Hierarchical Data Format prevalent in high performance scientific computing, sits directly on top of sequential or parallel file systems, providing block and stream operations on standardized or custom binary/text objects. Scientific computing platforms such as Python, R, Matlab, Fortran, Julia [and many more...] come with the necessary libraries to read write HDF5 dataset. This edition simplifies interactions with popular linear algebra libraries, provides compiler assisted seamless object persistence, Standard Template Library support and equipped with novel error handling architecture.

Accepted data types in BNF:
 T := ([unsigned] ( int8_t | int16_t | int32_t | int64_t )) | ( float | double )
   S := T | c/c++ struct | std::string
   ref := std::vector<S>
        | arma::Row<T> | arma::Col<T> | arma::Mat<T> | arma::Cube<T>
        | Eigen::Matrix<T,Dynamic,Dynamic> | Eigen::Matrix<T,Dynamic,1> | Eigen::Matrix<T,1,Dynamic>
        | Eigen::Array<T,Dynamic,Dynamic> | Eigen::Array<T,Dynamic,1> | Eigen::Array<T,1,Dynamic>
        | blaze::DynamicVector<T,rowVector> | blaze::DynamicVector<T,colVector>
        | blaze::DynamicVector<T,blaze::rowVector> | blaze::DynamicVector<T,blaze::colVector>
        | blaze::DynamicMatrix<T,blaze::rowMajor> | blaze::DynamicMatrix<T,blaze::colMajor>
        | itpp::Mat<T> | itpp::Vec<T>
        | blitz::Array<T,1> | blitz::Array<T,2> | blitz::Array<T,3>
        | dlib::Matrix<T> | dlib::Vector<T,1>
        | ublas::matrix<T> | ublas::vector<T>
     ptr := T*
     accept := ref | ptr