We’ve recently had discussions about the font of the VH site, changing it for readability etc. And now, for another interesting take on fonts… the ‘green’ font. From the AP, a recent news story from the UW – Green Bay.
UW-Green Bay: New e-mail font will save money
Updated: 03/25/2010 08:48:18 AM CDT
A Wisconsin college has found a new way to cut costs with e-mail — by changing the font.
The University of Wisconsin-Green Bay has switched the default font on its e-mail system from Arial to Century Gothic. It says the change sounds minor, but it will save money on printer ink when students print out e-mails in the new font.
Diane Blohowiak, the school’s director of computing, says the new font uses about 30 percent less ink than the previous one.
That could add up to real savings, since the cost of printer ink works out to about $10,000 per gallon.
Blohowiak says the decision is part of the school’s five-year plan to go green. She tells Wisconsin Public Radio it’s great that a change that’s eco-friendly also saves money.
I looked this up online and the story got picked up by several newspapers. Below a link to the story in the Washington Post — read the comments below the actual story. Some funny analysis as to whether this really is green…
Is serendipity necessary for innovation? Or in other words: Would an autonomous scientific discovery process that utilizes all available data at the time be incapable of innovation? Some think so. But not researcher Andrew Sparkes and colleagues who created Adam and Eve, two robot scientists, designed to carry out biomedical scientific research. The researchers claim that scientists robots will “make scientific information more accurate, reproducible and reusable”.
Adam and Eve are capable of generating hypotheses about a problem based on information obtained from publicly available databases, designing experiments to test these hypotheses, running the physical experiments, analyzing, interpreting the resulting data – and they even collaborate. Eve, for example, is a prototype system to demonstrate the automation of closed-loop learning in drug-screening and design.
So why not stretching this idea a bit? Could such a robot help support the clinical and translational research process? The authors of the recent paper “Translational Medicine – doing it backwards” may disagree. They argue that the general approach to hypothesis-driven research poorly suits the needs of translational biomedical research “unless efforts are spent in identifying clinically relevant hypotheses”. As Steinman pointed out, animal models, for example, can lead to results that are the opposite of what is ultimately seen in human disease. So, the authors propose “that hypothesis tested research should follow ‘factsdriven research’ and only when the collection of facts relevant to human disease has been extensive, should hypotheses be constructed to expand beyond what can be directly observed. What is needed is an approach that begins at the Bedside and then goes to the ‘Clinical Bench’.”
I guess once there are public databases available filled with “clinical realities” provided by clinically active physicians and non-physicians, robots like Adam and Eve could frame their research questions accordingly and reverse the discovery process starting with the “human reality”.
At our retreat in July this year, I listened to conversations about the challenge of bringing researchers with questions to those with new information and techniques. Using technology is one way to help scientists step outside their usual research network and find expertise. But since face to face still matters, some wondered how an event that gives researchers the chance to meet a lot of people within a short time could look like.
The Weill Cornell Medical College Clinical and Translational Science Center has tried something new in the field of “speed-networking”. In a “Translational Research Bazaar” the Center used a “format popularized by speed dating” – so I learned reading an article featured on the Clinical & Translational Science Network. We wrote about this new network site in our post “New Clinical and Translational Science Network”.
And this is how it works: The organizers “reserved a room that could accommodate 100 people. The tables were set up to minimize noise, maximize easy movement around the tables, and facilitate conversation”. Basic scientists and clinical & translational researchers “sit on opposite sides of a table and chat for 3 minutes until a bell rings, signaling that it’s time to move on and strike up a new conversation. This process continues until everyone in one group has met everyone in the other group”. As a result, “eighty-five percent of the participants said they met at least one potential collaborator”.
Interestingly, even those who were not looking for collaborators could benefit. For example, Even Robert Dottin, director of the Center for Study of Gene Structure and Function at Hunter College, suggested potential collaborators from within his center.
The first Translational Research Bazaar took place in October 2008. Since then the organizers have tracked the number of “new partners” who submitted grant proposals over the course of 2009 through follow-up surveys and phone calls. And what have they learned and will do differently next time?
- Require that registrants complete an online bio with photo, contact information, their research priorities and needs before the event.
