Data flow from instrument to publication; an X-ray experimentalist view on data generation, processing and analysis

This poster motivates and depicts the work path towards a FAIR data collection and processing at large scale X-ray infrastructure, especially for the X-ray reflectivity use case. The envisioned procedure is, the experimental data and meta data is collected by means of human input and automatic aggregation during the experiment. The (meta) data is stored in an open and standardized format and the meta data is made publicly available in a meta data catalogue. The sample related information is conserved in a sample registry and uniquely indentifiable by a PID, e.g. IGSN. During the experiment an automatic, machine learning based analysis is indentifying experimental and data quality related issues and reports these back to the human experimenters.

Publication Details

Journal
PUBLISSO (German National Library of Medicine)
Published
2024-10-11
DOI
https://doi.org/10.4126/frl01-006488507
Primary Topic
Research Data Management Practices
Type
article
Field-Weighted Citation Impact
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article

Data flow from instrument to publication; an X-ray experimentalist view on data generation, processing and analysis

PUBLISSO (German National Library of Medicine)
Research Data Management Practices
article

Data flow from instrument to publication; an X-ray experimentalist view on data generation, processing and analysis

article en

Abstract

This poster motivates and depicts the work path towards a FAIR data collection and processing at large scale X-ray infrastructure, especially for the X-ray reflectivity use case. The envisioned procedure is, the experimental data and meta data is collected by means of human input and automatic aggregation during the experiment. The (meta) data is stored in an open and standardized format and the meta data is made publicly available in a meta data catalogue. The sample related information is conserved in a sample registry and uniquely indentifiable by a PID, e.g. IGSN. During the experiment an automatic, machine learning based analysis is indentifying experimental and data quality related issues and reports these back to the human experimenters.

PUBLISSO (German National Library of Medicine)
Industry, innovation and infrastructure
Openalex Percentile: Top 47%
Research Data Management Practices
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