@article{
 title = {Image Archives Made Easy with Docker and ImageX},
 type = {article},
 year = {2020},
 pages = {267},
 volume = {522},
 id = {4e91b579-add6-3eab-920b-fffad3932654},
 created = {2020-07-08T15:03:43.166Z},
 file_attached = {false},
 profile_id = {42d295c0-0737-38d6-8b43-508cab6ea85d},
 group_id = {27e0553c-8ec0-31bd-b42c-825b8a5a9ae8},
 last_modified = {2020-07-08T15:03:43.166Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {We have developed a suite of integrated services called ImageX, which streamlines and automates the process of identifying and cataloguing large image data-sets (e.g. astronomical FITS images), harvesting metadata, converting the images to tile pyramids, and serving the images to clients. We have also developed a front-end server with an integrated open-source JavaScript-based viewer to display the images. This viewer, built on top of the widely used OpenSeaDragon framework, allows us to display even the largest of astronomical images to clients on standard internet connections. The viewer also supports displaying and interacting with whole sets of data simultaneously, allowing observers to visually inspect an entire night's worth of observing, for example. In addition we can present multiple images with spatial positioning, to visualize target coverage. By pulling and running our publicly available Docker containers, we will show how any observatory, data archive, or astronomer can start sharing their images by following a few simple steps. We describe here how many of the features of ImageX including intelligent image scaling with adaptive histograms, user level authorization, access authentication via JSON web tokens, load-balanced nginx image servers, and support for mobile clients. ImageX is fully open-sourced, and is built upon widely-supported industry standard frameworks, and is currently in use on the ODI-PPA portal serving astronomical images taken at the WIYN Observatory in near real-time. Finally, we show how our implementation for astronomy can be seamlessly adapted to serve any scientific imaging dataset that can be converted into a standard format like JPG or PNG.},
 bibtype = {article},
 author = {Young, Michael D. and Gopu, Arvind and Perigo, Raymond and Hayashi, Soichi},
 journal = {ASPC}
}

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