Qt is going LGPL

The Qt framework — developed by Trolltech but bought by Nokia — is going to be released under the LGPL license.

I used to program my GUI code in GTK+ but when I worked for Bioinformatics ApS four-five years ago I changed to Qt.  We needed the cross platform feature of Qt, but now that I’ve experienced both frameworks I must say that I much prefer Qt.

Anyway, these days, working in academia, I release all my software under the GPL license — where Qt has been available for a while now — so the change in license doesn’t matter much to me, but I can see the benefit for commercial companies.  The commercial license for Qt is pretty expensive, so an LGPL license can mean a lot here.

Molegro, for example, still use Qt 3 because moving to the Qt 4 license is too expensive.  This really is a shame. Qt 4 has a lot of improved features over Qt 3.

15-25=-10

Day three of APBC

Today started with two invited talks, both on transcript factors

Martin Vingron: Transcriptional regulation: computation methods, statistcs and coregulation

Michael Eisen: Understanding and exploiting the evolution of Drosphila regulation sequences

with the latter being more biological oriented — but in my opinion more interesting — than the former.

In the morning paper session, we followed the sequence alignment and evolution track with four interesting talks.  This track was more “computer science-ish” than those we followed yesterday and I am definitely going to read the papers when I have the time.

In the afternoon there were two paper sessions.  In the first we followed the association study and genomic variation track. We really had to, ’cause our paper was in this track, but we would have chosen that anyway.

The first paper was on population structure, then Besenbacher presented our paper, and then there were two presentations on searching for gene-gene interaction (epistasis) mapping.

The second session in the afternoon we skipped.  The topics didn’t look that interesting, and we could use a break.

Now we are getting ready for the conference dinner.

Over lunch we talked with one of the other danes here, and he told us where he’d found a bar, so we might go there for a beer after the dinner, if we are not too tired by then.  We still have not completely adjusted to the time zone and we get up pretty early in the morning.

15-23 = -8

Big Science vs small science

Just read this post on Big Science by Aaron Hirsch, describing how science has changed from small single group (or single researcher) projects to very large multi lab and international collaborations.

This is no less true in genetics and bioinformatics, where genome projects or large-scale population genetics projects like HapMap are only possible as these large collaborations.  For now at least.

A young discipline is bound to move first through the data it can gather most easily. And as it does, it also defines more exactly what it must measure to test its theories. As the low-hanging fruit vanish, and the most precious of fruits are spotted high above, bigger investments in harvesting equipment become necessary. Centralization is a way to extend scientists’ reach.

But of course, there are also some drawbacks. There’s something disturbingly hierarchical about the new architecture of the scientific community: what was before something like a network of small villages is today more like an urban high-rise, with big offices at the top and a lot of cubicles down below.

The trouble with this is not just what it means for the folks in the cubicles, but also that the entire business should rely so heavily on the creativity and vision of relatively few managers. If the glassy office is occupied by Einstein, that’s great, but of course there’s always a chance it won’t be. (Tellingly, this point was made to me by a friend who grew up in the Soviet Union. “Trust me,” he said, “centralization is risky.”)

Adaptive Complexity discusses this and the potential problems with moving to Big Science:

The danger today, in this era of big science, is that being exceptionally talented in herding resources becomes the major criterion of success. It means that creativity gets stifled: more senior scientists spend their time herding resources and being administrators, and more junior scientists end up being just cogs on the wheel until they manage to become administrators themselves.

The same thing is true in science. While some big, top-down projects designed by committees are important (like the Human Genome Project), the lifeblood of science are the ideas generated by creative individuals, working alone or with a small group of colleagues. Creative science based on individual initiative is one of the nicest aspects of the job, and it’s important to learn how to do that early in your career. If big science ends up being the way most science is done, a science career will become significantly less interesting and attractive.

In Big Science, junior scientists will find it hard to make a name.  Who remembers author 23 in a big genome paper?

But there is more to that story, as Genetics Future points out:

My reasoning is this: firstly, the sheer size of these projects encourages the emergence of a public data-sharing mentality that now (thankfully) permeates most of the field, because with no one group feeling complete ownership of the resulting data there are fewer barriers to the idea of dumping it all online for the benefit of the community as a whole. The free release of data into the research community, like an influx of nutrients into an ecosystem, ultimately results in the increased availability of niches for researchers to exist in. Basically, Big Genetics generates far more data than its participants can ever hope to analyse themselves, and the hefty remainder is fodder for a plethora of small labs exploring small but important facets of the bigger picture.

When you’re young in the field, getting your hands on data can be tricky.

Either you have to collect it yourself — which is a large-ish project in itself — or you need it from someone else which will be some senior scientist.

With data freely available from large scale projects, there is plenty of data to work on. And it hasn’t been mined out.

So what, if you cannot do Big Science in small groups.  Because the data is there, there are plenty of opportunities for small groups to do very interesting science.

In the original post, Hirsch continues:

And then there’s that problem of relating to Big Science when you’re standing outside the building, looking up. The difficulty is not just that the research is recondite, but that the work is institutional, as opposed to individual. After all, not many people really understood the paper on general relativity, but many did connect with Einstein. Sure, we all pitch in our tax dollars, but it’s hard to feel a whole lot of personal involvement in the search for the Higgs boson.

But if Big Science is what it takes to gather the truly precious data, what are we to do?

There is another way to extend our scientific reach, and I believe it can also restore some of what is lost in the process of centralization. It has been called Citizen Science, and it involves the enlistment of large numbers of relatively untrained individuals in the collection of scientific data. To return to our architectural metaphor, if Big Science builds the high-rise yet higher, Citizen Science extends outward the community of villages.

I’m not sure I would call what Joe Sixpack can contribute as “science”.  The science is in asking the right questions and figuring out how to answer them, not so much in gathering the data, important as that is.

It is true, though, that it helps people relate to large projects.

Distributed computing projects such as folding@home are very popular — they wouldn’t be worth much otherwise — so there is something to it.

15-22 = -7