https://doi.org/10.1140/epja/s10050-025-01760-w
Code Paper
The nucleardatapy toolkit for simple access to experimental nuclear data, astrophysical observations, and theoretical predictions
1
International Research Laboratory on Nuclear Physics and Astrophysics, Michigan State University and CNRS, 48824, East Lansing, MI, USA
2
Institute of Nuclear and Particle Physics (INPP), Ohio University, 45701, Athens, OH, USA
3
Facility for Rare Isotope Beams, Michigan State University, 48824, East Lansing, MI, USA
4
Departamento de Física e Laboratório de Computação Científica Avançada e Modelamento (Lab-CCAM), Instituto Tecnológico de Aeronáutica, DCTA, 12228-900, São José dos Campos, SP, Brazil
5
Theoretical Division, Los Alamos National Laboratory, 87545, Los Alamos, NM, USA
6
Department of Physics, University of Guelph, N1G 2W1, Guelph, ON, Canada
7
Institut für Physik und Astronomie, Universität Potsdam, Haus 28, Karl-Liebknecht-Str. 24/25, 14476, Potsdam, Germany
8
IRAP, CNRS, 9 avenue du Colonel Roche, BP 44346, 31028, Toulouse Cedex 4, France
9
Université de Toulouse, CNES, UPS-OMP, 31028, Toulouse, France
10
Istituto Nazionale di Fisica Nucleare, Sezione di Catania, Dipartimento di Fisica e Astronomia “Ettore Majorana”, Università di Catania, Via Santa Sofia 64, 95123, Catania, Italy
a
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Received:
30
June
2025
Accepted:
11
October
2025
Published online:
2
February
2026
Systematic comparisons across theoretical predictions for the properties of dense matter, nuclear physics data, and astrophysical observations (also called meta-analyses) are performed. Existing predictions for symmetric nuclear and neutron matter properties are considered, and they are shown in this paper as an illustration of the present knowledge. Asymmetric matter is constructed assuming the isospin asymmetry quadratic approximation. It is employed to predict the pressure at twice saturation energy-density based only on nuclear-physics constraints, and we find it compatible with the one from the gravitational-wave community. To make our meta-analysis transparent, updated in the future, and to publicly share our results, the Python toolkit nucleardatapy is described and released here. Hence, this paper accompanies nucleardatapy, which simplifies access to nuclear-physics data, including theoretical calculations, experimental measurements, and astrophysical observations. This Python toolkit is designed to easily provide data for: (i) predictions for uniform matter (from microscopic or phenomenological approaches); (ii) correlation among nuclear properties induced by experimental and theoretical constraints; (iii) measurements for finite nuclei (nuclear chart, charge radii, neutron skins or nuclear incompressibilities, etc.) and hypernuclei (single particle energies); and (iv) astrophysical observations. This toolkit provides data in a unified format for easy comparison and provides new meta-analysis tools. It will be continuously developed, and we expect contributions from the community in our endeavor.
© The Author(s) 2026
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