  Data analysis in fundamental sciences nowadays is an essential process that
pushes frontiers of our knowledge and leads to new discoveries. At the same
time we can see that complexity of those analyses increases fast due to
a)~enormous volumes of datasets being analyzed, b)~variety of techniques and
algorithms one have to check inside a single analysis, c)~distributed nature of
research teams that requires special communication media for knowledge and
information exchange between individual researchers. There is a lot of
resemblance between techniques and problems arising in the areas of industrial
information retrieval and particle physics. To address those problems we
propose Reproducible Experiment Platform (REP), a software infrastructure to
support collaborative ecosystem for computational science. It is a Python based
solution for research teams that allows running computational experiments on
shared datasets, obtaining repeatable results, and consistent comparisons of
the obtained results. We present some key features of REP based on case studies
which include trigger optimization and physics analysis studies at the LHCb
experiment.
