distributed computing solution, HDF5 offers an open format for the post DOS standalone world. Passive seismic and lithospheric scale electrical surveys mirror astronomy’s need for very large, portable storage. Ideally multiple surveys of multiple types should be combined into a monolithic standalone file without proprietary restraints or crashing because the file is too big. The ASEG Technical Standards Committee has met with petroleum consultants who report that converting a selection of SEG-Y data sets into a HDF5 store has meant that access to their data is faster, and the datasets are more stable and easier to move around their network. HDF5 and its variants offer a database that acts as single file with binary file speed but with text-like headers. Include the relevant package or header file and it is almost as simple to interrogate the resultant database as it is to interrogate a text file. The example below, from the website of the National Ecological Observatory Network (NEON) [2], shows how the opened object echoes the properties of a dictionary object with keys, values and items. The example enumerates keys to display the column names. Data trends Open data formats for the post DOS world The universally readable ASCII formats of ASEG GDF2, ESF or simple text files are still the preferred, official data exchange format for geophysics surveys in Australia. But, after 40 years, the Hierarchical Data Format (HDF) originally designed for supercomputers is growing in recognition as an efficient usage format [1]. Whether you collect large surveys, need simplified storage for many surveys, or a more efficient network and f = h5py.File(“NEON-DS-Imaging-Spectrometer-Data.h5”,“r”) datasetNames = [n for n in f.keys()] for n in datasetNames:   print(n) HDF5 and its variants may be of value to archivists and companies trying to analyse enormous data stores where tracking the files and not crashing the network is half the battle. Similarly, the format may assist with web distribution of data for use in apps, machine learning, and distributed number crunching in the cloud. The promise of running tenements from a mobile phone using data stored in the cloud rather than on workstations is not restricted by the supply of data, but how the data is being supplied. For more on these file formats, Alex Ip from Geoscience Australia (GA), with special guest Carina Kemp from AARNet, is running a workshop on how GA uses the NetCDF variant of HDF at the upcoming AEGC [3]. Notes [1] https://www.hdfgroup.org/ [2] https://www.neonscience.org/hdf5- intro-python [3] https://2019.aegc.com.au/ workshops/  Data trends 41 PREVIEW JUNE 2019 Tim Keeping Associate Editor for geophysical data management and analysis technical-standards@aseg.org.au