Well, Science 2.0 must be the next full release after Science 1.5.b13, right? Not quite. It takes its lead from applying Web 2.0 approaches to scientific research. So, what is Web 2.0?
In 2005, Tim O’Reilly described in detail what he meant by Web 2.0. Since then, there has been a lot of discussion on just what this means, if anything. So, I am going to add my own two bits to the mix. There really are not many technical differences between Web 1.0 and 2.0. The differences come from how they are used, and how usable they are.
Web 1.0 is static. Web 2.0 is dynamic.
As mentioned in the Wikipedia article on Web 2.0, Web 1.0 was about displaying information. Web 2.0 is about conversations, about participation in the flow of information.
Web 2.0 uses many new approaches for dealing with information including wikis, weblogs, syndication, aggregators, RSS, podcasts, forums and mashups. These often require the active participation of users. They have been used to create hugely popular social media sites, such as Facebook and YouTube, where the very content seen by all is created totally by the users. User-generated content.
From a priesthood to DIY.
The first uses of the Web were for presenting information that others could read. Web 1.0 was brought to us by the mystical powers of webmasters, who had been schooled in the arcane use of HTML, the language of the Web. Web 1.0 basically consisted of monologs.
Now web monologs were better than index cards or scouring Index Medicus. They permitted information to be made accessible to much larger groups. But the next step, Web 2.0, involves more than information. It involves conversation. It is a dialog.
Web 2.0 allows anyone to become a webmaster, to place information on the Internet. Essentially, the technologies of the Internet simply matured to the point where people could manipulate information, without having to understand why things worked.
In a Web 1.0 world, this page you are reading may have taken hours to recode from text into HTML. Then a couple of other applications would have had to have been used to move the file to the proper folder on a web site. It would have sat there, unaltered, for you to find and read.
In the Web 2.0 world, I entered the text into text area. I had buttons that did the formatting for me. Software took care of the all the posting and I can add a comment box. So this page can constantly change, on its own, as we begin a conversation. You add content and others can respond.
There is a novel emergent property of this mature technology. It is the ability to maintain conversations of the Internet. For example, these technologies allow to a comment box to easily be added to a blog, so that people can discuss what has been posted. Videos can be uploaded and examined, with responses also being viewed.
Wikis now allow anyone to add content for others to examine and to discuss. Each of these approaches create ever-changing, dynamic web pages. So as these pages constantly change, conversations begin.
A new idea – conversations
Now, the ability to carry on conversations and to decentralize the control of information flow are unique aspects of Web 2.0 technologies. Most information systems of the last century were centrally broadcast, making conversations difficult.
Newspapers and TV brought us information but did not really permit conversations to occur. A letter to an editor is a poor excuse for a dialog.
Large amounts of data were put into large, central databases. However, retrieving this data was often difficult, because the ones who usually created the databases were not the ones who actually used them. The data-users were not able to easily question the data-gatherers.
And different databases could not interact with one another due to different specifications. Much of the data just sat there.
Today, a majority of the top 10 largest databases in the world are fully online and at least partially accessible to all. Allowing the users to access the data and then to generate more content provides an accelerated creation of knowledge.
Humans manipulate data and create information by social interactions, by dialogs. Without dialog and context, the data are useless.
Conversations Create Knowledge
Knowledge is often created in the transformation of data into information in a social setting. This necessarily requires human interactions and conversations. Solo investigations can provide some usefulness. It can be very slow, however, for a single person to find all the relevant bits of information.
But having conversations greatly decreases the ‘friction’ of information transfer. Diverse viewpoints and experiences will produce better answers. Web 1.0 helped move the data out of the central databases, permitting more people to examine it.
It allowed many more people to provide some insight and context than before. Web 2.0 supercharges these interactions. Now people can respond almost instantaneously. They can rapidly move queries to a satisfactory answer.
Bad ideas get thrown away faster, Good ideas propagate farther. Great ideas get implemented immediately.
Information and Science
This has very real implications when applied to scientific research. Research lives by information transfer. There can be little advancement in the sum of human knowledge if no one knows about the work. That way lies alchemy.
Scientists want other scientists to see their work and to actually criticize it. They might not like the criticism but it is required for the Scientific Method to thrive. When properly applied, good science forces out bad.
