Core Scientific Dataset Model: A lightweight and portable model and file format for multi-dimensional scientific data

The Core Scientific Dataset (CSD) model with JavaScript Object Notation (JSON) serialization is presented as a lightweight, portable, and versatile standard for intra- and interdisciplinary scientific data exchange. This model supports datasets with a p-component dependent variable, {U0, …, Uq, …, Up−1}, discretely sampled at M unique points in a d-dimensional independent variable (X0, …, Xk, …, Xd−1) space. Moreover, this sampling is over an orthogonal grid, regular or rectilinear, where the principal coordinate axes of the grid are the independent variables. It can also hold correlated datasets assuming the different physical quantities (dependent variables) are sampled on the same orthogonal grid of independent variables. The model encapsulates the dependent variables’ sampled data values and the minimum metadata needed to accurately represent this data in an appropriate coordinate system of independent variables. The CSD model can serve as a re-usable building block in the development of more sophisticated portable scientific dataset file standards.

[1]  Prasanth H. Nair,et al.  Astropy: A community Python package for astronomy , 2013, 1307.6212.

[2]  John D. Hunter,et al.  Matplotlib: A 2D Graphics Environment , 2007, Computing in Science & Engineering.

[3]  A. Cardona,et al.  An Integrated Micro- and Macroarchitectural Analysis of the Drosophila Brain by Computer-Assisted Serial Section Electron Microscopy , 2010, PLoS biology.

[4]  Kangde Yao,et al.  A Brief Guide to the Standard Object Modelling Language , 2000 .

[5]  Gaël Varoquaux,et al.  The NumPy Array: A Structure for Efficient Numerical Computation , 2011, Computing in Science & Engineering.

[6]  Kristin Decker,et al.  Uml Distilled A Brief Guide To The Standard Object Modeling Language , 2016 .

[7]  T. Vosegaard,et al.  Fast Forward Maximum entropy reconstruction of sparsely sampled data. , 2012, Journal of magnetic resonance.

[8]  P. Florian,et al.  Correlating geminal 2JSi-O-Si couplings to structure in framework silicates. , 2017, Physical chemistry chemical physics : PCCP.

[9]  M Bak,et al.  SIMPSON: a general simulation program for solid-state NMR spectroscopy. , 2000, Journal of magnetic resonance.

[10]  Thomas Vosegaard,et al.  Computer-intensive simulation of solid-state NMR experiments using SIMPSON. , 2014, Journal of magnetic resonance.

[11]  G. Hoatson,et al.  Modelling one‐ and two‐dimensional solid‐state NMR spectra , 2002 .

[12]  Barry N. Taylor,et al.  Guide for the Use of the International System of Units (SI) , 1995 .

[13]  Thomas Vosegaard,et al.  jsNMR: an embedded platform‐independent NMR spectrum viewer , 2015, Magnetic resonance in chemistry : MRC.

[14]  N. White,et al.  Sea-Level Rise from the Late 19th to the Early 21st Century , 2011 .

[15]  John A. Church,et al.  Sea-Level Rise from the Late 19 th to the Early 21 st Century , 2011 .