• The boundaries of science are continually expanding as scientists become increasingly integral to finding solutions for larger social issues, such as poverty, conflict, financial crises, etc. On what specific issue/problem do you feel we need to bring the scientific lens to bear?
• Cross-disciplinary work has sparked provocative new technologies, solutions, and insights. What problems do you see as ripe for cross-disciplinary research, and which fields would you choose to combine?
• What cross-disciplinary approaches have you seen work for your field and why? When is a cross-disciplinary approach not appropriate?

These queries have been answered with imagination and insight and we've been privy to a stready stream of amazing projects that the RevMinds are directly involved in or actively tracking. From family dynamics to docuinformatics, the economics of sustainable environments to the cognitive life of things—we even learned about a provocative theatre piece dedicated to neurology. I've done my best to keep pace with this whirlwind of ingenuity with related commentary and links focusing on topics like data visualization, design fiction, precision farming and developments in healthcare and the information sciences. It has been a blast and I can't recall a publication where I was fortunate enough to be able to examine the economics of tourism one week and robotics the next. Many thanks to ScienceBlogs community manager Erin Johnson for the invitation to post here, but most importantly thanks to the RevMinds for their astute observations on technology, design and making the world a better place.

Anthony Dunne, Fernando Esponda, Gladys Kalema-Zikusoka, Edward Einhorn, John Wilbanks, Josh Ruxin, Margaret Turnbull, Moshe Pritsker, Lambros Malafouris, Nick Matzke, Michelle Borkin, Saleem Ali and Skylar Tibbits - please take a bow! We look forward to watching you continue to innovate and inspire.

image: illustration from the POWEr technical rider

In discussing fruitful interdisciplinary collaborations Edward Einhorn identifies a pair of independent theatre collectives that incorporate advanced projection technology and even a Tesla coil into their performances. These groups (3LD and the Collective Unconscious) exemplify how far production and set design has come since the era of Einstein on the Beach—science is not only the stuff of inspiration, or a means to create illusions onstage but experimental technologies can be directly incorporated into a production and foregrounded as part of the performance. An excellent examples that echoes the "weird science" of the Collective Unconscious theatre group is POWEr, a project presented by Alexandre Burton and Julien Roy of Artificiel at the 2009 edition of the MUTEK festival (held in Montreal this past spring).

Even amongst the impressive selection of experimental multimedia projects presented at the annual electronic music festival, POWEr made a distinct and visceral impression. An excerpt from a review of the performance that I wrote approximately a week after attending this idiosyncratic concert:

To be reductive, POWEr is electronic music in the purest sense. The project utilizes a custom made Tesla coil as the driving force in a dynamic musical performance. This concert was completely improvised and set out to explore the possibilities of using "electricity as a subtle but pressing instrument". Subtle is not the first word that comes to mind in attempting to describe POWEr as the Tesla coil device took up approximately half the stage and emitted violent arcs of electricity throughout the entire performance. The resulting crackling, buzz and uncanny sound of current became raw material with which Artificiel assembled on the fly electroacoustic sketches. In addition to modulating the electricity, "playing it" as if it were a rudimentary instrument, an array of cameras and microphones were used to collect and process the sound and images of the arcs. The concert was structured as a series of short vignettes in which Artificiel switched the focus between the device, their sound sketches and a range of simple but effective video sequences. These varying perspectives foregrounded different aspects of the electricity which moved and sounded so quickly that it was difficult to process in real time.

So a question to RevMinds readers: are you aware of any similar projects that deploy advanced, or fringe technology in a musical performance or a theatrical context?

