A generational war

 1288 1215052987 96482B2F3B by kentbye
Social Media vs. Knowledge Management: A Generational War:
[Via Enterprise 2.0 Blog]

You’d think Knowledge Management (KM), that venerable IT-based social engineering discipline which came up with evocative phrases like “community of practice,” “expertise locater,” and “knowledge capture,” would be in the vanguard of the 2.0 revolution. You’d be wrong. Inside organizations and at industry fora today, every other conversation around social media (SM) and Enterprise 2.0 seems to turn into a thinly-veiled skirmish within an industry-wide KM-SM shadow war. I suppose I must be a little dense, because it took not one, not two, but three separate incidents before I realized there was a war on. Here’s what’s going on: KM and SM look very similar on the surface, but are actually radically different at multiple levels, both cultural and technical, and are locked in an undeclared cultural war for the soul of Enterprise 2.0. And the most hilarious part is that most of the combatants don’t even realize they are in a war. They think they are loosely-aligned and working towards the same ends, with some minor differences of emphasis. So let me tell you about this war and how it is shaping up. Hint: I have credible neutral “war correspondent” status because I was born in 1974.


A very clear post that describes the conflict between Boomer and Millennial thinking when it comes to dealing with large amounts of data. Knowledge management (Boomer) is a top-down put the data in the proper bin sort of approach. There are names for each bin and everything needs to fit in the correct one.

Social media (Millennial) uses human social networks in a bottom-up approach that allows the data to determine where it should go. Any bin that it should go into is an emergent property of the network created by the community.

Read the whole post for a nice dissection of what is happening in this War. Just remember that Age is not as important as attitude. There are Boomers who get social media and Millennials who do not.

I think it is that one personality wants things to be black and white (the data is in a database on THIS computer) white the other deals great with shades of gray (the data is in the cloud and not really anyplace).

I did my post-doc in a chemistry lab, the only biologist. I saw something very valuable. Chemistry is very process-driven. The purpose of a process is to reproduce success. If a process, say a particular chemical synthesis, did not work, as in the yield was 10% instead of 90%, it was not the fault of the process. The reagents were bad or the investigator was incompetent. But the process was still valid.

So chemistry selected for people who were very process-driven, wanted things very tightly controlled and well defined.

Biology has a very different regard for process. The same process (say the cloning of a gene) can be done on two different days and get different results (10 colonies of cells one day; 500 the next). Biology is really too complex to be able to control everything. A lot of things can go wrong and it can be really easy to fool oneself with results.

So biology, particularly at the cutting edge, selects for people who can filter out extraneous bits of data, can be comfortable with conditional results and with the general anarchy that can occur. Every molecular biologist has experienced the dreaded ‘everything stops working, so I have to remake every buffer, order new reagents and spend a month trying to figure out what happened, knowing that things will start working again for no real reason.’

Chemists in my post-doc lab hated biology because of the large variance in results, compared to chemistry. Biologists are often happy to be within an order of magnitude of expected results

One way of thinking has to know whether Schrodinger’s cat is dead or alive, while the other is comfortable with knowing it is simultaneously dead and alive.

Biology needs the Millenial approach because it is creating data at too fast a pace to put it all into bins. Social networks can help tremendously with the filters needed to find knowledge in the huge amount of data.

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More information

pills by blmurch
Magical Thinking:
[Via FasterCures]
Margaret Anderson, COO, FasterCures

I appreciated the message of Carol Diamond and Clay Shirky’s recent piece in the August 2008 Health Affairs titled “Health Information Technology: A Few Years of Magical Thinking?” In it they say that “proponents of health IT must resist “magical thinking,” such as the notion that isolated work on technology will transform our broken system.” It’s interesting to think about systems change at the front end, and how easy it is to get stars in our eyes about how things like health IT or personalized medicine will transform the world as we know it, and how all of our problems will then magically go away.

The article discusses how it might be easier to implement IT in health if the whole system is redone, rather than bolting on IT. IT will not fix the problems without key changes in how medicine is practiced.

A press release discusses some of their points.

