Bench Press

One challenge with getting scientists to collaborate over the internet is the difficulty of representing scientific data in a way that can be readily manipulated and analyzed. Take Chemistry for an example. How does one share information about pathways and chemical structures in a way which allows for an entire group to collaborate on particular problems (e.g. synthesis pathways)?

Imaginitik, a startup specializing in software to help companies and institutions use crowdsourcing, and partially funded by Pfizer, has one such idea (HT: VentureBeat). Most scientists who are working or have worked in chemistry or biology are familiar with software company CambridgeSoft’s scientific software products like ChemDraw or BioDraw. What Imaginitik did was combine CambridgeSoft’s software with the collaborative features of Imaginitik’s Idea Central software to create ChemBioConnect, a crowdsourcing platform for a company or institution to deploy.

The idea is pretty simple. Imagitinik’s Idea Central platform creates web portal where scientists and management can list topics that can benefit from a multi-person collaborative approach and organize responses/suggestions/workflow and to rate individual ideas and contributions. But what differentiates ChemBioConnect from other life sciences collaboration solutions or more generic crowdsourcing platforms is integration with ChemBioDraw’s interface which provides more features than a standard collaboration platform (which will only let you share pictures/text) and a more familiar and robust user-interface than other life sciences-targeted solutions. Interestingly, Imagitinik’s platform also allows the creation of personality profiles (e.g. “creative” or “inquisitive”) to better help scientists network and target the right set of people to solve these problems. Not surprisingly, Imagitinik’s funder Pfizer has been rolling out this solution since Spring 2009!

A poorly scripted demo video is below (I personally think the speaker focuses too much time on basic ChemDraw functionality and less time on how this ties together with the collaborative features for my taste):

I, unfortunately, haven’t had the chance to actually try out the software (although reasonable pricing for enterprise software, I don’t have $50,000 – $500,000 to shell out to evaluate the software), but I think this is a great look into what a prototype for scientific collaborative software:

• Web-based: The need for ease of access across many machines and locations and the need for a central repository with which to organize a group’s information generally means that collaborative platforms should be web-based or, if not, sufficiently web-like as to not be an issue.
• Social networking features: It doesn’t have to be a full-fledged version of Facebook or MySpace, but a collaborative tool should encourage its users to network with one another and allow people to show off what projects they’ve contributed to. Not doing this fails to create the sense of community and personal attachment that crowdsourcing/community collaboration need
• Integration with existing tools: It’s a sad fact of life that inertia is a big factor when people are deciding whether or not to use something. But it’s a fact nevertheless. The best way to encourage quality adoption is to make sure that tools that are commonly used by the target user base tie in nicely for two reasons. First, new users won’t have to learn a new set of techniques, interfaces, and processes to adopt. And secondly, the tools that currently exist oftentimes support features that are harder to develop and more useful than developers of new platforms would like to admit. Sure, lots of people (including this humble commentator) have bashed ChemDraw as clunky and awkward, but someone developing a chemistry crowdsourcing platform is likely to skimp on things like NMR-simulation or smooth rotation of a structure.
• Managed workflow: Collaboration, even face-to-face, can be very difficult because information and suggestions and ideas are not organized effectively. It’s not enough to let people share their information and insights. You have to organize them and create tools with which to evaluate and encourage action on them.

As I haven’t actually put my hands on the software, I’m not sure if ChemBioConnect already supports these, but there are two additional features that I’d strongly suggest a collaboration platform to have:

• Easy way to export work: Too often, developers of a platform or tool forget that there is a world beyond their innovations. This is especially true when people are testing out a piece of software for the first time – it’s important that they can quickly move a piece of work off the tool to integrate with the rest of their work schedule, whether it be in printed form, in the form of a presentation, on a PDF, in web page/HTML form, or even just as a industry file format to share with another. Going the extra mile to make this easy makes it easier for someone to try out your software as well as provides a valuable service that just may win an adopter over.
• Semantics: This is harder to describe, but many web-based tools are very rigid, requiring a user to identify exactly what they want to do and figure out what part of the website is best suited for that particular type of work. Better, instead, to apply semantics/language processing to figure this out for the user. One example of a product that has done this is Google Calendar. Instead of requiring a user to try to figure out which fields correspond to what data when trying to create a calendar entry, a user can simply enter “Lunch with Jenny at Chez Carla on Sept 9, 2009 from 9 PM to 11 PM”. Google will decode the string and fill in the appropriate data. This feature is especially powerful for a collaborative tool where a user doesn’t want to have to figure out if something is a “task” or an “event” or an “idea” and doesn’t want to have to memorize what each of the tool’s special quirks and vocabulary are.

