EN100-web

Special theme: Scientific Data Sharing and Re-use
Beyond Data: Process Sharing and Reuse
by Tomasz Miksa and Andreas Rauber
Sharing and reuse of data is just an intermediate step on the way to reproducible computational
science. The next step, sharing and reuse of processes that transform data, is enabled by process
management plans, which benefit multiple stakeholders at all stages of research.
Nowadays almost every research
domain depends on data that is accessed
and processed using computers. Data
processing may range from simple calculations
made in a spreadsheet editor,
to distributed processes that transform
data using dedicated software and hardware
tools. The crucial focus is on the
data, because it underlies new scientific
breakthroughs and in many cases is irreproducible,
e.g. climate data. Funding
institutions have recognized the value of
data, and as a result data management
plans (DMPs) have become obligatory
for many scientists who receive public
funding. Data management plans are initiated
before the start of a project, and
evolve during its course. They aim not
only to ensure that the data is managed
properly during the project, e.g. by performing
backups, using file naming convention,
etc., but also that it is preserved
and available in the future.
In order to understand data, as well as
research results, data acquisition and
manipulation processes must also be
curated. Unfortunately, the underlying
processes are not included in DMPs. As
a consequence, information needed to
document, verify, preserve or re-execute
the experiment is lost. For this reason,
we extend DMPs to “process management
plans” (PMPs) [1] which complement
the description of scientific data
taking a process-centric view, viewing
data as the result of underlying
processes such as capture, (pre-) processing,
transformation, integration and
analyses. A PMP is a living document,
which is created at the time of process
design and is maintained and updated
during the lifetime of the experiment by
various stakeholders. Its structure as
outlined below necessarily appears relatively
abstract due to a wide range of
requirements and different practices in
scientific domains in which PMPs
should be used. The proposed structure
of a PMP is depicted in Figure 1.
Figure 1: Structure of a Process Management Plan.
The implementation of PMPs is to some
extent domain dependent, because it has
to incorporate already existing best practices.
During the course of the EUfunded
FP7 project TIMBUS, our team
at SBA Research in Vienna investigated
well-structured Taverna workflows, but
also unstructured processes from the
civil-engineering domain. In all cases,
the PMPs can be implemented by integrating
already existing tools. For
example, the automatically generated
TIMBUS Context Model [2] can be
used to describe the software and hardware
dependencies. The process of verification
and validation can be performed
using the VFramework.
Research Objects [3] can be used to
aggregate the high level information on
Figure 2: Stakeholders impacted by Process Management Plan.
the process, and the existing data management
plan templates and tools can be
refined to incorporate information on
processes.
Figure 2 depicts stakeholders impacted
by PMPs. Project applicants will benefit
by being able to better identify and plan
the resources needed for the research.
For example, if the process of transforming
the experimental data assumes
use of proprietary software with an
expensive license, this information will
be revealed at an early stage and can be
taken into the account when applying
for a grant.
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ERCIM NEWS 100 January 2015