Special Theme. By Esther Pacitti and Patrick Valduriez Modern science disciplines such as environmental science and astronomy must deal with overwhelming amounts of experimental data.
Such data must be processed (cleaned, transformed, analyzed) in all kinds of ways in order to draw new conclusions and test scientific theories. Despite their differences, certain features are common to scientific data of all disciplines: massive scale; manipulated through large, distributed workflows; complexity with uncertainty in the data values, eg, to reflect data capture or observation; important metadata about experiments and their provenance; and mostly append-only (with rare updates).
Furthermore, modern scientific research is highly collaborative, involving scientists from different disciplines (eg biologists, soil scientists, and geologists working on an environmental project), in some cases from different organizations in different countries.
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