Some communities within the sciences have long since organised their data management. They are the data intensive sciences which produce high volumes of data and which receive large science grants: Big Science. It is small science (as seen at 3TU) which is still getting started. 

Good practices within Big Science are, for example: 

• CERN the European Organisation for Nuclear Research which was in the news in September 2011 because a neutrino was found to have travelled faster than the speed of light¹. 
• KNMI (the Royal Netherlands Metereological Institute) 
• The Planetary Data System (NASA) contains peer reviewed datasets 
• Molecular biologists use the Protein Data Bank and GenBank 
• Earth and environmental scientists have PANGAEA: a network for research data relating to geosciences and the environment 
• Dryad is an international data repository for data on which peer-reviewed articles in the biosciences are based 

There are also some journals that publish datasets, such as: 

• Gigascience, launched in 2011 (an online open access, open data journal, publishing 'big data' studies from the entire spectrum of life and biomedical sciences) 

Social sciences 

Many social scientists use big externally collected and managed datasets. Access to those data is (at first) more important to them than internal data management or the permanent storage of their own datasets. Existing datasets are their starting point. See the module Acquisition and Advise. 

^{1. NOS. (2011, september 23rd). CERN: neutrino's sneller dan licht [video]. Retrieved 9-12-2011 from nos.nl/video/275189-cern-neutrinos-sneller-dan-het-licht.html (Dutch)}