Task #2035

A common trajectory analysis data exchange format

Added by Christian Blau over 4 years ago. Updated almost 4 years ago.

analysis tools
Target version:


Trajectory analysis tools to date lack a data exchange format for structured data.

A common sharing format for structured analysis result data shall simplify

  • splitting complex trajectory analysis tools into tools that perform minimal tasks. Complex output of a single tool will be parsable by the next tool.
  • import of data into external data analysis frameworks, e.g., python and matlab.
  • move away from the misuse of the trr format for eigenvalue/eigenvector calculations

Current data formats are

  • generic input data without specification .dat
  • generic output data without specification .dat
  • plain or annotated ascii time trace data format .xvg
  • ascii index files .ndx
  • binary and ascii matrix data formats .xpm and .mtx
  • matrix value to RGB-data format .map

Suggested alternatives are

  • leave everything as is
    • pro: little effort, complex data handling requirements are best represented by a diversity of file formats
    • contra: maintenance of many file formats, some of which might easily fall into obscurity; reduced transferability
  • JSON
    • pro: efforts already under way for implementing; very flexible; widely supported
    • contra: format specification might be too loose, a JSON file might contain anything, uncompressed JSON might be large
  • JSON with Base64 encoding for binary data
    • pro: mostly maintains human readability and only introduces compression where necessary
    • contra: not the most efficient (33% overhead for binary data) data storage and parsing method for binary data; not as widely supported
  • JSON with BSON for larger data files
    • pro: mostly maintains human readability and only introduces compression where deemed necessary
      supported by a number of tools; native format for MongoDB
    • contra: removes human readability in BSON files;
  • extended TNG format
    • pro: very effective, already implemented, tailored to huge time trace data
    • contra: not widely supported, analysis data might be more convoluted
  • HDF5 format
    • pro: very efficient data storage for complex data
      evolved to be a standard format
      native support from matlab (.mat is hdf5)
    • contra: very complex file specification, mostly accesible through library
  • SIRF (Self-contained Information Retention Format)
    • pro: designed to be read with any abstract future entity
    • contra: designed for archiving data, with future technology in mind
  • ASDF (Advanced Scientific Data Format)
    • pro: human readable and/or binary format based on YAML with hiearchical data representation
    • contra: very new, though well received
      using YAML-style for output, after finally deciding for JSON for input
  • XDR (eXternal Data Representation)
    • pro: already used within gromacs
    • contra: very non-human readable

for more formats also see


#1 Updated by Christian Blau over 4 years ago

  • Description updated (diff)

#2 Updated by Mark Abraham almost 4 years ago

  • Target version changed from 2018 to future

Not going into 2017 release

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