Diamond and Shirky propose an alternative route to using health IT to help transform the U.S. health system. “This alternative approach would focus on a minimal set of standards at first,” they say, and would make utility for the user and improved health outcomes, rather than vendor agreement, the key criteria.

Diamond and Shirky’s alternative approach “would mean working simultaneously on removing other obstacles while concentrating on those standards necessary for sharing the information, however formatted in the short term, to flow between willing and authorized participants. Finally, it would require clear policy statements that will guide the design of technology.”

Sounds like a bottom up approach with the end user driving the technology, rather than health vendors. More from Margaret Anderson:

Cell phones, email, and the Internet have certainly transformed things in ways we couldn’t have imagined, but they’ve introduced problems we couldn’t have imagined. Technologies such as FAX machines have been leapfrogged over. Problems such as the overabundance of information, and the speed of information flow are here to stay it seems. In the case of health IT, FasterCures sees it as a vital bridge to the future of more rapid information collection, characterization, and analysis which could speed our time to cures.

But there needs to be careful attention to the fact that too much information, particularly in the health field, can make it much harder to make accurate decision. eventually we will get the complexity of the system under control but in the meantime, there will be some problems. Faster Cures is examining them.

We are working on a white paper for the U.S. Department of Health and Human Services about educating and building awareness among consumers about personalized healthcare. This is another area where we must resist “magical thinking” and get down to brass tacks. Too often, the discussion about personalized medicine has been at a 30,000 foot level. For this paper, we’ve talked to many patient advocacy and disease research groups and everyone holds their breath about the potential power that these technologies may hold for their disease areas. They all want more targeted therapies with fewer side effects, which is ultimately the promise of personalized medicine. But they also recognize its complexities. It needs to take into account the world of co-morbidities we all live in; even if baby boomers are out running marathons and eating their greens and blueberries, the reality is that many of us are living with many conditions and diseases, not just one. It will probably raise costs before it can lower them. It’s unlikely many diseases will yield to the relatively easy HER2-Herceptin gene-to-drug relationship. Patients are likely to get much more information about their genetic makeup than they can act on in the near-term.

Health care is still too complex in most cases. The real magical thinking comes in the form of so many fraudulent ‘cures’ that have plagued mankind for thousands of years. Perhaps as we really get IT involved in health, we can begin to gain a fuller understanding of what causes disease and how to attempt a cure.

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Marketing for research

atomium by txd
Attention, science and money:
[Via business|bytes|genes|molecules]

Interesting observation by Kevin Kelly. He says

Where ever attention flows, money will follow

To some extent, that’s somewhat obvious. Peter Drucker, whom I admire a lot, said the following

Marketing and innovation produce results; all the rest are costs

Part of the problem with many corporations that commercialize science and technology is that they only focus on the marketing and not the innovation. I remember being told by a higher up that marketing made money – For every dollar we spend on Marketing, we get $3 back. But he told me that research cost money, money that was never directly recouped.

There are good metrics for marketing, not so much for innovation. Yet without the latter the former has nothing to do.

Attention can be driven by many mechanisms, marketing being the most effective one. The key is gaining sufficient mindshare, which is often accompanies by a flow of capital. In science, the money follows topics of research that have mindshare. Similarly people fund companies in areas that generate mindshare for whatever reason.

The question I often ask myself, both from my time as a marketer and as someone interested in science communication, is how can we bring more mindshare to some of our efforts and science in general. What does money flow mean? Is it just research funding? Is it investment in such concepts as “bursty work”? Take something else Kelly writes

New things that don’t work or serve no purpose are quickly weeded out of the system. But the fact that something does work or is helpful is no longer sufficient for success.

Part of the problem is that many researchers feel the data should speak for itself. They fail to realize that gaining mindshare or convincing people requires social interactions. It is a very rare thing that requires no further work in order to sell itself.

We all realize that nothing in science is this way. That is, when we deal with each other, we realize that further experimentation is required to convince us of a new innovation. Few things just emerge from Zeus’ head. we know the process to market to our peers – publications, conferences and seminars.