- Be prepared to be flexible: “More than 80 people signed up” for the event that was free of charge; “one-third of the registrants didn’t show up”. But “many new people appeared on the day of the event to register onsite”.
- Use a cowbell instead of a microphone to be sure the signal to switch partners will be heard “over the din”.
- Color-coded ‘dance cards’ are useful which were to match the side of the table people sat on. The cards “listed the names and top research interests of each registrant, with a blank line to scribble a quick note”.
- Provide bottles of water, as people will spend about 2 hours talking almost nonstop.
- Keep the speed networking to an hour as people get exhausted
- The “wine-and-cheese hour that followed turned out to be a critical, because “people had ideas they were anxious to discuss.”
- Send a follow-up email with a link to the bios registrants completed.
For those of you who like to delve into the research of networks a couple of articles, which were recently published in Science, might be interesting.
As I learned, scientists still “tend to describe how a complex system looks and behaves”, because it’s not clear what a complex system is exactly. So far researchers define it as something that “consists of many elements that interact so strongly that they tend to organize themselves in one way or another”. As a comparison: “A car may be complicated, but it is not a complex system, as each of its parts interacts with a few others in a predictable way. But cars in traffic form a complex system, as drivers’ jockeying for position can lead to surprises such as ‘phantom’ traffic jams that arise for no obvious reason”.
Some of the articles:
Reading “Predicting the Behavior of Techno-Social Systems” I learned about what brings us closer to achieving true predictive power of the behavior of techno-social systems and that there is the need for a “network” mindset. The article focuses on human interactions and mobility and talks about moving the analysis of networks from “small social groups” to the “quantitative analysis of social aggregate states”.
“Scale-Free Networks: A Decade and Beyond” talks about whether real networks as the society, the Internet, or the cell could function seamlessly if their people, nodes, or molecules, were wired randomly together.
“Revisiting the Foundations of Network Analysis” explores when a node is a node, standard frameworks, network processes and the choice of the right network presentation. Some of the key words for me: “reality mining”, which has been defined as the “collection of machine-sensed environmental data that are related to human social behavior”.
A Newsweek article is making waves. The author Sharon Begley asserts that academia and organized science essentially slow down the path from basic science to a meaningful “cure”. One of her major arguments is that academic science emphasizes basic science and novel discoveries at the expense of research around patient treatments. That explains why this article even sparked the interest of the CTSA. The solution that Sharon Begley offers? – “a powerful director who can get beyond the rhetoric about moving discoveries out of the lab and make it a reality.” In her view “that hasn’t happened yet, six years after a much-ballyhooed NIH ‘road map’ declared such bench-to-bedside research a priority and vowed to reward risk-taking, innovative studies, not the same old incremental research that has produced too few cures.”
But there seems to be disagreement. An interesting blog post comments on this article and provides interesting insights from a researcher’s perspective: “Begley’s criticisms rely on some anecdotal stories from researchers, who either had a hard time getting their research funded, or found their translational research being published in ‘less prestigious’ journals than their or others more basic science research. But there’s no evidence that this is a system-wide phenomena – indeed, I’d counter with my own anecdotes that translational research is currently the new golden child of the area of science I’m exposed to,…”
I call this post Tangential Thoughts, since it focuses on what the research community things about communication technology, which might be interesting to some of you.
I found two articles: One talks about how researchers more and more use mobile phones to collect data. In “Personal technology: Phoning in data” Roberta Kwok explores how “budget-conscious” and “digitally savvy scientists can write and distribute mobile-phone software for everything from monitoring traffic to reporting invasive species”. Maybe this is something for us to keep in mind and exploring?
Another article titled “Big Brother has evolved” by Jerome E. Dobson mentions human-tracking systems in scientific research. Dobson argues that the “social-networking benefits of human-tracking systems will surely be substantial” and that” the technology is bound to alter all sorts of social relationships”, including the one between researcher and subject. Yet, “investigators need to understand the risks as well as the benefits of new research opportunities”. His conclusion is not at all comforting, though: “We have entered a grand social experiment as momentous as any in our past and yet one so insidious that hardly anyone seems to have noticed”.