A computational EXFOR database

The EXFOR library is a useful resource for many people in the field of nuclear physics. In particular, the experimental data in the EXFOR library serves as a starting point for nuclear data evaluations. There is an ongoing discussion about how to make evaluations more transparent and reproducible. One important ingredient may be convenient programmatic access to the data in the EXFOR library from high-level languages. To this end, the complete EXFOR library can be converted to a MongoDB database. This database can be conveniently searched and accessed from a wide variety of programming languages, such as C++, Python, Java, Matlab, and R. This contribution provides some details about the successful conversion of the EXFOR library to a MongoDB database and shows simple usage examples to underline its merits. All codes required for the conversion have been made available online and are open-source. In addition, a Dockerfile has been created to facilitate the installation process

Learning from Google : About A Computational EXFOR Database for Efficient Data Retrieval and Analysis

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

Learning from Google : About A Computational EXFOR Database for Efficient Data Retrieval and Analysis

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