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.

A tool for Quality Controlling Darwin Core based datasets according to the EMODnet Biology guidelines. The tool performs a thorough QC on OBIS-env datasets and occurrence core datasets. It can use an IPT resource URL as input. Quality controlling a dataset is fundamental in order to ensure its appropriate usage. The EMODnetBiocheck R package is developed in the framework of the LifeWatch and EMODnet Biology projects, and managed by the EurOBIS (European Ocean Biodiversity Information System) Data Management Team at the Flanders Marine Institute (VLIZ). It helps users to Quality Control their (marine) biological datasets by performing a varied number of quality checks on both published and unpublished datasets. This R package also allows a thorough visual exploration of the dataset, while highlighting potential issues within the dataset. The R package can be used on: i) public IPT resources; ii) loaded data tables. The only requirement to use the R package is the existence of an Occurrence table in the dataset, although the analysis reaches its full potential using an IPT resource with OBIS-ENV data format (Core: "Event"; Extensions: "Occurrence" and "Extended Measurements or Facts").

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

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.

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/.

This interactive online tool gives access to all sensor data collected in the framework of the Flemish LifeWatch project, and provides an interface to explore and analyze these data. Several thematic portals have been set up as part of the Data Explorer: - Underway Data Explorer - Station Data Explorer - Zooplankton Data Explorer - GPS Bird Tracking Data Explorer - Fish Telemetry Data Explorer - Batcorder Data Explorer - CPOD (marine mammals) Data Explorer Within the RShiny LifeWatch Data Explorer, five general sections are available to explore, plot, visualize and download data. Some of the more recent data is temporarily under moratorium and therefore protected by password access. The LifeWatch Data Explorer is built using RShiny server, Leaflet, ggplot2, PLotly, Dygraph and DataTables. The system is able to query MSSQL, PostgreSQL, Geoserver (WFS) and MongoDB servers.

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.

The package lwdataexplorer retrieves biodiversity, environmental or genetic data from the projects supported by LifeWatch Belgium. These data are also available to explore and download through the LifeWatch Data Explorer: an R Shiny application that allows you to check and download data from your browser.

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.