Image via WikipediaLet us say you are a researcher and are doing a gene expression study on some tissue. Today, the chances are that you will run some microarrays and look at the expression profile and then try and correlate the expression profiles of a number of samples with associated data.
Fast forward a few years. I am convinced that a lot of such data will be available via search engines or data portals. Already you are beginning to see a number of commercial and public engines come to life (NextBio, Oncomine, etc). Earlier this week I read an announcement (sub reqd) by the NCI to create a Cancer Molecular Analysis Portal, which will integrate data sets from the Cancer Genome Atlas project and other cancer genomics studies.
The key here is that we already have a body of work using microarrays and other molecular profiling systems, and in many cases, people are just repeating experiments which someone, somewhere has already carried out. Unless there is something inherently proprietary in those studies (e.g specific dose-response studies), there is no reason to repeat that experiment, especially for technologies that are relatively stable and don’t have too much cross-platform/cross-lab variation (one of the goals of the MAQC projects has been to understand these variations). The second key, and to an extent perhaps even more important, is how these data are made available. Personally, I really like the NextBio interface. Will the business model work? I am not sure, but definitely the idea and concept make a lot of sense.
There is a lot of work being repeated over and over again because access to the data is not easily available. This is one of the big changes that will take place over the next few years, as the same principles that make PubMed or GenBank so useful start permeating all databases.
With the increase in the number of journals and articles being published every year and the possibility of having an even larger set of “gray literature” available online we face the challenge of filtering out those bits of information that are relevant for us.
Let us define as “perceived impact” this subjective measure of importance that some bit of information holds for us as scientists. This information is typically an article but it could be applied later to pre-prints and database entries in general.
So begins a nice essay looking at possible ways to filter articles AfTER publication on the Web, rather than BEFORE publication, which is what happens now with most journals.
No real answer except the idea that leveraging all the eyes on the web could accomplish a lot of this. RSS can be used to rapidly identify articles. Links to the PDFs plus any comments I may have can be quickly placed on my personal blog.
I can then come back later, when I have more time and spend it with the articles I have shown some interest in. If I am a good filter of articles, others can subscribe to my newsfeed, leveraging my abilities without having to do the filtering themselves.
Perhaps something could be done with a similar process. It would not replace other approaches but serve as an adjunct.
Lucas McDonnell has a list of 15 tips for successful knowledge sharing. Reading through them, I couldn’t help think of the Carnegie tips from How to Win Friends and Influence People. In both cases, the general principle is to listen more than you talk and let people have interesting ideas – no matter who really owns them.
Here is Lucas’ list (check the source for his comments):
Share failures as well as successes.
Don’t oversell your own work.
Ask questions about others’ work.
Ask before borrowing.
Give credit where it’s due.
Be genuine, avoid ‘networking’.
Don’t just connect with those doing identical work.
Be prepared to provide documentation.
Talk to people you already know as well.
Take lots of notes.
Take the first the step.
Learn more than you teach.
Be patient and listen.
Talk to people about talking to people.
Expect the best from people.
A very nice list, demonstrating the social aspects of knowledge flow. It does not happen in a vacuum. These are not new points as shown below:
And here are Carnegie’s 21 elements from HwWFaIP (courtesy of this page and listed in many other places as the summary of the book):
Don’t criticize, condemn or complain.
Give honest and sincere appreciation.
Arouse in the other person an eager want.
Become genuinely interested in other people.
Smile.
Remember that a person’s name is to that person the sweetest and most important sound in any language.
Be a good listener. Encourage others to talk about themselves.
Talk in terms of the other person’s interests.
Make the other person feel important – and do it sincerely.
The only way to get the best of an argument is to avoid it.
Show respect for the other person’s opinions. Never say, “You’re wrong.”
If you are wrong, admit it quickly and emphatically.
Begin in a friendly way.
Get the other person saying “yes, yes” immediately.
Let the other person do a great deal of the talking.
Let the other person feel that the idea is his or hers.
Try honestly to see things from the other person’s point of view.
Be sympathetic with the other person’s ideas and desires.
Appeal to the nobler motives.
Dramatize your ideas.
Throw down a challenge.
