"In the past 10 years, publishing has changed dramatically, and this includes a change in the core concept of publishing itself. It’s no longer just about making information publicly available in a singular format; it’s about allowing the reader to customize or manipulate the format of the information to use it more effectively. In academic journal publishing this change affects not only articles, but the raw research data that exists as the foundation for each article.
In the physical, biological, and medical sciences there is a growing trend toward Open Science – open publication of scientific research and findings – a key component of which is Open Data. Open Data is built on the idea of not only publishing findings in an article, but also publishing all the raw research data generated during the research phase of the project in an online data repository that is freely accessible.
This means readers have full access to all the data that contributed to a theory or hypothesis outlined in a published article. This is powerful and enticing, but also daunting. On the one hand, researchers can use the available open data to find patterns or breakthroughs originally unseen, overlooked, or untested. On the other hand, the quantity of available raw data is enormous and continually expanding. This begs a few questions: How can readers and researchers make sense of all this data? Are tools available to help analyze it or curate it to reveal patterns or new discoveries? Are these tools efficient and multidisciplinary?
Software designer and developer Bret Victor broaches these questions beautifully in a short 35-minute presentation at Stanford ...Victor sees the need for a tool that lets scientists and researchers directly and dynamically visualize a wide array of data.
Applying this sensibility within the context of Open Data is intriguing and has a great deal of potential. If scientific publishers offered a tool to help researchers analyze great volumes of raw data in a more intuitive and visual way, they would be able to work across disciplines more easily. The tool Victor is proposing could also be deployed within a suite of research tools that integrate with article content and other raw data sets or repositories. Imagine reading an article and taking notes on a platform like ReadCube, then, in a few clicks, moving on to visually analyze the raw data of the article more closely. This could be the foundation for a more useful research and analysis environment. The business model for this sort of tool is yet to be seen, but the most important thing is the sensibility underlying the tool and the way it could give researchers the power to process data with relative ease. This is something we will definitely see more of in all scientific publications and publishing in the coming years."