Publications by authors named "Damiano Oldoni"

3 Publications

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A checklist recipe: making species data open and FAIR.

Database (Oxford) 2020 01;2020

Meise Botanic Garden, Nieuwelaan 38, B-1860 Meise, Belgium.

Species checklists are a crucial source of information for research and policy. Unfortunately, many traditional species checklists vary wildly in their content, format, availability and maintenance. The fact that these are not open, findable, accessible, interoperable and reusable (FAIR) severely hampers fast and efficient information flow to policy and decision-making that are required to tackle the current biodiversity crisis. Here, we propose a reproducible, semi-automated workflow to transform traditional checklist data into a FAIR and open species registry. We showcase our workflow by applying it to the publication of the Manual of Alien Plants, a species checklist specifically developed for the Tracking Invasive Alien Species (TrIAS) project. Our approach combines source data management, reproducible data transformation to Darwin Core using R, version control, data documentation and publication to the Global Biodiversity Information Facility (GBIF). This checklist publication workflow is openly available for data holders and applicable to species registries varying in thematic, taxonomic or geographical scope and could serve as an important tool to open up research and strengthen environmental decision-making.
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http://dx.doi.org/10.1093/database/baaa084DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7661093PMC
January 2020

Multi-criteria anomaly detection in urban noise sensor networks.

Environ Sci Process Impacts 2014 ;16(10):2249-58

Department of Information Technology (INTEC) - IBCN, Ghent University - iMinds, Gaston Crommenlaan 8 bus 201, B-9050 Gent, Belgium.

The growing concern of citizens about the quality of their living environment and the emergence of low-cost microphones and data acquisition systems triggered the deployment of numerous noise monitoring networks spread over large geographical areas. Due to the local character of noise pollution in an urban environment, a dense measurement network is needed in order to accurately assess the spatial and temporal variations. The use of consumer grade microphones in this context appears to be very cost-efficient compared to the use of measurement microphones. However, the lower reliability of these sensing units requires a strong quality control of the measured data. To automatically validate sensor (microphone) data, prior to their use in further processing, a multi-criteria measurement quality assessment model for detecting anomalies such as microphone breakdowns, drifts and critical outliers was developed. Each of the criteria results in a quality score between 0 and 1. An ordered weighted average (OWA) operator combines these individual scores into a global quality score. The model is validated on datasets acquired from a real-world, extensive noise monitoring network consisting of more than 50 microphones. Over a period of more than a year, the proposed approach successfully detected several microphone faults and anomalies.
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http://dx.doi.org/10.1039/c4em00273cDOI Listing
May 2015

A computational model of auditory attention for use in soundscape research.

J Acoust Soc Am 2013 Jul;134(1):852-61

Acoustics Research Group, Department of Information Technology, Ghent University, St.-Pietersnieuwstraat 41, B-9000 Ghent, Belgium.

Urban soundscape design involves creating outdoor spaces that are pleasing to the ear. One way to achieve this goal is to add or accentuate sounds that are considered to be desired by most users of the space, such that the desired sounds mask undesired sounds, or at least distract attention away from undesired sounds. In view of removing the need for a listening panel to assess the effectiveness of such soundscape measures, the interest for new models and techniques is growing. In this paper, a model of auditory attention to environmental sound is presented, which balances computational complexity and biological plausibility. Once the model is trained for a particular location, it classifies the sounds that are present in the soundscape and simulates how a typical listener would switch attention over time between different sounds. The model provides an acoustic summary, giving the soundscape designer a quick overview of the typical sounds at a particular location, and allows assessment of the perceptual effect of introducing additional sounds.
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http://dx.doi.org/10.1121/1.4807798DOI Listing
July 2013
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