Hierarchical Data Format
Icon and logo for The HDF Group
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| Filename extension |
.hdf, .h4, .hdf4, .he2, .h5, .hdf5, .he5
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|---|---|
| Magic number | \211HDF\r\n\032\n |
| Developed by | The HDF Group |
| Latest release |
5-1.10.0
(March 31, 2016) |
| Type of format | scientific data format |
| Open format? | Yes |
| Website | www |
Hierarchical Data Format (HDF) is a set of file formats (HDF4, HDF5) designed to store and organize large amounts of data. Originally developed at the National Center for Supercomputing Applications, it is supported by The HDF Group, a non-profit corporation whose mission is to ensure continued development of HDF5 technologies and the continued accessibility of data stored in HDF.
In keeping with this goal, the HDF libraries and associated tools are available under a liberal, BSD-like license for general use. HDF is supported by many commercial and non-commercial software platforms, including Java, MATLAB, Scilab, Octave, Mathematica, IDL, Python, R, and Julia. The freely available HDF distribution consists of the library, command-line utilities, test suite source, Java interface, and the Java-based HDF Viewer (HDFView).
The current version, HDF5, differs significantly in design and API from the major legacy version HDF4.
The quest for a portable scientific data format, originally dubbed AEHOO (All Encompassing Hierarchical Object Oriented format) began in 1987 by the Graphics Foundations Task Force (GFTF) at the National Center for Supercomputing Applications (NCSA). NSF grants received in 1990 and 1992 were important to the project. Around this time NASA investigated 15 different file formats for use in the Earth Observing System (EOS) project. After a two-year review process, HDF was selected as the standard data and information system.
HDF4 is the older version of the format, although still actively supported by The HDF Group. It supports a proliferation of different data models, including multidimensional arrays, raster images, and tables. Each defines a specific aggregate data type and provides an API for reading, writing, and organizing the data and metadata. New data models can be added by the HDF developers or users.
HDF is self-describing, allowing an application to interpret the structure and contents of a file with no outside information. One HDF file can hold a mix of related objects which can be accessed as a group or as individual objects. Users can create their own grouping structures called "vgroups."
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