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).

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[wildlife] new EXACT runs

I've started up two new exact runs (they have feb_20 in the name). Let me know if you have any problems with them!

Travis Desell on Monday, February 20th
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[all] travel alert

Just a heads up that I'm currently traveling until February 28th, so I apologize for any slow responses. I'll still be checking in to email and the forums periodically while I'm away, just not as frequently as usual.

Travis Desell on Sunday, February 26th
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[all] Interview with Team Gridcoin

Last Saturday I had the pleasure of chatting with some people from Team Gridcoin, who recorded the session. If you'd like to check it out, here it is!

https://steemit.com/gridcoin/@erkan/interview-guide-with-boinc-admins

Looking forward to the next one! I'll try and inform everyone when I'll be joining next if anyone else would like to take part.

Travis Desell on Saturday, February 25th
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[wildlife] Paper on EXACT submitted to GECCO 2017

Sorry for radio silence the last few days, however I was furiously looking at results and working on a submission to this years Genetic and Evolutionary Computation Conference (GECCO).

GECCO uses a blind review, so mentions of authorship (and the CSG because that would make it pretty obvious who the author was) are removed until the camera ready version if it's accepted, but if you'd like to take a look at the submitted draft here it is:

Large Scale Evolution of Convolutional Neural Networks Using Volunteer Computing

It explains the algorithm I'm using in a bit of detail, and discusses some of the preliminary results. You may ask, how does recognizing numbers related to detecting wildlife -- well the idea is to use this on the datasets you've been helping us generate by classifying video and marking up the images. Thanks again everyone for the crunching and volunteering!

Travis Desell on Wednesday, February 8th
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[wildlife] EXACT v0.17 for windows xp

I've recompiled the windows app with some changes to help support windows xp. If anyone has a windows XP box please let me know if v0.17 is running or erroring out immediately. Thanks!

Travis Desell on Wednesday, February 8th
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