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Archive for the ‘Science’ Category
Scientists need to tell better stories
Author: richard22 Sep
by jurvetson
Stories Can Change The World:
[Via BIF Speak]
“Facts are facts, but stories are who we are, how we learn, and what it all means.” My friend Alan Webber, Co-founder of Fast Company and author of Rules of Thumb, has it exactly right. Storytelling is the most important tool for any innovator.
[More]
Scientists may not always realize it but they are always telling stories, providing narratives to illustrate the point to their research. This is often missed because the form the narrative takes is so structured that it does not appear like any story most of us have read.
But a story it is. It may be “Here is something no one has ever seen before and we don’t know what is going on.” Or “After years of work, we have completely delineated how this disease progresses.” Or “Here is an important piece to the puzzle that has been giving us fits for such a long time.” Or, sometimes, “What everyone else has written before is completely wrong and we show why!”
As a graduate student, I first ran across the expression, when putting a paper together, was “What story do we want to tell?” Few non-scientists really understand that every paper is simply a narrative. The best ones are incredible stories.
The structure of a paper throws many people off. There is an abstract, background, materials and methods, results and conclusions. It does not look like a standard text, it is presented in a stilted fashion and it has a structure that is unfamiliar but it actually does have a beginning, middle and end.
The abstract acts like a blurb on the back of a book, telling us whether the paper is worth reading. The background and methods act like a preface, giving us informative background.
The results are the meat of the story. Most start small, building up the knowledge as they move to data that have greater and greater ramifications. This leads to the climax of the paper, where they can state what it is they have now proven.
The conclusions often function as a denouement, recapitulating the action and providing context. It can also set up the action for the sequel.
Anyone reading Watson and Crick’s classic paper on the structure of DNA can see that it is a story. In fact, it compresses much of the normal scientific narrative in order to provide one of the classic “We figured it all out before everyone else!” stories.
It starts off with what others have proposed, continues with their model, demonstrates just how much better this fits all the data, and then ending with these words, setting up everything for the next series of papers:
It has not escaped our notice that the specific pairing we have postulated immediately suggest a possible copying mechanism for the genetic material.
Full details of the structures, including the conditions assumed in building it, together with a set of co-ordinates for the atoms, will be published elsewhere.
Science papers have an unusual format but they follow some of the standard things we see in any story. There has to a point to the paper. Why would anyone want to read the paper? It ca not just be a collection of random facts. The paper has to lead to some firm conclusions, including possible ramifications for current studies..
It must be focussed. It cannot meander through a lot of side streams. A science paper has to be kept on a very tight leash.
In every paper I have written, I have had to toss out very good experimental data, data that have no problems, because that they really do not fit the narrative that drives us to the conclusion. A well written paper focuses on the point and does not provide side trips into other areas.
The DNA paper did all this in one page. It left the detours for another time.
Most scientists realize at some level that a paper has to tell a story. But they do not realize that a scientific presentation at a conference really must do the same. There needs to be a beginning, middle and end. There has to be a point to it all, providing context to the data and its place in Nature.
Too many scientists forget this. They provide no frame for the discussion, leave needed background out and dump in all the data that was not fit for the focussed needs of a papers. Thus, most scientific presentations are unfocussed and boring. No structure and no real point.
The best presentations, the ones we all remember, use the data to provide a narrative, to help us understand just what story they are trying to tell.
We all tend to learn the needed tools to write a good science paper, incorporating the idea of a proper narrative. But few are provided any real tools to apply to presentations before a group of people. Most never learn the proper tools and simply give boring talk after boring talk.
Learning how to tell better stories, not just write good narratives, is something al researchers should learn how to do. But, whereas there is a real premium put on writing good papers, there is little pressure to speak well before a group.
That is why the best places to be at scientific conferences is usually not at the presentations but at the bars and pubs frequented by the conference goers. We get the real story there because every human being knows how to trade stories with others, even when the group is just a bunch of researchers.
Now if we could just get more researchers to adopt this approach to their public speaking trips, we might affect some real change.
