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  Advancing catalysis research through FAIR data principles implemented in a local data infrastructure - a case study of an automated test reactor

Moshantaf, A., Wesemann, M., Beinlich, S., Junkes, H., Schumann, J., Alkan, B., et al. (2024). Advancing catalysis research through FAIR data principles implemented in a local data infrastructure - a case study of an automated test reactor. Catalysis Science & Technology. doi:10.1039/d4cy00693c.

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 Creators:
Moshantaf, Abdulrhman 1, Author
Wesemann, Michael2, Author           
Beinlich, Simeon2, Author                 
Junkes, Heinz2, Author                 
Schumann, Julia1, Author           
Alkan, Baris1, Author                 
Kube, Pierre1, Author                 
Marshall, Clara Patricia1, Author                 
Pfister, Nils2, Author           
Trunschke, Annette1, Author                 
Affiliations:
1Inorganic Chemistry, Fritz Haber Institute, Max Planck Society, ou_24023              
2Interface Science, Fritz Haber Institute, Max Planck Society, ou_2461712              

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 Abstract: Findable, accessible, interoperable, and reusable (FAIR) data is currently emerging as an indispensable element in the advancement of science and requires the development of new methods for data acquisition, storage and sharing. This is becoming even more critical as the increasing application of artificial intelligence demands significantly higher data quality in terms of reliability, reproducibility and consistency of datasets. This paper presents methods for the digital and automatic acquisition and storage of data and metadata in catalysis experiments based on open-source software solutions. The successful implementation of a digitalization concept, which includes working according to machine-readable standard operating procedures (SOPs) is outlined using a reactor for catalytic tests that has been automated with the open source software tool EPICS (Experimental Physics and Industrial Control System). The process of data acquisition, standardized analysis, upload to a database and generation of relationships between database entries is fully automated. Application programming interfaces (APIs) have been developed to enable data exchange within the local data infrastructure and beyond to overarching repositories, paving the way for autonomous catalyst discovery and machine learning applications.

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Language(s): eng - English
 Dates: 2024-05-312024-08-252024-08-27
 Publication Status: Published online
 Pages: 12
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1039/d4cy00693c
 Degree: -

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Title: Catalysis Science & Technology
  Other : Catal. Sci. Technol.
Source Genre: Journal
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Publ. Info: Cambridge : Royal Society of Chemistry
Pages: 12 Volume / Issue: - Sequence Number: - Start / End Page: - Identifier: ISSN: 2044-4753
CoNE: https://pure.mpg.de/cone/journals/resource/2044-4753