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Detection range assessment Belwind offshore wind farm Universiteit Gent
Jolien Goossens, Jolien Buyse, Jan Reubens
The analysis of the data is detailed in the GitHub repository https://github.com/JolienGoossens/RangeTestingTime.
Data used for a detection range assessment of acoustic receivers, deployed in Belwind offshore wind farm in 2020. These data include transmission and detection data of VR2AR receiver built-in transmitters, deployment metadata and environmental data.
Data used for a detection range assessment of acoustic receivers, deployed in Belwind offshore wind farm in 2020. These data include transmission and detection data of VR2AR receiver built-in transmitters, deployment metadata and environmental data.
Detection range assessment Belwind offshore wind farm Instituut voor Landbouw-, Visserij- en Voedingsonderzoek
Jolien Goossens, Jolien Buyse, Jan Reubens
Data used for a detection range assessment of acoustic receivers, deployed in Belwind offshore wind farm in 2020. These data include transmission and detection data of VR2AR receiver built-in transmitters, deployment metadata and environmental data.,The analysis of the data is detailed in the GitHub repository https://github.com/JolienGoossens/RangeTestingTime.
Training materials on detection of plant viruses through high throughput sequencing - Accompanying information Kutnjak et al., Microorganisms, 2021,9, 841. Instituut voor Landbouw-, Visserij- en Voedingsonderzoek
Marie Lefebvre, Paolo Margaria, Annelies Haegeman, Lucie Tamisier, Mike Rott, Laura Miozzi, Olivier Schumpp, Martha Malapi-Wight, Neil Boonham, Michela Chiumenti, Benoit Remenant, Sebastien Massart, Kris De Jonghe, Denis Kutnjak, Thierry Candresse, Johan Rollin, Jean-Sébastien Reynard
This is a copy of the GitLab repository https://gitlab.com/ilvo/phbn-wp2-training. The repository contains training materials for pipelines for virus detection by means of high throughput sequencing. The information is accompanying the paper "A primer on the analysis of high-throughput sequencing data for detection of plant viruses" by Kutnjak et al, Microorganisms 2021, 9 841.
Replication package for "Smelly Variables in Ansible Infrastructure Code: Detection, Prevalence, and Lifetime" Vrije Universiteit Brussel
Infrastructure as Code is the practice of automating the provisioning, configuration, and orchestration of network nodes using code in which variable values such as configuration parameters, node hostnames, etc. play a central role. Mistakes in these values are an important cause of infrastructure defects and corresponding outages. Ansible, a popular IaC language, nonetheless features semantics which can cause confusion about the value of variables. In this paper, we identify six novel code smells related to Ansible's intricate variable precedence rules and lazy-evaluated template ...
RGB-statistics derived from Nile red-stained reference plastics for the construction of the PDM (Plastics Detection Model) Instituut voor Landbouw-, Visserij- en Voedingsonderzoek
Nelle Meyers, Ana Catarino, Annelies Declercq, Aisling Brenan, Lisa Devriese, Michiel Vandegehuchte, Bavo De Witte, Colin Janssen, Gert Everaert
Dataset containing RGB-statistics extracted from photographed fluorescent reference particles stained with Nile red. The most abundantly produced plastic polymers worldwide as well as natural materials with high prevalence in the marine environment were considered for this dataset. The spectral data was used to construct a supervised machine learning model that allows to accurately distinguish plastic from natural particles in a cost- and time-efficient way.,The dataset was built to train and validate the ‘Plastic Detection Model’ (PDM) in R and contains Red, Green and Blue (RGB) statistics ...
Sim2real flower detection towards automated Calendula harvesting Instituut voor Landbouw-, Visserij- en Voedingsonderzoek
This dataset serves as supplementary material for the research paper titled 'Sim2real flower detection towards automated Calendula harvesting', which was published in the October 2023 issue of Biosystems Engineering. Within this upload, you will find a collection of both authentic and computer-generated images featuring Calendula (Calendula officinalis L.) flowers. Additionally, we have included the resources and original data utilized in generating the synthetic images. This dataset proves instrumental in demonstrating the successful transference of a deep neural network from simulation to ...
Supplementary material 2: Towards harmonization of DNA metabarcoding for monitoring marine macrobenthos: the effect of technical replicates and pooled DNA extractions on species detection. Metabarcoding and Metagenomics 5: e71107. https://doi.org/10.3897/ Instituut voor Landbouw-, Visserij- en Voedingsonderzoek
Explanation note: ESM Table S1. Used primerset. ESM Table S2. Abundanties Morphological identified sample. ESM Table S3. Read numbers. ESM Table S4. Unassigned ASVs. ESM Table S5. Output UpSetPlots. Prediction of the number of species when using x number of replicates from the species accumulation method. This finds the expected richness following Coleman et al. 1982. ESM Table S6. Unique species genetic identified sample, with comparable species from the morphological identified sample. ESM Table S7. Table with results of PERMANOVA for DNA replicates and pooled DNA extractions with the ...
Data from: “Change point detection of peak tibial acceleration in over-ground running retraining” Universiteit Gent
This dataset contains the time series of axial peak tibial acceleration. We recruited 10 runners with high axial peak tibial acceleration. The participants performed a gait retraining session whilst running overground at 3.2 ± 0.2 m/s in self-selected footwear. Real-time auditory biofeedback on axial peak tibial acceleration was provided. The axial peak tibial acceleration was detected before and during the biofeedback-based intervention using a backpack system connected to a very lightweight accelerometer. We refer to the full paper for details on how the data were collected and processed. ...
COVID19 Rumor Detection Universiteit Gent
The data set contains information about the COVID-19 pandemic. Twitter data has been collected based on the hashtags #CoronaOutbreak, #CoronaVirus, #CoronaVirusOutbreak, #COVID19, #COVID-19, #COVID2019, and #SARSCoV2, between February 12, 2020 and June 15, 2020. The goal of this data set is to detect whether a tweet is identified as a rumor or not (given by the 'label' column). A tweet that is identified as a rumor is labeled as 1, and 0 otherwise. The tweets were labeled by two independent annotators using the following guidelines. Whether a tweet is a rumor or not depends on 3 important ...