Author Archives: Pawel Szczesny

All 2.0 – an attempt to connect disciplines

All 2.0Last year I bought a domain name Initially I had an idea to launch a huge portal around “2.0” meme – essentially tracking changes in communication methods across various areas. I wanted to quit science and start a consulting career in helping people to communicate more efficiently (new channels and tools, efficient visual communication, etc.). However, a market for such services in Poland is nonexistent, and I didn’t have a mood for relocation, so I’ve turned to other opportunities (and as effect, I’ve stayed in science). Neverthess, I still had a domain but no clear idea what to use it for.

So, with only a little time left, the next option I took was a tracker/aggregator. In theory, once done, it didn’t need much maintenance. There’s quite a lot of services for such purpose out there, but they didn’t necessarily allowed for certain things I wanted to have, so I had to code my own script. As I didn’t have much time, the resulting site is a little rough (it cannot compete with wonderful sites Euan is coding, such as recently released preview of Streamosphere). However, you should get an idea what I’m aiming for. Currently it tracks blog posts and conversations in areas of Science 2.0, Health 2.0 and Culture 2.0 (with Enterprise and Government to follow). Because within these types I sort all entries by date, I had to remove some bloggers from “Key People” list, as their high-speed blogging did not allow others to appear in the box at all. 🙂

At this stage, the set of sources is far from perfect – outside of science, conversations seem to be highly homogenous. When I improve the sources (maybe will use Twitter and custom FriendFeed searches), I plan to add some kind of visual summary to the tracked conversations to see if I can find some patterns that will let me establish a connection between disciplines. Let’s see…

While I was collecting links, I’ve found one interesting thing: you can find people interested in these three areas both over at FriendFeed and over at Twine. However, it seems that only scientists are actively talking with each other at these services – where are other groups storing their discussions?


Posted by on June 28, 2009 in bioinformatics


Open Science, what is your message?

It recently occured to me that maybe Open Science could be marketed more efficiently by simplyfying its messages and better targeting. I often find it difficult to convince scientists to support the idea, because Open Science idea does not seem to solve their problems. Western scientists have the main problem: not enough money – the rest are just details (I will be happy to be proven wrong, but I constantly notice that majority of scientists will happily play in the current academic system as long there’s enough money for their research). How about having the main message of OS movement along the lines of “Open Science = Cheaper Less Expensive Science”  (that’s something that Jean-Claude and Cameron say for some time)? I know that we don’t have enough evidence to say so, but on the other hand nobody seems to care that there are better measurements of scientific productivity than impact factor (and have some evidence for that).

Simple message – but also better targeting

Such message is not going to resonate at places that have much more significant problems than lack of money. To me, there are several places in the world that suffer from other issue – isolation. Thomas Erren in his short commentary on Phil’s Bourne “Ten simple rule for getting published” cites Rosalyn Yalow, a Nobel prize laureate:

… I am in full sympathy with rejecting papers from unknown authors working in unknown institutions. How does one know that the data are not fabricated? … on the average, the work of established investigators in good institutions is more likely to have had prior review from competent peers and associates even before reaching the journal.

And it’s just only one side of isolation – there are many more. So, maybe in such places the message of OS should be along the lines of “Open Science = Connected Science” (following one of Deepak’s blog themes), explaining that openness creates connection through which knowledge, experience and recognition can flow both ways?


Posted by on June 22, 2009 in bioinformatics


Dreaming about bio-spreadsheet

One of the often occuring task in my work is to present results of an analysis in some kind of table. I have used for such purpose quite a number of approaches, starting from generating simple HTML file, through fetching of SQL data into table stored in a wiki, up to using Rails. One of the dreams I have recently is a web-based spreadsheet that would allow me to apply some specific piece of code over every row/column and show resulting table.

ScreenshotA simple mockup is shown above. In this example, a code:

print " <img src="{column_1}_bio_r_250.jpg>"

… iterated over first column containing PDB codes, would substitute these codes with an image of a protein from PDB server.

