Science 2.0

Science 2.0 or research 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.

Ben Shneiderman, a computer science professor at the University of Maryland, argues that studying the interactions between people will be more important than studying the interactions between particles for finding solutions to bigger problems like disaster response, health care and energy sustainability. Because web 2.0 technologies make sure researchers and scientists can interact with each other through existing social networks, like facebook, twitter and linked in it's a great way to enhance our science and find these solutions.

Overview

The central concept of Science 2.0 is the connectivity of researchers and the sharing of scientific material. In conventional science, only final result are published. The used data set, the detailed results and the necessary (software) tools are typically only available to people directly involved in the research. In contrast, the science 2.0 community actively encourages scientists to share those elements. Even more openness is strongly encouraged by the Science 2.0 community: Not only final results, but also intermediate findings should be shared and updated. The exact amount of information to be shared is entirely up to the researcher. Early feedback, fast information exchange and enhancement of the research due to involvement by interested third parties are mentioned by the proponents as the possible advantages of science 2.0.

This concept is evidently closely related to Web 2.0: The Web is developing from a static system to a dynamic tool, in which people disseminate information and can collaborate. Where the static Web 1.0 is about displaying information, the new Web 2.0 is all about dynamics. It encourages sharing, conversation and participation in the flow of information. Web 2.0 is not new, it's just another approach. In Web 2.0, everyone is a publisher. Examples such as Wikis and social networks are known to everybody. Science 2.0 takes these tools to the research community. This perspective of Science 2.0 is to focus on how generic Web2.0 tools can be used by researchers, so as to scale up and make more efficient how scientist work together and get notified of new developments in their fields. This should accelerate the growth of knowledge. The availability of the information is supposed to increase and so is its quality.

Proponents imagine a shift from the traditional anonymous review process towards an open and continuous monitoring process performed by the community. This concept of reviewing should merge with filtering of information. The most true and valuable information should materialize due to this social selection. Science evolves from individual publishing towards collaboration of a community, facilitated through Wiki-like concepts. Proponents see the first signs of this change already appearing in several present evolutions. Old-fashioned lab notebooks have given way to Wikis, and the number of open-access journals is increasing. Raw scientific dataset are increasingly made available on several sites.

However, skeptics have raised some valuable remarks. How will authorship be granted? How is data protected? How will quality be assured?

Open data

In Science 2.0 research data is published online. That way millions of people can review the data or use it for further experiments. Nowadays data is only used by the handful of researchers that are conducting the experiment in question. An open data repository allows for the data of many different experiments to be aggregated and new patterns or conclusions to be found. Collaboration on a common data set becomes an integral part in the research process.

Open data may sound idyllic, but there are some real, practical challenges to getting it done. For one thing, as most people who have ever published a blog realizes, not everything posted on the Internet gets noticed and utilized.

Eisen puts it this way: "Just the technical details of releasing that data is not straightforward. Where do you put it? ... What format do you exactly put it in? How do you tell people that it has got a different data-release policy than the other data at that place?" Even when there is a prescribed format, such as for GenBank, he says that submitting the data "is not a trivial activity." And "that's just sequence data," he continues. "Imagine experimental data," which comes in infinite forms with an immeasurably wide range of experimental conditions.

And then there's the issue of licensing. Should you impose restrictions? "Anytime you have restrictions on use and reuse and rerelease of the data, it just becomes a complicated mess as to what you are allowed or not allowed to do," Eisen continues. So Eisen advocates completely open data. "If you release the data with no restrictions, it's very clear: anybody can do anything." The "Panton Principles" provide guidelines on how to liberate your data.

Doing it well might be challenging, but just getting it out there in the open, in any form, is a useful step, says Drexel's Bradley. "It's much, much easier to get automation involved in the scientific process if you make data open." The point is to get it out there, to put your data in play. Then "anyone in the world can come in, write a script, have some AI interact with the data, and you never know how it's going to be used in a productive way."

An example of open data in action is Gapminder, which offers a large amount of statistics data for free. People are encouraged to make contributions to the project or to use the data for their own research.

Open publishing

Peer review of scientific publications helps to filter out bad science or to correct errors. Unfortunately this is a slow process and the actual publication is often months after its submission.

By taking the papers themselves to the cloud, they become much more accessible. More peers will have a chance to read and review the paper, which could potentially lead to higher quality and faster publication.

Visualization

Information visualization becomes more and more important as the data sets grow bigger. As Shneiderman notes: The inherent complexity of social, political, and economic processes may finally become more understandable as information visualization tools for seeing temporal changes, relationships among variables, or surprising clusters become more widely used. This could lead to interesting discoveries that provoke livelier evidence-based discussions, which in turn are the basis for informed decision-making.

