Camera Trap Images used in "Identifying Animal Species in Camera Trap Images using Deep Learning and Citizen Science"

All images were downloaded from Zooniverse and have been resized to 330x330 pixels.This dataset provides the camera trap images used in "Identifying Animal Species in Camera Trap Images using Deep Learning and Citizen Science" as well as meta-data about the images. The Snapshop Serengeti collection includes 6,163,870 images in JPG format. The Snapshot Wisconsin collection includes 497,204 images in JPG format. The Camera CATalogue collection include 506,241 images in JPG format. Excluded are the images for the dataset "Elephant Expedition" which will be published separately outside DRUM. Also excluded are images of humans due to privacy reasons.This study was partially supported by the NSF under award IIS 1619177The development of the Zooniverse platform was partially supported by a Global Impact Award from Google.We also acknowledge support from STFC under grant ST/N003179/1.EE was funded by the University of Oxford’s Hertford College Mortimer May fund

A transient search using combined human and machine classifications

Large modern surveys require efficient review of data in order to find transient sources such as supernovae, and to distinguish such sources from artefacts and noise. Much effort has been put into the development of automatic algorithms, but surveys still rely on human review of targets. This paper presents an integrated system for the identification of supernovae in data from Pan-STARRS1, combining classifications from volunteers participating in a citizen science project with those from a convolutional neural network. The unique aspect of this work is the deployment, in combination, of both human and machine classifications for near real-time discovery in an astronomical project. We show that the combination of the two methods outperforms either one used individually. This result has important implications for the future development of transient searches, especially in the era of Large Synoptic Survey Telescope and other large-throughput surveys

Software, Data & Models used in "Identifying Animal Species in Camera Trap Images using Deep Learning and Citizen Science"

This dataset provides the software, the models, and other data used in "Identifying Animal Species in Camera Trap Images using Deep Learning and Citizen Science". This dataset contains the software to train convolutional neural networks, as well as all models trained for the study and code to apply them on new images. Additionally, data defining the conducted experiments are provided to ensure reproducibility.This study was partially supported by the NSF under award IIS 1619177The development of the Zooniverse platform was partially supported by a Global Impact Award from Google.We also acknowledge support from STFC under grant ST/N003179/1.EE was funded by the University of Oxford’s Hertford College Mortimer May fund

Survey of Gravitationally-lensed Objects in HSC Imaging (SuGOHI)

Context. Strong lenses are extremely useful probes of the distribution of matter on galaxy and cluster scales at cosmological distances, however, they are rare and difficult to find. The number of currently known lenses is on the order of 1000. Aims. The aim of this study is to use crowdsourcing to carry out a lens search targeting massive galaxies selected from over 442 square degrees of photometric data from the Hyper Suprime-Cam (HSC) survey. Methods. Based on the S16A internal data release of the HSC survey, we chose a sample of ∼300 000 galaxies with photometric redshifts in the range of 0.2  11.2. We crowdsourced lens finding on this sample of galaxies on the Zooniverse platform as part of the Space Warps project. The sample was complemented by a large set of simulated lenses and visually selected non-lenses for training purposes. Nearly 6000 citizen volunteers participated in the experiment. In parallel, we used YATT