A survey of biodiversity informatics: Concepts, practices, and challenges

Abstract

The unprecedented size of the human population, along with its associated economic activities, have an ever increasing impact on global environments. Across the world, countries are concerned about the growing resource consumption and the capacity of ecosystems to provide them. To effectively conserve biodiversity, it is essential to make indicators and knowledge openly available to decision-makers in ways that they can effectively use them. The development and deployment of mechanisms to produce these indicators depend on having access to trustworthy data from field surveys and automated sensors, biological collections, molecular data, and historic academic literature. The transformation of this raw data into synthesized information that is fit for use requires going through many refinement steps. The methodologies and techniques used to manage and analyze this data comprise an area often called biodiversity informatics (or e-Biodiversity). Biodiversity data follows a life cycle consisting of planning, collection, certification, description, preservation, discovery, integration, and analysis. Researchers, whether producers or consumers of biodiversity data, will likely perform activities related to at least one of these steps. This article explores each stage of the life cycle of biodiversity data, discussing its methodologies, tools, and challenges.

References in corpus (9)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Statistical mechanics of complex networks
• Deep Learning in Neural Networks: An Overview
• Deep Residual Learning for Image Recognition
• An introduction to Docker for reproducible research, with examples from the R environment
• Best Practices for Scientific Computing
• Parsl: Pervasive Parallel Programming in Python
• The Rise of Network Ecology: Maps of the topic diversity and scientific collaboration
• Containers for portable, productive and performant scientific computing