  The reproduction and replication of novel results has become a major issue
for a number of scientific disciplines. In computer science and related
computational disciplines such as systems biology, the issues closely revolve
around the ability to implement novel algorithms and approaches. Taking an
approach from the literature and applying it to a new codebase frequently
requires local knowledge missing from the published manuscripts and project
websites. Alongside this issue, benchmarking, and the development of fair ---
and widely available --- benchmark sets present another barrier.
  In this paper, we outline several suggestions to address these issues, driven
by specific examples from a range of scientific domains. Finally, based on
these suggestions, we propose a new open platform for scientific software
development which effectively isolates specific dependencies from the
individual researcher and their workstation and allows faster, more powerful
sharing of the results of scientific software engineering.