But the idea of doing something similar to get innovations out to non-scientists is not on an researcher’s radar screen. We don’t have enough time for that. Perhaps just a recognition that there is a process people go through to adopt an innovation and the attempt to facilitate some of those steps would go a long way.

I have written about the lack of marketing in science (stealing shamelessly from Larry Page). It’s critical that we do a better job of highlighting the power of our activities and learn some marketing tricks along the way. No I am not talking about the in your face stuff that gives marketing a bad name, but about the kinds of activities that maintain that attention, and get people to notice. The good news, many of us already do that, perhaps without even realizing it. It’s still niche awareness, but I have a feeling that we are close to actually crossing the hump and bringing some of our activities into the mainstream.

KK link via Michael Nielsen

Marketing is really just convincing people to make a change in their life, to adopt an innovation. It may have a bad odor in science (because ads make people want things that they do not really need) but marketing is really what everyone does who truly wants to compete for mindshare.

We just need to do it in a way that supports research while helping others through the process of adopting innovations.

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Browsing for researchers

I use a RSS reader and read feeds because it is part of my writing process. Lately, my RSS reading habits have changed. I haven’t given up on it completely, but my process has changed. My feeds are organized into folders and the folders ordered by priority. Like a farmer tending his crops, I’d scan through each folder, each feed, bookmarking and annotating what caught my eye, and looking for patterns and connections. This scan, capture, analyze patterns, and write a blog post is a part of my routine.

It still is, but I now use other methods for scanning. It’s more like hanging out in a village square or a pub — conversations, news, and resources come to me. I’m finding new links and posts either through twitter, comments on my blog post, or through people who have linked to me.

So, it’s like I have a left brain, orderly, linear way to scan and a right brain, wildly creative way to scan.

RSS and newsreaders present an incredible set of tools to filter through a lot of information very rapidly. It is like you are directly hooked into to a diverse group of communities in real time. You can see how different items spread through a linked community and drive communication.

And the orderly vs crazy approaches to connecting help one’s own creativity and innovation by interacting with our tacit information, producing the opportunity to alert other communities.

I like how Chris Brogan describes his reading goals.

1. Reading what friends write.
2. Reading about the “new marketing” industry and the tech industry (fishbowl).
3. Reading what people recommend.
4. Reading off the wall stuff that inspires new thoughts (outside the bowl).

This sounds very much like an early adopter, who has connection outside to other media outlets, but uses trusted insiders to decide what things to use.

Michele Martin wrote a post summarizing a paper titled How Knowledge Workers Use the Web and pulls out some the classifications referenced in the paper. My RSS reading is mostly information gathering or browsing.

Finding–Looking for something specific, such as an answer to a specific question.
Information gathering–Less specific than finding, this is research that’s focused on a particular goal that’s broader-based than simply getting a specific piece of information.
Browsing–Visiting personal or professional sites with no specific goal in mind other than to “stay up-to-date” or be entertained.
Transacting–Using the web to execute a transaction, such as banking or shopping.
Communicating–Participating in chat rooms or forums (remember–this was done in 2002, prior to Facebook and the explosive growth of blogs, etc.)
Housekeeping–Using the web to check or maintain the accuracy and functionality of web-based resources, such as looking for dead links, cleaning up outdated information, etc.

One of the major aspects of scientific research and innovation comes from browsing, from reading about something not directly related to a specific problem but which may provide valuable insight for the problem. This used to be relatively easy by doing things like sitting in the library once a week going through the table of contents of all the journals that came in that week, carefully writing down the bibliographic information on note cards, so they could be examined later at leisure.

Serendipity could raise its head. But the Internet made searching so much easier. So too many scientists spend their time on the first step, finding. This is, of course, very important but you will really only find what you are looking for. Serendipity is reduced.

A personal example. Many years ago, I was working on inducing protein production in E. coli from specific gene segments. We typically did this by shifting the temperature, which resulted in the inactivation of a repressor and the expression of the gene.

However, for large scale production (think 1000s of liters) this was not a tenable solution. It was really impossible to raise the temperature of the vessel quick enough to make it a viable solution.

