Open collaborative design/Open Source Scientific Research
Scientific research can also be done in a spirit of open collaboration. Scientific papers can be made available online to be read and reviewed (a process known as open publishing or open access), and the raw data collected from scientific studies can also be made publicly available (known as open notebook science). Some scientists 
are even taking this to the point of keeping detailed logs of everything done in their lab - all procedures, results, measurements - in a blog-style notebook that is updated in real time.
The main platform for the scientific community in the 20th century was the peer-reviewed scientific journal. A scientist writes a paper, submits it for review by other scientists and, if it is found to be up-to-standard, the paper is published in a copyrighted journal and made available to subscribers. The intellectual property rights to the paper are owned by the journal.
An alternative to the peer-reviewed journal is open scientific research. This means posting scientific papers freely on the Internet for anyone to access, without intellectual property restrictions.
The most obvious and most important advantage of open access publishing is that scientists have much more research available to them.
A different kind of peer-review then takes place, an open peer-review like what we see on Wikipedia. Other scientists can rate the paper or comment on it, allowing the best research to rise to the top. Data-mining algorithms can link related research studies together, showing studies that use similar methodologies together, even aggregating the results of several studies.
Open collaboration in science allows scientific experiments to be global collaborations of interested parties around the world. This allows for greater n numbers, as data can be aggregated from a large number of researchers. This leads to more reliable results.
A key tenet of the scientific method is that all experiments be repeatable. When anyone can read about an experiment and replicate it for themselves, false results - due to sloppy design, fluke or fraud - are neutralized. By making scientific papers open, experiments can be repeated by anyone, not just subscribers. This will ultimately lead to more reliable scientific results.
There are already millions of papers and datasets contributed to open scientific platforms. GenBank is a public library of over 120 million DNA nucleotide sequences and the proteins they produce. The Public Library of Science publishes open scientific journals in eight different areas. The Human Brain Project is a huge collaborative project that aims to share all known data about the human brain.
Grassroots science
Large areas of science have become dominated by highly institutionalised, heavily funded research projects. This has not only engendered public mistrust and fear of science, it has also led to a huge bias towards research that can serves commercial and military interests.
A new model of science is becoming possible thanks to two trends. The first is that equipment is becoming cheaper as a result of technological evolution. The second is that information is now being shared openly on a scale that dwarfs anything ever seen before.
Computer science, where hackers and hobbyists have always been in the vanguard, is the leading example of this. There are stirrings of biotechnology being done in this way, thanks to the equipment becoming much cheaper. There are now hackerspaces springing up for biotechnology, online communities for biohackers and cheap and open-source equipment available.
Open computing resources for science
Another application of open collaboration to scientific research is allowing people to contribute processing power to computing for scientific research. Rather than have a supercomputer crunching numbers in a lab, thousands of people's personal computers around the world can be used. This has been very successful at helping calculate how proteins fold — see http://folding.stanford.edu. The Open Science Grid is a cloud supercomputer used for data-rich research.
