I am a bit surprised that you continue to be freelancing scientist in the current trend of academia to be profitable and patent research.

I am not up to date about situation in Poland – do university technicians, floorsweepers etc. are also freelancers? Did any of you professors volunteered to share his/hers own salary with you?

I suggest that you are de facto exploited by the institution you are affiliated with, and you should simply find yourself a well paying job in the industry. Possibly abroad.

I’ve found your blog through mloss.org. It is a nice surprise to come across someone’s blog on the net and discover that the author works 2 min. walk away from me – presumably we crossed already many times in Biology Dept where I’m a frequent visitor in the lunch time I’m from Maths & Informatics Dept, working in the fields of data mining and machine learning – this includes occasionally bioinformatics, too.

I like your blog very much. Openness is the direction where science will go, no matter if someone likes this or not. You may take a look at recently finished Netflix Prize contest: http://www.netflixprize.com/ where $1M was awarded. Thousands of people out of academia struggled there to solve very difficult scientific / algorithmic problem and many scientific discoveries were done along the way.

I think for openness to arise two things are needed: (i) the request from the outside world for scientific innovations, with the world being ready to pay for them; and (ii) organization / infrastructure / tools / common platform – that will support openness and create a common place (like a website) where open science may live and grow. We’re trying to create something like this for data mining / machine learning – a website accompanied by a set of tools that will facilitate collaborative designing, evaluating, comparison and exchange of ML/DM algorithms. The system is called TunedIT and you may find it at:

http://tunedit.org

TunedIT is open for everyone – you can submit new algorithms, datasets and other resources no matter how many publications you’ve got in refereed journals or from what university you come. Everyone can download your algorithm and run a test to see how good it is. The result will be submitted back to TunedIT where everyone can view it and compare with results of other approaches to see which ideas are better – yours or someone else’s. So what finally matters is the objective quality of your algorithm and not your academic background.

I invite you to look at TunedIT – maybe you’ll find it useful for biology/bioinformatics research as well.

-Marcin

http://www.goarticles.com/cgi-bin/showa.cgi?C=1839563

I am strongly believing in Knowledge=People+Information, so just having people or information alone does not help, we need both, and we must work on our trust relationships ! In other words, as I will present on BioIT
http://www.bio-itworldexpoeurope.com/Bio-It_Europe_Content.aspx?id=90734
Drug Innovation 2.0 – Why we Need Knowledge Metrics for Democratic Action
Jörg Kurt Wegner, Ph.D., Scientist, Integrative Chem-/Bio-Informatics, Tibotec (J&J, Belgium); Blogger, Mining Drug Space; Project Administrator, Open Source Development
In this presentation we share three current challenges for innovation in drug design. 1) Group dynamics – Scientific collaboration typically occurs through networks. Inefficient collaboration and networking can lead to tunnel vision; innovation opportunities through more distant networks may be overlooked. Knowledge metrics can encourage new and productive networks, by appropriately rewarding contributing network members. 2) Information overload – Information and knowledge are not the same thing. We have too much (unstructured) information and we lack the time required to structure this information into knowledge. Knowledge metrics can encourage the ranking, structuring, and accessing of information on a reward-per-use basis. 3) Data silos – Data silos caused by technical or license hurdles, can reduce the number of efficient collaboration options. Knowledge metrics should clearly reward barrier-breaking and silo-bridging efforts, and favor new and diverse over redundant information…Drug designers, lawyers and computer scientists have a unique opportunity to take on these challenges – drug innovation 2.0!

Stumbled across “Freelancing Science” after this post was shared in the GenomeWeb e-newsletter.

Great discussion of open source’s failure to impact health 2.0 (or health in general) thus far…but as you said, I think there are now specific experimental designs that can be put into place to test empirically.

Comments (and one such experimental design proposition) here:
http://hmrx.posterous.com/calling-all-non-scientists

Hope our paths cross at some point in the future-
Jen