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R

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Control their (marine) biological datasets by performing a varied number of quality checks on both published and unpublished datasets.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

  • mregions2 provides access to the data from http://www.marineregions.org in R. It uses both the Marine Regions Gazetteer Web Services and the Marine Regions OGC Web Services in R. mregions2 superseedes the previous mregions R package.

  • mregions provides access to the data from http://www.marineregions.org in R. It uses both the Marine Regions Gazetteer Web Services and the Marine Regions OGC Web Services. mregions can help in a variety of use cases: - visualize marine regions alone; - visualize marine regions with associated data paired with analysis; - use marine region geospatial boundaries to query data providers (e.g., OBIS: http://www.iobis.org); - Geocode: get geolocation data from place names; - reverse Geocode: get place names from geolocation data.

  • 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.

  • A set of functions for reproducible and shareable analysis of models from an online repository, comparing and diagnostic models. The package has been developed specifically to improve reproducibility and comparability of SDMs in R by allowing users to encode entire SDM analyses as repeatable and extensible workflows consisting of independently executable, community-contributed modules. The module-workflow structure enables scientists to more easily create and share components of their analysis, and then, access, modify, reuse and combine the components of others.

  • 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 individual-based simulations of spatial eco-evolutionary dynamics. It is based on the software RangeShifter, making it flexible and fast. RangeShiftR models the processes of demography, dispersal and evolution in an inter-dependent way, offering substantial complexity in the corresponding modelling choices. It is entirely open-source and aims to facilitate the application of individual-based and mechanistic modelling to eco-evolutionary questions.

  • 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.

  • 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.

  • 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.