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      A Survey of e-Biodiversity: Concepts, Practices, and Challenges

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          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.

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          Most cited references67

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          Deep Learning in Neural Networks: An Overview

          (2014)
          In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarises relevant work, much of it from the previous millennium. Shallow and deep learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.
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            spThin: an R package for spatial thinning of species occurrence records for use in ecological niche models

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              Future paths for integer programming and links to artificial intelligence

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                Author and article information

                Journal
                29 September 2018
                Article
                1810.00224
                003303d5-a766-41cf-80c5-cb686cc670c5

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                Custom metadata
                q-bio.PE cs.DB

                Evolutionary Biology,Databases
                Evolutionary Biology, Databases

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