Even in my small area of astrobiology, the design of a single mission to find habitable planets orbiting other stars requires substantial input from the studies of astrophysics, space communications, space flight technology, optics, materials science, the interplanetary space environment, Earth's atmospheric system, microbiology, geology, computing, remote sensing, and signal processing. Within each of those areas, input from many sub-disciplines is required. For example, in the "astrophysics" portion of my work I communicate from astronomers from all across the field. Some of them know all about the variability of stars, some of them know about the multiplicity of stars and giant planetary companions. Some of them are experts in the details of previous observing campaigns that have collected the data that I now need. And this process of information gathering goes on and on. In science, we need people who can go deep, and people who can go broad. My colleagues and I can literally create new knowledge and capability simply by networking and pooling our talents. I am the kind of person whose purpose in the community is to build bridges between areas of knowledge and begin unfolding the bigger picture.

The appropriateness of cross-disciplinary sharing depends entirely on the situation to which your knowledge is being applied. The Fire Department does not need my knowledge of astronomy in order to douse a chimney fire, but they might like to know and prepare in advance if an asteroid is about to impact our town!

Writing theater about science, in general, has become somewhat more popular, thanks partly (but by no means wholly) on the fact that technology has slowly become a more integral part of theater. This is especially true in small, independent theaters where the technology is not just there to support the work but, in a way, take center stage.  This fascination ranges from modern technology, such as in the work of the group 3LD, which uses advance projection technology in every show, to technology of a definitely less modern sort—the Collective Unconscious, another small theater company, owns a huge Tesla coil which has been featured prominently in countless productions.

I think theater and science are natural partners. It is through the synthesis of art and science that breakthroughs in both are found, so I can't think of any instance it can't be appropriate. There are certain plays, of course, that lend themselves to the incorporation of science more than others.


So putting the Tesla coil in the middle of The Cherry Orchard might be inappropriate. Then again, it might not be. What's the concept?

image: a proposed example of an immune-inspired network system, source: SYMBRION & REPLICATOR

In identifying computer science as a nexus of interdisciplinary collaboration, Fernando Esponda cites Artificial Immune Systems (AIS) as research exemplifying this sentiment. Esponda describes AIS as an attempt by computer scientists and immunologists to "learn nature's algorithms for defending the body against pathogens and apply them as another security paradigm to other areas"—an intriguing notion. After a little investigation, one of the most incredible AIS initiatives that I came across was the SYMBRION robotics project (pictured above). Conducted by a consortium of EU researchers in conjunction with the partner REPLICATOR project, the goal of SYMBRION is to

...investigate and develop novel principles of adaptation and evolution for symbiotic multi-robot organisms based on bio-inspired approaches and modern computing paradigms. Such robot organisms consist of super-large-scale swarms of robots, which can dock with each other and symbiotically share energy and computational resources within a single artificial-life-form. When it is advantageous to do so, these swarm robots can dynamically aggregate into one or many symbiotic organisms and collectively interact with the physical world via a variety of sensors and actuators. The bio-inspired evolutionary paradigms combined with robot embodiment and swarm-emergent phenomena, enable the organisms to autonomously manage their own hardware and software organization. In this way, artificial robotic organisms become self-configuring, self-healing, self-optimizing and self-protecting from both hardware and software perspectives. This leads not only to extremely adaptive, evolve-able and scalable robotic systems, but also enables robot organisms to reprogram themselves without human supervision and for new, previously unforeseen, functionality to emerge.

Cross-disciplinary approaches have proved useful to gain insight into unknown territories, quickly change scale and application, push past a field's current boundaries and inspire new directions and connections. Varying skills and necessities often become beneficial characteristics for collaboration between domains. Each person can bring insight, real-world application, understanding of production, and specific expertise to complete even the most complicated tasks. Currently, mechanical and electrical engineers, computer scientists, architects, and fabricators are all collaborating on an exciting project at MIT that blurs the lines between programmable matter at minute scales to shape changing and self-organizing human-scale structures. This unique opportunity allows each individual to contribute their skill set while furthering the global project with specific local interests and goals.

On the opposite spectrum, cross-disciplinary approaches may be prohibitive when quickly applied across platforms at new scales and without contextual or informed decisions. These circumstances are far too easily mimicked across platforms and forcefully applied out of context. Regularly, cross-disciplinary approaches are applied for formal applications not interested in the original system's specific criteria. In terms of biological systems, each characteristic has explicit criteria that may not be suited for new scales, climates or applications. The re-wallpapering of existing systems most likely will not function appropriately or adapt for applications for which they were not intended.

