3 ms·

You have seriously underestimated 1) efforts needed to develop and to maintain such resources – your best hope is to work with government-funded institutes; 2)

by x1k 11y ago

You have seriously underestimated 1) efforts needed to develop and to maintain such resources – your best hope is to work with government-funded institutes; 2) resistance from the convention of a particular research field – you can rarely bend how people in a field work on things; 3) culture differences between biologists/doctors and programmers – biologists/doctors think very differently, which is frequently overlooked by programmers; 4) bureaucracy – everyone thinks he/she is the best; when you work with top groups to make things happen, you will find how problematic it is; 5) technical challenges – as you care about pheonotype data: there are no good ways to integrate various pheonotypes from multiple sources. Everyone in biomedical research dreams about integrated resources. I have heard multiple people advocating SPARQL as well. If it had been that easy, this would have occurred years ago. In the real world, no one is even close. If you want to attract collaborators, learn Linus: say you have a working prototype and demonstrate how wonderful it is. Your ideas are cheap. The difficult part is a clear roadmap to make it happen.

dekhn 11y ago
I agree strongly with this. I started out in biomedicine many years ago with the same aims as the OP, but after a lot of experience, I think that announcing the database resource is just the first step, it's an easy one, and all the hard problems are the ones listed by x1k. Based on what I see happening in large orgs with lots of machine learning resources is the development of new techniques to generate large amounts of homogenous phenotypic data across many measurement modalities. These large orgs have biologist/doctors: the small number of people cross-trained well enough to move between the two fields with ease. These orgs have gathered enough resources to compel the leading researchers to work with them, and they're starting to publish interesting papers.
- x1k 11y ago
  Yes, I think those large companies may finally have a slim chance to revolutionize data integration, but it is too early to tell yet. We will see.
hackuser 11y ago
Could the largest funding organizations, in order to get a much greater return on their investments (i.e., much wider use and re-use of the results), require use of some standard data format for projects they fund? (I know very little about the issue, but this seems to be a problem in many fields of academia.)

by x1k 11y ago

dekhn 11y ago

I agree strongly with this. I started out in biomedicine many years ago with the same aims as the OP, but after a lot of experience, I think that announcing the database resource is just the first step, it's an easy one, and all the hard problems are the ones listed by x1k. Based on what I see happening in large orgs with lots of machine learning resources is the development of new techniques to generate large amounts of homogenous phenotypic data across many measurement modalities. These large orgs have biologist/doctors: the small number of people cross-trained well enough to move between the two fields with ease. These orgs have gathered enough resources to compel the leading researchers to work with them, and they're starting to publish interesting papers.

x1k 11y ago
Yes, I think those large companies may finally have a slim chance to revolutionize data integration, but it is too early to tell yet. We will see.

hackuser 11y ago

Could the largest funding organizations, in order to get a much greater return on their investments (i.e., much wider use and re-use of the results), require use of some standard data format for projects they fund? (I know very little about the issue, but this seems to be a problem in many fields of academia.)