Results
Data from Snapshot Serengeti resulted in the following publications:
Reactive anti-predator behavioral strategy shaped by predator characteristics
Palmer & Packer August 2021
Citizen science, computing, and conservation: How can “Crowd AI” change the way we tackle large-scale ecological challenges?
Palmer et al. July 2021
Can citizen science analysis of camera trap data be used to study reproduction? Lessons from Snapshot Serengeti program
Thiel et al. May 2021
Iterative Human and Automated Identification of Wildlife Image
Miao et al. May 2021
Snapshot Safari: A large-scale collaborative to monitor Africa’s remarkable biodiversity
Pardo et al. January 2021
Zooming in on mechanistic predator-prey ecology: Integrating camera traps with experimental methods to reveal the drivers of ecological interactions
Smith et al. May 2020
Quantifying the severity of giraffe skin disease via photogrammetry analysis of camera trap data
Muneza et al. October 2019
The African Lion: A Long History of Interdisciplinary Research
Packer July 2019
Animal Scanner: Software for classifying humans, animals, and empty frames in camera trap images
Yousef et al. February 2019
Everyone counts? Design considerations in online citizen science
Spiers et al. January 2019
Evaluating relative abundance indices to produce abundance estimates for terrestrial herbivores from large-scale camera trap surveys
Palmer et al. November 2018
Identifying Animal Species in Camera Trap Images using Deep Learning and Citizen Science
Willi et al. September 2018
Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning
Norouzzadeh et al. June 2018
Identifying drivers of spatial variation in occupancy with limited replication camera trap data
Hepler et al. April 2018
No respect for apex carnivores: Distribution and activity patterns of honey badgers in the Serengeti
Allen et al. March 2018
Giraffe bed and breakfast: Camera traps reveal Tanzanian yellow‐billed oxpeckers roosting on their large mammalian hosts
Palmer and Packer February 2018
Towards automatic wild animal monitoring: Identification of animal species in camera-trap images using very deep convolutional neural networks
Gomez Villa et al. September 2017
A ‘dynamic’ landscape of fear: prey responses to spatiotemporal variations in predation risk across the lunar cycle
Palmer et al. September 2017
Assessing data quality in citizen science
Kosmala et al. November 2016
The spatial distribution of African savannah herbivores: species associations and habitat occupancy in a landscape context
Anderson et al. September 2016
A generalized approach for producing, quantifying, and validating citizen science data from wildlife images Swanson, Alexandra, Kosmala, Margaret, Lintott, Chris ; Packer, Craig. Conservation Biology, June 2016
In the absence of a “landscape of fear”: How lions, hyenas, and cheetahs coexist
Swanson et al. November 2016
Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna
Swanson et al. June 2015
Defining and Measuring Success in Online Citizen Science: A Case Study of Zooniverse Projects
Cox et al. July 2015
Applying a random encounter model to estimate lion density from camera traps in Serengeti National Park, Tanzania
Cusack et al. May 2015