Tapestry is a USDA-NIFA funded project to develop a transparent, open-access to an array of in-situ data, analysis protocols and analytic services that enables researchers, Extension professionals and policy makers to investigate interregional, intertemporal and intersectoral relationships at county-level regional economies of the United States.

Enabling open-access experiments with replicable sets of cross-sectional and time-series accounts will allow new insights into the causes and nature of income growth across the nation, differences in income distribution across the nation, differences in wealth distribution across the nation, calculating consistent economic contributions and impacts, and measures of the economic interdependency of different regions.

Tapestry’s current priority science workflows protocols:

• Input-output account transformations: Python, SQL and R library for transforming BEA’s 2007 and 2012 benchmark Supply-Use IO Tables (SUIOT) into a suite of Symmetric IO Tables (SIOT) (e.g., IxI vs CxC IO accounts).

• Extend input-output accounting transformations to a multi-regional context: Python, SQL and R libraries for transforming spatially-disaggregated multi-regional (MR) SUIOT into MR-SIOT.

• Income and transfer accounts to the US SUIOT/SIOT to produce a complete US SAM.

• Eliminate missing data from primary data sources such as BLS QCEW and BEA regional data and making these data available.

• A standard protocol for evaluating multi-sector economic contributions using input-output models with specifical applications to track food dollars and wood product supply chains through economies.

To access Tapestry data please visit: https://tapestry.nkn.uidaho.edu/.

To access the GitHub site that hosts the Tapestry source code visit: https://github.com/northwest-knowledge-network/tapestry-modeling.

Please read the article below for more information on Tapestry and to cite when using Tapestry data: https://rrs.scholasticahq.com/article/123153-tapestry-collaborative-tool-for-regional-data-and-modeling.