Register Abstract: You might be familiar with gzip / bzip2 / zip tools that can compress all types of files without losing data. With typical 3D research datasets, these tools will reduce your file sizes by ~30-50% -- in some cases more, depending on the nature of your data. Popular scientific data formats such as NetCDF and HDF5 also support built-in lossless compression most commonly implemented via zlib or szip libraries.On the other hand, we have all used lossy compression for audio, video and images. Lossy compression can be applied to multidimensional scientific datasets as well, with far better compression ratio than with lossless compression, as you really are disposing of some of the less important bits. In general, with 3D scalar fields you can expect a compression ratio of approximately 20:1 or even 30:1, without any visible degradation. This is especially fantastic for archiving the results of multidimensional simulations, as you can store your data in much less space than its original footprint.In this webinar we will cover at least two different approaches to lossy 3D data compression. We will focus on file (rather than in-memory) compression, with long-term data storage in mind.