In other words I dream about simple (single file would be the best – I like the approach Sinatra framework is taking) web-based programmable spreadsheet. Something like Resolver One, but simpler. Is there anything like that available?


Posted by on May 19, 2009 in bioinformatics, Software


Science 2.0 – introduction and perspectives for Poland

This is more-or-less transcript of Science 2.0 based on a presentation I gave on conference on open science organized in Warsaw earlier this month. Please remember that it’s not meant to be a general introduction to S2.0 – it was prepared for mixed audience and focused on perspectives for Poland.

Science 2.0 is a concept describing new forms of communication between scientists. Communication has been the essence of science; research become meaningful only after confronting results with the scientific community. So far it was thought that peer-reviewed publication is the best communication channel we had so far. New internet technologies had changed this picture – not by replacing the “best” channel, but by showing that the concept of “the best” covers only small part of a communication spectrum. We knew that already, but we keep forgetting: people didn’t stop calling each other after email had appeared – these two services complement each other. And in the same way many of new communication channels complement peer-reviewed publication.

Collaboration, exchanging information and confronting research results in 2.0 era have two important attributes in which they differ from traditional models: openness and communication time. Exploration towards increased openness and shorter communication time happens already in publishing industry (via Open Access movement and experiments with alternative/shorter ways of peer-review). However I wanted to say few words about experiments that go little or quite a lot beyond publication.

I have chosen My Experiment as an example of an important step towards openness because it’s probably the least radical idea you can find in modern Science 2.0 world. This service provides a virtual research environment in which the main focus is put on sharing scientific workflows. One of the use cases may be sharing a precise diagram of the “methods” sections from experimental (including bioinformatics analyses) publications. Small step towards openness towards other scientists – we can make it easier for others to understand what we did in a particular paper.

And while we can open towards other scientists we can also open towards non-experts. FoldIt is a game in which people from all over the world compete in improving structural models of proteins. There’s no deep knowledge required – a brief tutorial contains essential introduction to the topic and exercises testing if user understands some basic concepts. Playing the game does not contribute directly to the science, but helps in improving protein structure prediction software and in understanding protein folding. Two other examples of non-expert participation in science are Annotathon and Spectral Game. These are servers that combine “required” and “useful”, that is combine teaching and data annotation. Both servers aim at enhancing learning process and at the same time use crowdsourcing approach to curate data – metagenome sequences in first case and chemistry spectra in the second.


What about shorter communication time? The image above combines various data visualization techniques based on the Second Life platform. While Second Life was first sold to scientists as a conference platform, it turned out it’s not very useful for such purpose – but scientists stayed for SL’s very good visualization capabilities. How many times instead of explaining via email/phone some concept to a colleague, you said “come here, I’ll show you”? SL allows to prepare interactive visualizations of chemical structures, genomes, proteins or multidimensional data and as such, to communicate some difficult concepts faster than via other channels.

Last year’s ISMB conference became a major step towards new approaches in conference reporting. A few scientists that happened to be bloggers and users of a life streaming service called FriendFeed decided to report in real time from the conference. Their effort was followed by a number of people, including even the ones that were already on the conference. I’ve seen my colleagues creating an account on FriendFeed (which they sadly abandoned shortly after) only to follow this report. It is still available in permanent, searchable archive over at FriendFeed and resulted in an interesting publication. Life streaming service wasn’t designed strictly for conference reporting and similarly, virtual world platform wasn’t designed strictly for data visualization. But it obviously doesn’t matter to us, as long as it works. slajd13

