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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.19802 |
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| _version_ | 1866917511170097152 |
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| author | Mei, D. -M. Acharya, K. Adhikari, C. M. Adhikari, M. Aryal, S. Benson, B. V. Bhatta, K. Bhattarai, S. Budhathoki, N. Castillo, A. M. Chakraborty, D. Chhetri, S. Choudhury, S. Chowdhury, T. A. Cruz, R. D. Cui, B. Dhital, S. Dong, K. -M. Gapuz, R. Ghasemi, A. Gnimpieba, E. Z. Gurung, B. D. S. Hashim, H. A. Harry, R. I. Hasin, K. -E. Hassanzadeh, M. K. Jha, M. K. Kim, D. Kong, K. -C. Lama, B. Mahat, A. Maharjan, N. Majeed, A. Mammo, J. Masud, M. M. Moore, K. S. Mukherjee, T. Nawaz, A. Oli, H. Panamaldeniya, S. A. Pandey, L. Pandey, R. Peng, Z. Prem, A. Rana, M. M. Magar, K. Rana Rizk, R. Tadi, C. S. Wang, L. -W. Yang, Y. Yin, G. -L. Yu, C. -X. Zeng, D. Zhou, M. Zhou, Q. |
| author_facet | Mei, D. -M. Acharya, K. Adhikari, C. M. Adhikari, M. Aryal, S. Benson, B. V. Bhatta, K. Bhattarai, S. Budhathoki, N. Castillo, A. M. Chakraborty, D. Chhetri, S. Choudhury, S. Chowdhury, T. A. Cruz, R. D. Cui, B. Dhital, S. Dong, K. -M. Gapuz, R. Ghasemi, A. Gnimpieba, E. Z. Gurung, B. D. S. Hashim, H. A. Harry, R. I. Hasin, K. -E. Hassanzadeh, M. K. Jha, M. K. Kim, D. Kong, K. -C. Lama, B. Mahat, A. Maharjan, N. Majeed, A. Mammo, J. Masud, M. M. Moore, K. S. Mukherjee, T. Nawaz, A. Oli, H. Panamaldeniya, S. A. Pandey, L. Pandey, R. Peng, Z. Prem, A. Rana, M. M. Magar, K. Rana Rizk, R. Tadi, C. S. Wang, L. -W. Yang, Y. Yin, G. -L. Yu, C. -X. Zeng, D. Zhou, M. Zhou, Q. |
| contents | Data-centric materials science is changing how materials are discovered, optimized, manufactured, and qualified, yet many deployment-limiting materials problems still depend on experimental, processing-rich, device-level, and field-relevant data that are difficult to capture in conventional materials databases. This perspective argues that the Great Plains and adjacent interior research corridor can make a distinctive national contribution by organizing distributed experimental assets into a trusted regional materials-data ecosystem. The proposed model emphasizes FAIR metadata, provenance, persistent sample identifiers, uncertainty-aware modeling, semi-closed-loop workflows, stackable workforce training, and tiered governance for academic, public, controlled-access, and industry-protected data. We identify five coupled barriers -- fragmented data, weak algorithm--laboratory translation, uneven access to cyberinfrastructure and technical staff, workforce gaps at the materials--data interface, and insufficient incentives for sharing and reuse -- and propose a staged roadmap for addressing them. A high-purity germanium pilot illustrates how regional strengths can be converted into reusable datasets, benchmark models, trained personnel, and decision-improving workflows. The broader message is that regional leadership in data-centric materials science will depend less on geographic concentration than on trustworthy data practices, interoperable infrastructure, cross-trained people, and application-driven materials challenges. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_19802 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Building a Regional Data-Centric Materials Science Ecosystem for Processing-Rich Materials Innovation in the Great Plains Mei, D. -M. Acharya, K. Adhikari, C. M. Adhikari, M. Aryal, S. Benson, B. V. Bhatta, K. Bhattarai, S. Budhathoki, N. Castillo, A. M. Chakraborty, D. Chhetri, S. Choudhury, S. Chowdhury, T. A. Cruz, R. D. Cui, B. Dhital, S. Dong, K. -M. Gapuz, R. Ghasemi, A. Gnimpieba, E. Z. Gurung, B. D. S. Hashim, H. A. Harry, R. I. Hasin, K. -E. Hassanzadeh, M. K. Jha, M. K. Kim, D. Kong, K. -C. Lama, B. Mahat, A. Maharjan, N. Majeed, A. Mammo, J. Masud, M. M. Moore, K. S. Mukherjee, T. Nawaz, A. Oli, H. Panamaldeniya, S. A. Pandey, L. Pandey, R. Peng, Z. Prem, A. Rana, M. M. Magar, K. Rana Rizk, R. Tadi, C. S. Wang, L. -W. Yang, Y. Yin, G. -L. Yu, C. -X. Zeng, D. Zhou, M. Zhou, Q. Materials Science Applied Physics Computational Physics Data-centric materials science is changing how materials are discovered, optimized, manufactured, and qualified, yet many deployment-limiting materials problems still depend on experimental, processing-rich, device-level, and field-relevant data that are difficult to capture in conventional materials databases. This perspective argues that the Great Plains and adjacent interior research corridor can make a distinctive national contribution by organizing distributed experimental assets into a trusted regional materials-data ecosystem. The proposed model emphasizes FAIR metadata, provenance, persistent sample identifiers, uncertainty-aware modeling, semi-closed-loop workflows, stackable workforce training, and tiered governance for academic, public, controlled-access, and industry-protected data. We identify five coupled barriers -- fragmented data, weak algorithm--laboratory translation, uneven access to cyberinfrastructure and technical staff, workforce gaps at the materials--data interface, and insufficient incentives for sharing and reuse -- and propose a staged roadmap for addressing them. A high-purity germanium pilot illustrates how regional strengths can be converted into reusable datasets, benchmark models, trained personnel, and decision-improving workflows. The broader message is that regional leadership in data-centric materials science will depend less on geographic concentration than on trustworthy data practices, interoperable infrastructure, cross-trained people, and application-driven materials challenges. |
| title | Building a Regional Data-Centric Materials Science Ecosystem for Processing-Rich Materials Innovation in the Great Plains |
| topic | Materials Science Applied Physics Computational Physics |
| url | https://arxiv.org/abs/2605.19802 |