Flanders Marine Institute (VLIZ)
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Benthos Ecosystem Quality Index (BEQI). BEQI is a biological index that is used to assess the state of the benthos of coastal and transitional waters for the Water Framework Directive. Taking into consideration the large intrinsic variability of estuarine and coastal systems and the importance of ecosystem functioning within a water body, Ysebaert & Herman (2003) advocate a multilevel scale-dependent approach for the classification of the quality elements in coastal and transitional waters. The proposed multilevel approach consists of three levels: (1) level 1 - Ecosystem level; (2) level 2 - Habitat level; (3) level 3 - Community (within habitat) level. Where suitable input and reference data is provided, the tool will calculate Ecosystem quality scores and produce a list of species that are responsible for observed deviations from the reference state.
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pyporcc is an open-source Python package developed to detect and classify harbour porpoise’s clicks in audio files using the PorCC algorithm and offering the possibility to create new clicks classifiers. It provides a framework to train different models such as Support Vector Machines, Linear Support Vector Machines, Random Forest and K-Nearest Neighbour that classify sound clips in Noise, and Low-Quality and High-Quality harbour porpoises’ clicks. The algorithm from PAMGuard to detect possible clicks clips is also implemented.
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The goal of MarineSPEED is to provide a benchmark data set for presence-only species distribution modeling (SDM) in order to facilitate reproducible and comparable SDM research. It contains species occurrences (coordinates) from a wide diversity of marine species and associated environmental data from Bio-ORACLE and MARSPEC. Some additional information about MarineSPEED can be found in the R Shiny viewer at https://rshiny.lifewatch.be/marinespeed/.
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One of the virtual laboratories developed by LifeWatch Belgium is the Belgian LifeWatch eLab. This online application allows users to standardise, analyse and visualise their data, making use of web services built on top of internal and external reference databases. The ultimate goal of LifeWatch is to set up a network for data exchange and data analysis through web services. Web services are systems that allow communication between two computers over the web, and allow the user to access the most recent and up-to-date information directly from within other applications. Within LifeWatch Belgium, several web services are available to standardise, analyse and visualise your data, and to extract additional data from several sources. The user can select several data services (taxonomic, geographic, thematic, etc.) and run them successively through a straightforward online user interface. You can also use the LifeWatch.be web services in a concatenated way, i.e. the output of one web service is the input for the next web service. Establishing such workflows helps solving (complicated) biological questions. Several use cases demonstrate the use of the LifeWatch web services. So as to facilitate the use of the LifeWatch web services, several applications and tools were documented in use cases and tutorials. These can be found on the links below, as well as on the specific websites of software packages and Github repositories. The Belgian LifeWatch E-Lab online application allows users to standardize, analyze and visualize their data, making use of web services built on top of internal and external reference databases. A user can select several data services (taxonomic, geographic, thematic, etc.) and run them successively through a straightforward user interface. As explained in the user guide, the LifeWatch.be web services can be used in a concatenated way, i.e. the output of one web service is the input for the next web service.
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This online and interactive environment provides access to all data of the European Tracking Network. The VRE includes a database for storage and integration of acoustic telemetry data and a number of analytical tools for analysing the data using R that are: - the LifeWatch Data Explorer for fish telemetry, a RShiny GUI for data exploration; - an RStudio IDE that allows the user to develop and run R scripts online on the available telemetry data; - an Rpackage that incorporates specific functions to start a smooth analysis of telemetry data.
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pyhydrophone is an open-source Python package that has been developed to ease the import of underwater sound data recorded with a hydrophone to python, so postprocessing and AI can be easily performed on the data afterwards. Different recorders can be added with their different way of reading metadata, so the users do not have to worry about the format but just about the outcome.
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This marine biodiversity observatory has been developed in the framework of the Flemish contribution to LifeWatch and applies automated and innovative technology (imaging, acoustics, genomic profiling) to capture biodiversity data in the Belgian part of the North Sea and its coastal areas. The observatory combines regular ship-based station measurements with a number of operational sensor networks. The observatory provides biodiversity and ecosystem information for the LTSER Belgian coastal waters and sand bank systems. Collected data is made available through LifeWatch data explorers, other virtual labs and global biodiversity data systems.
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The Marine Regions team at VLIZ provides access to several data products in common GIS formats such as geopackage, shapefile, geojson or kml. Some of these are developed by the Marine Regions team. For example, the Maritime Boundaries which include all the provisions of the United Nations Convention on the Law of the Sea; or the sea areas delimited in the document "Limits of Oceans & Seas, Special Publication No. 23" published by the IHO in 1953. When possible, Marine Regions also offers access to these products following the specifications of the Open Geospatial Consortium (OGC), such as Web Feature Services (WFS), Web Map Services (WMS) or Catalogue Service for the Web (CSW). There is a wrapper in the R programming language for these services together with the Gazetteer webservices in the form of an R package: 'mregions', developed by the rOpenSci consortium and maintained by the Marine Regions team.
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Acoustic monitoring is a powerful technique for learning about the ecology of bats. Bat recorders provide continuous registration of bat activity by recording echolocation calls of passing bats. Since the bat calls are highly reliable taxonomic features, the acquired data can be used to estimate diversity and relative abundance of the occurring species. The generated data provides a basis for ecological and behavioral research of the bat species occurring in our coastal and marine areas. BatSounds is an open source repository that contains scripts to work with BatCorder data and BatCorder devices. The repository contains two scripts that are useful to process and send the audio data retrieved by ecoObs Batcorders (https://ecoobs.com/): - Raw_to_wav is a python script that adds a header to raw audio files from the BatCorder so it can more easily be used by different softwares as a wav file. - beaglebone_batcorder is a bash-script to run on a Beaglebone Black minicomputer connected to a BatCorder. It copies the data from the BatCorder daily to its own SD card and sends it to an FTP server over Ethernet. It allows for remote data transfer and memory management of the BatCorder. LifeWatch Belgium has currently four ecoObs BatCorders installed. You can find more information here: https://lifewatch.be/en/sensor-network-bat-detection
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The LifeWatch and EMODnet Biology QC Tool allows you to assess to what extend a dataset published on an IPT or a DwC-A file meets the EMODnet Biology Data Quality Criteria. This tool is based on the EMODnetBioCheck R package (https://github.com/EMODnet/EMODnetBiocheck) that was created using the obistools package (https://github.com/iobis/obistools) and it is available from the LifeWatch services at http://rshiny.lifewatch.be/BioCheck/. The Biocheck tool performs a detailed Quality Control on OBIS-env datasets and occurrence core dataset. It provides a way for a visual exploration of the dataset and highlights potential issues.