 If you've invested in generating these data sets, why would you keep them to yourself? I think that data sharing and open data is really important for the whole scientific society. I think there is great value in collaboration. I think nobody wants to reinvent the wheel which is already there. We make our data accessible, we make our results accessible, we also make our technology accessible. It saves time to use the data that is publicly available. It saves money too. And there is such power in the data collaboration because that increases confidence in results. So the first step in this process is that we typically pre-print our papers on a website called BioArchive. It's a paper repository. This is completely open access and it's slightly nerve-wracking. You finalize your manuscript, you hit submit and within a few hours that is completely open to the public. This is quite a new concept. You sometimes get a lot of interest on Twitter, a lot of debate and discussion about your paper. So you get immediate feedback. So it can be nerve-wracking but it's also extremely satisfying as well. Knowing that you are making your work available immediately and there's not an 18 month delay before the public see the work. I think it's the worst thing if you develop something new and then nobody will use it because they can't access it. So for us it's really to give us a wider reach to enable people to use it. So that's why we release as much as we can when we publish. It's changed the field of cancer research because again with this kind of data availability all the computational modelers can access this data freely. They can easily get into this field and bring new models, bring new insights to this data. They can work together, collaborate with cancer biologists to get new insights to get better prediction capability. I think our main aim is to find cure for diseases. In my research I have shared within WashU my code and my data and I have also used people's data in the campus. If I can go ahead and download the data or use the data that has already been analyzed. The entire idea is that so that people can go ahead and save their time and gain knowledge from the data that is already being used. We developed this technology to apply to our questions on stem cell biology so a very specific area. But people are seeing the opportunity to apply this technology to cancer biology questions, developmental biology questions and this is giving us direct feedback because they're saying to us, hey it works in the system, this is what we're finding. Then other people are telling us we're finding this issue with the technology, it doesn't translate very well to this system and that's giving us ideas and inspiration for how we can tweak our technology so it works in a more broadly applicable way. And I also think that the future trend is to have more of this collaborative effort and make precision medicine a reality. Ultimately as scientists we should be trying to make an impact, we should be trying to move the process of discovery forward. Reproducing each other's result and hoping that we have cure for diseases like cancer. Then eventually I think we have better prediction models, better technologies to help our patients. So why wouldn't we share our findings? I would definitely like to see this whole process expanded of open source public data and I'm very happy that I'm working at this age of where this is actively evolving and I'm happy to do my part.