Data from: Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna

Camera traps can be used to address large-scale community ecology questions by providing systematic data on an array of wide-ranging species. We deployed 225 camera traps across 1,125km2 in the centre of Serengeti National Park, Tanzania, to evaluate spatial and temporal inter-species dynamics. The cameras have operated continuously since 2010. From June 2010 to May 2013, the survey accumulated 101,315 camera-trap days and produced 1.51 million sets of pictures. Members of the general public classified the images via the citizen science website www.snapshotserengeti.org. Each image was viewed by multiple users who recorded the species present, number of individuals, behaviours observed, and presence of young. For the data described here, 28,040 registered users contributed 10.8 million classifications. We applied a simple algorithm to reduce the classifications into a final "consensus" dataset, yielding a final classification for each image and a measure of agreement among individual answers. The consensus dataset and images are a novel dataset for ecological research, providing an unparalleled opportunity to investigate multi-species dynamics among dozens of species in an intact ecosystem. The consensus dataset, raw classification dataset, and images are also a valuable resource for machine-learning and computer-vision research.红外相机陷阱（camera trap）可通过获取覆盖多种广布物种的系统性观测数据，助力大规模群落生态学研究问题的探究。本研究于坦桑尼亚塞伦盖蒂国家公园（Serengeti National Park）中心1125平方千米的范围内布设了225台红外相机陷阱，以探究物种种间的时空动态规律。该套相机自2010年起持续运行；2010年6月至2013年5月期间，本调查累计获得101315个相机有效工作日，产出151万组影像。普通公众可通过公民科学网站www.snapshotserengeti.org对影像进行标注，每张影像均由多名用户审阅，用户需记录影像中的物种、个体数量、观测到的行为以及幼崽存在情况。针对本数据集涵盖的影像，共有28040名注册用户完成总计1080万次标注。本研究通过简单算法将多轮标注整合为最终的"共识"数据集，为每张影像生成最终标注结果，并提供用户标注间的一致性衡量指标。该共识数据集及配套影像为生态学研究提供了全新数据源，为在完整生态系统中探究数十个物种种间动态关系提供了前所未有的研究契机。此外，共识数据集、原始标注数据集及配套影像，也可为机器学习（machine learning）与计算机视觉（computer vision）研究提供极具价值的研究资源。