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The Flexible Image Transport System (FITS) has been the de facto standard for storing and exchanging astronomical data for decades, but it is beginning to show its age. Developed in the late 1970s, the FITS authors made a number of implementation choices that, while common at the time, are now seen to limit its utility for the needs of modern science. As astronomy moves into a more varied set of data product types (data models) with richer and more complex metadata, FITS is being pushed to its breaking point. The issues with FITS are outlined in great detail in [Thomas2015].

Newer formats, such as VOTable have partially addressed the problem of richer, more structured metadata, by using tree structures rather than flat key/value pairs. However, those text-based formats are unsuitable for storing large amounts of binary data. On the other end of the spectrum, formats such as HDF5 and BLZ address problems with large data sets and distributed computing, but don’t really address the metadata needs of an interchange format. ASDF aims to exist in the same middle ground that made FITS so successful, by being a hybrid text and binary format: containing human editable metadata for interchange, and raw binary data that is fast to load and use. Unlike FITS, the metadata is highly structured and is designed up-front for extensibility.

ASDF has the following explicit goals:

  • It has a hierarchical metadata structure, made up of basic dynamic data types such as strings, numbers, lists and mappings.
  • It has human-readable metadata that can be edited directly in place in the file.
  • The structure of the data can be automatically validated using schema.
  • It’s designed for extensibility: new conventions may be used without breaking backward compatibility with tools that do not understand those conventions. Versioning systems are used to prevent conflicting with alternative conventions.
  • The binary array data (when compression is not used) is a raw memory dump, and techniques such as memory mapping can be used to efficiently access it.
  • It is possible to read and write the file in as a stream, without requiring random access.
  • It’s built on top of industry standards, such as YAML and JSON Schema to take advantage of a larger community working on the core problems of data representation. This also makes it easier to support ASDF in new programming languages and environments by building on top of existing libraries.
  • Since every ASDF file has the version of the specification to which it is written, it will be possible, through careful planning, to evolve the ASDF format over time, allowing for files that use new features while retaining backward compatibility with older tools.

ASDF is primarily intended as an interchange format for delivering products from instruments to scientists or between scientists. While it is reasonably efficient to work with and transfer, it may not be optimal for direct use on large data sets in distributed and high performance computing environments. That is explicitly not a goal of the ASDF standard, as those requirements can sometimes be at odds with the needs of an interchange format. ASDF still has a place in those environments as a delivery mechanism, even if it ultimately is not the actual format on which the computing is performed.


The ASDF standard is being developed concurrently with a reference implementation written in Python.

There is also a work-in-progress implementation for Go by Sebastian Binet.

Incorporated standards

The ASDF format is built on top of a number of existing standards:

[Thomas2015]Thomas, B., Jenness. T. et al. 2015, “The Future of Astronomical Data Formats I. Learning from FITS”. Astronomy & Computing, in press, arXiv e-print: 1502.00996.