Computer science is a discipline that is intrinsically interdisciplinary. Primarily because the computer itself—the externalization of our logic apparatus—is such an enticing and versatile tool. Therefore, it is not hard to find examples of cross-disciplinary approaches. Just think of Artificial Intelligence and all the areas it draws upon. A less well-known example is the subject of Artificial Immune Systems (AIS) in which I've done some work. Here, computer scientists and immunologists are collaborating to, among other things, learn nature's algorithms for defending the body against pathogens and apply them as another security paradigm to other areas—anomaly detection systems for networks as PCs, data protection schemes, and so forth. It remains to be seen if the road can be traveled in both directions and computer science (AIS) can inform questions about the natural immune system, it will be surprising if it can't.

The last part of the question is hard for me to answer without engaging in platitudes: Cross-disciplinary approaches are not appropriate when they are expected to bog down research; when they are forced upon; when they obfuscate ethical boundaries. There is no a-priori reason pertaining specifically to cross-disciplinary research that can be cited to avoid engaging in it. The main benefit of collaborating with people with other areas of expertise is the compulsion of viewing a problem from a different perspective; this usually increases the understanding of the issues at hand and the understanding of our understanding, which is, more often than not, a good thing.

image: history flow edit log of the Wikipedia article on evolution

Nick Matzke is ambitious when he exercises his imagination. In answering our final question, Matzke sketches out a methodology for tracking how public policies or scientific hypotheses were "copied, repeated, modified and propagated" to see how society (and the passage of time) nurtures the spread of ideas. Matzke rightly points to memetics as an important precedent and it is clear that this reference, when coupled with his earlier call for docuinformatics (data driven historical scholarship) clearly illustrates a desire to quantify and track the evolution of conceptual models—no small task. We do have one great example of a "discourse tracker" with Wikipedia where popular articles undergo thousands of revisions which are all logged and timestamped. Visualization projects such as history flow (2003), clearly delineate the manner in which these documents are "collaborative constructions" that provides stark contrast compared to, for example, the singular genius evidenced in Ben Fry's visualization of Charles Darwin's sequential revisions to The Origin of Species [discussed on RevMinds here]. It is interesting to read between the lines of Matzke's commentary on docuinformatics though, while he is clearly interested in big data and computational history, many of the techniques and types of analysis he is describing could be found in contemporary public relations. Think about it, real time trend analysis monitoring chatter across various social networks and microblogging services—Matzke essentially wants to apply this same scrutiny to the entire corpus of archived documents. It is an insanely ambitious proposal that would democratize knowledge production by diffusing "sole authorship" in favour of recognizing incremental advances. Matzke is not hyperbolizing when he states "the sky is the limit" for this kind of analysis as it could provide a fascinating reconsideration of knowledge production and decision making as a collective activity.

Cross-disciplinarity seems to work best when there's a problem that has a few facets that are apparently unconnected, but the disconnect comes from the artificial way we divide up the knowledge. Because in reality the problem is simply the problem, but scientists get trained into reductively narrow disciplines to become experts in those disciplines, get grants, and get tenure. Overcoming the narrow reductive natures that get trained is one of the challenges here—the scientists on cross-discipline teams spend a ton of time just learning the others' language of science! But some of the work taking place around sensors at UCLA in the Center for Embedded Network Sensing is a good pointer to what it's going to be like—see this link.

Where it's not appropriate is harder to figure out. My instinct is that in places where the local knowledge is sufficient enough to create falsifiable hypotheses and experiments, the time required to learn the language of others doesn't get rewarded by results—gene sequencing doesn't need a physicist, for example. My hope would be that we can get to enough technical standards so that this kind of science can be harvested, aggregated, and mashed up by people and machines into a higher level of discipline traversal. Right now the problem is we still think about cross-disciplinarity as a function of people choosing to work together. But the internet and the web give us a different model.