Technorati Tags: Science
Networks in academia
Author: richard22 Jul
The Networked Path to Breakthroughs:
[Via Dot Earth]
An expert on the history of technological leaps says a vital step is for scientists and engineers to build networks outside of their fields.
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This a nice interview that explains how the current methods of providing grants for academic researchers help to isolate scientists from social networks that may be critical for moving research out into society.
The ability to innovate requires social networks but there are few connections between the networks of academic scientists and the people who are needed to transfer technology to wider communities. Finding mechanisms to overcome this bottleneck can have huge effects on the rates that innovations traverse different communities.
As more and more scientific endeavors require collaboration between multidisciplinary groups the insular nature of research will begin to be breeched. But, the bottlenecks and consricted connections to outside networks will also have to be changed, if we are to truly solve the difficult problems facing us.
Technorati Tags: Science
Communicating science
Author: richard16 Jun
by hiddedevries
A Climate (Communication) Crisis?:
[Via Dot Earth]
If experts change how they describe global warming, will people wake up?
[More]
Interesting points but trying to be more emotional and dramatic is not very effective when facts are trying to be exchanged. There has been a lot of research done that exposes the steps individuals and communities progress through as they adopt new idea and change their viewpoints. It might be better to be aware of this than to try framing exercises.
The five steps are awareness, interest, evaluation, trial and adoption. Different people move through these steps at different rate.This results in a differentiation of a population into different groups: innovators, early adopters, early majority,
Scientists are generally on the innovator/early adopter spectrum of things, especially compared to the entire population, which, by definition, is mostly the 68% in the middle.
Innovators and early adopters take their cues from outside influences and their own experiences. They are open to ideas that come from outside the community and move much faster through the five steps than others. They are not as dependent on community influences as the majority are.
So scientists are influenced by people who are outside their direct social network. We are trained to do that in order to examine data, converting it into useful knowledge that gains us understanding of the natural world. We have a lot of training that helps us have the sagacity to determine the usefulness of a new idea. even if the idea comes from someone ‘outside.’
But, for the majority of people in the middle, outside influences are suspect. They usually will only adopt an innovation or change their opinion when a respected member of their own community, of their social network, tells them to. They are generally influenced only by those close connections in their social network.
If the scientist is viewed as the other, as outside the group, many people will not listen to them. They seldom are influenced by anyone outside the group. This is why being liked can be such a big plus when trying to change someone’s mind.
When you are liked, you are more easily admitted to the group and will be listened to. Politicians know this. That is why likability is so important for them. Unlikeable politicians won’t get elected.
But few scientists are influential in the scientific community because they are liked. They are influential because they are good at making data into knowledge. But this is not something the majority will ever use to shape their opinions on the reliability of an outside researcher.
Thus, scientists can and do listen to others but the majority will only change when they and their connections are directly effected. This is probably why knowing someone who is gay has a much greater influence on someone’s opinion that anything GLBT groups can say. Becoming trusted by the group is more important than presenting facts, even if the facts are correct.
Most scientists think that the data should speak for itself. That is because they are really good at the evaluation step. They use their tacit and explicit information to make a decision and move quickly through the last two steps. IIn fact, scientists have to be pretty good at moving through all five steps rapidly or they will not be a very successful researcher.
Most everyone else is stuck at the interest stage. They await the opinions of influential members of their community to move beyond evaluation.
So to engage and educate, scientists must move out into the community. They must be seen as unbiased members who provide information for others to deal with. This can be quite difficult for many scientists. Part of this is because science attracts people with very health egos. You have to be very strong because in science, you fail a lot of the time. To keep on doing something, knowing it is unlikely to succeed, oftenrequires a monster ego.
So it is often hard for a scientist, who has made it through all 5 steps, to accept that someone else will not just listen to them, just trust them. ‘We are a scientist, after all! We know more about it. Why are you unable to understand the simplest things?’
All things that are not going to make the scientist liked in the community.
The majority of people only see someone from outside dealing them to do something.The first thing they often think is “What is he trying to sell? What is his angle?”
