T3 Network Pilot Projects

Full Network Meeting Activity

September 19: Metadata Interoperability to Metadata Harmonization Webinar

The T3 Network pilot projects are organized into four specific areas of focus. Each pilot project is identified below with an outline of that project team's specific work tasks. 

Open Data Standards

Building the foundation to seamlessly share data across stakeholder systems.

  • Map and Harmonize Data Standards — Develop methods and tools for mapping and harmonizing existing data standards for improving interoperability and search and discovery on the web.
  • Employment and Earnings Record Standards — Develop public-private standards for employment and earnings records to improve data quality and utilization and reduce reporting costs.
  • Comprehensive Learner/Worker/Military Record Standards — Align and pilot test the use of data standards to enable individuals to better manage and use competencies documented in their records to pursue career and educational opportunities.
  • Public-Private Adoption of Open Data Standards — Improve public and private collaboration in the development and use of data standards.

Map and Harmonize Data Standards

Lead: Jeanne Kitchens, Southern Illinois University
Stuart Sutton, Sutton & Associates Consulting
Support: Natalie Evans Harris, BrightHive

Meeting Activity

Work Tasks

  • 1.1 Formalize and expand the Standards Mapping Group to address standards harmonization across the public-private talent marketplace (e.g. state and federal agencies, T3 Network, and participants in PP4) for both search and discovery and system to systems data transfer.

  • 1.2 Develop a standards harmonization process and platform (TBD if platform needs to be built or provided as a service by an outside partner).

  • 1.3 Develop two-year work plan starting with T3 Network mapping and harmonization priorities related to competency data (PP5); employment and earnings records (PP2); comprehensive learner records (PP3); and personal identifiers and related protocols for linking individual-level employment and learner records (PP9) as well as self-sovereign data management (PP10).

  • 1.4 Conduct data standards mapping and harmonization work based on the work plan.

  • 1.5 Develop a plan for long-term sustainability and how it will be governed and managed.

Employment and Earnings Record Standards

Lead: Bob Sheets, The George Washington University
Andrew Reamer, The George Washington University

Meeting Activity

Work Tasks

  • 2.1 Develop public and private stakeholder use cases and standards for employment and earnings records. Use cases will include applications of an enhanced Unemployment Insurance (UI) wage record and applications requiring the linkage of an individual's employment and earnings records with learner records (PP3 and PP9).

  • 2.2 Work with employers, HR technology vendors, and states to identify the benefits and cost of implementing enhanced UI wage records based on the public-private standards, including impact on state UI data systems  (Note: This only focuses on UI wage record applications of public-private employment and earnings record standards).

  • 2.3 Develop guidance and a plan for promoting the adoption and implementation of  public-private standards for enhanced UI wage records.

  • 2.4 Promote employer, state, and federal adoption and implementation of public-private standards for enhanced UI wage records.

  • 2.5 Develop a plan for exploring additional public and private use cases and applications of a comprehensive employment and earnings record standard.

Comprehensive Learner/Worker/Military Record Standards

Lead: Natalie Evans Harris, BrightHive
Jeanne Kitchens, Southern Illinois University
Support: Stuart Sutton, Sutton & Associates Consulting

Meeting Activity

November 6
Meeting scheduled for 11:30 a.m. ET using the following call-in information:
URL: https://zoom.us/j/7537215667 
Conference Line:  +1 929 205 6099  
Meeting ID:  753 721 5667

Work Tasks

  • 3.1 Develop public and private stakeholder use cases including search and discovery and system to system data sharing between employers; education, training, military, and credentialing organizations; federal and state agencies; and public-private data collaboratives.

  • 3.2 Facilitate cross-collaborative work among data standards bodies (i.e. learner records, HR, and government) to harmonize and address gaps in data standards including individual identifiers needed to match individual records (PP9).

  • 3.3 Harmonize and fill gaps in public-private standards for learner records necessary to address stakeholder use cases.

  • 3.4 Pilot test the harmonized and enhanced standards with T3 Network partners with a focus on system to system data sharing between employers and their education, training, military, and credentialing partners.

  • 3.5 Coordinate with PP4 to promote the use of harmonized and enhanced standards by federal and state data reporting systems and national and state data collaboratives to reduce the burden of inconsistent data requirements.

Public-Private Adoption of Open Data Standards

Lead: Bob Sheets, The George Washington University
Natalie Evans Harris, BrightHive
Support: Andrew Reamer, The George Washington University

Meeting Activity

  • May 2 open data standards webinar recording and slide deck
  • September 17 in-person meeting at the Center for Open Data Enterprise in Washington, D.C. to discuss a draft background paper on standards development as it relates to government, public-private, and international bodies. View the slide deck and a summary of the meeting

Work Tasks

  • 4.1 Review current policies and practices for, and barriers to, developing and adopting consensus-driven public-private data sharing standards. Draft a set of guiding principles and processes. Identify potential pilot opportunities for testing them (PP1, PP2, PP3, PP9).

