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.
DNA@Home
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.
Wildlife@Home
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+.
SubsetSum@Home
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).User of the Day
[SG]ATA-RolfBlick auf die beiden Sternwarten hinter dem Schulgebäude Kronshagen bei Kiel (Juli 2005) © Mario Lehwald. Danke Mario für die Erlaubnis, dieses Bild...
News 
[wildlife] assimilator bug
Looks like I didn't quite fully fix the assimilator bug. Will be working on it today and will keep you posted as I figure things out.
Travis Desell on Sunday, September 17th
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[wildlife] new application and version (v0.33)
I've added a new application (Exact Batch Norm with Scaled FMP) with some recent updates and improvements, as version 0.33. Let me know if you have any issues with this one!
Travis Desell on Monday, September 18th
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[wildlife] assimilator back up
Fixed the issue -- the assimilator is back up and running (which is also the work generator), so work should be flowing again!
Travis Desell on Friday, September 15th
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[wildlife] assimilator issue
It looks like there's an issue with the assimilator. I'm currently traveling and hosting a workshop, but I'm hoping I'll get things back up and running sometime today. Will keep you posted as I know more.
Travis Desell on Thursday, September 14th
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[wildlife] two publications accepted to this year's IEEE eScience conference
So we had two papers accepted to the 13th IEEE International Conference on eScience this year! One on EXACT (what you've been crunching) and the other on the unmanned aerial systems imagery you've been marking up. If you'd like to read pre-prints here they are:
Developing a Volunteer Computing Project to Evolve Convolutional Neural Networks and Their Hyperparameters.
Toward Using Citizen Scientists to Drive Automated Ecological Object Detection in Aerial Imagery.
Congrats to Marshall and Connor on their paper!
Travis Desell on Friday, August 18th
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