This document discusses decomposing MARC 21 records for analysis. To prepare the test dataset of the 1% sample of MARC 21 records from the WorldCat database for use in the Z39.50 Interoperability Testbed, the authors need to be able to efficiently analyze the records to determine relevant records to be returned for a set of test searches. The first step in that analysis is to determine the occurrence of test search terms in specific records. This document describes the general approach for this analysis and identifies specifications for the analysis.

This document describes the procedures that participants in the Z39.50 Interoperability Testbed (Z-Interop Participant) are to use when testing Z39.50 client (Z-client) applications. The testing of a Z-Interop reference implementation Z39.50 server. Specifically, the attribute combination and other query components (e.g., Boolean operators) are reviewed, and a report of the results will be prepared for each Z-Interop participant.

Data management plan for the grant, "Improving Student Learning While Decreasing Bias in Teaching Through Simulation." Research on the simEquity project which will implement a scalable model for developing equitable, culturally responsive teaching practices through simSchool, a simulated teaching environment. The project will identify best practices to help teachers recognize and mitigate implicit biases that often impact student success. The project team will iteratively develop and test a Teaching without Bias curriculum and an AI-driven set of bias reduction tools that can be added to existing simulation instruction modules.

Data management plan for the grant, "Distributed Learning for Undergraduate Programs in Data Science at Diverse Universities." Here, we propose to expand on our previous NSF-supported work in developing a suite of courses in data science (DS) that were taught across a network of 4 undergraduate institutions in a distributed learning (DL) format. The current proposal seeks to expand the size and diversity of our network while adapting the advanced distributed learning (ADL) technology of the U.S. military to an academic setting to harness the power of AI in tailoring optimal learning experiences for individual students.