Coming soon…
…watch this space later today.
Snapshot Serengeti
A Zooniverse Project Blog
Archives
- July 2019
- June 2019
- May 2019
- April 2019
- March 2019
- January 2019
- December 2018
- November 2018
- October 2018
- September 2018
- August 2018
- July 2018
- June 2018
- May 2018
- April 2018
- March 2018
- February 2018
- January 2018
- December 2017
- November 2017
- October 2017
- September 2017
- August 2017
- July 2017
- June 2017
- May 2017
- April 2017
- March 2017
- February 2017
- January 2017
- November 2016
- October 2016
- September 2016
- July 2016
- May 2016
- March 2016
- February 2016
- December 2015
- October 2015
- September 2015
- July 2015
- June 2015
- May 2015
- April 2015
- March 2015
- February 2015
- January 2015
- December 2014
- November 2014
- October 2014
- September 2014
- August 2014
- July 2014
- June 2014
- May 2014
- April 2014
- March 2014
- February 2014
- January 2014
- December 2013
- November 2013
- October 2013
- September 2013
- August 2013
- July 2013
- June 2013
- May 2013
- April 2013
- March 2013
- February 2013
- January 2013
- December 2012
aardwolves
Advent
algorithm
analysis
ants
bias
bird
birds
cameras
cheetah
cheetahs
coexistence
competition
conservation
Craig Packer
crowdfunding
Daniel Rosengren
data
detection
distribution
duiker
elephant
elephants
error
fire
food
funding
fundraising
giraffe
giraffes
Gombe Stream National Park
GPS collars
grass
hyena
hyenas
i-dont-know
impala
indiegogo
landscapes
leopards
lion
lions
Lisa O'Bryan
Lora Orme
Lucy Hughes
mud
National Geographic Magazine
News
other-bird
Pielou index
Poster
presentation
proposal
rain
random
science
science funding
season-4
Season 5
Season 6
Season 7
sequester
sequestration
shade
South Africa
stuck
trees
video
wild dog
wild dogs
wildebeest
wolves
zebra
zebras
Zooniverse
Snapshot Serengeti 
Doing this for moose and caribou in Newfoudland, Canada. Would gratly appreciate a copy of the study design.
Hi, I love this project already. However, I find that on some pics, the animal is so close to the camera that only an ear, a leg, a rump or a belly is seen. Or it’s too dark, backlit, or far away to be clear. Based on the filter options you have, there is no way to identify many of these partial or difficult to see animals. Perhaps a button that we can use to flag the pic as “something there but I can’t tell what it is” or “too close to identify” or some such term. Now, all I can do is label such images as “nothing there” and move on. This feels wrong! I want to let you know I found something in the image, I just can’t tell you what it is!
Thanks
It is wrong to identify an image with “nothing there”, when you can identify a brown rump or stomach. You simply guess, what it may be. Each image is presented to more persons. If the image gets different identifications, then is it presented for even more persons. It could be interesting to hear about this algorithm and the statistics behind.
Have to completely agree with Linda. Having a ‘no way i can meaningfully comment on this’ as a selection option is very much needed. You and the team can then look at them later and might have a better, professional, eye for determining what it is. And you’ll know local context so you might outright know what its likely to be. Otherwise, LOVE this. Excellent community engagement tool as well as helping you with the research!
Ditto all the above!
Same here – it seems a bit of a waste to have an animal in the picture that cannot be identified (at least by us tyros), yet could be by an expert.
It would also be helpful to have a color picture gallery of all the various tails.
Lastly, there are 33 varieties of mongoose (says Wikipedia), yet you show only one picture. There was one that I thought was a mongoos, but it didn’t seem too much like the picture you show. Opted for bat-eared fox – I’m not too sure which it was.