People like science because they enjoy understanding the world around them. But scientists do not have many useful organizations to help them engage with the public. They have very little training in how to deal with people that do not react to the world like they do. They often have to do it themselves. Perhaps if there was a more formal process to bring scientists into the community, we could get the majority convinced quicker.
The majority will move eventually and when they do, things can move rapidly. Facilitating this would be a worthwhile endeavor.
Technorati Tags: Social media
Five Researchers
Author: richard11 May
I published a new version of my Five Researchers Helped by Science 2.0. Hope you like it.
High tech helping biotech innovate
Author: richard18 Mar
by “T” altered art
Innovation, biotech, software, etc:
[Via business|bytes|genes|molecules]
There are a lot of synergistic effects of high tech on biotech. Much of the work done today requires high powered instruments running very complex algorithms.
But it still requires highly skilled people to do the work.
In a talk at E-Tech, Drew Endy apparently said that big money requirements of biotech are holding it back and one could make biotech innovation more like software and innovate much faster. Admittedly this is absent of context, but I responded to that tweet with one that said that while there is definitely a lot to learn, instruments and people cost money. My focus was actually on the latter. In the world of software, there is some specialization, but skills are more general, while in the life science world there is a lot of specialization of some very highly trained individuals (in fact one could argue that the amount these people get paid is a travesty compared to some other professions).
There are some things in biological research that can not be made easier by using computational approaches and processes. At least not yet. These systems are too complex and full of non-linear pathways.
There are a few things we can learn from the software world; DRY, iterative developments, organizational structure, etc, but biological systems are not perfect, they are not predictable, and most of all, our solutions have a lower margin of error. Whether it’s a drug, a diagnostic, or some kind of therapy, the process of development and associated regulations is always going to take time and it’s always going to throw nasty surprises at us. Biosimulation, protein structure prediction, robotics, improved collaborative tools, there are so many things to look into to make life science R&D faster and more efficient, and less prone to failure, but I find the idea that you can just use software development as a template a little insulting.
In fact, I think that in many ways biotech and high tech take very different approaches towards innovation. Computational techniques often take a procedural approach to solving a problem. Often, it is process driven and once the process has been found/optimized, you are pretty much done.
Process-driven sciences usually have well characterized components that act in defined manners. You start at point A and get to point C by going through point B.
Biological research at its base is not process driven. Not to say that there are not parts that can be encompassed in a process. But if a process is designed to provide a black and white answer (A to B to C), then the multitudes of gray that are biological results indicate its difference.
You start at point A and get to point C but you might go through points Q, R, and S before getting to point B. But only if the patient has a particular set of 20 different genes. For someone else, it could be a totally different game.
This is why it takes so long to develop any major drug. The model systems we use to develop them are not perfect. Then we have to hope that they will have greater beneficial effect in humans than deleterious.
We can, though, find ways to make some parts more efficient. Researchers are inundated with a surfeit of data these days. Disbursing these data throughout a social network helps alleviate this glut while making it more likely that the right data can get to the right person at the right time.
Human social networks are exquisitely formulated to tease out the underlying knowledge from a diverse set of information, and then pass this knowledge around quickly. Finding computational approaches to leverage these human social networks in order to solve these complex biological systems will have innovation as an emergent property.
It is a hardwired principle of humanity.
Technorati Tags: Open Access, Science, Social media, Web 2.0
Leo Durocher was wrong
Author: richard12 Mar
by Boston Public Library
Nice guys can finish first and so can their teams!:
[Via Eureka! Science News - Popular science news]
Ever thought the other guy was a loser for giving his all for the team even if others weren’t pulling their weight? A new study, published in the Journal of Personality and Social Psychology, says that person can influence a group to become more efficient in achieving its goals by making cooperative, collective behaviour seem acceptable and appropriate, and thereby encouraging others to act similarly.
The study, authored by a professor at the Rotman School of Management at the University of Toronto and his collaborator at Northwestern University, calls such individuals “consistent contributors” – people who contribute all the time, regardless of others’ choices.
The findings challenge assumptions made by many game and rational choice theorists that people should cooperate very little in situations with a known end-point when there are short-term incentives to act selfishly.
