# Collaborative Research: Conference: CI PAOS: AI and FAIR Data Infrastructure for Advanced Materials and Manufacturing

> **NSF 01002627DB NSF RESEARCH & RELATED ACTIVIT** · University of North Texas (TX) · $24,996

## Abstract

This project outlines a two-day workshop to be held in Norman, Oklahoma, focused on developing a community-wide roadmap for leveraging ontology-driven processes in advanced manufacturing (AM) to facilitate the integration and sharing of diverse, heterogeneous materials data - from synthesis and processing to characterization and performance. The central challenge is that materials data is often stored in disparate, isolated systems, creating "data silos" that hinder the ability to connect data with its critical contextual information, such as the material-process-structure-property relationship. This fragmentation is a fundamental bottleneck that slows the pace of innovation and discovery. The conference objective is to address this bottleneck by establishing a straightforward and systematic digitalization workflow that generates high-quality, semantically structured, and linked data adhering to the FAIR principles (Findable, Accessible, Interoperable, Reusable).

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

## Key facts

- **NSF award ID:** 2554299
- **Awardee organization:** University of North Texas (TX)
- **SAM.gov UEI:** G47WN1XZNWX9
- **PI:** Xinyi Xiao
- **Primary program:** 01002627DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** EXP PROG TO STIM COMP RES
- **Estimated total:** $24,996
- **Funds obligated:** $24,996
- **Transaction type:** Standard Grant
- **Period:** 06/01/2026 → 05/31/2027

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2554299

## Citation

> US National Science Foundation, Award 2554299, Collaborative Research: Conference: CI PAOS: AI and FAIR Data Infrastructure for Advanced Materials and Manufacturing. Retrieved via AI Analytics 2026-06-26 from https://api.ai-analytics.org/grant/nsf/2554299. Licensed CC0.

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