I happened to be reading the Table of Contents of the Journal of Bacteriology and saw a paper which discussed some of the biological effects on the bacteria when the pH of the media was shifted to a more acidic condition. I recognized some of the bacterial proteins involved as being similar to the repressor we used.

So I went out and did some experiments and determined that by dropping the pH, large amounts of the specific protein could be produced. Dropping some acid in a large vessel and stirring quickly can rapidly expose all the cells to the same conditions and induce protein production.

But it could also be done under some different conditions, resulting in up to 15 times more recombinant protein being produced.

So, for me, the really important aspect of RSS/newsreaders is bringing browsing back. Every journal has newsfeeds now. I can typically go through several thousand titles in an hour, bookmark the ones I want to examine later and even post the links to a blog, where I can add comments.

My blog becomes my online note card file for interesting articles.

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As always

cats by tanakawho
Digital intimacy:
[Via Bench Marks]

Recently, the NY Times had an article discussing the concept of “ambient awareness”, or as the article puts it, “incessant online contact”. Now, first off, I have to admit that I’m one of the over-30-year-olds the article mentions, who finds the concept of subjecting others to (and being subjected to) a stream of trivial details about one’s day completely unappealing. The proponents of Twitter and FriendFeed and the like feel that they’re getting a more intimate understanding of people, “something raw about my friends,” as one user puts it. I’m more in line with the critics quoted in the article that the end result is more “parasocial” than social, and that it ends up an extension of reading gossip magazines and following celebrities from afar.
So how do these new practices apply to the world of science research?

David always brings up really good points to discuss. I don’t expect every scientist will want or need to be a direct part of the ‘conversation’ happening on Twiiter or FriendFeed. Few have the time. But it will be important that the social network (ie. lab, department, etc.) they belong to includes people who are connected.

These tools are rapidly becoming a part of how human communities disperse information. This decreases the diameter of a social network tremendously, meaning information of every type has to traverse fewer nodes.

Research networks that normally involved publications, seminars, conferences, etc. will also include these social media approaches. Because labs that remain unconnected will not be able to compete with labs that do use these tools to decrease the diameter of their sphere of collaborations and fid out about relevant information faster.

These tools are just part of finding out what is happening in relevant fields. I’ll give an example of how these tools can help move information in ways not possible before.

I had looked a little bit at FriendFeed but just did not have the time to really dig. Then I noticed that there were a lot of hits at my website that were being referred from the Science 2.0 room.

Turns out they were having a conversation about my site and were asking a lot of questions, trying to get an idea of who I was , my reputation, etc. Seeing the conversation, I quickly joined and helped answer questions. Now I am a part of a group I can check in on every so often that does a great job finding and providing information I find useful.

Like any social setting, I introduced myself, answered some questions and provided insight. Now I am connected to a group that provides very useful information for me.
I don’t have to check it constantly to be able to see useful items that I would not have if I were not part of this particular conversation.

Human social networks are exceptionally great filters of information. The huge amounts of information being created today require human networks to help filter and disperse the info. These tools are simply one part.

All that will really be necessary is for a scientist just to be part of a research network, even just a lab, in which someone is connected to these online sites. What is important is the rate at which this information diffuses throughout the group, not that everyone in the group is connected to Twitter.

Each person in a network often has their own role, their own diverse viewpoint that helps the group. The best tools will be ones that allow people to use them for their own purposes and needs. They do not work by forcing everyone to join.

But they do work by spreading information farther and faster.

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Making Friends

friends by jurvetson

I wanted to bring my personal perspective of
the 5 steps people go through while adopting a new technology. It has to do with FriendFeed.

I have been aware of FriendFeed for several months, but never did much with it. I was not really sure what it provided, and I just did not have the time to explore it. But my interest built up as I saw more of the scientists whose newsfeeds I subscribe to begin to discuss their experiences with it. My interest increased seeing the mashups that were developing – such as the widgets that could connect a blog with FriendFeed comments, etc.