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This is a very interesting result. When people act selfishly in a group setting, they often change the behavior of others. There was a nice paper a few years ago that examined what a group did with cheats.
The game was set up in a similar fashion, with people in a group ‘donating’ their money into a pile. The group that donated the most got a bonus back. So, the way to make the most money was to be in a group that donated lots but donate little yourself. That is, freeload off of the rest.
What inevitably happened is that the rest of the group saw what was happening and started hoarding for themselves and the group would eventually fall apart. It was not stable. So what would create a stable group?
What worked was to allow people to sit out a round if they wanted. When people found a freeloader in the group they would all start to withdraw, making the parasite’s gambit worthless. When they came back in, the situation would remain stable until another cheat arose.
People would take a break until the cheat learned their lesson. So a relatively stable situation would develop if the group had a way to effectively deal with freeloaders. Otherwise it fell apart.
Now this study demonstrates that positive behavior can drive a groups approach simply by pushing forward no matter what. When people continue being consistently cooperative, they help everyone in the group.
“But our study found consistently cooperative actors even in places you might least expect them, and when they’re there they seem to set a tone and shape how their fellow group members understand situations,” says Prof. Weber. “Their clear, consistent behavior elicits cooperation, and once you get a few people cooperating with each other, they seem to enjoy cooperating. Groups become more productive, more economically efficient and, anecdotally, people enjoy being a part of them more as a result.”
In settings where there is an advantage to cooperating, groups with consistent cooperators were more successful than those who took a more ‘realistic’ approach. One can see why a social animal would evolve this way. Groups that cooperated would be more likely to survive than those where it was every man for themselves.
Given a level playing field, we want to cooperate with one another. The key is making the playing field level, insuring that the incentives do not push for behavior that is detrimental.
Technorati Tags: Social media, Web 2.0
No lines between disciplines
Author: richard11 Mar
by woodleywonderworks
Science Without Boundaries:
[Via AAAS News - RSS Feed]
AAAS Southwestern Meeting in Tulsa Explores Science Without Boundaries
The 2009 AAAS Southwestern and Rocky Mountain Division Annual Meeting will convene in Tulsa, Oklahoma., on 28 March for four days of events including a two-part special topic symposium on the climate and ecology of the Cross Timbers and South Great Plains.
The meeting—to be held on the campus of the University of Tulsa—will feature symposia on rainforest natural history, motor speech disorders, and alternative energies; along with student poster sessions and science communication workshops.
David Nash, executive director of the division, said this year’s meeting will emphasize the importance for science to transcend traditional boundaries.
“The largest problems facing society are so large and burdensome that no one scientific discipline, institution, or research method can find solutions,” said Nash. “This year’s meeting is going to show why scientific collaboration is vital to the scientific process.”
[More]
More meetings should be on exactly this same topic. Well, maybe not the same topic but the same underlying premise. Innovative research, and the underlying solutions that drive technology, can not be done anymore in silos of scientific disciplines.
The answers will be less and less likely to arise from a Department of Biochemistry or Oncology alone. It will take work across disciplines to find the answers.
It will require systems thinking and synthesis of information. Not reductionist approaches and analytical deconstruction.
The faster that organizations realize this and actually to something positive about it, the faster we will solve these problems. AAAS has recognized this as have several other organizations. Now if we can just change the ship of grants that is the NIH and then redo how research universities are put together we may get somewhere.
Baby Steps.
[Crossposted at Path to Sustainable and Path to Sustainable]
Technorati Tags: Science, Social media, Sustainability, Technology
Other matters
Author: richard6 Mar
For those interested, I’m hosting a open discussion group on science and sustainability called Idea Club here in Seattle on March 23. It is hosted by the Sustainable Path Foundation.
This month’s topic is on turning knowledge into action and how sustainable communities may be formed. It is based on some sessions that the AAAS Annual meeting in February.
I have written about some of these at my other blogs, Path to Sustainable and A Man with a PhD. You can register for the free event there.
One of the things we like to do is to ask people to submit their own ideas for a topic, even if you can not attend. If you have an idea,leave a comment at any of the other blogs.
