CNS - My Summary

Information management and the wealth of nations

In today’s information age, knowledge and innovation are main economic drivers. Those able to access and utilize high quality data, knowledge, tools, and expertise win. Those that duplicate efforts, re-invent the wheel, miss synergistic collaborations, or invest in obsolete areas lose.

Staying ahead of the game is tough: Today’s number of researchers exceeds the number of researchers ever alive. Some areas of science produce more than 40,000 papers a month. Datasets, tools, and expertise levels are continuously changing and increasing in number and complexity. We are expected to know more works than one can possibly read in a lifetime. We receive more emails per day than can be processed in 24 hours. All this while being reachable twenty four hours, seven days a week.

Researchers, practitioners, educators, and the general public at large are confronted with vast amounts of information that is streaming towards them in real time. No single man or machine can process and make sense of this enormous amount of data, information, knowledge, and expertise.

We need to better understand the process of knowledge and innovation based wealth creation in order to streamline and to accelerate it. We need tools that help us effectively manage our collective scholarly knowledge and to convert it into economic, social, and environmental wealth.

Designing Macroscopes

Access, management, and utilization of knowledge impacts knowledge creation and innovation. Widely used, proprietary search algorithms have a major impact on the growth of our collective knowledge. They are treated as a “black box” that either retrieves a result or not. The latter case is often equated with “does not exist”.

Search engines work well for fact-finding. They make it hard to identify patterns, trends, outliers, or the context in which a piece of knowledge was created or can be used. Without context, intelligent data selection, prioritization, and quality judgments become extremely difficult. We need an “up” button.

Just like world maps show us our world and our place in it, science maps show us what we know, who funds it, who uses it, and what the major players (experts, institutions, countries) are.

Just like we use microscopes and telescopes to see things that are too small or too far away, we are building “macroscopes” that help identify patterns, trends, and outliers in very large scale datasets and ultimately data streams.