But I was still too busy and I was not sure if it was worth the time to figure out the best way to use it, what was required, etc. So my progression through the first stages was a little slow as I still did not really see how it would help me. There were no ‘local’ authorities of mine that had adopted it.

Then, just a few days ago, I got a lot of hits and the referer was a specific FriendFeed page about Science 2.0, where the website was being discussed. In fact, there was quite a conversation going on, one that I had to join. Now I began to see what could be really useful about FirendFeed.

So I actually raced through the last 2 steps very fast. Trial took about 2 minutes since FriendFeed is pretty straightforward and i was congratulating myself for the adoption stage even as I was writing my second comment.

All this would suggest that I am an early adopter. not an innovator. Which is what I expected. I needed some interactions with members of the community rather than hearing it from outside experts.

But this also indicates just how rapidly a new innovation can move if it finds the right path. Especially when there are conversations happening, information being exchanged,

People will adopt a new innovation really fast if there is a conversation about them or their research interest, and they want to be a part of the conversation. I would expect most scientists would plow right through the latter stages of the 5 steps if their research was directly influenced by the conversation.

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A five step process

I’ve mentioned some of the work by Everett Rogers on technology adoption. The bell curve seen refers to the adoption of innovations by a community. But what about individuals? Is there a process whereby they adopt new technology?

Turns out there is. You can read the work by George Beal and Joe Bohlen in 1957. There is a five step path that each individual appears to go through, although some people are slower to transition between steps.

  1. Awareness. The individual is simply aware the innovation exists.
  2. Interest. The individual wants more information. They begin to wonder if the innovation can help them.
  3. Evaluation. The individual mentally examines the innovation using the information gathered, trying to determine whether it will really impact their work.
  4. Trial. The individual actually tests the innovation to see if reality matches expectations.
  5. Adoption. The individual likes the innovation and adopts it wholeheartedly.

Beal and Bohlen also described what sources of information were used at each stage. Through the first two, mass media and government agencies were most important.

This was really an attempt to get an ‘unbiased’ viewpoint since friends and salesmen (saesmen always came in last) were the next two sources. But for the last 3 stages, neighbors and friends were the largest source of information, moreso than any other group.

So, early in the diffusion process, unbiased experts are sought. But when the evaluation process is started, the experiences of close ties within a local social network become the most important. For most people, the opinions and personal experiences of their friends are most important for adoption of a new innovation than any external source.


Now the innovators in a community race through these steps. They often are connected to outside groups and use social interactions unavailable to others in the community to more rapidly move through the last 3 steps.

The early adopters take information from the innovators and use their own connections to move through the stages, not as fast as the innovators, but with reasonable speed.

But it is the majority of the community that relies on the early adopters and innovators within the community to inform themselves. Research has shown that they require much more information from trusted sources within the community than innovators and early adopters. Without this information from peers, they will not progress rapidly through the last 3 stages.

The laggards are the slowest to move through the 5 stages. They do not trust most outside sources, so the awareness and interest stages are slowed. Plus they will only listen to certain trusted sources within the community. Until those trusted sources make their own way through the 5 stages, the laggards will not progress.

So, to alter the rate of diffusion of innovation in a community, increased lines of communication must be available, increasing the information that can be provided to individuals.This helps with the first 2 steps. but mostly only for the 16% of the community at the left side of the curve.

However, of greatest importance are the connections between members within the community, particularly the thought-leaders found in the early adopters. About 70% of a community will not adopt new innovations unless they hear clear reasons why, from trusted individuals within the community.

No amount of salesmanship or external proof will easily move them. But, tgiven he right opinion from a community thought-leader and they will rapidly make the transition.

This is an area that Web 2.0 technologies can be of real value. Not only do they make it easier for members of a community to disburse information, they also help the community more accurately identify who is in each group, permitting more focused, explicit approaches to be used to move individuals through the 5 steps.

The thought-leaders can more rapidly progress through the stages and can extend their opinions much more rapidly to the majority because they are not required to be in the same place at the same time as the others in the group. Thus there will be more opportunities for their viewpoints to be assimilated by the majority.