What's more cross-disciplinary than Google? But the language barrier among scientists is preserved—indeed, made worse—by the lack of knowledge interoperability at the machine level. It's the Tower of Babel made digital. Until we can get past that one, we're going to be stuck doing human speed knowledge construction on machine speed data generation...

While astronomy and medical imaging seem very different, both fields search through large amounts of image data looking for meaningful patterns. For example, a physician may inspect a patient's MRI scans looking for signs of disease, while an astronomer will analyze radio telescope image data to find evidence of a new star being born. The two sciences have separately developed many techniques to analyze, visualize, and catalog complex multi-dimensional imaging data, but seldom have experts from the two areas worked together.

Image: The star-forming region IC 348 in 13CO as displayed by 3D Slicer.

Continuing the previous theme - I recently got interested in the origin of a particular apocryphal quote attributed to a famous scientist. The quote exists in hundreds of books and tens of thousands of webpages, but the scientist in question never said it, and no one seems to know where the quote came from (sorry to be coy, I am writing this up for publication and don't want to spoil the surprise). I believe I have finally traced it to its source, and the fascinating thing is that the quote has evolved through time, gradually becoming shorter and more pithy. The most popular versions of the quote even appear to exist in several clades, or evolutionary groups. Basically it is a great illustration of Dawkins' meme concept, and one can even construct a phylogeny (evolutionary tree) of the quote using modern computational methods. This sort of thing is not entirely novel - people have traced the evolutionary history of chain-letters and Biblical manuscripts and the like - but now, we can automatically monitor the propagation and evolution of this quote on the web, rather like scientists monitor the new flu viruses as they sweep around the world each year.

The above is a trivial example, but the sky is the limit for this sort of thing. Virtually any claim made for or against some public policy or scientific hypothesis might be tracked in a similar fashion, as the claim is copied, repeated, modified, and propagated. By studying this we might learn something profound about how individuals and societies end up making decisions.

As for the second question: there is probably never a problem, or at least never an interesting problem, where a cross-disciplinary approach is inappropriate. Almost any successful scientist these days is good in a number of areas, ranging from writing to speaking to programming to math and statistics to experiment to history to field work. But this perspective may be a product of being in an evolutionary biology program.

• Google health is a well known example of a medical information centralization service. Launched for public use in spring of 2008, the service promises to store patient information "securely and privately" while providing the patient with the ability to determine what is shared and with whom. Given Google's dominance of so many spheres, many users may not feel comfortable in handing over this additional sensitive information to the corporation. Privacy issues notwithstanding, an interesting technical note regarding Google Health is that the API for the service is related to the Continuity of Care Record, a patient health summary/XML standard that can be read by any Electronic Health Record (EHR) application.
• Earlier this year there was a public call for Health Data Rights that mobilized around the ability of patients to "own their data, source where it originated, take possession of this information and share it." Tim O'Reilly described the importance of the statement of principle as follows: "We may not yet have any idea what the exact format of an open health record system will look like, but we don't need to. If we establish the underlying principle of open exchange, the marketplace can sort out the details."

Even an examination of data ends up referring back to the marketplace—this seems the inevitable outcome of any conversation about healthcare. It goes without say that open source thinking and data ownership are definitely going to factor into this vital public conversation but it certainly remains to be seen exactly which forces will "sort out the details."

I have seen many cross-disciplinary approaches work successfully. These approaches are sometimes initially in the application of a tool or technique from one field to another, but ultimately lead to two-way conversations and better or new technologies to advance both fields. For my experience working at Harvard on the Astronomical Medicine project, the mutual need for sophisticated multidimensional data visualization techniques of image data cubes is the motivation for astronomers and radiologists working together. Both fields are able to borrow tools and techniques from each other and share in the new development of mutually beneficial tools.


