lifewatch/ebr-2022-data-combine

This is the repository of a workshop "Bringing together marine biodiversity, environmental and maritime boundaries data in R" happening as part of the Empowering Biodiversity Research II conference

ebr-2022-data-combine's Issues

Change course outline

1. Tell a story: @lauramarque

• Someone collect observations of sharks/cetaceans/sea turtle and wants to fit a species distribution model
  • Do you have enough data? Where can I find more?
  • From what region of the world?
  • How do you make sure you are using the same species name than your peers?
  • How do I link with environmental data? Where can I find this information?
• "In this workshop, we will answer all these questions" "and apply this to your own research!" "you will have an analysis-ready dataset"

2. Show what we will do in the workshop with a visual aid: Connection of the different projects. @whaleshark99

• Explain quickly the connections but we will go in detail about every project

3. Exercises:
Content: @salvafern
Outline: @whaleshark99

Exercises 3.1. Standardize your data: Get lifewatch data and standardize to MR and WoRMS rethink the wording of standardize

• Explain the 3 projects: show MRGID and AphiaID @lauramarque
• Show code examples
• "This can be applied to your own dataset!"
• Input: LifeWatch occurence data
• R packages: lwdataexplorer, worrms, mregions2
• Output: dataset

Exercise 3.2. Get EurOBIS data to complete your dataset

• Input: dataset from ex. 1
• R package: eurobis, explain what is EurOBIS @lauramarque
• Wrangle lifewatch and eurobis data to be in the same data.frame: species, lon, lat, time
• recommend use of darwin core terms

3.3. Get seabed habitats and bio-oracle data

• Input: none
• R packages: (emodnetWFS (seabed habitats)), sdmpredictors (Temp, pH, salinity, ...)
• Output: dataset with environmental info

3.4. Link occurrences dataset to environmental data and get an analysis ready dataset.

• Input: datasets from exercises 2 & 3
• Output: combined dataset

Add mregions2 exercise

Hi @whaleshark99, can you add some exercises using the mregions2 package?

I added a quick example that uses directly the API with httr2 but ideally we will showcase mregions2 (in an early development stage of course 😄 )

I would add:
Exercise 1.1. Find the Gazetteer MRGID for the Belgian Exclusive Economic Zone (using getGazetteerRecordByNames)
Exercise 1.2. Get the Geometry of the Belgian EEZ

And one or two bonus exercises. Some ideas:
3. Extra: get geometry of your region of interest.
4. Extra: find funny names of marine regions , e.g. Bay of Plenty (NZ): http://marineregions.org/gazetteer.php?p=details&id=8903; Point Disappointment (Antarctica): http://marineregions.org/gazetteer.php?p=details&id=13911

The exercises are in https://github.com/vlizBE/ebr-2022-data-combine/blob/master/src/exercises/01_mregions_solution.R

Cheers!