But you may ask: where is “science” in “science 2.0”, as these examples are not necessarily about doing research. And while I could provide some examples of using new communication channels in day-to-day work, I think it’s more important to tell you about people who test boundary conditions of communication spectrum in their research. In 2006 Jean-Claude Bradley coined a phrase “open notebook science” which means conducting research using publicly available, immediately updated laboratory notebook. Despite obvious disadvantages of such approach (competition, scooping etc.) he is quite successful in terms of getting grants, publishing his results and so on. A number of people followed his approach with smaller or bigger modifications and it is argued that ONS enables more effective collaboration and more effective (no repeated experiments) science. Is that true? Quite probably, but it’s important to recognize that Science 2.0 is not only about better communication. As Jean-Claude mentions is his talks, all these experiments are parts of a bigger process, a change in a way science is done. The outcome of Science 2.0 is not going to be limited to complementary channels to peer-reviewed publications – the more important part will be rules, formats and standards for communication between machines, not scientists. Those who think that removing scientists from scientific process is not going to happen all that soon, I would like to point to a recent publication in Nature presenting Adam, a robot that independently discovers scientific knowledge. While Adam seems to be an equivalent of a junior lab assistant (or a postdoc as some say) and is definitely too expensive to consider as your cheap lab workforce replacement, it’s a quite significant step towards automatising of many elements of scientific process.

There’s another aspect of Science 2.0 that is usually skipped in many articles. A screenshot I’ve shown you contains my conversation with Cameron Neylon over at FriendFeed in April 2008 and was presented in many talks on open science since then. The reason was clear – FriendFeed wasn’t designed to start scientific collaborations and this was the first example of such kind. However what is usually not said is the fact that I wouldn’t answer anonymous request. The reason I did a model for Cameron’s grant was that I subscribed to his feed before so I actually could see the request. However, I didn’t subscribe to Cameron because I knew his professional profile – I didn’t even know his affiliation then (and barely remember today). The reason I subscribed to Cameron was that I somehow “knew” him, this is I read his blog, I commented on it and he commented on mine, etc.


An important part of Science 2.0 is the fact that it has human face. In other words, through participation in online communities we become living persons, instead of anonymous scientists hiding behind publication list.

Why does it matter? Looking at Science 2.0 from perspective of a young scientists working in Poland, this aspect of Science 2.0 becomes a game changer. Poland has ca. 30 times less money in available grants than UK (according to calculation of the UK research budget by Duncan Hull). In biology, which is a very expensive field, it simply means that we have a hard time competing with other countries. And while that fact matters less on the country scale, it matters a lot at a level of individual researchers, because it closes many career opportunities.slajd20

This is not an example, this is a comparison of two real people I know, that finished their PhDs about the same time. The first was from a major Polish institute, the second from a major European one. And while impact factor is a poor measure of their research outcome, it’s exactly what a head of a lab both would apply to will see: few publications, not that impressive vs lots of publications, all in good journals. With research budget Poland has it’s hard to compete with Western Europe and even the EU money will not change that situation in a matter of days, because we also lack experience in spending money. While the system slowly transforms, young Polish scientists have already a way to fill this gap – by Science 2.0, or more precisely by participation in online scientific conversations. There’s no currently easier way to show that poor publication record does not equal poor skills.

And the last aspect of Science 2.0 I want to talk about today comes from the fact that I’ve just became a lecturer at the University of Warsaw. There’s another gap we must fill, this is between current research and lectures we give today. It is a hard task as the amount of knowledge doubles faster and faster. To adopt to such speed the structure of the university courses must change – or instead, we let the students to take care of the gaps. They can easily follow current research and decide what is important to learn by having an access to real-time scientific conversation, by participation in Science 2.0. It’s not an easy road, but at least it will allow them to learn important concepts long before we come up with an idea to teach these concepts.

Consider such example: a group of students from Department of Biology of University of Warsaw participated in 2008 in IGEM, International Genetically Engineered Machine competition. This is a part of a new field, called synthetic biology. This field is fresh and somehow still controversial and these are not the only reasons why not all universities in world have synthetic biology courses. University of Warsaw doesn’t have it either – but it didn’t stop these students, and they plan to participate in IGEM again this year. However, synthetic biology, as many emerging fields today, is done in the open. Scientific conversations are open. Ideas and thoughts are open. And students can learn from it before we organize these thoughts into textbooks.

The last slide shows a community of life scientists I’m a part of. These are not only scientists – there are librarians, science communicators, editors from scientific journals, people working in biotech industry or even people without direct connection to science. These people with diverse skills and background create a 24h a day, all year long online conference. Ability to interact with them and to learn from them was among biggest privileges I had in recent years. And even if this is the least visible aspect of Science 2.0, it’s the most important one and the main reason for my participation in online communities. Thank you.

