Citizen Science Grid

The Citizen Science Grid is run by Travis Desell, an Assistant Professor in the Computer Science Department at the University of North Dakota. It is hosted by UND's Computational Research Center and Information Technology Systems and Services. The Citizen Science Grid is dedicated to supporting a wide range of research and educational projects using volunteer computing and citizen science, which you can read about and visit below.


Wildlife@Home is citizen science project aimed at analyzing video gathered from various cameras recording wildlife. Currently the project is looking at video of sharp-tailed grouse, Tympanuchus phasianellus, and two federally protected species, interior least terns, Sternula antillarum, and piping plovers, Charadruis melodus to examine their nesting habits and ecology.

Climate Tweets

The Climate Tweets project is focused on personal opinions about climate change or global warming. The goal is to sort tweets and view the different views in various countries, how the discussion has changed over time, and how opinions change with political orientation. Classifying tweets allows us to discover patterns and coorelations in people's opinions about our world. It also helps us understand what people know about climate change. Please note that the tweets are unfiltered and may contain profanity or controversial views, and these are not the views of the Citizen Science Grid, any of our team, or funding agencies. Because of this the project is 18+.


The Subset Sum problem is described as follows: given a set of positive integers S and a target sum t, is there a subset of S whose sum is t? It is one of the well-know, so-called "hard" problems in computing. It's actually a very simple problem computationally, and the computer program to solve it is not extremely complicated. What's hard about it is the running time – all known exact algorithms have running time that is proportional to an exponential function of the number of elements in the set (for worst-case instances of the problem).


The goal of DNA@Home is to discover what regulates the genes in DNA. Ever notice that skin cells are different from a muscle cells, which are different from a bone cells, even though all these cells have every gene in your genome? That's because not all genes are "on" all the time. Depending on the cell type and what the cell is trying to do at any given moment, only a subset of the genes are used, and the remainder are shut off. DNA@home uses statistical algorithms to unlock the key to this differential regulation, using your volunteered computers.

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[wildlife] "monster" workunit questions

So I had a couple ideas about the monster workunits. For those of you who are having them, are you running other workunits at the same time? Could you let me know the memory usage of those workunits? Does their performance improve if you only run one EXACT workunit at a time?

What I'm thinking may be happening is that the larger workunits require more memory, and if there are enough other workunits (especially other large EXACT workunits) running at the same time things may end up paging to disk because of memory requirements.

Travis Desell on Friday, April 21st
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[wildlife] updated workunit deadlines

So I'm now dynamically generating workunit deadlines based on how much work they'll require (so deadlines are based on credit). Right now I'm setting the deadline to 1 day per 500 credit awarded -- let me know if this seems correct of if I should extend it further. I've also stopped the one search which should have been generating the really large workunits, so I imagine newly generated workunits should not have the excessively long multi-day runtimes you've been seeing.

Travis Desell on Friday, April 21st
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[wildlife] status update

Just a status update for everyone. Due to the "monster" workunits people have been seeing I've made a couple tweaks server side and started to run some new searches (although the results I've been getting in have been extremely cool).

These new searches will start back with the smaller workunits and get progressively larger in size, so if you start seeing results with names like 'exact_genome_<timestamp>_6_<more numbers>' or 'exact_genome_<timestamp>_7_<more numbers>' those are from the new searches. If these start to see runtimes wildly out of line with credit please let me know.

Also, I've updated the work generation to have dynamically generated deadlines as opposed to a fixed 7 days. This should make the deadlines get longer as the workunits get more complex, so people won't see WUs that will take longer to compute than their deadline anymore. If the deadlines seem a bit off please let me know.

On top of that, I've been working on a new version of the application which will also be able to run on GPUs. I think this may address some of the other weird issues we've been seeing with the "monster" workunits as well. Will keep you posted as this develops but hopefully in a few weeks I'll be able to start testing an OpenCL version of the application that can run on NVidia or AMD GPUs.

Travis Desell on Tuesday, April 18th
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[wildlife] grizzly chases geese

Susan has made a really cool video stitching together some of the imagery you've been looking at where a grizzly bear chases some geese around. Please take a look on youtube:

Feel free to talk to Susan if you have any questions in her thread in the video/image watching forum:

Travis Desell on Wednesday, April 12th
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[wildlife] credit inconsistencies

I've updated the algorithm which calculates credit in order to deal with credit inconsistencies we've been seeing recently. If you're still seeing wildly varying credit for workunits that take the same amount of time on your machines please let me know. Note that there are some 200-300 workunits in the queue that were still generated with the old credit algorithm so it may take a little time for the new workunits to start flowing out. Hope this fixes things!

Travis Desell on Friday, April 14th
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