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DTSTAMP:20210916T132455Z
LOCATION:Michel Mayor
DTSTART;TZID=Europe/Stockholm:20210709T153000
DTEND;TZID=Europe/Stockholm:20210709T160000
UID:submissions.pasc-conference.org_PASC21_sess110_msa273@linklings.com
SUMMARY:AiiDAlab: An Ccosystem for Developing, Executing, and Sharing Scie
 ntific Workflows
DESCRIPTION:Minisymposium\n\nAiiDAlab: An Ccosystem for Developing, Execut
 ing, and Sharing Scientific Workflows\n\nAdorf\n\nThe AiiDAlab platform [1
 ] is a computational environment that is tailored for the execution of com
 plex and reproducible scientific workflows and enables researchers to util
 ize cloud resources in the form of a web application accessible through th
 e browser. The platform is built on top of Jupyter and specifically target
 s users with only limited expertise within the domain of computational sci
 ence. Users can customize the environment for their needs by installing Ai
 iDAlab apps from the built-in app store that were contributed by colleague
 s, collaborators, or the wider scientific community – including the 
 AiiDAlab team – or by creating entirely new workflows and web interf
 aces. This is facilitated by providing a simple path for editing existing 
 apps and through the availability of a rich library of basic building bloc
 ks. Newly created workflows can be easily redistributed via the aforementi
 oned app store. AiiDAlab significantly simplifies the setup of a functiona
 l and well-integrated computational environment, which can present a major
  barrier especially to researchers with only limited computational experie
 nce, such as junior researchers and experimentalists. But the platform is 
 also of interest to experienced computationalists who would like to use an
 d develop intuitive graphical interfaces for AiiDA workflows based on Jupy
 ter technology or who would like to take advantage of integration with oth
 er platforms and web applications. AiiDAlab apps are powered by AiiDA and 
 thus enable the execution of fully-automated workflows that keep track of 
 full provenance of all data operations in accordance with FAIR data and op
 en science principles.<br />[1] <em>DOI:10.1016/j.commatsci.2020.110165</e
 m><br /><em></em>\n\nDomain: Chemistry and Materials, Physics, Engineering
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