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Posted by on May 18, 2009 in Community


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The Life Scientists room over at FriendFeed

It’s one of the slides prepared for “Open Science in Poland” conference. I captured screenshots of subscribers to The Life Scientists room over at FriendFeed (however, as you’ll notice, with uneven number of rows, so not all of subscribers did fit). I hesitated to share it at a high quality, but new FriendFeed layout does not look anywhere as pretty as the old one (and has much smaller number of avatars per page), so here it is.  The Life Scientists

The Life Scientists slide – PowerPoint format, PNG format.

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Posted by on May 3, 2009 in Community


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HMMER3 testing notes – my skills are (finally) becoming obsolete

Hidden Markov Model with Output
Image via Wikipedia

It’s already quite a while since I’ve started to extensively test performance of HMMER3. As many other people noticed before, speed of the search has improved dramatically – I’m really impressed how fast it is. However, it’s only part of the story. The smaller part actually.

As some of readers may know, most of my projects so far were revolving around protein sequence analysis and sequence-structure relationships. Mainly I was doing analysis of sequences that had no clear similarity to anything known, without functional annotation. Usual task was to run sequence comparison software and look at the end of the hit list, trying to make sense from hits beyond any reasonable E-value thresholds (for example I often run BLAST at E-value of 100 or 1000). I use very limited number of tools, because it takes quite a while to understand on which specific patterns a particular software fails.

The high-end tool I use most often is HHpred – HMM-HMM comparison software. It’s slow but very sensitive – my personal benchmarks show that it is able to identify very subtle patterns in sequence formed slightly above level of similar secondary structures (in other words, from the set of equally dissimilar sequences with identical secondary structure order, it correctly identifies the ones with similar tertiary structure).

The most surprising thing about HMMER3 is that in my personal benchmarks it’s almost as sensitive as HHpred. I wasn’t expecting that HMM-sequence comparison can be as good as HMM-HMM.  This observation suggests that there’s still a room for improvement for the latter approach, however it has already big implications.

PFAM will soon migrate to HMMER3 (the PFAM team is now resolving overlaps between families that arose due to increased sensitivity) and the moment it is be available, it will make a huge number of publications obsolete, or simply wrong. There are thousands of articles that discuss in detail evolutionary history of some particular domain (many of these will become obsolete) or draw some conclusions from the observation that some domain is not present in analyzed sequence/system (many of these will need to be revised). It will also make my skills quite obsolete, but that is always to be expected, no matter in what branch of science one is working. I also imagine that systems biology people will be very happy to have much better functional annotation of proteins.

I don’t want to call development of HMMER3 a revolution, but it will definitely have similar impact on biology as BLAST and HMMER2 had. Not only because of its speed, but also because it will create a picture of similarities between all proteins comparable to the picture state-of-the-art methods could only calculate for their small subset.

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Posted by on April 22, 2009 in bioinformatics, Research, Software


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Science and Art: limits in scientific creativity

This is a follow up to my recent post in this theme. I got encouraging (thank you!) and interesting responses to that post, some of which deserve a highlight. First quote comes from Gregory Lent, an artist:

art is simply listening inwards and being aware of what you feel. true of both art appreciation, and art making …. the permission to do that is more an emotional allowing than any sort of training or “creativity” … it is merely conscious sensitivity …

scientists have this ability, of course, but are too wedded to the intellect to allow it to emerge, or to be part of the daily flow …

The next one is from Steven Grand, AI researcher:

The thing is, all creative thinkers use some kind of analogy. At the artistic end of the scale these analogies tend to be loose, suggestive metaphors. At the scientific end of the scale we build mathematical models. But in between come many shades of analogy, some more concrete and some metaphorical; some symbolic and some more touchy-feely.

The trick, of course, is to be able to shift freely up and down the continuum as required. Not all artists or scientists can do this, sadly. Many artists are unable to anchor their thoughts in reality and many scientists are too scared to let go of certainty.