I have seen a different approach towards cross-disciplinary collaboration while working on Harvard's Multiscale Hemodynamics project. In this case a single problem requires multiple disciplines in order to find an answer. In this situation, the problem at hand is working to model blood flow through the human heart to better understand coronary artery disease. This problem can only be solved by multiple disciplines including physics, computational science, cardiology, radiology, and visualization working together. In the process of working together, sharing tools, techniques, and knowledge enlightens all participants while working to solve a common problem. I do not think there is any right or wrong way to collaborate as long as all fields involved participate and make an effort to learn more about what the others are doing.  It is only with this added effort that knowledge can be transferred and advancement can happen!

A little research yielded the above big think video interview where Ruxin outlines his experience directing Rwanda Works. Ruxin elaborates the ideas expressed in his RevMinds response in an extremely straightforward manner. As he states, "additional doctors, nurses and drugs" will not solve institutional shortcomings. He points to QuickBooks, accounting, HR and management training as bottom up actions that increase efficiency and profitability within individual clinics. Ruxin outlines that drug procurement alone is not the answer and instead points to the refining of knowledge-systems and optimization of workflows.

This thinking is echoed by Brian de Francesca, the Chief Operating Officer of Tawam Hospital in the UAE in an interview with FutureGov published this past summer:

People always want to make malls bigger and harder to manage, which I never understand. The same thing is happening with many hospitals. What matters to me is the management of information, different systems are unifying and we are getting a lot of information - we need to figure out how we design the systems to manage that enormous amount of data.

Hands down, the application of private sector management solutions to health care, particularly in developing countries, is vital, and, almost utterly unfinanced. The focus of public health systems continues to be on training and retraining personnel, and identifying gaps in specific administrative systems. There's a raft of research on drug procurement systems, billing systems, and electronic patient medical records. The industry has come to resemble the three blind men each touching a different part of the elephant and trying to describe the animal each has in hand. Why have we gotten to this point?  Quite simply, because we've addressed public health as a uniquely scientific endeavor when in fact, it's an extremely complex service industry.

Five years ago in Rwanda, my colleague, Dr. Blaise Karibushi, and I, took a close look at everything various donors and NGOs were doing in Rwanda, and came to the conclusion that the vast majority were focusing on clinical quick fixes (training in a specific surgical procedure for example) rather than a holistic and cross-cutting approach. In hundreds of health centers we found the overutilization of some services and personnel and underutilization of others. In nearly all cases, young people with some advanced training in nursing were also being asked to manage accounting, human resources, and the physical plants of health centers designed to serve tens of thousands of people. 
Other centers had similar characteristic features of non-management: failures to have daily schedules, having one person serve as accountant and cashier, and related breakdowns in every area of execution. The traditional approach to this sort of broad-spectrum failure is to pick an area where immediate improvement or diagnosis of the problem can be had, and follow it up with a one-off fix. Of course, that rarely results in the comprehensive change that benefits patients. On a nearly weekly basis, various organizations offer retreats for one sort of training or another. The training might be great, but when nurses and docs return to their facilities, plagued by institutional failure, it's nearly impossible for them to apply their learning.


The right response to the cascading problems that originate with non-management is core training in management and delivery. Through our work on The Access Project, we found that by providing on-the-ground, in-center training to health center leadership, the worst health centers could quickly make improvements that led to increased income and resources available for solving the day-to-day problems which had plagued them. Better-run health centers can afford more staff, and ultimately address their most systemic problems. We have found that with just six months of careful on-site training, centers that were seeing merely a couple dozen patients per day, began seeing on average 100 patients daily. Word travels fast when quality and efficiency increase!

The problem is, the appropriate management training is not offered by most donor programs and is certainly not offered in public health schools around the world. MBA programs come much closer - investing in financial modeling and case-based studies. Even better is the training that comes from working at well managed companies, though few health centers or hospitals in developing countries are looking outside of professional medical staff for management. Sadly, donors continue to focus on the medicalization of the workforce and ironically, that's not what is most urgently needed for good medicine. For clinical work, pure and simple medicine need not be combined with other disciplines. But when it comes to health service delivery, a cross-disciplinary approach is essential.