  • 4.2 Conduct meetings with standards bodies and federal and state agencies to review and revise the draft guiding principles and processes. Explore how federal and state agencies can promote and/or participate in one or more pilot opportunities.

  • 4.3 Conduct follow-up meetings to review pilot projects and their implications for revising guiding principles and processes.

  • 4.4 Meet to finalize draft guiding principles and processes and discuss recommendations for federal and state implementation.

  • 4.5 Prepare a  report on guiding principles and processes as well as recommendations for implementation.

Competency-Based Learning and Hiring

Using all available competency data to make all learning count.

  • Competency Data Collaborative — Develop an open-source infrastructure that can be used to better connect and link machine-actionable data from competency frameworks and repositories.
  • Competency Translation & Analysis — Analyze, compare, and translate competencies within and across industries using artificial intelligence and machine learning.

Pilot Projects 7 and 8 were consolidated into the above projects. 

Competency Data Collaborative

Lead: Tom Plagge, BrightHive
Stuart Sutton, Sutton & Associates Consulting
Support: Matt Gee, BrightHive
Jeanne Kitchens, Southern Illinois University

Meeting Activity

Work Tasks

  • 5.1 Organize an open, shared competency data collaborative in cooperation with PP6, PP7, and PP8. 

  • 5.2 Develop guidelines and protocols for publishing competency data as open linked data and support search and retrieval between linked repositories. 

  • 5.3 Enhance and harmonize competency data standards needed to implement guidelines (coordinate with PP1).

  • 5.4 Develop tools for publishing and maintaining open licensed competency frameworks that support systematic discovery and translation between alternate data formats.

  • 5.5 Pilot test publishing tools (coordinate with PP6).

  • 5.6 Promote widespread adoption and use of publishing tools.

Competency Translation & Analysis

Lead: Matt Gee, BrightHive
Tom Plagge, BrightHive 
Support: Stuart Sutton, Sutton & Associates Consulting

Meeting Activity

Work Tasks

  • 6.1 Organize an open, shared competency data collaborative in cooperation with PP5, PP7, and PP8. 

  • 6.2 Through the open, shared competency data collaborative, develop a plan for contributing and developing open AI tools to begin analyzing, translating, and comparing competencies.

  • 6.3 Determine and provide access to the necessary competency and contextual data to support the development of AI algorithms.

  • 6.4 Develop openly licensed AI algorithms best suited for interpreting, aligning, writing, and generating competencies along with defining necessary metrics for training given defined tasks and use cases.

  • 6.5 Pilot test openly licensed AI algorithms (contingent on additional funding).

  • 6.6 Work with user groups (PP8) to establish feedback and testing mechanisms. 

Empowering Learners and Workers

Ensuring data access and privacy for all individuals.

  • Data Collaboratives for Individual-Level Data — Promote best practices for managing public and private data, allowing for increased access while ensuring privacy and security.
  • Management and Use of Individual-Level Data Records — Develop open, selfsovereign protocols and data management guidance for learner, worker, and military records.

Data Collaboratives for Individual-Level Data

Lead: Matt Gee, BrightHive
Tom Plagge, BrightHive
Support: Bob Sheets, The George Washington University

Meeting Activity

Work Tasks

  • 9.1 Establish a work group composed of federal and state government agencies; public-private data collaboratives; employers;  education, training, military, and credentialing organizations; and related standards organizations to develop protocols for accessing, matching, and using individual-level employment and learner records. 

  • 9.2 Develop protocols for using individual identifiers for matching and linking individual records and their implications for employment and earnings (PP2) and comprehensive learner records (PP3). 

  • 9.3 Develop protocols (e.g. secure multiparty computation) for accessing and using linked individual employment, earnings, and comprehensive learner records for policy research and evaluation, consumer information systems, and workforce data analytics.

  • 9.4 Promote the use of these protocols in both the public and private sectors (PP4). 

Management and Use of Individual-Level Data Records

Lead: Matt Gee, BrightHive
Support: Bob Sheets, The George Washington University
Stuart Sutton, Sutton & Associates Consulting

Meeting Activity

November 5
Meeting scheduled for 2:00 p.m. ET using the following call-in information:
URL: https://zoom.us/j/7537215667 
Conference Line:  +1 929 205 6099
Meeting ID:  480 160 5697

Work Tasks

  • 10.1 Form a work group of stakeholders familiar with relevant data standards and individual-level data protocols to develop a plan for developing and testing protocols for self-sovereign data management.

  • 10.2 Review and analyze related initiatives in other sectors (e.g. healthcare) aimed at individual record management and promoting the adoption and use of existing data exchange standards with a new consumer-driven use case.

  • 10.3 Develop open, self-sovereign protocols and sandboxed data infrastructure. Then develop pilot projects with partners.

  • 10.4 Conduct pilot projects (contingent on additional funding).