And finally, a comment from Michael Nielsen, theoretical physicist (quantum information theorist to be precise), posted over at FriendFeed:

(…) I don’t think particularly verbally when I’m doing research. Not visually either. Instead, it’s a mishmash of spatial, kinesthetic, visual and linguistic; very, very hard to describe. In any case, I don’t think I fit your description. I suspect a lot of theoretical physicists don’t.

And actually I could end this post here, as these quotes nicely complement each other. However, there’s one more thing I wanted to add.

After noticing how limited my thinking patterns are, I suspect that there’s a lot of mental barriers for creative thinking in sciences, that are “inherited” during the training process (mainly the PhD studies). There’s quite a lot of “outside” barriers too (see brilliant post by Jean-Claude  on ego-less science), but my feeling is that great ideas don’t appear too often because we simply rarely fall off the track to find them. The times of Ansel Adams who took some of his most beatiful photographs from or in close proximity to his car are gone – science became a crowded tourists destination with thousands of eyes looking for a good picture from exactly the same spot.

I’m very happy where the topic has lead me. The whole theme of intersection between Science and Art becomes a quest for exploring limits in scientific creativity.

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Posted by on April 14, 2009 in Science and Art


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The Future of (Life) Scientists

This post is directly inspired by excellent essay by Michael Nielsen entitiled “The Future of Science“. While Michael writes about science itself (and how openness will be playing big role in scientific process) I wanted to write few words about how and where I see scientists in a near future (or rather how the research will be done – I’m not even touching the broad topic of alternative careers for scientists). While it sounds like a complementary essay to Michael’s work, I wouldn’t dare to call it so – think of it as a collection of loose notes gathered over months of learning from online science community. Also, please keep in mind that it’s written by a biologist and as such biased towards life sciences.

It’s no news that academic environment has changed so much that a joy of research spans only small fraction of day-to-day scientists’ life. “Publish or perish“, bureaucracy, money hunting, lack of tenure track positions, impact factor, ever-postdoc are only few of many issues within academic system. There’s quite a lot of interesting initiatives that aim at improving the system and some of them will certainly succeed by solving directly some of the issues above or more likely, by creating a niche within academia in which these issues will not apply. However, I think in the long run academia is not going to be the main environment where the research is being done and more importantly, there will be infinite gradation of research jobs, allowing people from many different fields with many different skills to contribute to scientific projects.

That said, I also believe that amount of data and knowledge produced will lead to enormous specialization of scientists. This does not contradict the previous statement: I don’t think that some teenager will design and develop in his spare time a new molecular dynamics algorithm, but finding new genetic associations or inventing another way to modify bacterial genome so it has better biodegradation features sounds to me like a reachable project for many people. Specialization will be one of many factors influencing creation of new types of scientists. And what are these types? Let me describe a few.

Mind, brain, intelligence amplification – future Nobel Prize winners

This category emerged pretty recently, after reading Deepak’s post on uniqueness (or lack of it) of someone’s contribution to science. I always had this notion that no matter what I did, it would be done in a near future by someone else, but this time I could put it into words: science is like sports –  winner takes it all and there’s always a winner. Because prestige of an institution or fame of a scientist plays a big role in getting one’s research funded, competition for money will lead to development of procedures that will aim at producing Nobel Prize winners (or equivalents) analogous to sports training programs.

1933 Nobel Peace Prize awarded to Norman Angel...
Image via Wikipedia

Techniques like neurolinguistic programming, biofeedback or binaural bits (just to name a few) are surrounded by such a hype, that it’s hard to believe they are worth something. However I think there’s a solid field emerging from these inventions that aims at dealing with issues we create in our lives. Have you heard that Google had opened School of Personal Growth as a part of the Google University, teaching things like mental development, emotional development, holistic health, well-being  and finally a Buddhist notion, “beyond the self”? I think it’s no mistake – it’s an attempt to help employees to consistently work at their optimal speed. And there’s story is published by Nature in April last year results of a poll of using brain-doping drugs among scientists. And there’s an inspiring talk by Juan Enriquez on arrival of Homo evolutis. I believe it’s just a matter of time big universities will launch (probably secretly) their own programs for training high profile scientists. And judging from the comments to the Nature’s poll I don’t think many people will object – science, unlike sports, doesn’t have to pretend it’s fair.