Gresham’s Law, and its derivatives, are not applicable because good science and bad science are not of equal value. One explains the natural world around use and the other does not.
For the Scientific Method to work, information dispersal must be free flowing. Greater accessibility to the data enhances the ability to do science. Very little science would be worthwhile today if no one had access to the data.
Now, most of the current methods used to disseminate scientific information were developed over the last century or so. One example is publication of research in a scientific journal, after proper vetting by other scientists.
Such peer review of scientific publications has helped trigger an explosion of extraordinary research. While it has some problems – few things involving humans are perfect – it does help tremendously by filtering out some bad science or just helping correct errors before they become part of the scientific literature.
However, it can take quite a bit of time from original submission of the work until actual publication. Other scientists can publish similar work in the meantime. There is a limitation in the number of pages that can be physically published, so that often getting a paper accepted in a major journal is a matter of luck and politics.
There are faster approaches for disseminating information. Another example is public presentation of data at a scientific meeting. This is faster because the work is often not reviewed prior to the discussion.
But since the presentation may take place in front of several hundred of the author’s peers, there will be a form self imposed review. No one wants to look like an idiot to be shot down in the Q&A afterwards.
And the question periods provided by these presentations create conversations that can have huge effects on research. The interactions between scientists can provide intense focus on specific scientific problems.
Most scientists understand the importance of conferences. Even many companies realize that sending their scientists to such meetings are an important part of maintaining competitiveness.
So, call that Science 1.0. Information moved about by very defined routes, often hampered by the length of time it took to actually get research published or for the annual meeting to occur. Vetting took a long time. Publication was arduous. Dissemination of information was constrained.
The tremendous amount of information being created often increased the size of the haystack without making it easier to find the needle. Information flow was dependent on direct human interactions.
Lots of data. A little more information. Some knowledge creation.
Now, the effect of the Web 1.0 on information dispersal for science was to really just move the data to a digital realm. Instead of trucking over to the library to read a journal, researchers could download the PDF online. They could also dowload important data to their own databases.
Sometimes they could access a journal without having to possess a subscription – so-called Open Access. Registration for conferences could be done online and scientists could see the agenda immediately, rather than waiting for the special edition of the journal to arrive.
Organizations realized how useful it was to move information off of people’s computers and out of their heads and onto an accessible intranet. Meeting minutes could be posted, as could experimental protocols. Internal documents could be provided online.
New Ways of Doing Old Things
So, information flowed faster but it was really just taking old ways of doing things and moving them online. It was great for pushing data out but not for pulling it in.
Lots of data could be presented but there was no context for it since there could be no real discussion of the data. If you wanted to have such a discussion, it would have to occur offline.
Web 1.0 speeded up some areas of information flow in science. But, in a variant of Parkinson’s Law, the amount of information available expanded, often resulting in diminished flow.
A bigger pipe just filled up faster. Without the context and filters that human interactions provided, the enhanced information flow was suboptimal.
Nothing New. Just Better.
Science 2.0 takes its cue from the technologies of Web 2.0. It creates conversations between researchers, lets them discuss the data and connect it with other data that might be relevant. Blogs, wikis and such permit users to make information available in ways that create a conversation.
Web 2.0 permits scientists to create digitized conversations that provide context for the data. They can use our powerful filtering processes found in our brains to find the needle in the much larger haystack.
When applied to research, this has the tremendous potential to greatly enhance our ability to understand much more complex data sets. The human mind and our social networks are incredible filters. They find patterns in very dense data sets.
The Promise of Science 2.0
Science 2.0 is, at heart, just Science 1.0. By taking the conversations online, it permits millions of minds to interact with the data instead of tens. Then the modified data can be examined. The cycle of knowledge creation cranks much faster because of Science 2.0
We have used social networks for thousands of years to filter complex information. Nothing moves us forward faster than a group of diverse minds examining information. However, the physical limitations found in Science 1.0 have hampered enquiries that require examination of very large amounts of data.
The tools of Web 2.0 allow much larger and diverse social networks to work together, sifting and filtering information.
The research organizations that can effectively leverage Web 2.0 conversations to enable the solution of difficult scientific questions will prosper.