Science 2.0

Science 2.0 uses the technologies of web 2.0 to enhance conversations between researchers, let them discuss data and connect 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 technology permits scientists to create digitized conversations that provide context for the data.

Overview

Science 2.0 provides a new level of connectivity between researchers using the Internet to share scientific material. It signifies a shift from the publication of final results by well-defined collaborative groups towards a more open approach to sharing raw data, preliminary experimental results, and related information. To facilitate this shift, Science 2.0 relies heavily upon Web-based software tools and principles as a way to foster greater cooperation and collaboration between interested third parties. Such an approach has the potential to: speed up the process of scientific discovery; overcome problems associated with academic publishing and peer review; and remove time and cost barriers limiting the commercialization of scientific technology and IP. Central to the effectiveness of this is fast and effective communication.

The application of Web-based tools to the realm of academic research requires a clear understanding of the particular wants and needs of scientists.

• Data, for instance, can be shared in a way that allows colleagues to both verify it or to add to it. The sharing of data creates the potential for metadata analysis, and the open nature of the sharing creates standards that can be more easily compared.
• Highly specialized skills and services can first be brought together from the global community, and then coordinated through tools that help communicate results and logistical developments. Even non-scientific services can be included to contribute within the framework of collaboraton.
• Software applications can be used to draw knowledge from various perspectives to add significant sophistication to data analysis.

The original Science 2.0 vision was an evolution of communication and outreach beyond individual publishing and towards collaborative publications and tools, facilitated through Wikis and the like. The trend towards open access publishing, online lab books and the inclusion of raw data when publishing could be interpreted as the first steps in this process. There is also the potential to shift from traditional anonymous review processes towards more open and ongoing monitoring processes. This concept of 'reviews and ratings' is already highly advanced in other manifestations of Web 2.0 as a basis for validating, ranking, and even sorting information.

One example of an 'evolutionary' advantage that Science 2.0 may have over traditional approaches to collaboration is that highly technical problems are more likely to be resolved by appealing to a larger group of specialists than a smaller group. In research, such problems are very often the rate limiting step, and so any advantage would provide the lab with a competitive advantage. Such an advantage should be particularly evident during the establishment phase of Science 2.0, as an increasing number of scientists explore and benefit from the potential of upgrading to the new system.

Despite the potential of Science 2.0, its broad uptake will be limited until concerns are addressed, such as how will authorship be granted? How is data protected? How will quality be assured? While Science 2.0 is developing as a concept, its broad application within research itself lags behind industries where the benefits of sharing information are self-evident, such as software, automotive, and a range of service-based industries.

Open data

In Science 2.0, research data may be published online so that others can immediately review the data or use it for further experiments. The level of access provided can be defined by the supplier, who must balance his need to protect his own interests from competitors, while attempting to collect ideas from those whom he shares his information with. The former attitude is currently prevalent due to traditional processes of collaboration and publication, which make it difficult to demonstrate the true source of an idea or result (if communicated verbally or to a single individual). Providing open access to knowledge within the context of Science 2.0 has the potential to create an open data repository in which all contributions are openly recognized with authorship. This not only benefits the creator of content, but aggregates data or information so that new patterns or conclusions can be explored. Moreover, collaboration, not competition, based upon a common data set becomes an integral part in the research process.

Open data may sound idyllic, but there are some practical challenges to getting it done. For one thing, as most people who have ever published a blog realize, 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 re-release 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."

Data driven science

The scientific method was traditionally driven by hypothesis. First scientists predict a good response, then collect experimental data to finally test the data against its predictions. However, in the new data-driven approach researchers start with collecting data and afterwards analyse data. This approach becomes feasible when experiments produce enormous amounts of data, enough data to test hypotheses more than any scientist could ever think of. [1]

Criticism

Ben Shneiderman, a computer science professor, argues that too much focus is placed upon Web 2.0 technology rather than actual usage. 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.

Open Science

One element of science 2.0 is the Open Science 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.

Rabbit hole risk

Finding the right information on the web is difficult because of all the abundance. Because of the overload of information available, one may have rabbit hole risk of information overload. Because of links that are followed by other links, researchers tend to lose focus and dwell on non-relevant websites, when searching for just the right information. Because all websites are linked, there is no significant barrier of what is relevant, and what information is not. This may cause a researcher to create a huge list of papers, which he isn't even sure if they are relevant.

Spigot risk

Another problem with the current information flow is the spigot risk. This relates apparently exponential growth of information, with limited filtering and reading capacity. This makes it harder to find out which information is the most important to read.

Challenge

The aim is to create an open scientific culture where as much information as possible is moved out of people’s heads and labs, onto the network, and into tools which can help us structure and filter the information. This means everything – data, scientific opinions, questions, ideas, folk knowledge, workflows, and everything else – the works. Information not on the network can’t do any good. Tools such as Creative Commons licensing and free and open source software help to promote such a more open culture.

Concerns

Openness of information

When developing a Science 2.0 application there are a few concerns you need to keep in mind. Since research can have a high cost (both time and money) it is important that users can trust the application. For example, users sharing papers must be assured that there is plagiarism protection.

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."

However, the difference between unethical theft of ideas and a genuine new insight isn't always clear. In a competitive research industry, where received grants are largely dependant on obtained results, one might ultimately be at a disadvantage by sharing your research information. This doesn't apply to all fields of science though, there are many non-competitive fields where better results can be acquired by cooperating.

Quality

Allowing more people to publish more frequently may lead to documents of lesser quality than most researchers are accustomed to. However, this only applies to intermediate documents. Since review techniques as peer review can still be used, the quality of finished documents isn't necessarily lower.

Looking unprofessional

Some researchers have argued that publishing uncompleted documents will make them look unprofessional. However, this is certainly not true for every topic and having a dynamic update process might be seen as an advantage.

External links

• Science 2.0 is a registered trademarkof Ion Publications