Hope to see some of you there.
Technorati Tags: Social media, Sustainability, Web 2.0
Opening sources for Biotech
Author: richard3 Mar
Genentech open sources Unison: [Via business|bytes|genes|molecules]
Image by Getty Images via Daylife While on the subject of open and pharma, a bioinform article (sub reqd) tells us about Unison, a protein sequence analysis platform from Genentech that has been released under the Academic Free License (why not the Apache License since they are very similar). What is Unison? Unison is a compendium of protein sequences and extensive precomputed predictions. Integration of these and other data within Unison enables holistic mining of sequences based on protein features, analysis of individual and sets of sequences, and refinement of hypotheses regarding the composition of protein families
Essentially Unison is a data warehouse, which includes a number of protein sequences, and a bunch of pre-computed data. They have also released the complete schema, API, and some of the predictions. The backend is PostgreSQL and the platform leverages the BioPerl API. So the web service serves as a reference implementation of the Unison platform. People can essentially replicate the system and contribute code within their own servers using.
I think that biotech/pharma companies may do this more and more. The advantages for a company do not really come from these particular tools but how they are used and interpreted. Making this available to a much larger group means it is more likely to yield useful results. Genentech can only do so much with these tools. If someone else uses them to find something novel, some thing that Genentech did not recognize at all, Genentech might be able to reap some rewards that they would not have if they had kept things to themselves. Even if they do not get rewards directly, the publicity is worth something. They see this as a way to extend their influence rather than something for competitors to use against them. By furthering collaboration and increasing the number of eyeballs using their tools, Genentech can accomplish some things that would be difficult to do with their cards held close.
Technorati Tags: Bioinformatics, Health, Science, Web 2.0
Not such a killer, perhaps
Author: richard25 Feb
by Hey Paul
Why article tagging doesn’t work:
[Via Bench Marks]
Reading William Gunn’s recent blog posting, Could this be the Science Social Networking killer app? got me thinking more about the many online scientific reference list repositories like Connotea, CiteULike and 2Collab, and why they are failing to catch on. William is suggesting a Pandora-like system of expert reviewers tagging papers to set up a recommendation system. I’m not sure this would be really helpful–what you get from a scientific paper is very different from what you get from listening to a song, and their interconnectedness works in very different ways. And it brings to mind the failings of organizing your references by tags.
If you’ve ever dealt with any of these social bookmarking sites, you know how incredibly tedious they are to use. Even for journals like CSH Protocols, where we have buttons on every article to add it directly to these sites, you still end up jumping through hoops, filling out forms, writing summaries, adding tags. You’re on the spot at that moment to come up with a list of tags that will remind you about the content of that paper. As your worldview changes over time, and with it your research priorities, you’re probably going to want to revisit many papers and add additional tags. Even with all this time-consuming work, you still may not have added an appropriate tag to let you find what you want to find at a given moment. Did you add a tag for every method used in the paper? Every conclusion, every subject referenced? That band on the gel in figure 3 that you’re ignoring today might be very important to you tomorrow. How are you going to tag the paper in case you need to find it again?
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I totally agree with David. There are two kinds of list-making people in the world – those that make lists and those that don’t. Applying tags to articles works well for the list-makers but many, many scientists are just too time-deprived to fill in boxes or check off squares.
But the real problem, as notes, is that in research the semantics change very rapidly. A paper that was really useful for a its description of a new cell surface marker may, at a later date, become important for a particular technique. How are you supposed to know beforehand which tags to use.
And in many cases since the research is at the cutting edge, there are no appropriate tags. So I make up one – call it IL-99. But someone else working on the same protein, adds a new tag called EDFWR. How in the world to the tags properly link these papers?
And, finally, no researcher gets any credit for really annotating a paper. Taking the time to do this, or to recommend a paper, is time they can be focussing on getting tenure, getting a grant in or writing a paper. Where is the payoff to the individual scientist?
Tagging research is not an easy problem to fix. We may all agree that it is worthwhile but we are a long way from any reasonable solution.
These works by SpreadingScience and Richard Gayle are licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.