CarbMap aims to provide users with easy access to nutritional information when eating out at restaurants. This type of information is valuable to many health conscious people, especially patients with diabetes.
This location-based app will aggregate carbohydrate totals in two separate ways. First, it will compile official nutritional estimates from chain restaurants. This information is already public, but not accessed as often as it could because it requires users to load the websites of each restaurant they may visit.
CarbMap also looks to provide nutritional estimates from small, independent restaurants as well. This will rely on individual users providing information about the menus and portion size from restaurants that do not provide this information themselves. This citizen cyberscience project thus looks to tap the “wisdom of the crowd” and network the insight of people who have already visited these restaurants, providing a knowledge base where there currently is none.
The process of estimating carb counts of menu items is in itself beneficial for patients with diabetes and other users. CarbMap will provide an educational guide to help users learn how to accurately estimate these totals. This will include a breakdown of common ingredients as well as general estimates of standard menu items. With local participation, a user will help judge how a particular dish at a particular restaurant falls within these general guidelines. The site will also allow users to upload pictures of their food. As a registered user, this will allow them to create a personal log of their eating history.
While the process of estimating carb counts requires human intervention at this time, there is interesting research being done at such places as Purdue University that could simplify this process. Their TADA (Technology Assisted Dietary Assessment) project uses photos from mobile phones and translate it into nutritional breakdowns.
Nonetheless, the current process of manually estimating carb counts is beneficial because it gets users to be more self-aware about the food they are eating. The simple behavior of either taking a picture and recording the carb count or loading a saved reading from the site is a valuable moment of reflection.
For our first assignment in Citizen Cyberscience, we were asked to try using two examples of web-based citizen scientist projects.
The first was BOINC, the Berkeley Open Infrastructure Network Computing. This software provides scientists with a platform to harness the power of interested volunteers. Downloading and installing the software was easy enough. But in general, the service is not that polished or user-friendly for volunteers. When browsing through the projects, most groups have made little effort to determine whether a potential user’s computer is up to the task. For instance, the GPUGrid project has too complicated a process. In asking people to determine GPU compatibility, I think they lose many potential volunteers. Instead I think they should write a small script to check a volunteer’s computer and automatically complete what is now a manual task.
There are many more examples of these types of interaction issues throughout the whole experience. In doing so, they have limited their pool of potential participants to just those with better computer skills.
Zooniverse provides a much more user-friendly experience for volunteers. The site has a nice design, easy navigation and a good feedback loop. I chose the MoonZoo project, where I was asked to help identify craters and compare the amount of boulders in pictures.
Two YouTube videos explained the process effectively and I was able to get to work within minutes. The tasks each take just a few seconds up to a minute to complete and submit. The site then provides another similar task. Providing these bite-sized pieces is a good solution and keeps the experience rather light. As a result I clicked through tasks for a while.
For BOINC, the first task is to choose a project on the platform. I went with the World Community Project, which has some support from IBM. I was curious to see how this combination of corporate sponsorship and community effort is hashed out.
With that, I launched the BOINC software. This brings up a window that starts a task which runs for about 8 hours. No action is required of me other than allowing for the project to use my computer’s process power.
It is a pretty uninspiring experience, outside of the knowledge that this small role is helpful. To be quite honest, I doubt I will return to this software much after completing this assignment.
In designing projects for the rest of this term, I will certainly lean towards creating an experience modeled on Zooniverse. I expect that most people at ITP would make the same choice since our program is more focused on design and user experience than producing a processor-heavy big data project.
Finally, we have been asked for this assignment to share which skills we can bring to citizen science projects this term. I have made data visualization a large part of my studies at ITP. I hope to integrate that experience into my classwork. I also bring a deep personal interest in tapping the energies of a specific community, namely the millions of diabetes patients in the U.S. (and potentially the world). I want to ask them to participate in ways that share their knowledge so that we as a community can aggregate our collective wisdom about managing the disease. Together, I believe we can provide better self-management support and help educate fellow patients.