Shneiderman thinks that it’s time for the laboratory research that has defined science for the last 400 years to make room for a revolutionary new method of scientific discovery. He calls it Science 2.0. It combines the hypothesis based inquiry of laboratory science with the methods of social science research to understand and improve the use of new human networks made possible by today’s digital connectivity. Through Science 2.0, the societal potential of such networks can be realized for applications ranging from homeland security to medical care to the environment.

“It’s time for researchers in science to take network collaboration like this to the next phase and reap the potential intellectual and societal payoffs. We need to understand the principles that are at work in these systems,” said Shneiderman.

Shneiderman and a number of colleagues at the University of Maryland are already on the frontier of applying Science 2.0 methods to the computer-based human networks that Shneiderman calls socio-technical systems. An example of this, is the work done by Shneiderman, Jennifer Preece and several other colleagues, who are developing 911.gov Community Response Grid, an emergency response system that would rely on the Internet and mobile communication devices to allow citizens to receive and submit information about significant homeland security community problems.

Implementations

There are 2 kinds of implementations for science 2.0 applications, the first one focuses on collaboration between scientists so they can work together on something bigger. The second one focusses on research, sharing of papers and improving the communication between researchers.

Collaboration

Examples of collaboration tools are OpenWetware and WikiSpecies. In the case of Wikispecies, it's a specifies directory anyone can edit an collaborate to.

Research

Mendeley and Epernicus are examples of an science 2.0 implementation focused on research. Epernicus wants to help researchers to connect and Mendeley wants to help you organize, share and discover research papers. A special tool is the More application http://sites.google.com/site/kulmoreapp/ it helps researchers connect online.

Open Science

An important element of science 2.0 is the Open Science (or Open Research) movement, which has the goal of increasing transparency of scientific research and wider sharing of its results both within and beyond the scientific community, e.g. by means of Open Data, Open Source and Open Access.

Snowflake Effect

The Snowflake Effect signifies that every individual, just like every snowflake in a snowstorm, is somehow unique. As such, science 2.0 should exploit this and provide a list of 'just the right things' to any user. Science 2.0, ideally, will provide just the right content to just the right person with just the right partners at just the right time on just the right device in just the right context and just the right way. This is not to be confused with perfection, but

E-learning

E-learning is difficult because of all the abundance. The solution is found in science 2.0, by terms of what is described above as the Snowflake effect.

Concerns

When developing a science 2.0 application there are a few concerns you need to keep in mind. The most important concern is how to make people trust your application? You have to try to do this by making your application look professional and secure. Users must have the right permissions, their own space and have to be defended against vandalism. For example, if you make an application on which you can upload papers. How do you make sure users can't upload someone else his paper and take credit for it?

For example Steve Koch, assistant professor of physics at the University of New Mexico, says that he isn't too worried about getting scooped, even though -- unlike most of his fellow Open Notebook Science practitioners -- he is not yet tenured. Open Notebook Science advocates claim that being open may protect a scientist's ideas rather than exposing them to theft. Newton's decision to conceal his findings within an anagram made it harder for him to prove priority over rival Gottfried Leibniz. Open Notebook scientists say all they need to do is point to their open notebooks to show that they had an idea or found a result first. "I've been able to cite my [online] lab notebook pages in a peer-reviewed paper," Bradley says. "That's clearly citing your priority." In the case of an unethical theft of ideas, "the published track record would make it easier to shame the person who did the scooping," Koch wrote in his blog."

Examples

Example science 2.0 applications are:

• Epernicus - social network for investigators looking for people and techniques to solve research problems
• Journal of Visualized Experiments - peer-reviewed web journal publishing videos demonstrating experiment protocols
• More - using QR codes to find more information about speakers and presentation topics
• Open Source Science Project - web tools for communication between researchers, the public, and other researchers
• OpenWetWare - wiki for sharing laboratory information and protocols
• Proteome Commons - collaboration on proteomics (proteins expressed by a genome, cell, tissue or organ)
• Public Library of Science, a nonprofit open-access scientific publishing project
• WikiSpecies - open directory of all species of life
• Mendeley - academic software for research papers
• Gapminder - open statistics data
• researchgate - scientific network
• 911.gov - Community Response Grids, E-government, and Emergencies
• friendfeed - online tools to do science in new ways.
• academicsnet - facilitate and support science and scientists 2.0
• scientificblogging- community where everyone can write articles and discuss issues without being filtered
• labspaces.net- aims to make the scientific process more public and open
• UsefulChem - an Open Notebook Science project on the synthesis of anti-malarial compounds and other organic chemistry related projects
• ONS Solubility Challenge an Open Notebook Science project designed to collect non-aqueous solubility measurements

External links

• "What Is Science 2.0?" spreadingscience.com
• Brodie, M.L. (2007) "Computer science 2.0: a new world of data management" Very Large Data Bases (Proceedings of the 33rd international conference on very large data bases, Vienna, Austria) p. 1161
• Over 50 biomedical community sites for scientists and physicians "Scienceroll.com"