Idea for Compute Commons originated out of personal frustrations with access to sensitive data.
Plenty of researchers that benefit from open data. But openness of data is limited by the privacy and security concerns of data donors and subjects. You cannot share things like medical information or locations of rare species. But these concerns are not only about the obvious things, like the security of a person’s life. For example, you shouldn’t share your genetic information with everybody, as not only it tells a lot about your past and your future, but it also tells a lot about the past and the future of your children. So, these concerns aren’t artificial. And this is the reason research community is self-correcting when it comes to sharing sensitive data.
Imagine a situation where you work on an important but rare medical condition. You need the sensitive data from the patients, but you cannot share the data openly. The level of detail in the data you collect is large enough to detect way more just a condition-related physiology. But you want other researchers to benefit from these data! Whether this is testing for a possibility of early diagnosis of other disorders or the general assessment of well-being. What can you do? Make the computations on data possible. And this is the idea behind Compute Commons: generic computations on sensitive data.
Imagine a situation where somebody holds a sensitive data. You’re coming as an outsider, asking the holder to run an analysis for you, as you have a research question that the holder didn’t come up with. The holder runs in his/her silo the computations, and if the answer is confirming the hypothesis, you can publish a new result together. At no point, you’re allowed to see the data and you need to trust the holder that the computations were done correctly.
There’s nothing wrong with this scenario, except that it’s not scalable and it’s not democratic. If you’re from less prestigious institution or you have not-so-fancy research question, you have a chance to be stuck in long queue of requests for assistance.
Compute Commons is aimed at automation of the process, based on existing, specialized solution for dealing with sensitive data. It’s not inventing a new process from the scratch, but rather rebuilding existing solutions and refocusing the community on them. Instead of manual judgment on the access to the computations, we will design the filter that will take care of that.
While there are several platforms for citizen science projects (including the largest, Zooniverse, developed by Citizen Science Alliance), none of the covers the whole research process, from ideation to publication. As, a part of an service ecosystem for large scale collaborative science and innovation, OSF is working on such a platform, named Mare Apertum (“open sea” in latin).
CSR-CS funding mechanism
We have a common dream. This is a dream where open citizen science and open innovation solve major world’s problems. A major like cure for disease, green energy or really sustainable development.
Why we are not there yet?
It’s the long road ahead, and we are just at the beginning. We need larger participation. In the world where two billion people use facebook less than a one in a thousand participate in citizen science projects. We need larger and better funding. Currently almost all funding goes to researchers and hardly any is allocated to the marketing of the project or to the communication with participants. We need a better structure of such projects. How can you expect that a project will move science substantially forward if hundred thousand people are performing exactly the same task? And finally we need better incentives. Instead of primitive gamification such as having the highest score, the project should offer a real value to the participants.
None of these will happen, unless we start to think about open citizen science in a different fashion.
The solution to a stagnant development is corporate sponsored open citizen science.
A corporation, possibly as a part of its corporate social responsibility programs, funds the citizen science project. Because the corporation is interested in outcomes, it does not hesitate to consider large funding being spent on marketing and communication. This translates into a wider than typically participation, that allows to make the project as complex as it is needed. The wider participation is amplified, by the fact that the project is socially-aware. Such awareness is either the topic of the project (such as cure for disease) or transferable skills that are developed by participants during the course of the project.
Let’s consider an example of Thomas Edison. Let’s assume that Edison wants to invent a new source of lighting and he wants the general public to participate in his research. His idea is augmented by two teams of people. One team analyzes the project to recognize a business value in his project assuming a wide participation. Such a value could be a recognition by potential customers, increased sales, retention of customers or increased stock prices due to media coverage. The second team analyzes the project to recognize a social value. This can be the product itself, skills, like engineering or design, or a knowledge that can be applied outside of the project domain.
If the recognized values are satisfactory for all parties, the business closes the gap by deciding to fund the project and also possibly by a direct participation in it. This process can be repeated again and again, and desired outcome is that everybody wins here.