Scientific Computing: Analytical Knowledge Transfer presents a Challenging Landscape in an Externalized World

Article Series on the Topic of Externalization of Scientific Research and Development

Published online at Scientific Computing.

One of the hottest topics in laboratory informatics discussions today encompasses externalization of scientific research and development. Organizations spanning many industries have increasingly outsourced a variety of R&D activities. Trying to extend existing informatics systems into an externalized world that they were never designed to address presents myriad more predicaments. Stringent regulations in development and the pressures of early discovery have, in turn, brought focus to the lack of systems in place to adequately handle data generated and shared by partner organizations.

Following is a series of articles around this topic published in Scientific Computing.

The Business Challenges of Externalizing R&D

Written by Brian Fahie and Evan Guggenheim, Biogen

A robust and well-articulated sourcing strategy coupled with a seamless way to transfer LIVE data between CROs and innovators allows innovators to define CROs in the context of “partners.” This paradigm enables conversations to focus more around what work should be performed internally and what work should be performed at the CRO. This paradigm can enable both the innovator and CRO to efficiently utilize resources to meet their long-term strategic goals.

Analytical Knowledge Transfer presents a Challenging Landscape in an Externalized World

Written by Sanji Bhal, ACD/Labs

The changing landscape requires organizations to tackle emerging issues from a technology perspective head-on. An ACD/Labs perspective is to pose the fundamental question: Is any of your data live or dead? Some live data and associated knowledge are surely essential. However, in some cases, not all data needs to be live. Not all data will be re-purposed in a useful way, data mined, or re-examined — meaning that organizations must be wary of creating a data dumpster.
  • Can your organization automatically convert any vital analytical data to knowledge, managing and storing it so that it can be effectively accessed by scientists and other corporate decision makers?
  • Which data do you need to have access to at a moment's notice to drive key scientific and business decisions that help keep your organization educated, sustainable and innovative into the next several decades?

The Future as a Service

Written by Michael H. Elliott

IT is often a secondary consideration after partnerships are formed. That is why, even today, the majority of data are transferred in document format, most often by e-mail. In the October 2012 edition of Scientific Computing I called this the “De-Evolution of Informatics.” In other words, very sophisticated internal systems are being supplanted with PDFs and Excel files, creating ever-increasing stores of “dark data.” Data are trapped inside these limited formats and are often so poorly organized (usually in project-specific SharePoint sites), that they are lost for long term preservation and knowledge reuse.

Open Innovation and IT Infrastructure Considerations for Information Assembly in Analytical Sciences

Written by Andrew Anderson and Graham A. McGibbon, ACD/Labs

Taking into consideration the points and observations raised in the preceding list of articles, this article will provide some additional recommendations for industrial R&D stakeholders to consider, with particular emphasis on measurement or analytical sciences.