Table of Contents

1 Introduction.- 2 Developing mobile applications for environmental and biodiversity citizen science: considerations and recommendations.- 3 A toolbox for understanding and implementing a citizens' observatory on air monitoring.- 4 A Real-Time Streaming and Detection System for Bio-acoustic Ecological Studies after the Fukushima Accident.- 5 Towards improved air quality monitoring using publicly available sky images.- 6 Traits: Structuring species information for discoverability, navigation and identification.- 7 Unsupervised Bioacoustic Segmentation by Hierarchical Dirichlet Process Hidden Markov Model.- 8 Plant Identification: Experts vs. Machines in the Era of Deep Learning.- 9 Automated identification of herbarium specimens at different taxonomic levels.- 10 A deep learning approach to species distribution modelling.- A Existing Data and Metadata Standards and Schemas related to Citizen Science.- B Creative Commons (CC) and Open Data Commons (ODC) Licenses.- C List of Apps, Platforms and their Functionalities in Citizen Science Projects.- D Examples of Symbolic and Non-symbolic Rewards in Citizen Science Projects.- E List of Apps for Sonic Environment and Noise Pollution Monitoring.