Increasing the rate of diffusion of innovation in a community really means increasing the speed with which each individual progresses through the 5 step.

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Mining with friends

digging by Untitled blue
Friends and feedy thoughts:
[Via business|bytes|genes|molecules]

I hope Bret and co are paying attention. I’ve heard people say that Friendfeed is too noisy, that they don’t get the value, etc. The tech world has the unique ability to make anything too noisy and the worlds ultimate echo chamber. The scientific community on the other hand (life scientists, physicists, librarians and technologists) have made it a second home. We use it to discuss ideas and ask questions. Of course, every conference seems to get it’s own backchannel on Friendfeed, e.g. ISMB, BioBarCamp, Science in the 21st Century, Science Blogging 2008, etc. We even have rooms for programming and development efforts now, e.g.for Ruby for Python and for the Chemistry Development Kit.

It’s a classic example of successful micro-communities, all coming together, driven by common interests. Makes you want to think ahead. Friendfeed has an API, a decent search engine, but what I would love to see is some way of mining all that data, cause in all the science rooms there is a ton of interesting information. I suspect you can do it today, just not sure what the best approach might be, and the graph of likes and comments and connections just HAS to be fascinating.

To me, at least, Friendfeed conversations actually have a different ‘feel’ to them than the different ‘Web 2.0’ tools that make them up. Things like blog entries, direct links, messages, etc. each have their own flavor. But put them all together, with added comments, more links, more blogs and you end up with something that is much richer than simply the sum of each part.

It is interesting that many scientists have gravitated to Friendfeed. I suspect that the ability to rapidly aggregate a wide variety of different types of conversations and the information they disburse would be one reason. Mining this would be a very interesting proposition.

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An Announcement

spirals by hendriko
All the details have been finalized for a three hour seminar SpreadingScience is sponsoring entitled

Transformed! Information, Bioscience and Web 2.0

October 7, 2008 6-9 PM
Lake Washington Rowing Club
910 N. Northlake Way
Seattle WA

The seminar will be given by Richard Gayle, Ph.D. and Mark Minie, Ph.D. It is geared for a general audience that includes researchers, lawyers, clinicians and anyone else interested in using modern technology to solve today’s problems. It will have three segments:

  1. The Transformation of Information into Knowledge
    Knowledge is the ability to make a decision, to perform an action. The knowledge creation cycle begins with data. Human social interactions transform data into knowledge. Social networks evolved to provide primates with diverse solutions to complex problems. However, there appear to be hardwired barriers to the size of these social networks, limiting the scope and complexities of the problems that can be solved. The huge amount of information being generated overwhelms these barriers. The difficult problems facing us today are too complex to be solved only the tools we evolved. We must use new digital tools to amplify our inherent abilities.
  2. The Transformation of Bioscience by Information
    Biology is now a branch of Information Science, and important new research, discovery and invention is taking place on the World Wide Web. From computer gaming/education to personal genomics, biological engineering and robotics, bioscience is undergoing a true renaissance with previously unexpected impact and dividends. This segment will explore bioscience’s new life on the Internet. It will focus on specific examples and new tools with potential practical uses for both scientists and non-scientists alike.
  3. The Web 2.0 Transformation
    Web 2.0 is about online conversations. These tools often remove the need for people to occupy the same space at the same time in order to transform information into knowledge. They permit the examination and understanding of human social networks many times larger than our hardwired limits. This enhances the ability to create knowledge and to increase the rate of diffusion of information in an organization. Communities that can use Web 2.0 tools to leverage human social networks will solve complex problems more rapidly than those that do not.

There is a glut of data in the world today. Our normal processes to deal with this glut – the interactions in a human social network – are overwhelmed. However, the same technologies that are permitting such huge amount of data to be created can also help us enhance our social network interactions, providing organizations with the possibility of solving much more complex problems than before.


Please join us on October 7 as we provide a foundation for understanding how Bioscience is being transformed by information and how we can use novel tools to leverage this transformation into critical solutions .

Until September 23, the cost is $175. After that date it rises to $225. So register early!

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