Getting research done – staff scientist

This type doesn’t require introduction. If one doesn’t have to waste time on advancing career and hunting for money, one becomes a very efficient scientist. Staff scientist positions are available in many countries and I wish it could be more of them in the future, especially in bioinformatics – where a single person can be trained to do everything from microarrays analysis to molecular dynamics in a relatively (!) short time (and become then a very important asset in the lab).

Experienced specialist – nomadic freelancer

Nissan_NV200 photographed in Tokyo Motor Show 2007
Image via Wikipedia

This is category I was aspiring to. Here you can read little details how I tried, and here when and why it failed. I still think it can be done, although not in every field and not all the time. My hope was that telecommuting is the future of freelance scientists, but Bora offered entirely different solution: co-researching spaces/science hostels:

A coworking space has three important components: the physical space, the technological infrastructure, and the people. A Science Hostel that accommodates people who need more than armchairs and wifi, would need to be topical – rooms designed as labs of a particular kind, common equipment that will be used by most people there, all the people being in roughly the same field who use roughly the same tools.

From what I’ve seen, people doing structural biology (especially NMR-related research) tend to enjoy similar to a freelancer status: they can do a crucial high tech task, which takes no more than several weeks to finish and often the task is needed so rarely that there’s no point in employing the specialist  full time (or to do in-house training).

The main disadvantage of this mode is something called “consultant’s dilemma” (hat tip Harold Jarche): when you’re working you’re not generating new ideas or business, and vice versa.

In a  failure of interdisciplinary approach – translator, integrator

I expect that lots of people will disagree with me on that, but I think on the long run interdisciplinary approaches are going to fail. The area where a reason for failure is most visible is genome sequencing. Deep knowledge about single simple organism such as bacteria is beyond capability of most (if not all) laboratories and teams and that’s why publishing a genome is just a starting point, not end to a process. It takes years of work of experts in their own small fields to extract all useful information from the single sequence.

Once this situation becomes more of an issue, scientific translators may emerge. Such person will track scientific literature in two (or three or four, such as language translators) small fields and will tell group of researchers from one field what important has been published in other field. Will similar service become part of libraries or such people will become independent consultants? I have no idea.

I don’t think that gaps in knowledge will be corrected by talking to colleagues or by review process. Here’s a perfect example (in used-to-be prestigious journal): neither authors nor reviewers have noticed that the structure containing so-called trimerization “octads” is a perfectly fine, quite regular, heptad-based coiled-coil (you guess it right, these “octads” were separated by six residues, giving together fourteen – two coiled-coil heptads). It was already visible in the sequence figure – but only if you knew that things like coiled-coils exist and were already studied by Francis Crick. After almost a year and a half correction wasn’t submitted which means the community does not care either.


As soon as we have our own Paul Graham and a clear, well-described path of how to make a startup in life sciences successful, we will have a bloom of bioentrepreneurs. Life science is a field comparable to high-tech, not software industry. It requires different skills and different approach, but no one has so far put it into words that we can follow. Also, we need more hardware providers in area of life sciences. If you want to build a mobile phone, it’s a matter of days to order its every single part. If you want to build your own sequencing machine, I wish you good luck, because it will take considerably longer (you need to wait until respective companies are built and offer their products).

Nevertheless, I’m sure it will happen. Streamlining life sciences is something that lots of people are talking about.

Clean data needed – biocurator

The more data the more errors. Recently, I’ve stumbled upon interesting functional annotation of a protein: will die slowly. Search on NCBI reveals few dozens of proteins with such annotation. This is a terse description of a phenotype, however I don’t think should be used as a protein name. Paul Davis suggested that this propagated from Drosophila, since fruit fly gene names have a long history of names blurb:

Early work refers to the gene as fruity, an apparent pun on both the common name of D. melanogaster, the fruit fly, as well as a slang word for homosexual. As social attitudes towards homosexuality changed, fruity came to be regarded as offensive, or at best, not politically correct. Thus, the gene was re-dubbed fruitless, alluding to the lack of offspring produced by flies with the mutation.

