  Since the MEDLINE database was released, the number of documents indexed by
this entity has risen every year. Several tools have been developed by the
National Institutes of Health (NIH) to query this corpus of scientific
publications. However, in terms of advances in big data, text-mining and data
science, an option to build a local relational database containing all metadata
available on MEDLINE would be truly useful to optimally exploit these
resources. MEDOC (MEdline DOwnloading Contrivance) is a Python program designed
to download data on an FTP and to load all extracted information into a local
MySQL database. It took MEDOC 4 days and 17 hours to load the 26 million
documents available on this server onto a standard computer. This indexed
relational database allows the user to build complex and rapid queries. All
fields can thus be searched for desired information, a task that is difficult
to accomplish through the PubMed graphical interface. MEDOC is free and
publicly available at https://github.com/MrMimic/MEDOC.
