Scirex – Creating a Next Generation Research Data Management Platform
Generic office software such as Excel, PowerPoint, Outlook and Project are still heavily
used for research data management and project communication – thus, often depriving life
science research of the full benefits of informatics, such as saving time doing data
processing, optimization of laboratory efficiency, increased data quality, improved
analysis of aggregated data, and timely notifications of project results.
This state of affairs is largely caused by the complexity of the current generation of
research data management software, which takes too long to implement and cannot easily
and quickly be adapted to changing research workflows; scientists are therefore forced
to rely on isolated Excel files, offering great flexibility but very poor data management.
Sciformatics has recently started development of Scirex, a Next Generation research data
management platform for building highly customized systems. Scirex is built as a collection
of building blocks that can be composed into workflow-specific solutions in an agile process
by scientists or local informatics staff; by enabling fast changes and short feedback cycles,
the solutions can quickly be adapted to changing workflows, and the project risk is
dramatically reduced through close communication with the end users.
Scirex is focused on two areas of research informatics:
Research execution support: Experiment planning, communication between scientists,
sample tracking as well as data capture and processing.
Assay data management: Rescuing results from Excel by automatically importing them into
databases, enabling aggregated data analysis. The key challenge here is to combine the
flexibility of Excel and Prism with the power of database systems.
The main drivers for the design of Scirex are:
Agility: Small, fast deliveries enables informatics systems to match research process
requirements and reduces project risk by facilitating immediate feedback from users.
Composability: Scirex functionality is implemented as building blocks that can be combined
in different ways to match specific workflow requirements.
Convenience: Convenient systems save end user time, promote system adoption and reduce
cognitive stress, freeing scientist to focus on research rather than on operating obscure
Customizability: Customizing IT systems to specific workflows improves convenience, and
increases efficiency and quality gains through automation of data processing, integration
Discoverability: System adoption is improved if users can discover functionality while
using the system rather than rely on distant and long forgotten training.
Flexibility: Research workflows often change from one experiment to the next, e.g. to
rerun failed steps or further investigate interesting results. The informatics systems
must be able to accomodate the variation without reprogramming.
Future posts will go into concreate details about each of these priority guidelines.