTER reports via SUSHI?
The request URL and requestor ID specific to an institution is located on the usage reports page on the Librarian Administration site. The customer will see this when logging in to their Librarian account at pubs.acs.org/4librarians, and can transfer this information to their electronic resource management (ERM) system or web service.
Do customers need an ERM system to use SUSHI?
No. Many customers interested in SUSHI have a commercial electronic resource management (ERM) system that will integrate this usage data into their overall management of e-resources, but this is not a requirement. The SUSHI protocol does not presume an end-of-the-line repository for the retrieved report. Some libraries are creating master spreadsheets or in-house databases for their usage data. Any software that can initiate a web service request using the SUSHI WSDL and Schema can use the SUSHI protocol. More information for customers looking to implement SUSHI without using and ERM can be found at http://www.niso.org/workrooms/sushi.
How is SUSHI related to COUNTER?
The SUSHI protocol has been incorporated into the COUNTER Code of Practice. SUSHI was developed by NISO (National Information Standards Organization) in cooperation with COUNTER and in 2007 became a NISO standard (Z39.93).Release 3 of the Code of Practice for Journals and Databases specifically addressed the needs of consortia with the introduction of two new usage reports. These Consortia Reports must be provided in XML format and are designed to include detailed usage for consortium members in a single report. SUSHI can greatly facilitate the handling of large volumes of usage data.