We try to analyse bibliographical data using big data technology (flink, elasticsearch, metafacture).

- more info will follow…

- see also:

1. https://github.com/dataramblers
2. https://github.com/dataramblers/hackathon17/wiki

here a first sketch of what we're aiming at:

We use biographical metadata:

Swissbib bibliographical data https://www.swissbib.ch/

• Catalog of all the Swiss University Libraries, the Swiss Nationallibrary, etc.
• 960 Libraries / 23 repositories (Bibliotheksverbunde)
• ca. 30 Mio records
• MARC21 XML Format
• → raw data stored in Mongo DB
• → transformed and clustered data stored in CBS (central library system)

edoc http://edoc.unibas.ch/

• Institutional Repository der Universität Basel (Dokumentenserver, Open Access Publications)
• ca. 50'000 records
• JSON File

crossref https://www.crossref.org/

• Digital Object Identifier (DOI) Registration Agency
• ca. 90 Mio records (we only use 30 Mio)
• JSON scraped from API

• Data Ramblers https://github.com/dataramblers
• Dominique Blaser
• Jean-Baptiste Genicot
• Günter Hipler
• Jacqueline Martinelli
• Rémy Meja
• Andrea Notroff
• Sebastian Schüpbach
• T
• Silvia Witzig