Information into action

Online Activism: The Movie – Ten Tactics for Info Activism
[Via Beth’s Blog: How Nonprofits Can Use Social Media]

Activists around the world are using social media tools to make change. A new 50- minute documentary film called “10 Tactics for Turning Information into Action” is a guide to how best to use take advantage of the power of these tools and avoid hidden dangers. The site and film include inspiring info-activism stories from around the world, a set of cards with tool tips and advice. The project comes from Tactical Technology, inspired their info-activism camp in India.

The film is being shown in 35 countries, showcasing the experiences of 25 human rights advocates from around the globe who have masterfully incorporated tools like Twitter and Facebook to take on governments and corporations.   The film also covers the security and privacy issues faced by human rights activists.


In today’s world, the huge amount of information makes it impossible for one or a few people to quickly examine a complex situation and begin to formulate a successful response. It requires a knowledge of social interactions as well as an appreciation for systems thinking.

These ten tactics fit right in the sweet spot of systems approaches and social interactions. There ned to be good facts and information. There need to be great stories and an understanding of policy. Here are the ten (there are more but ten is such a great number):

The Ten Tactics

1. Mobilise People

2. Witness and Record

3. Visualise Your Message

4. Amplify Personal Stories

5. Just Add Humour

6. Investigate and Expose

7. How to Use Complex Data

8. Use Collective Intelligence

9. Let People Ask the Questions

10. Manage Your Contacts

These tactics can be incorporated into several strategies for systems thinking to produce some powerful solutions to complex problems.

My model is the bacterium. It does not know where a food source is, yet moves quite rapidly towards it. Bacteria, such as E. coil, have no eyes or nose. How does it find the sugar, or other nutrient it needs? It is called chemotaxis and is actually a very simple solution to a complex problem.

E. coli has a few flagella to propel itself. When they all rotate counter-clockwise, they work together and move the bacterium forward. This is called swimming. When they rotate clockwise, the bundle of flagella breaks apart and the bacterium rotates in a random fashion called tumbling. Here is an example:

The combination of these two behaviors allows the bacterium to move towards a food source. As long as the concentration of the attractant is increasing the bacterium swims. If it gets off track, and the concentration begins declining, it tumbles, eventually picking a new, random direction to swim. No attractant, more tumbling. More attractant, swimming.

This actually results in a very efficient method to move to, or away, from things.

When the bacterium fails to successfully follow the correct path, it makes corrections to see if a better path arises. If these corrections fail, more tumbling until it succeeds. It has a process for dealing with failure that inevitably leads to success.

Same with systems approaches. Intermediate evaluations, rapid failure, path to success. Incorporate the ten tactics into these strategies and you are well on your way.

More collaboration in biology

[Crossposted at A Man with a PhD]

analysis by Cushing Memorial Library and Archives, Texas A&M
I’ve collected my data, now what do I do with it?:
[Via Bench Marks]

4-dimensional live cell imaging has gone from being a rare technique used only by cutting-edge laboratories to a mainstream method in use everywhere. While more and more labs are becoming comfortable with the equipment and protocols needed to collect imaging data, performing detailed analyses is often problematic. The application of computational image processing is still far from routine. Researchers need to determine which measurements are necessary and sufficient to characterize a system and they need to find the appropriate tools to extract these data. In Computational Image Analysis of Cellular Dynamics: A Case Study Based on Particle Tracking, Gaudenz Danuser and Khuloud Jaqaman introduce the basic concepts that make the application of computational image processing to live cell imaging data successful. As one of the featured articles in December’s issue of Cold Spring Harbor Protocols, it is freely accessible for subscribers and non-subscribers alike.

The article is adapted from the new edition of Live Cell Imaging: A Laboratory Manual, now available from CSHL Press.


My first year as a biochemistry graduate student, one of the classes simply dealt with the analytical technologies we would be using. Things like NMR, UV spectroscopy, circular dichroism, fluorescence and X-ray crystallography. They would help us understand the properties of isolated biological molecules

This paper gives a great view of some of the new analytical approaches that examine entire living cells, not just isolated molecules. Now it looks like students will also have to get some firm understanding of image analysis. There will be some really interesting results from these sorts of technologies. The conclusions provide insights into the promise and the problems:

Computational image analysis is a complex yet increasingly central component of live cell imaging experiments. Much has to be done to make these techniques useful for cell biological investigation. First, algorithms must be transparent, not necessarily at the level of the code but in terms of their sensitivity to changing image quality and the effect that control parameters have on the output. Second, the design of imaging experiments must be tightly coupled to the design of the analysis software. All too often, images are taken without careful consideration of the subsequent analysis and are forwarded to the computer scientist to retrieve information from the images. To avoid these problems, communication must be initiated early on, and experiments must be designed with the appreciation that data acquisition and analysis are equivalent components. Third, software development and application require careful controls, as is customary for molecular cell biology experiments. This article provides a brief introduction to the ideas useful for implementing such controls. Hopefully, the cell biological literature will include a more extensive discussion of the measures taken to substantiate the validity of results from image analysis. On the other hand, manual image analysis should no longer be an option. As discussed in this article, manual analyses fall short in consistency and completeness, two essential criteria underlying the validity of a scientific model derived from image data.

While the results can be amazing, there needs to be close collaboration between the different researchers involved. Because very few people will have all the expertise necessary for success. This tight coupling of researchers with vastly different backgrounds and focus (i.e. cell biology and bioinformatics) is a relative new aspect of modern biological research.

There may be slowing of this coupling in some labs but the successful results by those that can accomplish this type of collaboration will rapidly overtake those who take a slower course. As I mentioned below, large collaborations may be a big part of the published record as we move forward.

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