It’s nothing new that to reach holy grail of many fields (text mining, ontologies, automated discoveries, predictions), we need manual curation of biological data (even Wolfram Alpha is based on curated data). Similarly to staff scientists, biocurator jobs are already appearing in science job listing.

Science as creative hobby – “not even a scientist”

In the introduction I’ve mentioned a teenager inventing new genetic modification of an organism. While to some it may sound difficult, unquestionable success of iGEM competition shows that it doesn’t require 20 years of research experience to come up with such ideas. Lots of knowledge and lots of data create opportunity for people outside academia to jump in and make a valuable contribution. The necessary requirement in “openness” – as long as the data and publications are freely available, there’s a space for outsiders.

I expect (or I hope) amateur science to grow in the following years – especially in the less bureaucratic countries. If we don’t see many of such examples yet, it’s the education system to blame – kids don’t realize that remixing data and remixing video are very similar things that differ only by a target audience, but both can be cool :).

Knowing your position – “lighthouse” scientist

Lighthouse’s primary role is to assists in navigation – it helps you find your position on the map. Lighthouse is not a point of reference – as a point on the map is usually no more important than any other points. Lighthouse helps you understand where you are. Tech crowd has its own “lighthouse” people, for example Tim O’Reilly. Our small online science community has Bill Hooker. Neither of them seem to have outstanding resume (sorry to write that, I’ve seen better ones), but to understand where you are it’s worth to pay attention to what they say. They seem to understand particular part of our world much better than anybody else.

To put it in other words, a lighthouse scientist isn’t necessarily a person with the biggest achievements or a person who has a brilliant vision of the future – it’s a person who sees trends and movements, has a wider perspective and most importantly knows what’s important. In recent discussions on the blogosphere about bioinformatics as a field of science, Sean Eddy didn’t express his opinion – which I think is a very meaningful response.

Final thoughts

I’ve sketched this map to organize lots of thoughts and discussions around future directions of science. It is far from being complete and full of wishful thinking, but still helped me to wrap my mind around couple of issues in this area. Probably the most important thing I’ve realized is what was put into introduction: that the future may open lots of options for people willing to stay close to science. Those who realize this will benefit from them as first.

Update: there are interesting comments over at FriendFeed already.

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Posted by on March 26, 2009 in Comments, Community, Research


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What would you (do you) teach your kids?

Image via Wikipedia

This post is inspired by a question Iddo posted over at FriendFeed, in The Life Scientists room:

Teaching my 7 year old Logo (using kturtle). Any ideas for a good programming book for kids?

Other than programming, what would you (or do you) teach your kids? What kind of skills science geeks consider important, that aren’t really tought in schools (at least not at the level you’d want schools to teach them)?

My first thoughts were about remixing digital media, 3D modelling and printing (I believe 3D printers will become quite cheap in few years) and technical side of photography but I didn’t really spend much time on this topic (it’s obvious from this list). What’s your opinion?

UPDATE: Feel free to comment on this post’s FriendFeed thread.

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Posted by on March 8, 2009 in Comments


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(Do not) Beat ideas half to death

This is short post/note to self – to see if the way I think about science today will change in couple of years. Its enigmatic title comes from Stu Jenks, one of my favourite photographers. He wrote in the introduction to his works:

I’ve been doing a series of spirals. You know how it is with us artists. We take one idea, and then beat it half to death.

If we substitute “artists” with “scientists” it still sounds true. This is efficient (in modern terms of scientific productivity) way of doing research, but probably not always the best one. While I know that many breakthrough discoveries in science were results of years of hard work, not all of them required fifteen years to establish a procedure only. So, the question is if rapid switching between fields (every few years or so) is a good idea? It probably depends. Ask me in a few years how it works in my case.

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Posted by on February 17, 2009 in Comments


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