Read and write Frictionless Data Packages. A 'Data Package' (https://specs.frictionlessdata.io/data-package/) is a simple container format and standard to describe and package a collection of (tabular) data. It is typically used to publish FAIR (https://www.go-fair.org/fair-principles/) and open datasets.

bioRad provides standardized methods for extracting and reporting biological signals from weather radars. It includes functionality to inspect low-level radar data, process these data into meaningful biological information on animal speeds and directions at different altitudes in the atmosphere, visualize these biological extractions, and calculate further summary statistics.

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Control their (marine) biological datasets by performing a varied number of quality checks on both published and unpublished datasets.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").

This package provides functionality to access data from the European Tracking Network (ETN) database hosted by the Flanders Marine Institute (VLIZ) as part of the Flemish contribution to LifeWatch. ETN data is subject to the ETN data policy and can be: - restricted: under moratorium and only accessible to logged-in data owners/collaborators - unrestricted: publicly accessible without login and routinely published to international biodiversity facilities The ETN infrastructure currently requires the package to be run within the LifeWatch.be RStudio server, which is password protected. A login can be requested at http://www.lifewatch.be/etn/contact.

The package provides a framework for generating virtual species distributions, a procedure increasingly used in ecology to improve species distribution models. It integrates the existing methodological approaches with the objective of generating virtual species distributions with increased ecological realism.

Automated assessment of accuracy and geographical status of georeferenced biological data. The methods rely on reference regions, namely checklists and range maps. The package includes functions to obtain data from the Global Biodiversity Information Facility (https://www.gbif.org) and from the Global Inventory of Floras and Traits (https://gift.uni-goettingen.de/home). Alternatively, the user can input their own data. Furthermore, it provides easy visualisation of the data and the results through the plotting functions. It is especially suited for large datasets. The reference for the methodology is: Arlé et al. https://doi.org/10.1111/2041-210X.13629

The package provides features to manage the complete workflow for biodiversity data cleaning: uploading data, gathering input from users (in order to adjust cleaning procedures), cleaning data and finally, generating various reports and several versions of the data. It facilitates user-level data cleaning, designed for the inexperienced R users.

The package contains a number of discrete functions, each of which can be used to assess a particular form of bias, uncertainty or coverage. Generally, users must pass their occurrence data to the functions along with a list of time periods into which the outputs will be split. The functions generally return a list with two elements: a ggplot2 object, and the data that underpins that plot.

The package allows creating spatially or environmentally separated folds for cross-validation to provide a robust error estimation in spatially structured environments. Moreover, it permits to investigate and visualise the effective range of spatial autocorrelation in continuous raster covariates and point samples to find an initial realistic distance band to separate training and testing datasets spatially described in Valavi, R. et al. (2019), https://doi.org/10.1111/2041-210X.13107.

The package estmates the importance and the relative contribution of factors to explain species distribution by using several plots. A global geographic raster file for each environmental variable may be also obtained with the mean relative contribution, considering all species present in each raster cell, of the factor to explain species distribution. Finally, for each variable it is also possible to compare the frequencies of any variable obtained in the cells where the species is present with the frequencies of the same variable in the cells of the extent.