Online knowledge sharing will only be effective if it follows similar core elements. Web 2,0 is about human conversations, not IT tools. The tools are important based on how they further these core elements.
Online sharing still happens between people, through human social networks. The same tips that further healthy human communication face-to-face, as exemplified by Carnegie’s list, are just a helpful with Web 2.0. What Carnegie wrote in 1936 for Depression era audiences is just a relevant today for online communities.plus ça change, plus c’est la même chose (from 1849)
Organizationsthat forget this will not have successful online experiences.
James Robertson of StepTwoposted his slides for a recent presentation entitled, Ten tips for succeeding at collaboration (with audio). Along with the tips, he includes some background around the ideas he presents. Nice.
Always nice to see presentations using newer technology like Slideshare. Being able to also hear a presentation rather than just see them is another huge bonus.
Here are the enumerated tips (in American English :-). And a few of my thoughts sprinkled through. Get ahead of the curve. “Thousands of uncoordinated wikis and blogs in your company is anti-knowledge-sharing.” Recognize when collaboration will work. In James’ talk, he gives two key criteria: A sense of purpose and a Clear sense of community. And he talks through several examples of using these criteria. Understand where collaboration fits in. Internet need not be in conflict with the Intranet, as the purposes are different between the outside world and the internal world. Establish a portfolio of tools. One tool will NOT unite them all. Identify an owner of collaboration. Not sure who should be the “collaboration czar” or where it should fit. Define boundaries and relationships. Establish policies and support. Start by ‘gardening.’ x Focus on business needs. “Pilot in an area that people care about.” Don’t bother piloting in IT or in the KM team. They aren’t normal people! Don’t forget it’s all about the people! Of course! But I hope we can say this with a bit of a smile, as it seems a cliche. Just like “It’s not about the tools.”
A very nice list and a fun presentation. The important thing is to focus on a specific area, a specific need, and some specific people. Once they find the usefulness of collaboration, it will spread to others who will use it.
While the people are important, the tools are also. The key is to make sure the tools fit the group of people, not the other wat around. You would not give someone a screwdriver to hammer a nail. Same thing here.
But recognize that not every needs to collaborate, at least initially.
Giving talks can be stressful as is, so this requires some congratulating for the effort. Great work Cameron!
It is very likely that presentations in the near future will not only be in-person and streamed on the web, but also include much larger back channels using FriendFeed and Twitter.
There will not only be a way to enlarge the audience, but all these conversations can be examined in order to get a much better idea of how the presentation went and what effect any new data will have on other investigations.
Presentations will not simply be monologs anymore but will have be just a part of the overall conversation. This means scientific information travels farther, faster, with greater vetting by peers than ever before.
No matter what one does for a living, everyone today needs good online research skills. According to Jane John, past president of the Association of Independent Information Professionals, the key is to first clearly define what it is you need to know. In this interview John also explains what specific skills teachers and students need to navigate our ever-growing sea of information, and how leaving time for reflection can help illuminate the real meaning and value of that information.
Some discussion of the approaches students will take, including the importance of synthesis. Current education spends a lot of time on analysis, breaking things down into simple easily remembered bits.
Web 2.0 promotes synthesis. Being able to bring facts together in order to create knowledge will be important aspects of future learning.
If you think of yourself as someone who understands creativity, this is an essential talk by Ken Robinson, from the TED Conference. He calls into question the antiquated teaching models we have in the Western world, and asks many great questions about creative thinking and the business world.
Key quotes from Mr. Robinson:
“My contention is that creativity is as important as literacy and should be treated with the same status.”
“They’re [children] not frightened of being wrong. I don’t mean to say being wrong is the same as being creative. What we do know is if you’re not prepared to be wrong, you won’t come up with anything original. And by the time most children get to be adults most children have lost that capacity.”
It’s a funny, enlightening and well reasoned 20 minutes. Highly recommended.
Ahh. Education. Always good for some nice discussion. This is a very entertaining talk, though.
Most schools do drive out creativity in many students but I think they also force some students to become more creative, in order to get around the roadblocks presented by schools.
Just as patents/copyrights put barriers in the way of innovators, requiring them to find a new way forward , so too can public education.
However, these students would probably be creative no matter what, while the vast majority will have creativity pushed out of them. It is a real waste. This is one reason I expect public education to see vast changes.
