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Learning from Google: About A Computational EXFOR Database for Efficient Data Retrieval and Analysis
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.ORCID iD: 0000-0001-5007-2975
2019 (English)Conference paper, Oral presentation with published abstract (Other academic)
Abstract [en]

High-level languages, such as Python and R, find broad adoption for data science and machine learning due to their expressive power and the many community-contributed packages to apply sophisticated algorithms in just a few lines of code. Despite the fast progress in these fields in recent years, the field of nuclear data evaluation remained relatively unaffected by these developments. An essential reason for this observation may be the fact that the original EXFOR format is cumbersome to deal with in highlevel languages. In this contribution, I present details about the successful conversion of the complete original EXFOR database to a NoSQL database as, e.g., employed by Google, discuss the advantages of this database architecture for nuclear data evaluation, and provide examples demonstrating the ease and flexibility of data retrieval. Finally, I show some possibilities of quick data visualization and manipulation, such as the inversion of huge experimental covariance matrices (e.g., 105×105 including correlations between data sets), underpinning the benefits of performing nuclear data evaluation in a high-level language. Conversion codes and program packages will be made available for everyone. The availability of these codes will also enable outsiders of the nuclear data field, e.g., mathematicians, statisticians, and data scientists, to test their ideas and contribute to the field.

Place, publisher, year, edition, pages
2019.
National Category
Subatomic Physics
Identifiers
URN: urn:nbn:se:uu:diva-396399OAI: oai:DiVA.org:uu-396399DiVA, id: diva2:1367691
Conference
International Conference on Nuclear Data for Science and Technology, May 19-24, 2019, Beijing, China
Available from: 2019-11-04 Created: 2019-11-04 Last updated: 2019-11-11Bibliographically approved

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