Does anyone else have any thoughts on ChemBioConnect or on other principles of good collaborative tool design?

We’ve mentioned before the power of Cloud Computing as a means of expanding the computational power in the hands of researchers and scientists (by “outsourcing” it to someone with excess computing capacity like Amazon or Google), such as those at CERN studying high-energy particle physics. So, it was very heartwarming to see a Chemical & Engineering News cover story on the use of new cloud computing systems by large biotech/pharmaceutical companies like Genentech, Pfizer, and Eli Lilly.

Lilly has demonstrated the viability of cloud computing in pharmaceutical R&D, according to Dave Powers, the firm’s associate information consultant for discovery IT. "We were recently able to launch a 64-machine cluster computer working on bioinformatics sequence information, complete the work, and shut it down in 20 minutes," he says, describing a project the firm executed using Amazon’s Elastic Compute Cloud (EC2) service. "It cost $6.40. To do that internally—to go from nothing to getting a 64-machine cluster installed and qualified—is a 12-week process."

What was most interesting about the article was the assessment of the cost/benefit analysis that the companies each went through before adopting the technology, something which is important to understand both for researchers/companies interested in adopting cloud computing techniques as well as for technologists/developers striving for broad adoption of their own special technologies. This particular case is especially enlightening as it flips the conventional wisdom – who said that all pharma IT systems and managers are slow to embrace change?

There are 4 key factors that seemed to play a role in “tipping” the large bio/pharma companies: (1) cost, (2) maturity, (3) third parties, and (4) the ability to achieve sizable benefits on a number of scales.

1. Cost: This is usually the “easy” part of new technologies. After all, if a technology is being considered as an alternative to conventional approaches, it usually has major cost or efficiency advantages. In this case, Cloud promoters/IT experts were able to make the case that the cost of buying, setting up, qualifying, and supporting IT infrastructure needed to support all internal demand for computing resources was much greater than the cost of outsourcing some computing needs to Cloud Computing providers. This cost, of course, is measured not only in terms of IT dollars spent,  but also in terms of the time needed to set up, qualify, and develop the necessary IT infrastructure as well as the potential dollars a company might lose by being less nimble.
2. Maturity: One of the major obstacles that new technologies face is that nobody trusts them. After all, why trust a new player with an unproven product for your ultra-important needs (especially when your job is on the line if your needs aren’t fulfilled), when your current setup is working “good enough”? In the case of Cloud Computing, however, the availability of well-known vendors (Microsoft, Google, Amazon, Rackspace, etc) who have had years of experience developing their own systems and product offering has been instrumental in wearing down the traditional resistance that new technologies tend to face. Amazon, especially, has built a well-known cloud platform (their Elastic Compute Cloud more commonly referred to as EC2), with:
   • Easy-to-use web interface
   • Range of offerings that are attractive to many customers (e.g. targeted offerings which distinguish the needs of web application providers versus high-performance scientific computing clusters)
   • Support for standard enterprise software to minimize the amount of re-work that CIOs/IT people will need to do
   • Professional SLA (service-level agreement) which legally obligates Amazon to provide a certain level of service
   • Simple billing system
3. Third party: The existence of third party players on a particular type of technology provide two things to companies interested in adopting a new type of technology. First, it is proof of a concept’s maturity. If a technology really is great, then there will be companies that aim to provide services or add-on products for users of that technology. Second, it simplifies the job of using the technology, as there will be companies who specialize in providing support and useful add-ons. In the case of Cloud Computing, there are a number of providers who specialize in helping companies deploy Cloud computing software or manage their use of computing resources. Companies like Eucalyptus and CycleComputing can provide support (for a fee) for companies interested in setting up their own cloud, and open source projects like Hadoop and research papers on how to use Amazon’s EC2 to do proteomics research not only validate Cloud Computing but provide free advice on how to set up research “clouds”!
4. Benefits achievable on multiple scales: Too often, new technologies promise grand benefits which can only be realized on very specific scales. Small pilots and full-scale transformations are not how companies make decisions and do not reflect the reality of how technology is used. Technology that only delivers significant benefits in small pilots or only work if all old technology is replaced by a new one will be very challenging to adopt. The beauty of the Cloud efforts detailed in the article is that they cover a wide range of scales. Benefits are easily realized regardless of if you outsource your entire IT infrastructure, or if you choose to only use the Cloud some of the time, or selectively avoid using the Cloud when regulatory concerns dictate it.

This is not to say that the adoption of cloud computing in these cases went perfectly smoothly. Amazon does experience service outages and there are still issues to be sorted out regarding SLAs and regulatory concerns, but their adoption by companies in an industry known for conservative IT practices is something that can and should be learned from.

(Image credit)