The Data Science Journal is, as the title suggests, a journal that is
ddedicated to the advancement of data science. The first thing that’s good about it is that you won’t get random emails about it with poor grammar, wild claims about its impact factor and begin with DEAR ESTEEMED RESEARCHER….
Even though it’s about data science, it’s not a journal that is obsessed with
building ever better recommender algorithms for Netflix or mining twitter feeds. Its focus is very much on its application in the policies, practices and management of Open Data.
It tries to take as wide a definition as possible when considering Open Data; data can be originally digital (would “source digitale” make any sense at all in French? don’t laugh…) or converted from other sources.
It also considers every research discipline. The journal will have digital humanities papers rubbing shoulders with bioinformatics papers with social science papers.
It’s also a journal that is interested in applications, so papers that are descriptions of data systems are great. Naturally, it’s a grown up journal and is entirely electronic and Open Access.
The journal has been in existance since 2002 but has recently been relaunched by CODATA and moved to the Ubiquity Press platform with the excellent Sarah Callaghan (@as editor (full disclosure :- I am on the board for this). There is a call for papers for the journal which is discussed in detail here.
If you are interested have a look at its web site http://datascience.codata.org/ to find out more about the types of articles they are interested in receiving.