The following represents a model of the approaches that may be taken using Web 2.0 tools. It is really simplification of what is possible. The question is how fast this model in some form is adopted.
Working together to solve problems in a collaborative fashion results in faster innovation cycles. This will be true in school also. Ones that use Web 2.0 approaches to teach will find that their students are more creative and better able to solve difficult problems. This will be superior for solving the complex problems seen today than the 19th century approaches we use today.
This is not about teaching a curriculum that will solve all our problems. This has been attempted for the last 150 years. This is about changing the basic manner in which we teach children.
It will be less authoritarian, with less of a stern headmaster using a top-down approach and more of a collaborative approach. Rote memorization of things like what year an event happened will not be as important, since this will be too easy to find online. But understanding the results of an event, how it changed the world, will be among the important skills that will be taught.
In a Web 2.0 world, finding information is easy. Using it to create knowledge is more rapidly facilitated by working in groups.
And because Web 2.0 tools make explicit many things that are usually hidden in today’s approaches, it will be possible to tease out data such as each person’s contribution. Those that try to freeload on the backs of their group can be more easily identified and dealt with.
I do not expect it to be Nirvana but schools that adopt more of the approaches, particularly with older students, will find they are more successful at meeting many of the metrics being used today, at least the ones examining creativity and innovation.
Shankar Vedantam explores the widely accepted belief that extrinsic rewards can get people to do things. Research studies, Vedantam shows, points the other way around — external rewards kills the inner drive.
“External rewards and punishments are counterproductive when it comes to activities that are meaningful — tasks that telegraph something about a person’s intellectual abilities, generosity, courage or values. People will voluntarily perform intellectually arduous work, for example, because it gives them pleasure to solve a puzzle or win a game of wits.”
For this to work, there needs to be a way for the telegraphing to get disbursed throughout the community and allow others to know about the intellectual abilities, etc.
If this is not done, most people will feel like schmucks, taking on arduous work with no real group compensation. The reward does not have to be much. Intellipedia just used a small spade.
In fact, it works better when the reward is something small, almost frivolous, when the reward is for the work done, not when the work is done for the reward.
There is a great story (it may be from HP) whose details I can not recall. Essentially, an engineer walked into the boss’s office and let him know that a big project was complete. To show his pleasure, the boss picked up some incidental object in the office (like a hammer) and gave the engineer the first Golden Hammer award.
The group passed the award around whenever someone did something outstanding. The rewards have to help strengthen the connections, not break them.
Had a conversation earlier today, all about RDF and linked data. I am a big believer, which is why posts like this one by Cameron Neylon on A new way of looking at science? bring a smile.
Andrew Milsted, a PhD student, enabled an RDF dump of the content in the lab notebook used by Cameron’s group (and others I suspect). The result, a graph that shows each post in the notebook as a node and links between posts as edges. It is a universe of the work going on in the lab, and how that work interacts. It would be interesting to see the dynamics of this graph evolve, and various other ways of visualizing the underlying data and relationships. It would also be cool to put this up on the web as linked data and link it to data outside Cameron’s lab. Might even lead to some very interesting observations and relationships.
This is a simple example, but highlights why it is so important to be able to put data into machine readable formats. RDF is a naturally good model, since it highlights relationships within the underlying data.
In the not too distant future, lab notebooks will be digitized and all the info will be available online, at least for the use of the researchers creating the data. This will be because most of the experimental results will be in digital form, making it much easier to attach them to the electronic notebook but also because the work can be accessed and examined in totally novel ways.
As shown here, the digital material can be examined for links and mined in ways that are just impossible today. Linkages between pages, data and comments could be examined. Possible relationships between projects could be highlighted. Areas for collaboration could be determined.
Context can be added to data in order to create a deeper examination of the information created.
The groups that more rapidly embrace these sorts of approaches will be able to turn the creativity cycle faster, increase the rates of diffusion of innovation in the community and find solutions to complex problems that are unsolvable by simply analog approaches.
Image via WikipediaLet us say you are a researcher and are doing a gene expression study on some tissue. Today, the chances are that you will run some microarrays and look at the expression profile and then try and correlate the expression profiles of a number of samples with associated data.
