pedrobeltrao

I agree with the need for better visualization tools in biology. There is an interesting group/project started at UCSF and headed by Graham Johnson (www.grahamjlab.org/). Some of these tools are still closer to art than science but I think it is really worth the investment. It is clear that these will help us think about biological objects.

Scientific publishers (as it stands) provide 3 services (publishing, filtering, accreditation) - these could be decoupled and that is what PLOS One, PeerJ and other folks working on "alternative" metrics of evaluation for scientific publishing are pushing for. If pusblishers really only did publishing we would be putting our papers in blogs. It is very very frustrating that it is taking so long to decouple those functions and to have true publishing systems that are as cheap as blog hosting with services on top that are open for competition.

It was tried and failed in the life sciences. Genome Biology and Nature tried to create pre-print servers but there was no adoption. You can still find Nature's server online (precedings.nature.com). Why have they failed ? I don't know. I think it is a critical mass issue. Usually it takes a switch from a whole community and for some reason it is hard to get life scientists to switch. arXiv does have a quantitative biology section and the genetics and genomics people are using it increasingly.

Protein folding is an interesting and computational challenging task. So challenging that some groups have sort of given up on it and move to other fields. Look up David Baker and Rosetta for more info. This is just an example, there are many many problems to work on. I feel sorry for the author of the post, bioinformatics is only getting more interesting as our capacity to make experimental measurements grow. There have been so many interesting findings that are just the product of bioinformaticians digging into existing databases and analyzing them to come up with new theories that have since then been experimentally validated.

I just got a group leader position in the UK (currently a postdoc in the bayarea) and I am paying some attention to these experiments in crowdfunding for academic projects. In almost all group leader or faculty position, the research budget is covered by applying for grants. These are very competitive with typical rejection rates of 90%. This is an interesting alternative but there are many caveats. As someone has already noted in one of the comments, you would typically need to write a scientifically grounded proposal. One fear of crowdsourcing for science would be that many "unreasonable" or pseudo-science projects might attract funding if they are popular. This is slightly less of a concern with for-profit projects since popularity needed for the crowdsourcing might be correlated with financial success. The same is not true at all for academic success. The other issue that has already been hinted at is that the required funds are probably too high. A good grant might bring in on the order of 1 million so is it worth more fighting to improve your social networking and fundraising skills or getting more grants submitted ?

I also disagree with a proliferation of micro self publishing without having an aggregation systems in place first. For the HN community it is obvious that the aggregation can be done post-publication and in theory there are many different ways to achieve this. However, it somehow never gets done. There is no Techmeme for science, no real effort to create filtering tools for the stream of scientific articles that are produced daily. Maybe it is a small market but there is a market. I would pay for a service that would give me a (useful) personalized stream of articles. Without the filtering/aggregation the idea of self publishing or the current trend for open access mega journals (PLoS ONE, Scientific Reports, etc) make content discovery a challenge.

I think the author of the post is completely underestimating how many different fields require some proficiency in managing data. I work in biology (genomics and proteomics) and I teach a summer course to biomed PhD students where I try to convince them that it is essential to learn some aspects of data mining, including a scripting language (Python/Perl) and/or R/Matlab. To be successful in bio-medical research these days you need to be able to reach out to available datasets that relate to your work and combine them with your own observations.Each experiment is also producing a lot more observations than in the past so you need to be able to handle larger datasets as well. My impression is that anyone who does not have some proficiency in programming will not be able to compete in these areas of research. This does not mean that we have to be able to do great code and build great software, a small increase in programming skills really goes a long way. My impression is that this is not just true in biology but it is happening everywhere (ex. business intelligence, social sciences, marketing, etc).

well, you can find a job in a bio related research lab as a programmer but you would earn (at least) 2/3 times more in a company.

You can consider just volunteering to do a project for a biology lab. You have the tools (quantitative thinking, programming, etc) but to work in biology you need to know about some specific biology knowledge domain. There is shortcut, you need to read text-book and then review articles in a sub-discipline until you are comfortable with the area. After that, reading current research papers will get you to the cutting edge and you will start to see where you can contribute. It is possible to do this on your own but it would be much faster to get guidance, either through a course or as I said above to work directly in a lab that is doing some biological research. You would also have to figure out what areas of biology you want to work on.

Unfortunately, as far as I know there is very little useful phenotypic data along with these genomes to work with. I would rather have fewer genome sequences with better phenotypic information. Even with just 35 genome sequences of "individual" yeast genomes I could do a lot more interesting analysis because they are well studied in turns of phenotypes.

Scientists still publish Elsevier because of the so called "impact factor" of some of these journals. As a young researcher currently looking for tenure-track positions in academia I can tell you that it is still extremely important to publish in some of these journals (ex Cell). Unfortunately there are still very few (or none) open access journals that have the same perceived impact. I try to publish in PLoS journals or other open access journals as much as I can but given the chance to publish in higher impact journals I have to take it or risk not having a job in the future. Once you have the job you need to secure funding and so there is always pressure to publish in the "best" possible journal. The ridiculous thing about this situation is that would be fairly easy to create a new open access and highly prestigious journal in any field if everyone would switch to publishing in these new journals at the same time. You would probably just need to convince some of the leaders in the field to do it. This was how PLoS was born in fact. Unfortunately for the PhD students and postdocs it is hard to do anything about it.